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The Neo4j Manual v1.6
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The Neo4j Team neo4j.org neotechnology.com
Copyright © 2012 Neo Technology
License: Creative Commons 3.0
2012-01-22 14:58:33
Quickstart
* Section 2.1, “What is a Graph Database?”
* Chapter 4, Using Neo4j embedded in Java applications
* Chapter 11, Using Neo4j embedded in Python applications
* Chapter 17, Neo4j Server
* Chapter 18, REST API
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Table of Contents
Preface
I. Introduction
1. Neo4j Highlights
2. Graph Database Concepts
2.1. What is a Graph Database?
2.1.1. A Graph contains Nodes and Relationships
2.1.2. Relationships organize the Graph
2.1.3. Query a Graph with a Traversal
2.1.4. Indexes look-up Nodes or Relationships
2.1.5. Neo4j is a Graph Database
2.2. Comparing Database Models
2.2.1. A Graph Database transforms a RDBMS
2.2.2. A Graph Database elaborates a Key-Value Store
2.2.3. A Graph Database relates Column-Family
2.2.4. A Graph Database navigates a Document Store
3. The Neo4j Graph Database
3.1. Nodes
3.2. Relationships
3.3. Properties
3.4. Paths
3.5. Traversal
II. Tutorials
4. Using Neo4j embedded in Java applications
4.1. Include Neo4j in your project
4.1.1. Add Neo4j to the build path
4.1.2. Add Neo4j as a dependency
4.1.3. Starting and stopping
4.2. Hello World
4.2.1. Prepare the database
4.2.2. Wrap mutating operations in a transaction
4.2.3. Create a small graph
4.2.4. Print the result
4.2.5. Remove the data
4.2.6. Shut down the database server
4.3. User database with index
4.4. Traversal
4.4.1. The Matrix
4.4.2. New traversal framework
4.4.3. Uniqueness of Paths in traversals
4.4.4. Social network
4.5. Domain entities
4.6. Graph Algorithm examples
4.7. Reading a management attribute
4.8. Cypher Queries
5. Cypher Cookbook
5.1. Hyperedges and Cypher
5.1.1. Find Groups
5.1.2. Find all groups and roles for a user
5.2. Basic Friend finding based on social neighborhood
5.2.1. Simple Friend Finder
5.3. Co-favorited places
5.3.1. Co-Favorited Places - Users Who Like x Also Like y
5.3.2. Co-Tagged Places - Places Related through Tags
5.4. Find people based on similar favorites
5.4.1. Find people based on similar favorites
5.5. Multirelational (social) graphs
5.5.1. Who FOLLOWS or LOVES me back
6. Using the Neo4j REST API
6.1. How to use the REST API from Java
6.1.1. Creating a graph through the REST API from Java
6.1.2. Start the server
6.1.3. Creating a node
6.1.4. Adding properties
6.1.5. Adding relationships
6.1.6. Add properties to a relationship
6.1.7. Querying graphs
6.1.8. Phew, is that it?
6.1.9. What’s next?
6.1.10. Appendix: the code
7. Extending the Neo4j Server
7.1. Server Plugins
7.2. Unmanaged Extensions
8. The Traversal Framework
8.1. Main concepts
8.2. Traversal Framework Java API
8.2.1. TraversalDescription
8.2.2. Evaluator
8.2.3. Traverser
8.2.4. Uniqueness
8.2.5. Order - How to move through branches?
8.2.6. BranchSelector
8.2.7. Path
8.2.8. RelationshipExpander
8.2.9. Expander
8.2.10. How to use the Traversal framework
9. Domain Modeling Gallery
9.1. User roles in graphs
9.1.1. Get the admins
9.1.2. Get the group memberships of a user
9.1.3. Get all groups
9.1.4. Get all members of all groups
9.2. ACL structures in graphs
9.2.1. Generic approach
9.2.2. Read-permission example
10. Languages
11. Using Neo4j embedded in Python applications
11.1. Hello, world!
11.2. A sample app using traversals and indexes
11.2.1. Domain logic
11.2.2. Creating data and getting it back
III. Reference
12. Capabilities
12.1. Data Security
12.2. Data Integrity
12.2.1. Core Graph Engine
12.2.2. Different Data Sources
12.3. Data Integration
12.3.1. Event-based Synchronization
12.3.2. Periodic Synchronization
12.3.3. Periodic Full Export/Import of Data
12.4. Availability and Reliability
12.4.1. Operational Availability
12.4.2. Disaster Recovery/ Resiliency
12.5. Capacity
12.5.1. File Sizes
12.5.2. Read speed
12.5.3. Write speed
12.5.4. Data size
13. Transaction Management
13.1. Interaction cycle
13.2. Isolation levels
13.3. Default locking behavior
13.4. Deadlocks
13.5. Delete semantics
13.6. Creating unique nodes
13.6.1. Single thread
13.6.2. Get or create
13.6.3. Pessimistic locking
13.7. Transaction events
14. Indexing
14.1. Introduction
14.2. Create
14.3. Delete
14.4. Add
14.5. Remove
14.6. Update
14.7. Search
14.7.1. Get
14.7.2. Query
14.8. Relationship indexes
14.9. Scores
14.10. Configuration and fulltext indexes
14.11. Extra features for Lucene indexes
14.11.1. Numeric ranges
14.11.2. Sorting
14.11.3. Querying with Lucene Query objects
14.11.4. Compound queries
14.11.5. Default operator
14.11.6. Caching
14.12. Batch insertion
14.12.1. Best practices
14.13. Automatic Indexing
14.13.1. Configuration
14.13.2. Search
14.13.3. Runtime Configuration
14.13.4. Updating the Automatic Index
15. Cypher Query Language
15.1. Compatibility
15.2. Parameters
15.3. Identifiers
15.4. Start
15.4.1. Node by id
15.4.2. Relationship by id
15.4.3. Multiple nodes by id
15.4.4. Node by index lookup
15.4.5. Relationship by index lookup
15.4.6. Node by index query
15.4.7. Multiple start points
15.5. Match
15.5.1. Related nodes
15.5.2. Outgoing relationships
15.5.3. Directed relationships and identifier
15.5.4. Match by relationship type
15.5.5. Match by relationship type and use an identifier
15.5.6. Relationship types with uncommon characters
15.5.7. Multiple relationships
15.5.8. Variable length relationships
15.5.9. Relationship identifier in variable length relationships
15.5.10. Zero length paths
15.5.11. Optional relationship
15.5.12. Optional typed and named relationship
15.5.13. Properties on optional elements
15.5.14. Complex matching
15.5.15. Shortest path
15.5.16. All shortest paths
15.5.17. Named path
15.5.18. Matching on a bound relationship
15.6. Where
15.6.1. Boolean operations
15.6.2. Filter on node property
15.6.3. Regular expressions
15.6.4. Escaping in regular expressions
15.6.5. Case insensitive regular expressions
15.6.6. Filtering on relationship type
15.6.7. Property exists
15.6.8. Compare if property exists
15.6.9. Filter on null values
15.6.10. Filter on relationships
15.7. Return
15.7.1. Return nodes
15.7.2. Return relationships
15.7.3. Return property
15.7.4. Identifier with uncommon characters
15.7.5. Column alias
15.7.6. Optional properties
15.7.7. Unique results
15.8. Aggregation
15.8.1. COUNT
15.8.2. Count nodes
15.8.3. Group Count Relationship Types
15.8.4. Count entities
15.8.5. Count non null values
15.8.6. SUM
15.8.7. AVG
15.8.8. MAX
15.8.9. MIN
15.8.10. COLLECT
15.8.11. DISTINCT
15.9. Order by
15.9.1. Order nodes by property
15.9.2. Order nodes by multiple properties
15.9.3. Order nodes in descending order
15.9.4. Ordering null
15.10. Skip
15.10.1. Skip first three
15.10.2. Return middle two
15.11. Limit
15.11.1. Return first part
15.12. Functions
15.12.1. Predicates
15.12.2. ALL
15.12.3. ANY
15.12.4. NONE
15.12.5. SINGLE
15.12.6. Scalar functions
15.12.7. LENGTH
15.12.8. TYPE
15.12.9. ID
15.12.10. COALESCE
15.12.11. Iterable functions
15.12.12. NODES
15.12.13. RELATIONSHIPS
15.12.14. EXTRACT
16. Graph Algorithms
16.1. Introduction
17. Neo4j Server
17.1. Server Installation
17.1.1. As a Windows service
17.1.2. Linux Service
17.1.3. Mac OSX
17.1.4. Multiple Server instances on one machine
17.1.5. High Availability Mode
17.2. Server Configuration
17.2.1. Important server configurations parameters
17.2.2. Neo4j Database performance configuration
17.2.3. Logging configuration
17.2.4. Other configuration options
17.3. Setup for remote debugging
17.4. Using the server (including web administration) with an embedded
database
17.4.1. Getting the libraries
17.4.2. Starting the Server from Java
17.4.3. Providing custom configuration
17.5. Server Performance Tuning
17.5.1. Specifying Neo4j tuning properties
17.5.2. Specifying JVM tuning properties
17.6. Server Installation in the Cloud
17.6.1. Heroku
18. REST API
18.1. Service root
18.1.1. Get service root
18.2. Nodes
18.2.1. Create Node
18.2.2. Create Node with properties
18.2.3. Get node
18.2.4. Get non-existent node
18.2.5. Delete node
18.2.6. Nodes with relationships can not be deleted
18.3. Relationships
18.3.1. Get Relationship by ID
18.3.2. Create relationship
18.3.3. Create a relationship with properties
18.3.4. Delete relationship
18.3.5. Get all properties on a relationship
18.3.6. Set all properties on a relationship
18.3.7. Get single property on a relationship
18.3.8. Set single property on a relationship
18.3.9. Get all relationships
18.3.10. Get incoming relationships
18.3.11. Get outgoing relationships
18.3.12. Get typed relationships
18.3.13. Get relationships on a node without relationships
18.4. Relationship types
18.4.1. Get relationship types
18.5. Node properties
18.5.1. Set property on node
18.5.2. Update node properties
18.5.3. Get properties for node
18.5.4. Property values can not be null
18.5.5. Property values can not be nested
18.5.6. Delete all properties from node
18.6. Relationship properties
18.6.1. Update relationship properties
18.6.2. Remove property from a relationship
18.6.3. Remove non-existent property from a relationship
18.6.4. Remove properties from a non-existing relationship
18.6.5. Remove property from a non-existing relationship
18.7. Indexes
18.7.1. Create node index
18.7.2. Create node index with configuration
18.7.3. Delete node index
18.7.4. List node indexes
18.7.5. Add node to index
18.7.6. Remove all entries with a given node from an index
18.7.7. Remove all entries with a given node and key from an index
18.7.8. Remove all entries with a given node, key and value from an
index
18.7.9. Find node by exact match
18.7.10. Find node by query
18.8. Unique Indexes
18.8.1. Create a unique node in an index
18.8.2. Create a unique node in an index (the case where it exists)
18.8.3. Add a node to an index unless a node already exists for the
given mapping
18.8.4. Create a unique relationship in an index
18.8.5. Add a relationship to an index unless a relationship
already exists for the given mapping
18.9. Automatic Indexes
18.9.1. Find node by exact match from an automatic index
18.9.2. Find node by query from an automatic index
18.10. Configurable Automatic Indexing
18.10.1. Create an auto index for nodes with specific configuration
18.10.2. Create an auto index for relationships with specific
configuration
18.11. Traversals
18.11.1. Traversal using a return filter
18.11.2. Return relationships from a traversal
18.11.3. Return paths from a traversal
18.11.4. Traversal returning nodes below a certain depth
18.11.5. Creating a paged traverser
18.11.6. Paging through the results of a paged traverser
18.11.7. Paged traverser page size
18.11.8. Paged traverser timeout
18.12. Cypher queries
18.12.1. Send a Query
18.12.2. Return paths
18.12.3. Send queries with parameters
18.12.4. Nested results
18.12.5. Server errors
18.13. Built-in Graph Algorithms
18.13.1. Find all shortest paths
18.13.2. Find one of the shortest paths between nodes
18.13.3. Execute a Dijkstra algorithm with similar weights on
relationships
18.13.4. Execute a Dijkstra algorithm with weights on relationships
18.14. Batch operations
18.14.1. Execute multiple operations in batch
18.14.2. Refer to items created earlier in the same batch job
18.15. Cypher Plugin
18.15.1. Send a Query
18.15.2. Return paths
18.15.3. Send queries with parameters
18.15.4. Server errors
18.16. Gremlin Plugin
18.16.1. Send a Gremlin Script - URL encoded
18.16.2. Load a sample graph
18.16.3. Sort a result using raw Groovy operations
18.16.4. Send a Gremlin Script - JSON encoded with table results
18.16.5. Returning nested pipes
18.16.6. Set script variables
18.16.7. Send a Gremlin Script with variables in a JSON Map
18.16.8. Return paths from a Gremlin script
18.16.9. Send an arbitrary Groovy script - Lucene sorting
18.16.10. Emit a sample graph
18.16.11. HyperEdges - find user roles in groups
18.16.12. Group count
18.16.13. Collect multiple traversal results
18.16.14. Collaborative filtering
18.16.15. Chunking and offsetting in Gremlin
18.16.16. Modify the graph while traversing
18.16.17. Flow algorithms with Gremlin
19. Python embedded bindings
19.1. Installation
19.1.1. Installation on OSX/Linux
19.1.2. Installation on Windows
19.2. Core API
19.2.1. Getting started
19.2.2. Transactions
19.2.3. Nodes
19.2.4. Relationships
19.2.5. Properties
19.2.6. Paths
19.3. Indexes
19.3.1. Index management
19.3.2. Indexing things
19.3.3. Searching the index
19.4. Cypher Queries
19.4.1. Querying and reading the result
19.4.2. Parameterized and prepared queries
19.5. Traversals
19.5.1. Basic traversals
19.5.2. Traversal results
19.5.3. Uniqueness
19.5.4. Ordering
19.5.5. Evaluators - advanced filtering
IV. Operations
20. Installation & Deployment
20.1. Deployment Scenarios
20.1.1. Server
20.1.2. Embedded
20.2. System Requirements
20.2.1. CPU
20.2.2. Memory
20.2.3. Disk
20.2.4. Filesystem
20.2.5. Software
20.2.6. JDK Version
20.3. Installation
20.3.1. Embedded Installation
20.3.2. Server Installation
20.4. Upgrading
20.4.1. Automatic Upgrade
20.4.2. Explicit Upgrade
20.4.3. Upgrade 1.4 → 1.5
20.4.4. Upgrade 1.5 → 1.6
20.5. Usage Data Collector
20.5.1. Technical Information
20.5.2. How to disable UDC
21. Configuration & Performance
21.1. Introduction
21.1.1. How to add configuration settings
21.2. Performance Guide
21.2.1. Try this first
21.2.2. Neo4j primitives' lifecycle
21.2.3. Configuring Neo4j
21.3. Caches in Neo4j
21.3.1. File buffer cache
21.3.2. Object cache
21.4. JVM Settings
21.4.1. Configuring heap size and GC
21.5. Compressed storage of short strings
21.6. Compressed storage of short arrays
21.7. Memory mapped IO settings
21.7.1. Optimizing for traversal speed example
21.7.2. Batch insert example
21.8. Linux Performance Guide
21.8.1. Setup
21.8.2. Running the benchmark
21.8.3. Fixing the problem
21.9. Linux specific notes
21.9.1. File system tuning for high IO
21.9.2. Setting the number of open files
22. High Availability
22.1. Architecture
22.2. Setup and configuration
22.2.1. Small
22.2.2. Medium
22.2.3. Large
22.2.4. Installation Notes
22.3. How Neo4j HA operates
22.4. High Availability setup tutorial
22.4.1. Set up a Coordinator cluster
22.5. Setting up HAProxy as a load balancer
22.5.1. Installing HAProxy
22.5.2. Configuring HAProxy
22.5.3. Configuring separate sets for master and slaves
22.5.4. Cache-based sharding with HAProxy
23. Backup
23.1. Embedded and Server
23.2. Online Backup from Java
23.3. High Availability
23.4. Restoring Your Data
24. Security
24.1. Securing access to the Neo4j Server
24.1.1. Secure the port and remote client connection accepts
24.1.2. Server Authorization Rules
24.1.3. Hosted Scripting
24.1.4. Security in Depth
24.1.5. Rewriting URLs with a Proxy installation
25. Monitoring
25.1. JMX
25.1.1. Adjusting remote JMX access to the Neo4j Server
25.1.2. How to connect to a Neo4j instance using JMX and JConsole
25.1.3. How to connect to the JMX monitoring programmatically
25.1.4. Reference of supported JMX MBeans
V. Tools
26. Web Administration
26.1. Dashboard tab
26.1.1. Entity chart
26.1.2. Status monitoring
26.2. Data tab
26.3. Console tab
26.4. The Server Info tab
27. Neo4j Shell
27.1. Starting the shell
27.1.1. Enabling the shell server
27.1.2. Connecting to a shell server
27.1.3. Pointing the shell to a path
27.1.4. Read-only mode
27.1.5. Run a command and then exit
27.2. Passing options and arguments
27.3. Enum options
27.4. Filters
27.5. Node titles
27.6. How to use (individual commands)
27.6.1. Current node/relationship and path
27.6.2. Listing the contents of a node/relationship
27.6.3. Creating nodes and relationships
27.6.4. Setting, renaming and removing properties
27.6.5. Deleting nodes and relationships
27.6.6. Environment variables
27.6.7. Executing groovy/python scripts
27.6.8. Traverse
27.6.9. Query with Cypher
27.6.10. Indexing
27.7. Extending the shell: Adding your own commands
27.8. An example shell session
27.9. A Matrix example
VI. Community
28. Community Support
29. Contributing to Neo4j
29.1. Contributor License Agreement
29.1.1. Summary
29.1.2. Common questions
29.1.3. How to sign
29.2. Writing Neo4j Documentation
29.2.1. Overall Flow
29.2.2. File Structure in docs.jar
29.2.3. Headings and document structure
29.2.4. Writing
29.2.5. Gotchas
29.2.6. Links
29.2.7. Text Formatting
29.2.8. Admonitions
29.2.9. Images
29.2.10. Attributes
29.2.11. Comments
29.2.12. Code Snippets
29.2.13. A sample Java based documentation test
29.2.14. Hello world Sample Chapter
29.2.15. Toolchain
29.3. Areas for contribution
29.3.1. Neo4j Distribution
29.3.2. Maintaining Neo4j Documentation
29.3.3. Drivers and bindings to Neo4j
29.4. Contributors
A. Manpages
neo4j — Neo4j Server control and management
neo4j-shell — a command-line tool for exploring and manipulating a graph
database
neo4j-coordinator — Neo4j Coordinator for High-Availability clusters
neo4j-coordinator-shell — Neo4j Coordinator Shell interactive interface
B. Questions & Answers
List of Figures
2.1. RDBMS
2.2. Graph Database as RDBMS
2.3. Key-Value Store
2.4. Graph Database as Key-Value Store
2.5. Document Store
2.6. Graph Database as Document Store
4.1. Hello World Graph
4.2. Node space view of users
4.3. Matrix node space view
4.4. Descendants Example Graph
4.5. Social network data model
8.1. Traversal Example Graph
15.1. Example Graph
18.1. Final Graph
18.2. Final Graph
18.3. Final Graph
18.4. Final Graph
18.5. Final Graph
18.6. Final Graph
18.7. Final Graph
18.8. Final Graph
18.9. Final Graph
18.10. Starting Graph
18.11. Final Graph
18.12. Starting Graph
18.13. Final Graph
18.14. Final Graph
18.15. Starting Graph
18.16. Final Graph
18.17. Final Graph
18.18. Starting Graph
18.19. Final Graph
18.20. Final Graph
18.21. Final Graph
18.22. Final Graph
18.23. Final Graph
18.24. Final Graph
18.25. Final Graph
18.26. Final Graph
18.27. Final Graph
18.28. Final Graph
18.29. Final Graph
18.30. Final Graph
18.31. Final Graph
18.32. Final Graph
18.33. Starting Graph
18.34. Final Graph
18.35. Final Graph
18.36. Final Graph
18.37. Final Graph
18.38. Final Graph
18.39. Final Graph
18.40. Final Graph
18.41. Final Graph
18.42. Final Graph
18.43. Final Graph
18.44. Final Graph
18.45. Final Graph
18.46. Final Graph
18.47. Final Graph
18.48. Final Graph
18.49. Final Graph
18.50. Final Graph
18.51. Final Graph
18.52. Final Graph
18.53. Final Graph
18.54. Final Graph
18.55. Final Graph
18.56. Final Graph
18.57. Final Graph
18.58. Final Graph
18.59. Final Graph
18.60. Final Graph
18.61. Final Graph
18.62. Final Graph
18.63. Final Graph
18.64. Final Graph
18.65. Final Graph
18.66. Final Graph
18.67. Final Graph
18.68. Final Graph
18.69. Final Graph
18.70. Final Graph
18.71. Final Graph
18.72. Final Graph
18.73. Final Graph
18.74. Final Graph
18.75. Final Graph
18.76. Final Graph
18.77. Final Graph
18.78. Final Graph
18.79. Final Graph
18.80. Final Graph
18.81. Final Graph
18.82. Final Graph
18.83. Final Graph
18.84. Final Graph
18.85. Final Graph
18.86. Final Graph
18.87. Final Graph
18.88. Final Graph
18.89. Starting Graph
18.90. Final Graph
18.91. Starting Graph
18.92. Final Graph
22.1. Typical setup when running multiple Neo4j instances in HA mode
25.1. Connecting JConsole to the Neo4j Java process
25.2. Neo4j MBeans View
26.1. Web Administration Dashboard
26.2. Entity charting
26.3. Status indicator panels
26.4. Browsing and manipulating data
26.5. Editing properties
26.6. Traverse data with Gremlin
26.7. Query data with Cypher
26.8. Interact over HTTP
26.9. JMX Attributes
29.1. Hello World Graph
List of Tables
3.1. Using relationship direction and type
3.2. Property value types
5.1. Result
5.2. Result
5.3. Result
5.4. Result
5.5. Result
5.6. Result
5.7. Result
6.1. Neo4j REST clients contributed by the community.
10.1. Neo4j embedded drivers contributed by the community.
14.1. Lucene indexing configuration parameters
15.1. Result
15.2. Result
15.3. Result
15.4. Result
15.5. Result
15.6. Result
15.7. Result
15.8. Result
15.9. Result
15.10. Result
15.11. Result
15.12. Result
15.13. Result
15.14. Result
15.15. Result
15.16. Result
15.17. Result
15.18. Result
15.19. Result
15.20. Result
15.21. Result
15.22. Result
15.23. Result
15.24. Result
15.25. Result
15.26. Result
15.27. Result
15.28. Result
15.29. Result
15.30. Result
15.31. Result
15.32. Result
15.33. Result
15.34. Result
15.35. Result
15.36. Result
15.37. Result
15.38. Result
15.39. Result
15.40. Result
15.41. Result
15.42. Result
15.43. Result
15.44. Result
15.45. Result
15.46. Result
15.47. Result
15.48. Result
15.49. Result
15.50. Result
15.51. Result
15.52. Result
15.53. Result
15.54. Result
15.55. Result
15.56. Result
15.57. Result
15.58. Result
15.59. Result
15.60. Result
15.61. Result
15.62. Result
15.63. Result
15.64. Result
15.65. Result
15.66. Result
15.67. Result
15.68. Result
15.69. Result
15.70. Result
17.1. neo4j-wrapper.conf JVM tuning properties
20.1. Neo4j deployment options
20.2. Neo4j editions
20.3. Upgrade process for Neo4J version
21.1. Guidelines for heap size
22.1. HighlyAvailableGraphDatabase configuration parameters
25.1. MBeans exposed by the Neo4j Kernel
25.2. MBean Memory Mapping
25.3. MBean Locking
25.4. MBean Transactions
25.5. MBean Cache
25.6. MBean Configuration
25.7. MBean Primitive count
25.8. MBean XA Resources
25.9. MBean Store file sizes
25.10. MBean Kernel
25.11. MBean High Availability
29.1. Result
-------------------------------------------------------------------------------
Preface
-------------------------------------------------------------------------------
This is the reference manual for Neo4j version 1.6, written by the Neo4j Team.
The main parts of the manual are:
* Part I, “Introduction” — introducing graph database concepts and Neo4j.
* Part II, “Tutorials” — learn how to use Neo4j.
* Part III, “Reference” — detailed information on Neo4j.
* Part IV, “Operations” — how to install and maintain Neo4j.
* Part V, “Tools” — guides on tools.
* Part VI, “Community” — getting help from, contributing to.
* Appendix A, Manpages — command line documentation.
* Appendix B, Questions & Answers — common questions.
The material is practical, technical, and focused on answering specific
questions. It addresses how things work, what to do and what to avoid to
successfully run Neo4j in a production environment.
The goal is to be thumb-through and rule-of-thumb friendly.
Each section should stand on its own, so you can hop right to whatever
interests you. When possible, the sections distill "rules of thumb" which you
can keep in mind whenever you wander out of the house without this manual in
your back pocket.
The included code examples are executed when Neo4j is built and tested. Also,
the REST API request and response examples are captured from real interaction
with a Neo4j server. Thus, the examples are always in sync with Neo4j.
Who should read this?
The topics should be relevant to architects, administrators, developers and
operations personnel.
Part I. Introduction
This part gives a bird’s eye view of what a graph database is, and then
outlines some specifics of Neo4j.
Table of Contents
1. Neo4j Highlights
2. Graph Database Concepts
2.1. What is a Graph Database?
2.1.1. A Graph contains Nodes and Relationships
2.1.2. Relationships organize the Graph
2.1.3. Query a Graph with a Traversal
2.1.4. Indexes look-up Nodes or Relationships
2.1.5. Neo4j is a Graph Database
2.2. Comparing Database Models
2.2.1. A Graph Database transforms a RDBMS
2.2.2. A Graph Database elaborates a Key-Value Store
2.2.3. A Graph Database relates Column-Family
2.2.4. A Graph Database navigates a Document Store
3. The Neo4j Graph Database
3.1. Nodes
3.2. Relationships
3.3. Properties
3.4. Paths
3.5. Traversal
Chapter 1. Neo4j Highlights
As a robust, scalable and high-performance database, Neo4j is suitable for full
enterprise deployment or a subset of the full server can be used in lightweight
projects.
It features:
* true ACID transactions
* high availability
* scales to billions of nodes and relationships
* high speed querying through traversals
Proper ACID behavior is the foundation of data reliability. Neo4j enforces that
all mutating operations occur within a transaction, guaranteeing consistent
data. This robustness extends from single instance embedded graphs to
multi-server high availability installations. For details, see Chapter 13,
Transaction Management.
Reliable graph storage can easily be added to any application. A property graph
can scale in size and complexity as the application evolves, with little impact
on performance. Whether starting new development, or augmenting existing
functionality, Neo4j is only limited by physical hardware.
A single server instance can handle a graph of billions of nodes and
relationships. When data throughput is insufficient, the graph database can be
distributed among multiple servers in a high availability configuration. See
Chapter 22, High Availability to learn more.
The graph database storage shines when storing richly-connected data. Querying
is performed through traversals, which can perform millions of traversal steps
per second. A traversal step resembles a join in a RDBMS.
Chapter 2. Graph Database Concepts
This chapter contains an introduction to the graph data model and also compares
it to other data models used when persisting data.
2.1. What is a Graph Database?
A graph database stores data in a graph, the most generic of data structures,
capable of elegantly representing any kind of data in a highly accessible way.
Let’s follow along some graphs, using them to express graph concepts. We’ll
“read” a graph by following arrows around the diagram to form sentences.
2.1.1. A Graph contains Nodes and Relationships
“A Graph —records data in→ Nodes —which have→ Properties”
The simplest possible graph is a single Node, a record that has named values
referred to as Properties. A Node could start with a single Property and grow
to a few million, though that can get a little awkward. At some point it makes
sense to distribute the data into multiple nodes, organized with explicit
Relationships.
graphdb-GVE.svg
2.1.2. Relationships organize the Graph
“Nodes —are organized by→ Relationships —which also have→ Properties”
Relationships organize Nodes into arbitrary structures, allowing a Graph to
resemble a List, a Tree, a Map, or a compound Entity – any of which can be
combined into yet more complex, richly inter-connected structures.
2.1.3. Query a Graph with a Traversal
“A Traversal —navigates→ a Graph; it —identifies→ Paths —which order→
Nodes”
A Traversal is how you query a Graph, navigating from starting Nodes to related
Nodes according to an algorithm, finding answers to questions like “what music
do my friends like that I don’t yet own,” or “if this power supply goes down,
what web services are affected?”
graphdb-traversal.svg
2.1.4. Indexes look-up Nodes or Relationships
“An Index —maps from→ Properties —to either→ Nodes or Relationships”
Often, you want to find a specific Node or Relationship according to a Property
it has. Rather than traversing the entire graph, use an Index to perform a
look-up, for questions like “find the Account for username master-of-graphs.”
graphdb-indexes.svg
2.1.5. Neo4j is a Graph Database
“A Graph Database —manages a→ Graph and —also manages related→ Indexes”
Neo4j is a commercially supported open-source graph database. It was designed
and built from the ground-up to be a reliable database, optimized for graph
structures instead of tables. Working with Neo4j, your application gets all the
expressiveness of a graph, with all the dependability you expect out of a
database.
graphdb-overview.svg
2.2. Comparing Database Models
A Graph Database stores data structured in the Nodes and Relationships of a
graph. How does this compare to other persistence models? Because a graph is a
generic structure, let’s compare how a few models would look in a graph.
2.2.1. A Graph Database transforms a RDBMS
Topple the stacks of records in a relational database while keeping all the
relationships, and you’ll see a graph. Where an RDBMS is optimized for
aggregated data, Neo4j is optimized for highly connected data.
Figure 2.1. RDBMS
graphdb-compare-rdbms.svg
Figure 2.2. Graph Database as RDBMS
graphdb-compare-rdbms-g.svg
2.2.2. A Graph Database elaborates a Key-Value Store
A Key-Value model is great for lookups of simple values or lists. When the
values are themselves interconnected, you’ve got a graph. Neo4j lets you
elaborate the simple data structures into more complex, interconnected data.
Figure 2.3. Key-Value Store
graphdb-compare-kvstore.svg
K* represents a key, V* a value. Note that some keys point to other keys as
well as plain values.
Figure 2.4. Graph Database as Key-Value Store
graphdb-compare-kvstore-g.svg
2.2.3. A Graph Database relates Column-Family
Column Family (BigTable-style) databases are an evolution of key-value, using
"families" to allow grouping of rows. Stored in a graph, the families could
become hierarchical, and the relationships among data becomes explicit.
2.2.4. A Graph Database navigates a Document Store
The container hierarchy of a document database accommodates nice, schema-free
data that can easily be represented as a tree. Which is of course a graph.
Refer to other documents (or document elements) within that tree and you have a
more expressive representation of the same data. When in Neo4j, those
relationships are easily navigable.
Figure 2.5. Document Store
graphdb-compare-docdb.svg
D=Document, S=Subdocument, V=Value, D2/S2 = reference to subdocument in (other)
document.
Figure 2.6. Graph Database as Document Store
graphdb-compare-docdb-g.svg
Chapter 3. The Neo4j Graph Database
This chapter goes into more detail on the data model and behavior of Neo4j.
3.1. Nodes
The fundamental units that form a graph are nodes and relationships. In Neo4j,
both nodes and relationships can contain properties.
Nodes are often used to represent entities, but depending on the domain
relationships may be used for that purpose as well.
graphdb-nodes-overview.svg
Let’s start out with a really simple graph, containing only a single node with
one property:
graphdb-nodes.svg
3.2. Relationships
Relationships between nodes are a key part of a graph database. They allow for
finding related data. Just like nodes, relationships can have properties.
graphdb-rels-overview.svg
A relationship connects two nodes, and is guaranteed to have valid start and
end nodes.
graphdb-rels.svg
As relationships are always directed, they can be viewed as outgoing or
incoming relative to a node, which is useful when traversing the graph:
graphdb-rels-dir.svg
Relationships are equally well traversed in either direction. This means that
there is no need to add duplicate relationships in the opposite direction (with
regard to traversal or performance).
While relationships always have a direction, you can ignore the direction where
it is not useful in your application.
Note that a node can have relationships to itself as well:
graphdb-rels-loop.svg
To further enhance graph traversal all relationships have a relationship type.
Note that the word type might be misleading here, you could rather think of it
as a label. The following example shows a simple social network with two
relationship types.
graphdb-rels-twitter.svg
Table 3.1. Using relationship direction and type
+------------------------------------------------------------------------+
|What |How |
|------------------------------+-----------------------------------------|
|get who a person follows |outgoing follows relationships, depth one|
|------------------------------+-----------------------------------------|
|get the followers of a person |incoming follows relationships, depth one|
|------------------------------+-----------------------------------------|
|get who a person blocks |outgoing blocks relationships, depth one |
|------------------------------+-----------------------------------------|
|get who a person is blocked by|incoming blocks relationships, depth one |
+------------------------------------------------------------------------+
This example is a simple model of a file system, which includes symbolic links:
graphdb-rels-filesys.svg
Depending on what you are looking for, you will use the direction and type of
relationships during traversal.
+-----------------------------------------------------------------------------+
|What |How |
|-------------------------------------+---------------------------------------|
|get the full path of a file |incoming file relationships |
|-------------------------------------+---------------------------------------|
|get all paths for a file |incoming file and symbolic link |
| |relationships |
|-------------------------------------+---------------------------------------|
|get all files in a directory |outgoing file and symbolic link |
| |relationships, depth one |
|-------------------------------------+---------------------------------------|
|get all files in a directory, |outgoing file relationships, depth one |
|excluding symbolic links | |
|-------------------------------------+---------------------------------------|
|get all files in a directory, |outgoing file and symbolic link |
|recursively |relationships |
+-----------------------------------------------------------------------------+
3.3. Properties
Both nodes and relationships can have properties.
Properties are key-value pairs where the key is a string. Property values can
be either a primitive or an array of one primitive type. For example String,
int and int[] values are valid for properties.
Note
null is not a valid property value. Nulls can instead be modeled by the absence
of a key.
graphdb-properties.svg
Table 3.2. Property value types
+-----------------------------------------------------------------------------+
|Type |Description |Value range |
|-------+-----------------------------------+---------------------------------|
|boolean| |true/false |
|-------+-----------------------------------+---------------------------------|
|byte |8-bit integer |-128 to 127, inclusive |
|-------+-----------------------------------+---------------------------------|
|short |16-bit integer |-32768 to 32767, inclusive |
|-------+-----------------------------------+---------------------------------|
|int |32-bit integer |-2147483648 to 2147483647, |
| | |inclusive |
|-------+-----------------------------------+---------------------------------|
|long |64-bit integer |-9223372036854775808 to |
| | |9223372036854775807, inclusive |
|-------+-----------------------------------+---------------------------------|
|float |32-bit IEEE 754 floating-point | |
| |number | |
|-------+-----------------------------------+---------------------------------|
|double |64-bit IEEE 754 floating-point | |
| |number | |
|-------+-----------------------------------+---------------------------------|
|char |16-bit unsigned integers |u0000 to uffff (0 to 65535) |
| |representing Unicode characters | |
|-------+-----------------------------------+---------------------------------|
|String |sequence of Unicode characters | |
+-----------------------------------------------------------------------------+
For further details on float/double values, see Java Language Specification
.
3.4. Paths
A path is one or more nodes with connecting relationships, typically retrieved
as a query or traversal result.
graphdb-path.svg
The shortest possible path has length zero and looks like this:
graphdb-path-example1.svg
A path of length one:
graphdb-path-example2.svg
3.5. Traversal
Traversing a graph means visiting its nodes, following relationships according
to some rules. In most cases only a subgraph is visited, as you already know
where in the graph the interesting nodes and relationships are found.
Neo4j comes with a callback based traversal API which lets you specify the
traversal rules. At a basic level there’s a choice between traversing breadth-
or depth-first.
For an in-depth introduction to the traversal framework, see Chapter 8, The
Traversal Framework. For Java code examples see Section 4.4, “Traversal”.
Other options to traverse or query graphs in Neo4j are Cypher and Gremlin.
Part II. Tutorials
The tutorial part describes how to set up your environment, and write programs
using Neo4j. It takes you from Hello World to advanced usage of graphs.
Table of Contents
4. Using Neo4j embedded in Java applications
4.1. Include Neo4j in your project
4.1.1. Add Neo4j to the build path
4.1.2. Add Neo4j as a dependency
4.1.3. Starting and stopping
4.2. Hello World
4.2.1. Prepare the database
4.2.2. Wrap mutating operations in a transaction
4.2.3. Create a small graph
4.2.4. Print the result
4.2.5. Remove the data
4.2.6. Shut down the database server
4.3. User database with index
4.4. Traversal
4.4.1. The Matrix
4.4.2. New traversal framework
4.4.3. Uniqueness of Paths in traversals
4.4.4. Social network
4.5. Domain entities
4.6. Graph Algorithm examples
4.7. Reading a management attribute
4.8. Cypher Queries
5. Cypher Cookbook
5.1. Hyperedges and Cypher
5.1.1. Find Groups
5.1.2. Find all groups and roles for a user
5.2. Basic Friend finding based on social neighborhood
5.2.1. Simple Friend Finder
5.3. Co-favorited places
5.3.1. Co-Favorited Places - Users Who Like x Also Like y
5.3.2. Co-Tagged Places - Places Related through Tags
5.4. Find people based on similar favorites
5.4.1. Find people based on similar favorites
5.5. Multirelational (social) graphs
5.5.1. Who FOLLOWS or LOVES me back
6. Using the Neo4j REST API
6.1. How to use the REST API from Java
6.1.1. Creating a graph through the REST API from Java
6.1.2. Start the server
6.1.3. Creating a node
6.1.4. Adding properties
6.1.5. Adding relationships
6.1.6. Add properties to a relationship
6.1.7. Querying graphs
6.1.8. Phew, is that it?
6.1.9. What’s next?
6.1.10. Appendix: the code
7. Extending the Neo4j Server
7.1. Server Plugins
7.2. Unmanaged Extensions
8. The Traversal Framework
8.1. Main concepts
8.2. Traversal Framework Java API
8.2.1. TraversalDescription
8.2.2. Evaluator
8.2.3. Traverser
8.2.4. Uniqueness
8.2.5. Order - How to move through branches?
8.2.6. BranchSelector
8.2.7. Path
8.2.8. RelationshipExpander
8.2.9. Expander
8.2.10. How to use the Traversal framework
9. Domain Modeling Gallery
9.1. User roles in graphs
9.1.1. Get the admins
9.1.2. Get the group memberships of a user
9.1.3. Get all groups
9.1.4. Get all members of all groups
9.2. ACL structures in graphs
9.2.1. Generic approach
9.2.2. Read-permission example
10. Languages
11. Using Neo4j embedded in Python applications
11.1. Hello, world!
11.2. A sample app using traversals and indexes
11.2.1. Domain logic
11.2.2. Creating data and getting it back
Chapter 4. Using Neo4j embedded in Java applications
It’s easy to use Neo4j embedded in Java applications. In this chapter you will
find everything needed — from setting up the environment to doing something
useful with your data.
4.1. Include Neo4j in your project
After selecting the appropriate edition for your platform, embed Neo4j in your
Java application by including the Neo4j library jars in your build. The
following sections will show how to do this by either altering the build path
directly or by using dependency management.
4.1.1. Add Neo4j to the build path
Get the Neo4j libraries from one of these sources:
* Extract a Neo4j download zip/tarball, and use
the jar files found in the lib/ directory.
* Use the jar files available from Maven Central Repository
Add the jar files to your project:
JDK tools
Append to -classpath
Eclipse
* Right-click on the project and then go Build Path → Configure Build
Path. In the dialog, choose Add External JARs, browse to the Neo4j lib/
directory and select all of the jar files.
* Another option is to use User Libraries .
IntelliJ IDEA
See Libraries, Global Libraries, and the Configure Library dialog
NetBeans
* Right-click on the Libraries node of the project, choose Add JAR/Folder
, browse to the Neo4j lib/ directory and select all of the jar files.
* You can also handle libraries from the project node, see Managing a
Project’s Classpath .
4.1.2. Add Neo4j as a dependency
For an overview of the main Neo4j artifacts, see Table 20.2, “Neo4j editions”.
The artifacts listed there are top-level artifacts that will transitively
include the actual Neo4j implementation. You can either go with the top-level
artifact or include the individual components directly. The examples included
here use the top-level artifact approach.
4.1.2.1. Maven
Maven dependency.
...
org.neo4j
neo4j
${neo4j-version}
...
...
Where ${neo4j-version} is the desired version and the artifactId is found in
Table 20.2, “Neo4j editions”.
4.1.2.2. Eclipse and Maven
For development in Eclipse , it is recommended to
install the M2Eclipse plugin and let Maven manage
the project build classpath instead, see above. This also adds the possibility
to build your project both via the command line with Maven and have a working
Eclipse setup for development.
4.1.2.3. Ivy
Make sure to resolve dependencies from Maven Central, for example using this
configuration in your ivysettings.xml file:
With that in place you can add Neo4j to the mix by having something along these
lines to your ivy.xml file:
..
..
..
..
Where ${neo4j-version} is the desired version and the name is found in
Table 20.2, “Neo4j editions”.
4.1.2.4. Gradle
The example below shows an example gradle build script for including the Neo4j
libraries.
def neo4jVersion = "[set-version-here]"
apply plugin: 'java'
repositories {
mavenCentral()
}
dependencies {
compile "org.neo4j:neo4j:${neo4jVersion}"
}
Where neo4jVersion is the desired version and the name ("neo4j" in the example)
is found in Table 20.2, “Neo4j editions”.
4.1.3. Starting and stopping
To create a new database or ópen an existing one you instantiate an
EmbeddedGraphDatabase .
graphDb = new EmbeddedGraphDatabase( DB_PATH );
registerShutdownHook( graphDb );
Note
The EmbeddedGraphDatabase instance can be shared among multiple threads. Note
however that you can’t create multiple instances pointing to the same database.
To stop the database, call the shutdown() method:
graphDb.shutdown();
To make sure Neo4j is shut down properly you can add a shutdown hook:
private static void registerShutdownHook( final GraphDatabaseService graphDb )
{
// Registers a shutdown hook for the Neo4j instance so that it
// shuts down nicely when the VM exits (even if you "Ctrl-C" the
// running example before it's completed)
Runtime.getRuntime().addShutdownHook( new Thread()
{
@Override
public void run()
{
graphDb.shutdown();
}
} );
}
If you want a read-only view of the database, use EmbeddedReadOnlyGraphDatabase
.
To start Neo4j with configuration settings, a Neo4j properties file can be
loaded like this:
Map config = EmbeddedGraphDatabase.loadConfigurations( pathToConfig
+ "neo4j.properties" );
GraphDatabaseService graphDb = new EmbeddedGraphDatabase(
"target/database/location", config );
Or you could of course create you own Map programatically and
use that instead.
For configuration settings, see Chapter 21, Configuration & Performance.
4.2. Hello World
Learn how to create and access nodes and relationships. For information on
project setup, see Section 4.1, “Include Neo4j in your project”.
Remember, from Section 2.1, “What is a Graph Database?”, that a Neo4j graph
consist of:
* Nodes that are connected by
* Relationships, with
* Properties on both nodes and relationships.
All relationships have a type. For example, if the graph represents a social
network, a relationship type could be KNOWS. If a relationship of the type
KNOWS connects two nodes, that probably represents two people that know each
other. A lot of the semantics (that is the meaning) of a graph is encoded in
the relationship types of the application. And although relationships are
directed they are equally well traversed regardless of which direction they are
traversed.
4.2.1. Prepare the database
Relationship types can be created by using an enum. In this example we only
need a single relationship type. This is how to define it:
private static enum RelTypes implements RelationshipType
{
KNOWS
}
We also prepare some variables to use:
GraphDatabaseService graphDb;
Node firstNode;
Node secondNode;
Relationship relationship;
The next step is to start the database server. Note that if the directory given
for the database doesn’t already exist, it will be created.
graphDb = new EmbeddedGraphDatabase( DB_PATH );
registerShutdownHook( graphDb );
Note that starting a database server is an expensive operation, so don’t start
up a new instance every time you need to interact with the database! The
instance can be shared by multiple threads. Transactions are thread confined.
As seen, we register a shutdown hook that will make sure the database shuts
down when the JVM exits. Now it’s time to interact with the database.
4.2.2. Wrap mutating operations in a transaction
All mutating transactions have to be performed in a transaction. This is a
conscious design decision, since we believe transaction demarcation to be an
important part of working with a real enterprise database. Now, transaction
handling in Neo4j is very easy:
Transaction tx = graphDb.beginTx();
try
{
// Mutating operations go here
tx.success();
}
finally
{
tx.finish();
}
For more information on transactions, see Chapter 13, Transaction Management
and Java API for Transaction .
4.2.3. Create a small graph
Now, let’s create a few nodes. The API is very intuitive. Feel free to have a
look at the JavaDocs at http://components.neo4j.org/neo4j/1.6/apidocs/ . They’re included in the distribution,
as well. Here’s how to create a small graph consisting of two nodes, connected
with one relationship and some properties:
firstNode = graphDb.createNode();
firstNode.setProperty( "message", "Hello, " );
secondNode = graphDb.createNode();
secondNode.setProperty( "message", "World!" );
relationship = firstNode.createRelationshipTo( secondNode, RelTypes.KNOWS );
relationship.setProperty( "message", "brave Neo4j " );
We now have a graph that looks like this:
Figure 4.1. Hello World Graph
Hello-World-Graph-java.svg
4.2.4. Print the result
After we’ve created our graph, let’s read from it and print the result.
System.out.print( firstNode.getProperty( "message" ) );
System.out.print( relationship.getProperty( "message" ) );
System.out.print( secondNode.getProperty( "message" ) );
Which will output:
Hello, brave Neo4j World!
4.2.5. Remove the data
In this case we’ll remove the data before committing:
// let's remove the data
firstNode.getSingleRelationship( RelTypes.KNOWS, Direction.OUTGOING ).delete();
firstNode.delete();
secondNode.delete();
Note that deleting a node which still has relationships when the transaction
commits will fail. This is to make sure relationships always have a start node
and an end node.
4.2.6. Shut down the database server
Finally, shut down the database server when the application finishes:
graphDb.shutdown();
Full source code: EmbeddedNeo4j.java
4.3. User database with index
You have a user database, and want to retrieve users by name. To begin with,
this is the structure of the database we want to create:
Figure 4.2. Node space view of users
users.png
That is, the reference node is connected to a users-reference node to which all
users are connected.
To begin with, we define the relationship types we want to use:
private static enum RelTypes implements RelationshipType
{
USERS_REFERENCE,
USER
}
Then we have created two helper methods to handle user names and adding users
to the database:
private static String idToUserName( final int id )
{
return "user" + id + "@neo4j.org";
}
private static Node createAndIndexUser( final String username )
{
Node node = graphDb.createNode();
node.setProperty( USERNAME_KEY, username );
nodeIndex.add( node, USERNAME_KEY, username );
return node;
}
The next step is to start the database server:
graphDb = new EmbeddedGraphDatabase( DB_PATH );
nodeIndex = graphDb.index().forNodes( "nodes" );
registerShutdownHook();
It’s time to add the users:
Transaction tx = graphDb.beginTx();
try
{
// Create users sub reference node (see design guidelines on
// http://wiki.neo4j.org/ )
Node usersReferenceNode = graphDb.createNode();
graphDb.getReferenceNode().createRelationshipTo(
usersReferenceNode, RelTypes.USERS_REFERENCE );
// Create some users and index their names with the IndexService
for ( int id = 0; id < 100; id++ )
{
Node userNode = createAndIndexUser( idToUserName( id ) );
usersReferenceNode.createRelationshipTo( userNode,
RelTypes.USER );
}
And here’s how to find a user by Id:
int idToFind = 45;
Node foundUser = nodeIndex.get( USERNAME_KEY,
idToUserName( idToFind ) ).getSingle();
System.out.println( "The username of user " + idToFind + " is "
+ foundUser.getProperty( USERNAME_KEY ) );
Full source code: EmbeddedNeo4jWithIndexing.java
4.4. Traversal
For reading about traversals, see Chapter 8, The Traversal Framework.
For more examples of traversals, see Chapter 9, Domain Modeling Gallery.
4.4.1. The Matrix
This is the first node space we want to traverse into:
Figure 4.3. Matrix node space view
examples-matrix.png
Friends and friends of friends.
private static Traverser getFriends( final Node person )
{
return person.traverse( Order.BREADTH_FIRST,
StopEvaluator.END_OF_GRAPH,
ReturnableEvaluator.ALL_BUT_START_NODE, RelTypes.KNOWS,
Direction.OUTGOING );
}
Let’s perform the actual traversal and print the results:
String output = neoNode.getProperty( "name" ) + "'s friends:\n";
Traverser friendsTraverser = getFriends( neoNode );
for ( Node friendNode : friendsTraverser )
{
output += "At depth " +
friendsTraverser.currentPosition().depth() +
" => " +
friendNode.getProperty( "name" ) + "\n";
}
Which will give us the following output:
Thomas Anderson's friends:
At depth 1 => Trinity
At depth 1 => Morpheus
At depth 2 => Cypher
At depth 3 => Agent Smith
Who coded the Matrix?
private static Traverser findHackers( final Node startNode )
{
return startNode.traverse( Order.BREADTH_FIRST,
StopEvaluator.END_OF_GRAPH, new ReturnableEvaluator()
{
@Override
public boolean isReturnableNode(
final TraversalPosition currentPos )
{
return !currentPos.isStartNode()
&& currentPos.lastRelationshipTraversed()
.isType( RelTypes.CODED_BY );
}
}, RelTypes.CODED_BY, Direction.OUTGOING, RelTypes.KNOWS,
Direction.OUTGOING );
}
Print out the result:
Traverser traverser = findHackers( getNeoNode() );
for ( Node hackerNode : traverser )
{
output += "At depth " +
traverser.currentPosition().depth() +
" => " +
hackerNode.getProperty( "name" ) + "\n";
}
Now we know who coded the Matrix:
Hackers:
At depth 4 => The Architect
Full source code: Matrix.java
4.4.2. New traversal framework
Note
The following examples use a new experimental traversal API. It shares the
underlying implementation with the old traversal API, so performance-wise they
should be equal. However, expect the new API to evolve and thus undergo
changes.
4.4.2.1. The Matrix
The traversals from the Matrix example above, this time using the new traversal
API:
Friends and friends of friends.
private static Traverser getFriends( final Node person )
{
TraversalDescription td = Traversal.description()
.breadthFirst()
.relationships( RelTypes.KNOWS, Direction.OUTGOING )
.evaluator( Evaluators.excludeStartPosition() );
return td.traverse( person );
}
Let’s perform the actual traversal and print the results:
Traverser friendsTraverser = getFriends( neoNode );
for ( Path friendPath : friendsTraverser )
{
System.out.println( "At depth " + friendPath.length() + " => "
+ friendPath.endNode()
.getProperty( "name" ) );
}
Who coded the Matrix?
private static Traverser findHackers( final Node startNode )
{
TraversalDescription td = Traversal.description()
.breadthFirst()
.relationships( RelTypes.CODED_BY, Direction.OUTGOING )
.relationships( RelTypes.KNOWS, Direction.OUTGOING )
.evaluator(
Evaluators.returnWhereLastRelationshipTypeIs( RelTypes.CODED_BY ) );
return td.traverse( startNode );
}
Print out the result:
Traverser traverser = findHackers( getNeoNode() );
for ( Path hackerPath : traverser )
{
System.out.println( "At depth " + hackerPath.length() + " => "
+ hackerPath.endNode()
.getProperty( "name" ) );
}
Full source code: MatrixNewTravTest.java
4.4.2.2. Walking an ordered path
This example shows how to use a path context holding a representation of a
path.
Create a toy graph.
Node A = db.createNode();
Node B = db.createNode();
Node C = db.createNode();
Node D = db.createNode();
A.createRelationshipTo( B, REL1 );
B.createRelationshipTo( C, REL2 );
C.createRelationshipTo( D, REL3 );
A.createRelationshipTo( C, REL2 );
example-ordered-path.svg
Now, the order of relationships (REL1 → REL2 → REL3) is stored in an ArrayList.
Upon traversal, the Evaluator can check against it to ensure that only paths
are included and returned that have the predefined order of relationships:
Walk the path.
final ArrayList orderedPathContext = new ArrayList();
orderedPathContext.add( REL1 );
orderedPathContext.add( withName( "REL2" ) );
orderedPathContext.add( withName( "REL3" ) );
TraversalDescription td = Traversal.description().evaluator(
new Evaluator()
{
@Override
public Evaluation evaluate( final Path path )
{
if ( path.length() == 0 )
{
return Evaluation.EXCLUDE_AND_CONTINUE;
}
RelationshipType expectedType = orderedPathContext.get( path.length() - 1 );
boolean isExpectedType = path.lastRelationship().isType( expectedType );
boolean included = path.length() == orderedPathContext.size() && isExpectedType;
boolean continued = path.length() < orderedPathContext.size() && isExpectedType;
return Evaluation.of( included, continued );
}
} );
Traverser t = td.traverse( db.getNodeById( 1 ) );
for ( Path path : t )
{
System.out.println( path );
}
Full source code: OrderedPathTest.java
4.4.3. Uniqueness of Paths in traversals
This example is demonstrating the use of node uniqueness. Below an imaginary
domain graph with Principals that own pets that are descendant to other pets.
Figure 4.4. Descendants Example Graph
Descendants-Example-Graph-Uniqueness-of-Paths-in-traversals.svg
In order to return all descendants of Pet0 which have the relation owns to
Principal1 (Pet1 and Pet3), the Uniqueness of the traversal needs to be set to
NODE_PATH rather than the default NODE_GLOBAL so that nodes can be traversed
more that once, and paths that have different nodes but can have some nodes in
common (like the start and end node) can be returned.
final Node target = data.get().get( "Principal1" );
TraversalDescription td = Traversal.description()
.uniqueness( Uniqueness.NODE_PATH )
.evaluator( new Evaluator()
{
@Override
public Evaluation evaluate( Path path )
{
if ( path.endNode().equals( target ) )
{
return Evaluation.INCLUDE_AND_PRUNE;
}
return Evaluation.EXCLUDE_AND_CONTINUE;
}
} );
Traverser results = td.traverse( start );
This will return the following paths:
(3)--[descendant,0]-->(1)<--[owns,3]--(5)
(3)--[descendant,2]-->(4)<--[owns,5]--(5)
In the default path.toString() implementation, (1)--[knows,2]-->(4) denotes a
node with ID=1 having a relationship with ID 2 or type knows to a node with
ID-4.
Let’s create a new TraversalDescription from the old one, having NODE_GLOBAL
uniqueness to see the difference.
Tip
The TraversalDescription object is immutable, so we have to use the new
instance returned with the new uniqueness setting.
TraversalDescription nodeGlobalTd = td.uniqueness( Uniqueness.NODE_GLOBAL );
results = nodeGlobalTd.traverse( start );
Now only one path is returned:
(3)--[descendant,0]-->(1)<--[owns,3]--(5)
4.4.4. Social network
Note
The following example uses the new experimental traversal API.
Social networks (know as social graphs out on the web) are natural to model
with a graph. This example shows a very simple social model that connects
friends and keeps track of status updates.
4.4.4.1. Simple social model
Figure 4.5. Social network data model
socnet-model.png
The data model for a social network is pretty simple: Persons with names and
StatusUpdates with timestamped text. These entities are then connected by
specific relationships.
* Person
o friend: relates two distinct Person instances (no self-reference)
o status: connects to the most recent StatusUpdate
* StatusUpdate
o next: points to the next StatusUpdate in the chain, which was posted
before the current one
4.4.4.2. Status graph instance
The StatusUpdate list for a Person is a linked list. The head of the list (the
most recent status) is found by following status. Each subsequent StatusUpdate
is connected by next.
Here’s an example where Andreas Kollegger micro-blogged his way to work in the
morning:
andreas-status-updates.svg
To read the status updates, we can create a traversal, like so:
TraversalDescription traversal = Traversal.description().
depthFirst().
relationships( NEXT ).
filter( Traversal.returnAll() );
This gives us a traverser that will start at one StatusUpdate, and will follow
the chain of updates until they run out. Traversers are lazy loading, so it’s
performant even when dealing with thousands of statuses - they are not loaded
until we actually consume them.
4.4.4.3. Activity stream
Once we have friends, and they have status messages, we might want to read our
friends status' messages, in reverse time order - latest first. To do this, we
go through these steps:
1. Gather all friend’s status update iterators in a list - latest date first.
2. Sort the list.
3. Return the first item in the list.
4. If the first iterator is exhausted, remove it from the list. Otherwise, get
the next item in that iterator.
5. Go to step 2 until there are no iterators left in the list.
Animated, the sequence looks like this .
The code looks like:
PositionedIterator first = statuses.get(0);
StatusUpdate returnVal = first.current();
if ( !first.hasNext() )
{
statuses.remove( 0 );
}
else
{
first.next();
sort();
}
return returnVal;
Full source code: socnet
4.5. Domain entities
This page demonstrates one way to handle domain entities when using Neo4j. The
principle at use is to wrap the entities around a node (the same approach can
be used with relationships as well).
First off, store the node and make it accessible inside the package:
private final Node underlyingNode;
Person( Node personNode )
{
this.underlyingNode = personNode;
}
protected Node getUnderlyingNode()
{
return underlyingNode;
}
Delegate attributes to the node:
public String getName()
{
return (String)underlyingNode.getProperty( NAME );
}
Make sure to override these methods:
@Override
public int hashCode()
{
return underlyingNode.hashCode();
}
@Override
public boolean equals( Object o )
{
return o instanceof Person &&
underlyingNode.equals( ( (Person)o ).getUnderlyingNode() );
}
@Override
public String toString()
{
return "Person[" + getName() + "]";
}
Full source code: Person.java
4.6. Graph Algorithm examples
Calculating the shortest path (least number of relationships) between two
nodes:
Node startNode = graphDb.createNode();
Node middleNode1 = graphDb.createNode();
Node middleNode2 = graphDb.createNode();
Node middleNode3 = graphDb.createNode();
Node endNode = graphDb.createNode();
createRelationshipsBetween( startNode, middleNode1, endNode );
createRelationshipsBetween( startNode, middleNode2, middleNode3, endNode );
// Will find the shortest path between startNode and endNode via
// "MY_TYPE" relationships (in OUTGOING direction), like f.ex:
//
// (startNode)-->(middleNode1)-->(endNode)
//
PathFinder finder = GraphAlgoFactory.shortestPath(
Traversal.expanderForTypes( ExampleTypes.MY_TYPE, Direction.OUTGOING ), 15 );
Iterable paths = finder.findAllPaths( startNode, endNode );
Using Dijkstra’s algorithm to calculate cheapest path between node A and B where
each relationship can have a weight (i.e. cost) and the path(s) with least cost
are found.
PathFinder finder = GraphAlgoFactory.dijkstra(
Traversal.expanderForTypes( ExampleTypes.MY_TYPE, Direction.BOTH ), "cost" );
WeightedPath path = finder.findSinglePath( nodeA, nodeB );
// Get the weight for the found path
path.weight();
Using A* to calculate the
cheapest path between node A and B, where cheapest is for example the path in a
network of roads which has the shortest length between node A and B. Here’s our
example graph:
A* algorithm example graph
Node nodeA = createNode( "name", "A", "x", 0d, "y", 0d );
Node nodeB = createNode( "name", "B", "x", 7d, "y", 0d );
Node nodeC = createNode( "name", "C", "x", 2d, "y", 1d );
Relationship relAB = createRelationship( nodeA, nodeC, "length", 2d );
Relationship relBC = createRelationship( nodeC, nodeB, "length", 3d );
Relationship relAC = createRelationship( nodeA, nodeB, "length", 10d );
EstimateEvaluator estimateEvaluator = new EstimateEvaluator()
{
public Double getCost( final Node node, final Node goal )
{
double dx = (Double) node.getProperty( "x" ) - (Double) goal.getProperty( "x" );
double dy = (Double) node.getProperty( "y" ) - (Double) goal.getProperty( "y" );
double result = Math.sqrt( Math.pow( dx, 2 ) + Math.pow( dy, 2 ) );
return result;
}
};
PathFinder astar = GraphAlgoFactory.aStar(
Traversal.expanderForAllTypes(),
CommonEvaluators.doubleCostEvaluator( "length" ), estimateEvaluator );
WeightedPath path = astar.findSinglePath( nodeA, nodeB );
Full source code: PathFindingExamplesTest.java
4.7. Reading a management attribute
The EmbeddedGraphDatabase class includes a convenience method
to get
instances of Neo4j management beans. The common JMX service can be used as
well, but from your code you probably rather want to use the approach outlined
here.
This example shows how to get the start time of a database:
private static Date getStartTimeFromManagementBean(
GraphDatabaseService graphDbService )
{
// use EmbeddedGraphDatabase to access management beans
AbstractGraphDatabase graphDb = (AbstractGraphDatabase) graphDbService;
Kernel kernel = graphDb.getSingleManagementBean( Kernel.class );
Date startTime = kernel.getKernelStartTime();
return startTime;
}
Depending on which Neo4j edition you are using different sets of management
beans are available.
* For all editions, see the org.neo4j.jmx package.
* For the Advanced and Enterprise editions, see the org.neo4j.management
package as well.
Full source code: JmxTest.java
4.8. Cypher Queries
In Java, you can use the query language like this:
AbstractGraphDatabase db = new EmbeddedGraphDatabase( DB_PATH );
ExecutionEngine engine = new ExecutionEngine( db );
ExecutionResult result = engine.execute( "start n=node(0) where 1=1 return n" );
System.out.println( result );
Note
The classes used here are from the org.neo4j.cypher.javacompat package, see
link to the Java API below.
Which will output:
+-----------+
| n |
+-----------+
| Node[0]{} |
+-----------+
1 rows, 0 ms
You can get a list of columns in the result:
List columns = result.columns();
System.out.println( columns );
This outputs:
[n]
To fetch the result items in a single column, do like this:
Iterator n_column = result.columnAs( "n" );
for ( Node node : asIterable( n_column ) )
{
System.out.println( node );
}
In this case there’s only one node in the result:
Node[0]
Full source code of the example: JavaQuery.java
For more information on the Java interface to Cypher, see the Java API .
Chapter 5. Cypher Cookbook
The following cookbook aims to provide a few snippets, examples and use-cases
and their query-solutions in Cypher. FOr the Cypher reference documentation,
see Chapter 15, Cypher Query Language.
5.1. Hyperedges and Cypher
Imagine a user being part of different groups. A group can have different
roles, and a user can be part of different groups. He also can have different
roles in different groups apart from the membership. The association of a User,
a Group and a Role can be referred to as a HyperEdge. However, it can be easily
modeled in a property graph as a node that captures this n-ary relationship, as
depicted below in the U1G2R1 node.
Graph
cypher-hyperedge-graph.svg
5.1.1. Find Groups
To find out in what roles a user is for a particular groups (here Group2), the
following Cypher Query can traverse this HyperEdge node and provide answers.
Query
START n=node:node_auto_index(name = "User1")
MATCH n-[:hasRoleInGroup]->hyperEdge-[:hasGroup]->group, hyperEdge-[:hasRole]->role
WHERE group.name = "Group2"
RETURN role.name
The role of User1:
Table 5.1. Result
+------------+
|role.name |
|------------|
|1 rows, 4 ms|
|------------|
|"Role1" |
+------------+
5.1.2. Find all groups and roles for a user
Here, find all groups and the roles a user has, sorted by the roles names.
Query
START n=node:node_auto_index(name = "User1")
MATCH n-[:hasRoleInGroup]->hyperEdge-[:hasGroup]->group, hyperEdge-[:hasRole]->role
RETURN role.name, group.name
ORDER BY role.name ASC
The groups and roles of User1
Table 5.2. Result
+---------------------+
|role.name|group.name |
|---------------------|
|2 rows, 3 ms |
|---------------------|
|"Role1" |"Group2" |
|---------+-----------|
|"Role2" |"Group1" |
+---------------------+
5.2. Basic Friend finding based on social neighborhood
Imagine an example graph like
Graph
cypher-collabfiltering-graph.svg
5.2.1. Simple Friend Finder
To find out the friends of Joes friends that are not already his friends,
Cypher looks like:
Query
START joe=node:node_auto_index(name = "Joe")
MATCH joe-[:knows]->friend-[:knows]->friend_of_friend, joe-[r?:knows]->friend_of_friend
WHERE r IS NULL
RETURN friend_of_friend.name, COUNT(*)
ORDER BY COUNT(*) DESC, friend_of_friend.name
The list of Friends-of-friends order by the number of connections to them,
secondly by their name.
Table 5.3. Result
+-------------------------------+
|friend_of_friend.name |count(*)|
|-------------------------------|
|3 rows, 11 ms |
|-------------------------------|
|"Ian" |2 |
|----------------------+--------|
|"Derrick" |1 |
|----------------------+--------|
|"Jill" |1 |
+-------------------------------+
5.3. Co-favorited places
Graph
cypher-cofavoritedplaces-graph.svg
5.3.1. Co-Favorited Places - Users Who Like x Also Like y
Find places that people also like who favorite this place:
* Determine who has favorited place x.
* What else have they favorited that is not place x.
Query
START place=node:node_auto_index(name = "CoffeeShop1")
MATCH place<-[:favorite]-person-[:favorite]->stuff
RETURN stuff.name, count(*)
ORDER BY count(*) DESC, stuff.name
The list of places that are favorited by people that favorited the start place.
Table 5.4. Result
+----------------------+
|stuff.name |count(*)|
|----------------------|
|3 rows, 4 ms |
|----------------------|
|"MelsPlace" |2 |
|-------------+--------|
|"CoffeShop2" |1 |
|-------------+--------|
|"SaunaX" |1 |
+----------------------+
5.3.2. Co-Tagged Places - Places Related through Tags
Find places that are tagged with the same tags:
* Determine the tags for place x.
* What else is tagged the same as x that is not x.
Query
START place=node:node_auto_index(name = "CoffeeShop1")
MATCH place-[:tagged]->tag<-[:tagged]-otherPlace
RETURN otherPlace.name, collect(tag.name)
ORDER By otherPlace.name DESC
The list of possible friends ranked by them liking similar stuff that are not
yet friends.
Table 5.5. Result
+---------------------------------+
|otherPlace.name|collect(tag.name)|
|---------------------------------|
|3 rows, 3 ms |
|---------------------------------|
|"MelsPlace" |List(Cool, Cosy) |
|---------------+-----------------|
|"CoffeeShop3" |List(Cosy) |
|---------------+-----------------|
|"CoffeeShop2" |List(Cool) |
+---------------------------------+
5.4. Find people based on similar favorites
Graph
cypher-peoplesimilarityfavorites-graph.svg
5.4.1. Find people based on similar favorites
To find out the possible new friends based on them liking similar things as the
asking person:
Query
START me=node:node_auto_index(name = "Joe")
MATCH me-[:favorite]->stuff<-[:favorite]-person
WHERE NOT(me-[:friend]-person)
RETURN person.name, count(stuff)
ORDER BY count(stuff) DESC
The list of possible friends ranked by them liking similar stuff that are not
yet friends.
Table 5.6. Result
+-------------------------+
|person.name|count(stuff) |
|-------------------------|
|2 rows, 5 ms |
|-------------------------|
|"Derrick" |2 |
|-----------+-------------|
|"Jill" |1 |
+-------------------------+
5.5. Multirelational (social) graphs
Graph
cypher-multirelationalsocialnetwork-graph.svg
5.5.1. Who FOLLOWS or LOVES me back
This example shows a multi-relational * network between persons and things they
like. * A multi-relational graph is a graph with more than * one kind of
relationship between nodes.
Query
START me=node:node_auto_index(name = 'Joe')
MATCH me-[r1]->other-[r2]->me
WHERE type(r1)=type(r2) AND type(r1) =~ /FOLLOWS|LOVES/
RETURN other.name, type(r1)
People that FOLLOWS or LOVES Joe back.
Table 5.7. Result
+---------------------+
|other.name |TYPE(r1) |
|---------------------|
|3 rows, 4 ms |
|---------------------|
|"Sara" |"FOLLOWS"|
|-----------+---------|
|"Maria" |"FOLLOWS"|
|-----------+---------|
|"Maria" |"LOVES" |
+---------------------+
Chapter 6. Using the Neo4j REST API
The included Java example shows a “low-level” approach to using the Neo4j REST
API from Java. For other options, see below.
Table 6.1. Neo4j REST clients contributed by the community.
+-----------------------------------------------------------------------------+
|name |language / |URL |
| |framework | |
|---------------+-----------+-------------------------------------------------|
|Neo4jClient |.NET |http://hg.readify.net/neo4jclient/ |
|---------------+-----------+-------------------------------------------------|
|Neo4jRestNet |.NET |https://github.com/SepiaGroup/Neo4jRestNet |
| | | |
|---------------+-----------+-------------------------------------------------|
|py2neo |Python |http://py2neo.org/ |
|---------------+-----------+-------------------------------------------------|
|Bulbflow |Python |http://bulbflow.com/ |
|---------------+-----------+-------------------------------------------------|
|neo4jrestclient|Python |https://github.com/versae/neo4j-rest-client |
| | | |
|---------------+-----------+-------------------------------------------------|
|neo4django |Django |https://github.com/scholrly/neo4django |
|---------------+-----------+-------------------------------------------------|
|Neo4jPHP |PHP |https://github.com/jadell/Neo4jPHP |
|---------------+-----------+-------------------------------------------------|
|neography |Ruby |https://github.com/maxdemarzi/neography |
|---------------+-----------+-------------------------------------------------|
|node.js |JavaScript |https://github.com/thingdom/node-neo4j |
+-----------------------------------------------------------------------------+
6.1. How to use the REST API from Java
6.1.1. Creating a graph through the REST API from Java
The REST API uses HTTP and JSON, so that it can be used from many languages and
platforms. Still, when geting started it’s useful to see some patterns that can
be re-used. In this brief overview, we’ll show you how to create and manipulate
a simple graph through the REST API and also how to query it.
For these examples, we’ve chosen the Jersey client
components, which are easily downloaded via Maven.
6.1.2. Start the server
Before we can perform any actions on the server, we need to start it as per
Section 17.1, “Server Installation”.
WebResource resource = Client.create()
.resource( SERVER_ROOT_URI );
ClientResponse response = resource.get( ClientResponse.class );
System.out.println( String.format( "GET on [%s], status code [%d]",
SERVER_ROOT_URI, response.getStatus() ) );
response.close();
If the status of the response is 200 OK, then we know the server is running
fine and we can continue. If the code fails to conenct to the server, then
please have a look at Chapter 17, Neo4j Server.
Note
If you get any other response than 200 OK (particularly 4xx or 5xx responses)
then please check your configuration and look in the log files in the data/log
directory.
6.1.3. Creating a node
The REST API uses POST to create nodes. Encapsulating that in Java is
straightforward using the Jersey client:
final String nodeEntryPointUri = SERVER_ROOT_URI + "node";
// http://localhost:7474/db/data/node
WebResource resource = Client.create()
.resource( nodeEntryPointUri );
// POST {} to the node entry point URI
ClientResponse response = resource.accept( MediaType.APPLICATION_JSON )
.type( MediaType.APPLICATION_JSON )
.entity( "{}" )
.post( ClientResponse.class );
final URI location = response.getLocation();
System.out.println( String.format(
"POST to [%s], status code [%d], location header [%s]",
nodeEntryPointUri, response.getStatus(), location.toString() ) );
response.close();
return location;
If the call completes successfully, under the covers it will have sent a HTTP
request containing a JSON payload to the server. The server will then have
created a new node in the database and responded with a 201 Created response
and a Location header with the URI of the newly created node.
In our example, we call this functionality twice to create two nodes in our
database.
6.1.4. Adding properties
Once we have nodes in our datatabase, we can use them to store useful data. In
this case, we’re going to store information about music in our database. Let’s
start by looking at the code that we use to create nodes and add properties.
Here we’ve added nodes to represent "Joe Strummer" and a band called "The
Clash".
URI firstNode = createNode();
addProperty( firstNode, "name", "Joe Strummer" );
URI secondNode = createNode();
addProperty( secondNode, "band", "The Clash" );
Inside the addProperty method we determine the resource that represents
properties for the node and decide on a name for that property. We then proceed
to PUT the value of that property to the server.
String propertyUri = nodeUri.toString() + "/properties/" + propertyName;
// http://localhost:7474/db/data/node/{node_id}/properties/{property_name}
WebResource resource = Client.create()
.resource( propertyUri );
ClientResponse response = resource.accept( MediaType.APPLICATION_JSON )
.type( MediaType.APPLICATION_JSON )
.entity( "\"" + propertyValue + "\"" )
.put( ClientResponse.class );
System.out.println( String.format( "PUT to [%s], status code [%d]",
propertyUri, response.getStatus() ) );
response.close();
If everything goes well, we’ll get a 204 No Content back indicating that the
server processed the request but didn’t echo back the property value.
6.1.5. Adding relationships
Now that we have nodes to represent Joe Strummer and The Clash, we can relate
them. The REST API supports this through a POST of a relationship
representation to the start node of the relationship. Correspondingly in Java
we POST some JSON to the URI of our node that represents Joe Strummer, to
establish a relationship between that node and the node representing The Clash.
URI relationshipUri = addRelationship( firstNode, secondNode, "singer",
"{ \"from\" : \"1976\", \"until\" : \"1986\" }" );
Inside the addRelationship method, we determine the URI of the Joe Strummer
node’s relationships, and then POST a JSON description of our intended
relationship. This description contains the destination node, a label for the
relationship type, and any attributes for the relation as a JSON collection.
private static URI addRelationship( URI startNode, URI endNode,
String relationshipType, String jsonAttributes )
throws URISyntaxException
{
URI fromUri = new URI( startNode.toString() + "/relationships" );
String relationshipJson = generateJsonRelationship( endNode,
relationshipType, jsonAttributes );
WebResource resource = Client.create()
.resource( fromUri );
// POST JSON to the relationships URI
ClientResponse response = resource.accept( MediaType.APPLICATION_JSON )
.type( MediaType.APPLICATION_JSON )
.entity( relationshipJson )
.post( ClientResponse.class );
final URI location = response.getLocation();
System.out.println( String.format(
"POST to [%s], status code [%d], location header [%s]",
fromUri, response.getStatus(), location.toString() ) );
response.close();
return location;
}
If all goes well, we receive a 201 Created status code and a Location header
which contains a URI of the newly created relation.
6.1.6. Add properties to a relationship
Like nodes, relationships can have properties. Since we’re big fans of both Joe
Strummer and the Clash, we’ll add a rating to the relationship so that others
can see he’s a 5-star singer with the band.
addMetadataToProperty( relationshipUri, "stars", "5" );
Inside the addMetadataToProperty method, we determine the URI of the properties
of the relationship and PUT our new values (since it’s PUT it will always
overwrite existing values, so be careful).
private static void addMetadataToProperty( URI relationshipUri,
String name, String value ) throws URISyntaxException
{
URI propertyUri = new URI( relationshipUri.toString() + "/properties" );
String entity = toJsonNameValuePairCollection( name, value );
WebResource resource = Client.create()
.resource( propertyUri );
ClientResponse response = resource.accept( MediaType.APPLICATION_JSON )
.type( MediaType.APPLICATION_JSON )
.entity( entity )
.put( ClientResponse.class );
System.out.println( String.format(
"PUT [%s] to [%s], status code [%d]", entity, propertyUri,
response.getStatus() ) );
response.close();
}
Assuming all goes well, we’ll get a 200 OK response back from the server (which
we can check by calling ClientResponse.getStatus()) and we’ve now established a
very small graph that we can query.
6.1.7. Querying graphs
As with the embedded version of the database, the Neo4j server uses graph
traversals to look for data in graphs. Currently the Neo4j server expects a
JSON payload describing the traversal to be POST-ed at the starting node for
the traversal (though this is likely to change in time to a GET-based
approach).
To start this process, we use a simple class that can turn itself into the
equivalent JSON, ready for POST-ing to the server, and in this case we’ve
hardcoded the traverser to look for all nodes with outgoing relationships with
the type "singer".
// TraversalDescription turns into JSON to send to the Server
TraversalDescription t = new TraversalDescription();
t.setOrder( TraversalDescription.DEPTH_FIRST );
t.setUniqueness( TraversalDescription.NODE );
t.setMaxDepth( 10 );
t.setReturnFilter( TraversalDescription.ALL );
t.setRelationships( new Relationship( "singer", Relationship.OUT ) );
Once we have defined the parameters of our traversal, we just need to transfer
it. We do this by determining the URI of the traversers for the start node, and
then POST-ing the JSON representation of the traverser to it.
URI traverserUri = new URI( startNode.toString() + "/traverse/node" );
WebResource resource = Client.create()
.resource( traverserUri );
String jsonTraverserPayload = t.toJson();
ClientResponse response = resource.accept( MediaType.APPLICATION_JSON )
.type( MediaType.APPLICATION_JSON )
.entity( jsonTraverserPayload )
.post( ClientResponse.class );
System.out.println( String.format(
"POST [%s] to [%s], status code [%d], returned data: "
+ System.getProperty( "line.separator" ) + "%s",
jsonTraverserPayload, traverserUri, response.getStatus(),
response.getEntity( String.class ) ) );
response.close();
Once that request has completed, we get back our dataset of singers and the
bands they belong to:
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/82/relationships/out",
"data" : {
"band" : "The Clash",
"name" : "Joe Strummer"
},
"traverse" : "http://localhost:7474/db/data/node/82/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/82/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/82/properties/{key}",
"all_relationships" : "http://localhost:7474/db/data/node/82/relationships/all",
"self" : "http://localhost:7474/db/data/node/82",
"properties" : "http://localhost:7474/db/data/node/82/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/82/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/82/relationships/in",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/82/relationships/in/{-list|&|types}",
"create_relationship" : "http://localhost:7474/db/data/node/82/relationships"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/83/relationships/out",
"data" : {
},
"traverse" : "http://localhost:7474/db/data/node/83/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/83/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/83/properties/{key}",
"all_relationships" : "http://localhost:7474/db/data/node/83/relationships/all",
"self" : "http://localhost:7474/db/data/node/83",
"properties" : "http://localhost:7474/db/data/node/83/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/83/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/83/relationships/in",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/83/relationships/in/{-list|&|types}",
"create_relationship" : "http://localhost:7474/db/data/node/83/relationships"
} ]
6.1.8. Phew, is that it?
That’s a flavor of what we can do with the REST API. Naturally any of the HTTP
idioms we provide on the server can be easily wrapped, including removing nodes
and relationships through DELETE. Still if you’ve gotten this far, then
switching .post() for .delete() in the Jersey client code should be
straightforward.
6.1.9. What’s next?
The HTTP API provides a good basis for implementers of client libraries, it’s
also great for HTTP and REST folks. In the future though we expect that
idiomatic language bindings will appear to take advantage of the REST API while
providing comfortable language-level constructs for developers to use, much as
there are similar bindings for the embedded database. For a list of current
Neo4j REST clients and embedded wrappers, see http://www.delicious.com/neo4j/
drivers .
6.1.10. Appendix: the code
* CreateSimpleGraph.java
* Relationship.java
* TraversalDescription.java
Chapter 7. Extending the Neo4j Server
The Neo4j Server can be extended by either plugins or unmanaged extensions. For
more information on the server, see Chapter 17, Neo4j Server.
7.1. Server Plugins
Quick info
* The server’s functionality can be extended by adding plugins.
* Plugins are user-specified code which extend the capabilities of the
database, nodes, or relationships.
* The neo4j server will then advertise the plugin functionality within
representations as clients interact via HTTP.
Plugins provide an easy way to extend the Neo4j REST API with new
functionality, without the need to invent your own API. Think of plugins as
server-side scripts that can add functions for retrieving and manipulating
nodes, relationships, paths, properties or indices.
Tip
If you want to have full control over your API, and are willing to put in the
effort, and understand the risks, then Neo4j server also provides hooks for
unmanaged extensions based on JAX-RS.
The needed classes reside in the org.neo4j:server-api jar file. See
the linked page for downloads and instructions on how to include it using
dependency management. For Maven projects, add the Server API dependencies in
your pom.xml like this:
org.neo4j
server-api
${neo4j-version}
Where ${neo4j-version} is the intended version.
To create a plugin, your code must inherit from the ServerPlugin class. Your plugin should also:
* ensure that it can produce an (Iterable of) Node, Relationship or Path, or
any Java primitive or String
* specify parameters,
* specify a point of extension and of course
* contain the application logic.
* make sure that the discovery point type in the @PluginTarget and the
@Source parameter are of the same type.
An example of a plugin which augments the database (as opposed to nodes or
relationships) follows:
Get all nodes or relationships plugin.
@Description( "An extension to the Neo4j Server for getting all nodes or relationships" )
public class GetAll extends ServerPlugin
{
@Name( "get_all_nodes" )
@Description( "Get all nodes from the Neo4j graph database" )
@PluginTarget( GraphDatabaseService.class )
public Iterable getAllNodes( @Source GraphDatabaseService graphDb )
{
return GlobalGraphOperations.at( graphDb ).getAllNodes();
}
@Description( "Get all relationships from the Neo4j graph database" )
@PluginTarget( GraphDatabaseService.class )
public Iterable getAllRelationships( @Source GraphDatabaseService graphDb )
{
return GlobalGraphOperations.at( graphDb ).getAllRelationships();
}
}
The full source code is found here: GetAll.java
Find the shortest path between two nodes plugin.
public class ShortestPath extends ServerPlugin
{
@Description( "Find the shortest path between two nodes." )
@PluginTarget( Node.class )
public Iterable shortestPath(
@Source Node source,
@Description( "The node to find the shortest path to." )
@Parameter( name = "target" ) Node target,
@Description( "The relationship types to follow when searching for the shortest path(s). " +
"Order is insignificant, if omitted all types are followed." )
@Parameter( name = "types", optional = true ) String[] types,
@Description( "The maximum path length to search for, default value (if omitted) is 4." )
@Parameter( name = "depth", optional = true ) Integer depth )
{
Expander expander;
if ( types == null )
{
expander = Traversal.expanderForAllTypes();
}
else
{
expander = Traversal.emptyExpander();
for ( int i = 0; i < types.length; i++ )
{
expander = expander.add( DynamicRelationshipType.withName( types[i] ) );
}
}
PathFinder shortestPath = GraphAlgoFactory.shortestPath(
expander, depth == null ? 4 : depth.intValue() );
return shortestPath.findAllPaths( source, target );
}
}
The full source code is found here: ShortestPath.java
To deploy the code, simply compile it into a .jar file and place it onto the
server classpath (which by convention is the plugins directory under the Neo4j
server home directory).
Tip
Make sure the directories listings are retained in the jarfile by either
building with default Maven, or with jar -cvf myext.jar *, making sure to jar
directories instead of specifying single files.
The .jar file must include the file META-INF/services/
org.neo4j.server.plugins.ServerPlugin with the fully qualified name of the
implementation class. This is an example with multiple entries, each on a
separate line:
org.neo4j.examples.server.plugins.GetAll
org.neo4j.examples.server.plugins.DepthTwo
org.neo4j.examples.server.plugins.ShortestPath
The code above makes an extension visible in the database representation (via
the @PluginTarget annotation) whenever it is served from the Neo4j Server.
Simply changing the @PluginTarget parameter to Node.class or Relationship.class
allows us to target those parts of the data model should we wish. The
functionality extensions provided by the plugin are automatically advertised in
representations on the wire. For example, clients can discover the extension
implemented by the above plugin easily by examining the representations they
receive as responses from the server, e.g. by performing a GET on the default
database URI:
curl -v http://localhost:7474/db/data/
The response to the GET request will contain (by default) a JSON container that
itself contains a container called "extensions" where the available plugins are
listed. In the following case, we only have the GetAll plugin registered with
the server, so only its extension functionality is available. Extension names
will be automatically assigned, based on method names, if not specifically
specified using the @Name annotation.
{
"extensions-info" : "http://localhost:7474/db/data/ext",
"node" : "http://localhost:7474/db/data/node",
"node_index" : "http://localhost:7474/db/data/index/node",
"relationship_index" : "http://localhost:7474/db/data/index/relationship",
"reference_node" : "http://localhost:7474/db/data/node/0",
"extensions_info" : "http://localhost:7474/db/data/ext",
"extensions" : {
"GetAll" : {
"get_all_nodes" : "http://localhost:7474/db/data/ext/GetAll/graphdb/get_all_nodes",
"get_all_relationships" : "http://localhost:7474/db/data/ext/GetAll/graphdb/getAllRelationships"
}
}
Performing a GET on one of the two extension URIs gives back the meta
information about the service:
curl http://localhost:7474/db/data/ext/GetAll/graphdb/get_all_nodes
{
"extends" : "graphdb",
"description" : "Get all nodes from the Neo4j graph database",
"name" : "get_all_nodes",
"parameters" : [ ]
}
To use it, just POST to this URL, with parameters as specified in the
description and encoded as JSON data content. F.ex for calling the shortest
path extension (URI gotten from a GET to http://localhost:7474/db/data/node/123
):
curl -X POST http://localhost:7474/db/data/ext/GetAll/node/123/shortestPath \
-H "Content-Type: application/json" \
-d '{"target":"http://localhost:7474/db/data/node/456&depth=5"}'
If everything is OK a response code 200 and a list of zero or more items will
be returned. If nothing is returned (null returned from extension) an empty
result and response code 204 will be returned. If the extension throws an
exception response code 500 and a detailed error message is returned.
Extensions that do any kind of write operation will have to manage their own
transactions, i.e. transactions aren’t managed automatically.
Through this model, any plugin can naturally fit into the general hypermedia
scheme that Neo4j espouses - meaning that clients can still take advantage of
abstractions like Nodes, Relationships and Paths with a straightforward upgrade
path as servers are enriched with plugins (old clients don’t break).
7.2. Unmanaged Extensions
Quick info
* Danger: Men at Work! The unmanaged extensions are a way of deploying
arbitrary JAX-RS code into the Neo4j server.
* The unmanaged extensions are exactly that: unmanaged. If you drop poorly
tested code into the server, it’s highly likely you’ll degrade its
performance, so be careful.
Some projects want extremely fine control over their server-side code. For this
we’ve introduced an unmanaged extension API.
Warning
This is a sharp tool, allowing users to deploy arbitrary JAX-RS classes to the server and so you should be
careful when thinking about using this. In particular you should understand
that it’s easy to consume lots of heap space on the server and hinder
performance if you’re not careful.
Still, if you understand the disclaimer, then you load your JAX-RS classes into
the Neo4j server simply by adding adding a @Context annotation to your code,
compiling against the JAX-RS jar and any Neo4j jars you’re making use of. Then
add your classes to the runtime classpath (just drop it in the lib directory of
the Neo4j server). In return you get access to the hosted environment of the
Neo4j server like logging through the org.neo4j.server.logging.Logger.
In your code, you get access to the underlying GraphDatabaseService through the
@Context annotation like so:
public MyCoolService( @Context GraphDatabaseService database )
{
// Have fun here, but be safe!
}
Remember, the unmanaged API is a very sharp tool. It’s all to easy to
compromise the server by deploying code this way, so think first and see if you
can’t use the managed extensions in preference. However, a number of context
parameters can be automatically provided for you, like the reference to the
database.
In order to specify the mount point of your extension, a full class looks like
this:
Unmanaged extension example.
@Path( "/helloworld" )
public class HelloWorldResource
{
private final GraphDatabaseService database;
public HelloWorldResource( @Context GraphDatabaseService database )
{
this.database = database;
}
@GET
@Produces( MediaType.TEXT_PLAIN )
@Path( "/{nodeId}" )
public Response hello( @PathParam( "nodeId" ) long nodeId )
{
// Do stuff with the database
return Response.status( Status.OK ).entity(
( "Hello World, nodeId=" + nodeId ).getBytes() ).build();
}
}
The full source code is found here: HelloWorldResource.java
Build this code, and place the resulting jar file (and any custom dependencies)
into the $NEO4J_SERVER_HOME/plugins directory, and include this class in the
neo4j-server.properties file, like so:
Tip
Make sure the directories listings are retained in the jarfile by either
building with default Maven, or with jar -cvf myext.jar *, making sure to jar
directories instead of specifying single files.
#Comma separated list of JAXRS packages containing JAXRS Resource, one package name for each mountpoint.
org.neo4j.server.thirdparty_jaxrs_classes=org.neo4j.examples.server.unmanaged=/examples/unmanaged
Which binds the hello method to respond to GET requests at the URI: http://
{neo4j_server}:{neo4j_port}/examples/unmanaged/helloworld/{nodeId}
curl http://localhost:7474/examples/unmanaged/helloworld/123
which results in
Hello World, nodeId=123
Chapter 8. The Traversal Framework
The Neo4j Traversal API is a callback based, lazily
executed way of specifying desired movements through a graph in Java. Some
traversal examples are collected under Section 4.4, “Traversal”.
There is also a more restricted way to perform traversals, Node.traverse()
is a good starting point to read more about that.
Other options to traverse or query graphs in Neo4j are Cypher and Gremlin.
8.1. Main concepts
Here follows a short explanation of all different methods that can modify or
add to a traversal description.
* Expanders — define what to traverse, typically in terms of relationships
direction and type.
* Order — for example depth-first or breadth-first.
* Uniqueness — visit nodes (relationships, paths) only once.
* Evaluator — decide what to return and whether to stop or continue traversal
beyond the current position.
* A starting node where the traversal will begin.
graphdb-traversal-description.svg
See Section 8.2, “Traversal Framework Java API” for more details.
8.2. Traversal Framework Java API
The traversal framework consists of a few main interfaces in addition to Node
and Relationship: TraversalDescription, Evaluator, Traverser and Uniqueness are
the main ones. The Path interface also has a special purpose in traversals,
since it is used to represent a position in the graph when evaluating that
position. Furthermore the RelationshipExpander and Expander interfaces are
central to traversals, but users of the API rarely need to implement them.
There are also a set of interfaces for advanced use, when explicit control over
the traversal order is required: BranchSelector, BranchOrderingPolicy and
TraversalBranch.
8.2.1. TraversalDescription
The TraversalDescription is the main interface used
for defining and initializing traversals. It is not meant to be implemented by
users of the traversal framework, but rather to be provided by the
implementation of the traversal framework as a way for the user to describe
traversals. TraversalDescription instances are immutable and its methods
returns a new TraversalDescription that is modified compared to the object the
method was invoked on with the arguments of the method.
8.2.1.1. Relationships
Adds a relationship type to the list of relationship types to traverse. By
default that list is empty and it means that it will traverse all relationships
, irregardless of type. If one or more relationships are added to this list
only the added types will be traversed. There are two methods, one including
direction and another one excluding
direction , where the latter traverses
relationships in both directions .
8.2.2. Evaluator
Evaluator s are used for deciding, at each position (represented
as a Path): should the traversal continue, and/or should the node be included
in the result. Given a Path, it asks for one of four actions for that branch of
the traversal:
* Evaluation.INCLUDE_AND_CONTINUE: Include this node in the result and
continue the traversal
* Evaluation.INCLUDE_AND_PRUNE: Include this node in the result, but don’t
continue the traversal
* Evaluation.EXCLUDE_AND_CONTINUE: Exclude this node from the result, but
continue the traversal
* Evaluation.EXCLUDE_AND_PRUNE: Exclude this node from the result and don’t
continue the traversal
More than one evaluator can be added. Note that evaluators will be called for
all positions the traverser encounters, even for the start node.
8.2.3. Traverser
The Traverser object is the result of invoking traverse() of a
TraversalDescription object. It represents a traversal positioned in the graph,
and a specification of the format of the result. The actual traversal is
performed lazily each time the next()-method of the iterator of the Traverser
is invoked.
8.2.4. Uniqueness
Sets the rules for how positions can be revisited during a traversal as stated
in Uniqueness . Default if not set is NODE_GLOBAL .
A Uniqueness can be supplied to the TraversalDescription to dictate under what
circumstances a traversal may revisit the same position in the graph. The
various uniqueness levels that can be used in Neo4j are:
* NONE - any position in the graph may be revisited.
* NODE_GLOBAL uniqueness - no node in the entire graph may be visited more
than once. This could potentially consume a lot of memory since it requires
keeping an in-memory data structure remembering all the visited nodes.
* RELATIONSHIP_GLOBAL uniqueness - no relationship in the entire graph may be
visited more than once. For the same reasons as NODE_GLOBAL uniqueness,
this could use up a lot of memory. But since graphs typically have a larger
number of relationships than nodes, the memory overhead of this uniqueness
level could grow even quicker.
* NODE_PATH uniqueness - a node may not occur previously in the path reaching
up to it.
* RELATIONSHIP_PATH uniqueness - a relationship may not occur previously in
the path reaching up to it.
* NODE_RECENT uniqueness - Similar to NODE_GLOBAL uniqueness in that there is
a global collection of visited nodes each position is checked against. This
uniqueness level does however have a cap on how much memory it may consume
in the form of a collection that only contains the most recently visited
nodes. The size of this collection can be specified by providing a number
as the second argument to the TraversalDescription.uniqueness()-method
along with the uniqueness level.
* RELATIONSHIP_RECENT uniqueness - works like NODE_RECENT uniqueness, but
with relationships instead of nodes.
8.2.4.1. Depth First / Breadth First
These are convenience methods for setting preorder depth-first / breadth-first BranchSelector|ordering policies.
The same result can be achieved by calling the order method with ordering policies from the
Traversal factory ,
or to write your own BranchSelector/BranchOrderingPolicy and pass in.
8.2.5. Order - How to move through branches?
A more generic version of depthFirst/breadthFirst methods in that it allows an
arbitrary BranchOrderingPolicy to be injected into the
description.
8.2.6. BranchSelector
A BranchSelector is used for selecting which branch of the traversal to attempt
next. This is used for implementing traversal orderings. The traversal
framework provides a few basic ordering implementations:
* Traversal.preorderDepthFirst() - Traversing depth first, visiting each node
before visiting its child nodes.
* Traversal.postorderDepthFirst() - Traversing depth first, visiting each
node after visiting its child nodes.
* Traversal.preorderBreadthFirst() - Traversing breadth first, visiting each
node before visiting its child nodes.
* Traversal.postorderBreadthFirst() - Traversing breadth first, visiting each
node after visiting its child nodes.
Note
Please note that breadth first traversals have a higher memory overhead than
depth first traversals.
BranchSelectors carries state and hence needs to be uniquely instantiated for
each traversal. Therefore it is supplied to the TraversalDescription through a
BranchOrderingPolicy interface, which is a factory of BranchSelector instances.
A user of the Traversal framework rarely needs to implement his own
BranchSelector or BranchOrderingPolicy, it is provided to let graph algorithm
implementors provide their own traversal orders. The Neo4j Graph Algorithms
package contains for example a BestFirst order BranchSelector/
BranchOrderingPolicy that is used in BestFirst search algorithms such as A* and
Dijkstra.
8.2.6.1. BranchOrderingPolicy
A factory for creating BranchSelectors to decide in what order branches are
returned (where a branch’s position is represented as a Path from the
start node to the current node). Common policies are depth-first and breadth-first and that’s why there are convenience
methods for those. For example, calling TraversalDescription#depthFirst()
is equivalent to:
description.order( Traversal.preorderDepthFirst() );
8.2.6.2. TraversalBranch
An object used by the BranchSelector to get more branches from a certain
branch. In essence these are a composite of a Path and a RelationshipExpander
that can be used to get new TraversalBranch es from the
current one.
8.2.7. Path
A Path is a general interface that is part of the Neo4j API. In the
traversal API of Neo4j the use of Paths are twofold. Traversers can return
their results in the form of the Paths of the visited positions in the graph
that are marked for being returned. Path objects are also used in the
evaluation of positions in the graph, for determining if the traversal should
continue from a certain point or not, and whether a certain position should be
included in the result set or not.
8.2.8. RelationshipExpander
The traversal framework use RelationshipExpanders to discover the relationships
that should be followed from a particular node to further branches in the
traversal.
8.2.9. Expander
A more generic version of relationships where a RelationshipExpander is
injected, defining all relationships to be traversed for any given node. By
default (and when using relationships) a default expander is used, where any particular order of relationships isn’t
guaranteed. There’s another implementation which guarantees that relationships
are traversed in order of relationship type , where types are
iterated in the order they were added.
The Expander interface is an extension of the RelationshipExpander interface
that makes it possible to build customized versions of an Expander. The
implementation of TraversalDescription uses this to provide methods for
defining which relationship types to traverse, this is the usual way a user of
the API would define a RelationshipExpander - by building it internally in the
TraversalDescription.
All the RelationshipExpanders provided by the Neo4j traversal framework also
implement the Expander interface. For a user of the traversal API it is easier
to implement the RelationshipExpander interface, since it only contains one
method - the method for doing getting the relationships from a node, the
methods that the Expander interface adds are just for building new Expanders.
8.2.10. How to use the Traversal framework
In contrary to Node#traverse a traversal description is built (using a fluent interface) and such a
description can spawn traversers .
Figure 8.1. Traversal Example Graph
Traversal-Example-Graph-how-to-use-the-Traversal-framework.svg
With the definition of the RelationshipTypes as
private enum Rels implements RelationshipType
{
LIKES, KNOWS
}
The graph can be traversed with for example the following traverser, starting
at the “Joe” node:
for ( Path position : Traversal.description()
.depthFirst()
.relationships( Rels.KNOWS )
.relationships( Rels.LIKES, Direction.INCOMING )
.evaluator( Evaluators.toDepth( 5 ) )
.traverse( joe ) )
{
output += position + "\n";
}
The traversal will output:
(7)
(7)<--[LIKES,1]--(4)
(7)<--[LIKES,1]--(4)--[KNOWS,6]-->(1)
(7)<--[LIKES,1]--(4)--[KNOWS,6]-->(1)--[KNOWS,4]-->(6)
(7)<--[LIKES,1]--(4)--[KNOWS,6]-->(1)--[KNOWS,4]-->(6)--[KNOWS,3]-->(5)
(7)<--[LIKES,1]--(4)--[KNOWS,6]-->(1)--[KNOWS,4]-->(6)--[KNOWS,3]-->(5)--[KNOWS,2]-->(2)
(7)<--[LIKES,1]--(4)--[KNOWS,6]-->(1)<--[KNOWS,5]--(3)
Since TraversalDescription s are immutable it is also
useful to create template descriptions which holds common settings shared by
different traversals. For example, let’s start with this traverser:
final TraversalDescription FRIENDS_TRAVERSAL = Traversal.description()
.depthFirst()
.relationships( Rels.KNOWS )
.uniqueness( Uniqueness.RELATIONSHIP_GLOBAL );
This traverser would yield the following output (we will keep starting from the
“Joe” node):
(7)
(7)--[KNOWS,0]-->(2)
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)<--[KNOWS,3]--(6)
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)<--[KNOWS,3]--(6)<--[KNOWS,4]--(1)
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)<--[KNOWS,3]--(6)<--[KNOWS,4]--(1)<--[KNOWS,5]--(3)
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)<--[KNOWS,3]--(6)<--[KNOWS,4]--(1)<--[KNOWS,6]--(4)
Now let’s create a new traverser from it, restricting depth to three:
for ( Path path : FRIENDS_TRAVERSAL
.evaluator( Evaluators.toDepth( 3 ) )
.traverse( joe ) )
{
output += path + "\n";
}
This will give us the following result:
(7)
(7)--[KNOWS,0]-->(2)
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)<--[KNOWS,3]--(6)
Or how about from depth two to four? That’s done like this:
for ( Path path : FRIENDS_TRAVERSAL
.evaluator( Evaluators.fromDepth( 2 ) )
.evaluator( Evaluators.toDepth( 4 ) )
.traverse( joe ) )
{
output += path + "\n";
}
This traversal gives us:
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)<--[KNOWS,3]--(6)
(7)--[KNOWS,0]-->(2)<--[KNOWS,2]--(5)<--[KNOWS,3]--(6)<--[KNOWS,4]--(1)
For various useful evaluators, see the Evaluators Java API or
simply implement the Evaluator interface yourself.
If you’re not interested in the Path s, but the Node s you can
transform the traverser into an iterable of nodes like
this:
for ( Node node : FRIENDS_TRAVERSAL
.traverse( joe )
.nodes() )
{
output += node.getProperty( "name" ) + "\n";
}
In this case we use it to retrieve the names:
Joe
Sara
Peter
Dirk
Lars
Ed
Lisa
Relationships are fine as well, here’s how to get
them:
for ( Relationship relationship : FRIENDS_TRAVERSAL
.traverse( joe )
.relationships() )
{
output += relationship.getType() + "\n";
}
Here the relationship types are written, and we get:
KNOWS
KNOWS
KNOWS
KNOWS
KNOWS
KNOWS
The full source for this example is available at
TraversalTest.java
Chapter 9. Domain Modeling Gallery
The following chapters contain simplified examples of how different domains can
be modeled using Neo4j. The aim is not to give full examples, but to suggest
possible ways to think using the graph patterns and data locality in
traversals.
9.1. User roles in graphs
This is an example showing a hierarchy of roles. What’s interesting is that a
tree is not sufficient for storing this structure, as elaborated below.
roles.png
This is an implementation of an example found in the article A Model to
Represent Directed Acyclic Graphs (DAG) on SQL Databases by Kemal Erdogan
.
The article discusses how to store directed acyclic graphs (DAGs) in SQL based DBs. DAGs are
almost trees, but with a twist: it may be possible to reach the same node
through different paths. Trees are restricted from this possibility, which
makes them much easier to handle. In our case it is "Ali" and "Engin", as they
are both admins and users and thus reachable through these group nodes. Reality
often looks this way and can’t be captured by tree structures.
In the article an SQL Stored Procedure solution is provided. The main idea,
that also have some support from scientists, is to pre-calculate all possible
(transitive) paths. Pros and cons of this approach:
* decent performance on read
* low performance on insert
* wastes lots of space
* relies on stored procedures
In Neo4j storing the roles is trivial. In this case we use PART_OF (green
edges) relationships to model the group hierarchy and MEMBER_OF (blue edges) to
model membership in groups. We also connect the top level groups to the
reference node by ROOT relationships. This gives us a useful partitioning of
the graph. Neo4j has no predefined relationship types, you are free to create
any relationship types and give them any semantics you want.
Lets now have a look at how to retrieve information from the graph. The Java
code is using the Neo4j Traversal API (see Section 8.2, “Traversal Framework
Java API”), the queries are done using Cypher.
9.1.1. Get the admins
Node admins = getNodeByName( "Admins" );
Traverser traverser = admins.traverse(
Traverser.Order.BREADTH_FIRST,
StopEvaluator.END_OF_GRAPH,
ReturnableEvaluator.ALL_BUT_START_NODE,
RoleRels.PART_OF, Direction.INCOMING,
RoleRels.MEMBER_OF, Direction.INCOMING );
resulting in the output
Found: Ali at depth: 0
Found: HelpDesk at depth: 0
Found: Engin at depth: 1
Found: Demet at depth: 1
The result is collected from the traverser using this code:
String output = "";
for ( Node node : traverser )
{
output += "Found: " + node.getProperty( NAME ) + " at depth: "
+ ( traverser.currentPosition().depth() - 1 ) + "\n";
}
In Cypher, a similar query would be:
START admins=node(14)
MATCH admins<-[:PART_OF*0..]-group<-[:MEMBER_OF]-user
RETURN user.name, group.name
resulting in:
+---------------------+
|user.name|group.name |
|---------------------|
|3 rows, 37 ms |
|---------------------|
|"Ali" |"Admins" |
|---------+-----------|
|"Engin" |"HelpDesk" |
|---------+-----------|
|"Demet" |"HelpDesk" |
+---------------------+
9.1.2. Get the group memberships of a user
Using the Neo4j Java Traversal API, this query looks like:
Node jale = getNodeByName( "Jale" );
traverser = jale.traverse(
Traverser.Order.DEPTH_FIRST,
StopEvaluator.END_OF_GRAPH,
ReturnableEvaluator.ALL_BUT_START_NODE,
RoleRels.MEMBER_OF, Direction.OUTGOING,
RoleRels.PART_OF, Direction.OUTGOING );
resuling in:
Found: ABCTechnicians at depth: 0
Found: Technicians at depth: 1
Found: Users at depth: 2
In Cypher:
START jale=node(10)
MATCH jale-[:MEMBER_OF]->()-[:PART_OF*0..]->group
RETURN group.name
+----------------+
|group.name |
|----------------|
|3 rows, 3 ms |
|----------------|
|"ABCTechnicians"|
|----------------|
|"Technicians" |
|----------------|
|"Users" |
+----------------+
9.1.3. Get all groups
In Java:
Node referenceNode = getNodeByName( "Reference_Node") ;
traverser = referenceNode.traverse(
Traverser.Order.BREADTH_FIRST,
StopEvaluator.END_OF_GRAPH,
ReturnableEvaluator.ALL_BUT_START_NODE,
RoleRels.ROOT, Direction.INCOMING,
RoleRels.PART_OF, Direction.INCOMING );
resulting in:
Found: Admins at depth: 0
Found: Users at depth: 0
Found: HelpDesk at depth: 1
Found: Managers at depth: 1
Found: Technicians at depth: 1
Found: ABCTechnicians at depth: 2
In Cypher:
START refNode=node(16)
MATCH refNode<-[:ROOT]->()<-[:PART_OF*0..]-group
RETURN group.name
+----------------+
|group.name |
|----------------|
|6 rows, 5 ms |
|----------------|
|"Admins" |
|----------------|
|"HelpDesk" |
|----------------|
|"Users" |
|----------------|
|"Managers" |
|----------------|
|"Technicians" |
|----------------|
|"ABCTechnicians"|
+----------------+
9.1.4. Get all members of all groups
Now, let’s try to find all users in the system being part of any group.
in Java:
traverser = referenceNode.traverse(
Traverser.Order.BREADTH_FIRST,
StopEvaluator.END_OF_GRAPH,
new ReturnableEvaluator()
{
@Override
public boolean isReturnableNode(
TraversalPosition currentPos )
{
if ( currentPos.isStartNode() )
{
return false;
}
Relationship rel = currentPos.lastRelationshipTraversed();
return rel.isType( RoleRels.MEMBER_OF );
}
},
RoleRels.ROOT, Direction.INCOMING,
RoleRels.PART_OF, Direction.INCOMING,
RoleRels.MEMBER_OF, Direction.INCOMING );
Found: Ali at depth: 1
Found: Engin at depth: 1
Found: Burcu at depth: 1
Found: Can at depth: 1
Found: Demet at depth: 2
Found: Gul at depth: 2
Found: Fuat at depth: 2
Found: Hakan at depth: 2
Found: Irmak at depth: 2
Found: Jale at depth: 3
In Cypher, this looks like:
START refNode=node(16)
MATCH refNode<-[:ROOT]->root, p=root<-[PART_OF*0..]-()<-[:MEMBER_OF]-user
RETURN user.name, min(length(p))
ORDER BY min(length(p)), user.name
and results in the following output:
+-------------------------+
|user.name|min(LENGTH(p)) |
|-------------------------|
|10 rows, 43 ms |
|-------------------------|
|"Ali" |1 |
|---------+---------------|
|"Burcu" |1 |
|---------+---------------|
|"Can" |1 |
|---------+---------------|
|"Engin" |1 |
|---------+---------------|
|"Demet" |2 |
|---------+---------------|
|"Fuat" |2 |
|---------+---------------|
|"Gul" |2 |
|---------+---------------|
|"Hakan" |2 |
|---------+---------------|
|"Irmak" |2 |
|---------+---------------|
|"Jale" |3 |
+-------------------------+
As seen above, querying even more complex scenarios can be done using
comparatively short constructs in Java and other query mechanisms.
9.2. ACL structures in graphs
This example gives a generic overview of an approach to handling ACLs in
graphs, and a simplified example with concrete queries.
9.2.1. Generic approach
In many scenarios, an application needs to handle security on some form of
managed objects. This example describes one pattern to handle that through use
a the graph structure and traversers that build a full permissions-structure
for any managed object with exclude and include overriding possibilities. This
results in a dynamic construction of Access Control Lists (ACLs) based on the
position and context of the managed object.
The result is a complex security scheme that can easily be implemented in a
graph structure, supporting permissions overriding, principal and content
composition, and does not duplicate data anywhere.
ACL.png
9.2.1.1. Technique
As seen in the example graph layout, there are some key concepts in this domain
model:
* The managed content (folders and files) that are connected by
HAS_CHILD_CONTENT relationships
* The Principal subtree pointing out principals that can act as ACL members,
pointed out by the PRINCIPAL relationships.
* The aggregation of principals into groups, connected by the IS_MEMBER_OF
relationship. One principal (user or group) can be part of many groups at
the same time.
* The SECURITY - relationships, connecting the content composite structure to
the principal composite structure, containing a addition/removal modifier
property ("+RW")
9.2.1.2. Constructing the ACL
The calculation of the effective permissions (e.g. Read, Write, Execute) for a
principal for any given ACL-managed node (content) follows a number of rules
that will be encoded into the permissions-traversal:
9.2.1.3. Top-down-Traversal
This approach will let you define a generic permission pattern on the root
content, and then refine that for specific sub-content nodes and specific
principals.
1. Start at the content node in question traverse upwards to the content root
node to determine the path to it.
2. Start with a effective optimistic permissions list of "all permitted" (111
in a bit encoded ReadWriteExecute case) or 000 if you like pessimistic
security handling (everything is forbidden unless explicitly allowed).
3. Beginning from the topmost content node, look for any SECURITY
relationships on it.
4. If found, look if the principal in question is part of the end-principal of
the SECURITY relationship.
5. If yes, add the "+" permission modifiers to the existing permission
pattern, revoke the "-" permission modifiers from the pattern.
6. If two principal nodes link to the same content node, first apply the more
generic prinipals modifiers.
7. Repeat the security modifier search all the way down to the target content
node, thus overriding more generic permissions with the set on nodes closer
to the target node.
The same algorithm is applicable for the bottom-up approach, basically just
traversing from the target content node upwards and applying the security
modifiers dynamically as the traverser goes up.
9.2.1.4. Example
Now, to get the resulting access rights for e.g. "user 1" on the "My File.pdf"
in a Top-Down approach on the model in the graph above would go like:
1. Traveling upward, we start with "Root folder", and set the permissions to
11 initially (only considering Read, Write).
2. There are two SECURITY relationships to that folder. User 1 is contained in
both of them, but "root" is more generic, so apply it first then "All
principals" +W +R → 11.
3. "Home" has no SECURITY instructions, continue.
4. "user1 Home" has SECURITY. First apply "Regular Users" (-R -W) → 00, Then
"user 1" (+R +W) → 11.
5. The target node "My File.pdf" has no SECURITY modifiers on it, so the
effective permissions for "User 1" on "My File.pdf" are ReadWrite → 11.
9.2.2. Read-permission example
In this example, we are going to examine a tree structure of directories and
files. Also, there are users that own files and roles that can be assigned to
users. Roles can have permissions on directory or files structures (here we
model only canRead, as opposed to full rwx Unix permissions) and be nested. A
more thorough example of modeling ACL structures can be found at How to Build
Role-Based Access Control in SQL .
The-Domain-Structure-ACL-structures-in-graphs.svg
9.2.2.1. Find all files in the directory structure
In order to find all files contained in this structure, we need a variable
length query that follows all contains relationships and retrieves the nodes at
the other end of the leaf relationships.
START root=node:node_auto_index(name = 'FileRoot')
MATCH root-[:contains*0..]->(parentDir)-[:leaf]->file
RETURN file
resulting in:
+-----------------------+
|file |
|-----------------------|
|2 rows, 5 ms |
|-----------------------|
|Node[11]{name->"File1"}|
|-----------------------|
|Node[10]{name->"File2"}|
+-----------------------+
9.2.2.2. What files are owned by whom?
If we introduce the concept of ownership on files, we then can ask for the
owners of the files we find — connected via owns relationships to file nodes.
START root=node:node_auto_index(name = 'FileRoot')
MATCH root-[:contains*0..]->()-[:leaf]->file<-[:owns]-user
RETURN file, user
Returning the owners of all files below the FileRoot node.
+----------------------------------------------+
|file |user |
|----------------------------------------------|
|2 rows, 5 ms |
|----------------------------------------------|
|Node[11]{name->"File1"}|Node[8]{name->"User1"}|
|-----------------------+----------------------|
|Node[10]{name->"File2"}|Node[7]{name->"User2"}|
+----------------------------------------------+
9.2.2.3. Who has access to a File?
If we now want to check what users have read access to all Files, and define
our ACL as
* The root directory has no access granted.
* Any user having a role that has been granted canRead access to one of the
parent folders of a File has read access.
In order to find users that can read any part of the parent folder hierarchy
above the files, Cypher provides optional variable length path.
START file=node:node_auto_index('name:File*')
MATCH file<-[:leaf]-()<-[:contains*0..]-dir<-[?:canRead]-role-[:member]->readUser
RETURN file.name, dir.name, role.name, readUser.name
This will return the file, and the directory where the user has the canRead
permission along with the user and their role.
+--------------------------------------------+
|file.name|dir.name |role.name|readUser.name|
|--------------------------------------------|
|9 rows, 50 ms |
|--------------------------------------------|
|"File2" |"Desktop" | | |
|---------+----------+---------+-------------|
|"File2" |"HomeU2" | | |
|---------+----------+---------+-------------|
|"File2" |"Home" | | |
|---------+----------+---------+-------------|
|"File2" |"FileRoot"|"SUDOers"|"Admin1" |
|---------+----------+---------+-------------|
|"File2" |"FileRoot"|"SUDOers"|"Admin2" |
|---------+----------+---------+-------------|
|"File1" |"HomeU1" | | |
|---------+----------+---------+-------------|
|"File1" |"Home" | | |
|---------+----------+---------+-------------|
|"File1" |"FileRoot"|"SUDOers"|"Admin1" |
|---------+----------+---------+-------------|
|"File1" |"FileRoot"|"SUDOers"|"Admin2" |
+--------------------------------------------+
The results listed above contain null values for optional path segments, which
can be mitigated by either asking several queries or returning just the really
needed values.
Chapter 10. Languages
The table below lists community contributed language- and framework bindings
for using Neo4j in embedded mode.
Table 10.1. Neo4j embedded drivers contributed by the community.
+-----------------------------------------------------------------------------+
|name |language /|URL |
| |framework | |
|-----------+----------+------------------------------------------------------|
|Neo4j.rb |JRuby |https://github.com/andreasronge/neo4j |
|-----------+----------+------------------------------------------------------|
|Neo4django |Python, |https://github.com/scholrly/neo4django |
|-----------+----------+------------------------------------------------------|
|Neo4js |JavaScript|https://github.com/neo4j/neo4js |
|-----------+----------+------------------------------------------------------|
|Gremlin |Java, |Section 18.16, “Gremlin Plugin”, https://github.com/ |
| |Groovy |tinkerpop/gremlin/wiki |
|-----------+----------+------------------------------------------------------|
|Neo4j-Scala|Scala |https://github.com/FaKod/neo4j-scala |
|-----------+----------+------------------------------------------------------|
|Borneo |Clojure |https://github.com/wagjo/borneo |
+-----------------------------------------------------------------------------+
For information on REST clients for different languages, see Chapter 6, Using
the Neo4j REST API.
Chapter 11. Using Neo4j embedded in Python applications
For instructions on how to install the Python Neo4j driver, see Section 19.1,
“Installation”.
For general information on the Python language binding, see Chapter 19, Python
embedded bindings.
11.1. Hello, world!
Here is a simple example to get you started.
from neo4j import GraphDatabase
# Create a database
db = GraphDatabase(folder_to_put_db_in)
# All write operations happen in a transaction
with db.transaction:
firstNode = db.node(name='Hello')
secondNode = db.node(name='world!')
# Create a relationship with type 'knows'
relationship = firstNode.knows(secondNode, name='graphy')
# Read operations can happen anywhere
message = ' '.join([firstNode['name'], relationship['name'], secondNode['name']])
print message
# Delete the data
with db.transaction:
firstNode.knows.single.delete()
firstNode.delete()
secondNode.delete()
# Always shut down your database when your application exits
db.shutdown()
11.2. A sample app using traversals and indexes
For detailed documentation on the concepts use here, see Section 19.3,
“Indexes” and Section 19.5, “Traversals”.
This example shows you how to get started building something like a simple
invoice tracking application with Neo4j.
We start out by importing Neo4j, and creating some meta data that we will use
to organize our actual data with.
from neo4j import GraphDatabase, INCOMING, Evaluation
# Create a database
db = GraphDatabase(folder_to_put_db_in)
# All write operations happen in a transaction
with db.transaction:
# A node to connect customers to
customers = db.node()
# A node to connect invoices to
invoices = db.node()
# Connected to the reference node, so
# that we can always find them.
db.reference_node.CUSTOMERS(customers)
db.reference_node.INVOICES(invoices)
# An index, helps us rapidly look up customers
customer_idx = db.node.indexes.create('customers')
11.2.1. Domain logic
Then we define some domain logic that we want our application to be able to
perform. Our application has two domain objects, Customers and Invoices. Let’s
create methods to add new customers and invoices.
def create_customer(name):
with db.transaction:
customer = db.node(name=name)
customer.INSTANCE_OF(customers)
# Index the customer by name
customer_idx['name'][name] = customer
return customer
def create_invoice(customer, amount):
with db.transaction:
invoice = db.node(amount=amount)
invoice.INSTANCE_OF(invoices)
invoice.RECIPIENT(customer)
return customer
In the customer case, we create a new node to represent the customer and
connect it to the customers node. This helps us find customers later on, as
well as determine if a given node is a customer.
We also index the name of the customer, to allow for quickly finding customers
by name.
In the invoice case, we do the same, except no indexing. We also connect each
new invoice to the customer it was sent to, using a relationship of type
SENT_TO.
Next, we want to be able to retrieve customers and invoices that we have added.
Because we are indexing customer names, finding them is quite simple.
def get_customer(name):
return customer_idx['name'][name].single
Lets say we also like to do something like finding all invoices for a given
customer that are above some given amount. This could be done by writing a
traversal, like this:
def get_invoices_with_amount_over(customer, min_sum):
def evaluator(path):
node = path.end
if node.has_key('amount') and node['amount'] > min_sum:
return Evaluation.INCLUDE_AND_CONTINUE
return Evaluation.EXCLUDE_AND_CONTINUE
return db.traversal()\
.relationships('RECIPIENT', INCOMING)\
.evaluator(evaluator)\
.traverse(customer)\
.nodes
11.2.2. Creating data and getting it back
Putting it all together, we can create customers and invoices, and use the
search methods we wrote to find them.
for name in ['Acme Inc.', 'Example Ltd.']:
create_customer(name)
# Loop through customers
for relationship in customers.INSTANCE_OF:
customer = relationship.start
for i in range(1,12):
create_invoice(customer, 100 * i)
# Finding large invoices
large_invoices = get_invoices_with_amount_over(get_customer('Acme Inc.'), 500)
# Getting all invoices per customer:
for relationship in get_customer('Acme Inc.').RECIPIENT.incoming:
invoice = relationship.start
Part III. Reference
The reference part is the authoritative source for details on Neo4j usage. It
covers details on capabilities, transactions, indexing and queries among other
topics.
Table of Contents
12. Capabilities
12.1. Data Security
12.2. Data Integrity
12.2.1. Core Graph Engine
12.2.2. Different Data Sources
12.3. Data Integration
12.3.1. Event-based Synchronization
12.3.2. Periodic Synchronization
12.3.3. Periodic Full Export/Import of Data
12.4. Availability and Reliability
12.4.1. Operational Availability
12.4.2. Disaster Recovery/ Resiliency
12.5. Capacity
12.5.1. File Sizes
12.5.2. Read speed
12.5.3. Write speed
12.5.4. Data size
13. Transaction Management
13.1. Interaction cycle
13.2. Isolation levels
13.3. Default locking behavior
13.4. Deadlocks
13.5. Delete semantics
13.6. Creating unique nodes
13.6.1. Single thread
13.6.2. Get or create
13.6.3. Pessimistic locking
13.7. Transaction events
14. Indexing
14.1. Introduction
14.2. Create
14.3. Delete
14.4. Add
14.5. Remove
14.6. Update
14.7. Search
14.7.1. Get
14.7.2. Query
14.8. Relationship indexes
14.9. Scores
14.10. Configuration and fulltext indexes
14.11. Extra features for Lucene indexes
14.11.1. Numeric ranges
14.11.2. Sorting
14.11.3. Querying with Lucene Query objects
14.11.4. Compound queries
14.11.5. Default operator
14.11.6. Caching
14.12. Batch insertion
14.12.1. Best practices
14.13. Automatic Indexing
14.13.1. Configuration
14.13.2. Search
14.13.3. Runtime Configuration
14.13.4. Updating the Automatic Index
15. Cypher Query Language
15.1. Compatibility
15.2. Parameters
15.3. Identifiers
15.4. Start
15.4.1. Node by id
15.4.2. Relationship by id
15.4.3. Multiple nodes by id
15.4.4. Node by index lookup
15.4.5. Relationship by index lookup
15.4.6. Node by index query
15.4.7. Multiple start points
15.5. Match
15.5.1. Related nodes
15.5.2. Outgoing relationships
15.5.3. Directed relationships and identifier
15.5.4. Match by relationship type
15.5.5. Match by relationship type and use an identifier
15.5.6. Relationship types with uncommon characters
15.5.7. Multiple relationships
15.5.8. Variable length relationships
15.5.9. Relationship identifier in variable length relationships
15.5.10. Zero length paths
15.5.11. Optional relationship
15.5.12. Optional typed and named relationship
15.5.13. Properties on optional elements
15.5.14. Complex matching
15.5.15. Shortest path
15.5.16. All shortest paths
15.5.17. Named path
15.5.18. Matching on a bound relationship
15.6. Where
15.6.1. Boolean operations
15.6.2. Filter on node property
15.6.3. Regular expressions
15.6.4. Escaping in regular expressions
15.6.5. Case insensitive regular expressions
15.6.6. Filtering on relationship type
15.6.7. Property exists
15.6.8. Compare if property exists
15.6.9. Filter on null values
15.6.10. Filter on relationships
15.7. Return
15.7.1. Return nodes
15.7.2. Return relationships
15.7.3. Return property
15.7.4. Identifier with uncommon characters
15.7.5. Column alias
15.7.6. Optional properties
15.7.7. Unique results
15.8. Aggregation
15.8.1. COUNT
15.8.2. Count nodes
15.8.3. Group Count Relationship Types
15.8.4. Count entities
15.8.5. Count non null values
15.8.6. SUM
15.8.7. AVG
15.8.8. MAX
15.8.9. MIN
15.8.10. COLLECT
15.8.11. DISTINCT
15.9. Order by
15.9.1. Order nodes by property
15.9.2. Order nodes by multiple properties
15.9.3. Order nodes in descending order
15.9.4. Ordering null
15.10. Skip
15.10.1. Skip first three
15.10.2. Return middle two
15.11. Limit
15.11.1. Return first part
15.12. Functions
15.12.1. Predicates
15.12.2. ALL
15.12.3. ANY
15.12.4. NONE
15.12.5. SINGLE
15.12.6. Scalar functions
15.12.7. LENGTH
15.12.8. TYPE
15.12.9. ID
15.12.10. COALESCE
15.12.11. Iterable functions
15.12.12. NODES
15.12.13. RELATIONSHIPS
15.12.14. EXTRACT
16. Graph Algorithms
16.1. Introduction
17. Neo4j Server
17.1. Server Installation
17.1.1. As a Windows service
17.1.2. Linux Service
17.1.3. Mac OSX
17.1.4. Multiple Server instances on one machine
17.1.5. High Availability Mode
17.2. Server Configuration
17.2.1. Important server configurations parameters
17.2.2. Neo4j Database performance configuration
17.2.3. Logging configuration
17.2.4. Other configuration options
17.3. Setup for remote debugging
17.4. Using the server (including web administration) with an embedded
database
17.4.1. Getting the libraries
17.4.2. Starting the Server from Java
17.4.3. Providing custom configuration
17.5. Server Performance Tuning
17.5.1. Specifying Neo4j tuning properties
17.5.2. Specifying JVM tuning properties
17.6. Server Installation in the Cloud
17.6.1. Heroku
18. REST API
18.1. Service root
18.1.1. Get service root
18.2. Nodes
18.2.1. Create Node
18.2.2. Create Node with properties
18.2.3. Get node
18.2.4. Get non-existent node
18.2.5. Delete node
18.2.6. Nodes with relationships can not be deleted
18.3. Relationships
18.3.1. Get Relationship by ID
18.3.2. Create relationship
18.3.3. Create a relationship with properties
18.3.4. Delete relationship
18.3.5. Get all properties on a relationship
18.3.6. Set all properties on a relationship
18.3.7. Get single property on a relationship
18.3.8. Set single property on a relationship
18.3.9. Get all relationships
18.3.10. Get incoming relationships
18.3.11. Get outgoing relationships
18.3.12. Get typed relationships
18.3.13. Get relationships on a node without relationships
18.4. Relationship types
18.4.1. Get relationship types
18.5. Node properties
18.5.1. Set property on node
18.5.2. Update node properties
18.5.3. Get properties for node
18.5.4. Property values can not be null
18.5.5. Property values can not be nested
18.5.6. Delete all properties from node
18.6. Relationship properties
18.6.1. Update relationship properties
18.6.2. Remove property from a relationship
18.6.3. Remove non-existent property from a relationship
18.6.4. Remove properties from a non-existing relationship
18.6.5. Remove property from a non-existing relationship
18.7. Indexes
18.7.1. Create node index
18.7.2. Create node index with configuration
18.7.3. Delete node index
18.7.4. List node indexes
18.7.5. Add node to index
18.7.6. Remove all entries with a given node from an index
18.7.7. Remove all entries with a given node and key from an index
18.7.8. Remove all entries with a given node, key and value from an
index
18.7.9. Find node by exact match
18.7.10. Find node by query
18.8. Unique Indexes
18.8.1. Create a unique node in an index
18.8.2. Create a unique node in an index (the case where it exists)
18.8.3. Add a node to an index unless a node already exists for the
given mapping
18.8.4. Create a unique relationship in an index
18.8.5. Add a relationship to an index unless a relationship already
exists for the given mapping
18.9. Automatic Indexes
18.9.1. Find node by exact match from an automatic index
18.9.2. Find node by query from an automatic index
18.10. Configurable Automatic Indexing
18.10.1. Create an auto index for nodes with specific configuration
18.10.2. Create an auto index for relationships with specific
configuration
18.11. Traversals
18.11.1. Traversal using a return filter
18.11.2. Return relationships from a traversal
18.11.3. Return paths from a traversal
18.11.4. Traversal returning nodes below a certain depth
18.11.5. Creating a paged traverser
18.11.6. Paging through the results of a paged traverser
18.11.7. Paged traverser page size
18.11.8. Paged traverser timeout
18.12. Cypher queries
18.12.1. Send a Query
18.12.2. Return paths
18.12.3. Send queries with parameters
18.12.4. Nested results
18.12.5. Server errors
18.13. Built-in Graph Algorithms
18.13.1. Find all shortest paths
18.13.2. Find one of the shortest paths between nodes
18.13.3. Execute a Dijkstra algorithm with similar weights on
relationships
18.13.4. Execute a Dijkstra algorithm with weights on relationships
18.14. Batch operations
18.14.1. Execute multiple operations in batch
18.14.2. Refer to items created earlier in the same batch job
18.15. Cypher Plugin
18.15.1. Send a Query
18.15.2. Return paths
18.15.3. Send queries with parameters
18.15.4. Server errors
18.16. Gremlin Plugin
18.16.1. Send a Gremlin Script - URL encoded
18.16.2. Load a sample graph
18.16.3. Sort a result using raw Groovy operations
18.16.4. Send a Gremlin Script - JSON encoded with table results
18.16.5. Returning nested pipes
18.16.6. Set script variables
18.16.7. Send a Gremlin Script with variables in a JSON Map
18.16.8. Return paths from a Gremlin script
18.16.9. Send an arbitrary Groovy script - Lucene sorting
18.16.10. Emit a sample graph
18.16.11. HyperEdges - find user roles in groups
18.16.12. Group count
18.16.13. Collect multiple traversal results
18.16.14. Collaborative filtering
18.16.15. Chunking and offsetting in Gremlin
18.16.16. Modify the graph while traversing
18.16.17. Flow algorithms with Gremlin
19. Python embedded bindings
19.1. Installation
19.1.1. Installation on OSX/Linux
19.1.2. Installation on Windows
19.2. Core API
19.2.1. Getting started
19.2.2. Transactions
19.2.3. Nodes
19.2.4. Relationships
19.2.5. Properties
19.2.6. Paths
19.3. Indexes
19.3.1. Index management
19.3.2. Indexing things
19.3.3. Searching the index
19.4. Cypher Queries
19.4.1. Querying and reading the result
19.4.2. Parameterized and prepared queries
19.5. Traversals
19.5.1. Basic traversals
19.5.2. Traversal results
19.5.3. Uniqueness
19.5.4. Ordering
19.5.5. Evaluators - advanced filtering
Chapter 12. Capabilities
12.1. Data Security
Some data may need to be protected from unauthorized access (e.g., theft,
modification). Neo4j does not deal with data encryption explicitly, but
supports all means built into the Java programming language and the JVM to
protect data by encrypting it before storing.
Furthermore, data can be easily secured by running on an encrypted datastore at
the file system level. Finally, data protection should be considered in the
upper layers of the surrounding system in order to prevent problems with
scraping, malicious data insertion, and other threats.
12.2. Data Integrity
In order to keep data consistent, there needs to be mechanisms and structures
that guarantee the integrity of all stored data. In Neo4j, data integrity is
maintained for the core graph engine together with other data sources - see
below.
12.2.1. Core Graph Engine
In Neo4j, the whole data model is stored as a graph on disk and persisted as
part of every committed transaction. In the storage layer, Relationships,
Nodes, and Properties have direct pointers to each other. This maintains
integrity without the need for data duplication between the different backend
store files.
12.2.2. Different Data Sources
In a number of scenarios, the core graph engine is combined with other systems
in order to achieve optimal performance for non-graph lookups. For example,
Apache Lucene is frequently used as an additional index system for text queries
that would otherwise be very processing-intensive in the graph layer.
To keep these external systems in synchronization with each other, Neo4j
provides full Two Phase Commit transaction management, with rollback support
over all data sources. Thus, failed index insertions into Lucene can be
transparently rolled back in all data sources and thus keep data up-to-date.
12.3. Data Integration
Most enterprises rely primarily on relational databases to store their data,
but this may cause performance limitations. In some of these cases, Neo4j can
be used as an extension to supplement search/lookup for faster decision making.
However, in any situation where multiple data repositories contain the same
data, synchronization can be an issue.
In some applications, it is acceptable for the search platform to be slightly
out of sync with the relational database. In others, tight data integrity (eg.,
between Neo4j and RDBMS) is necessary. Typically, this has to be addressed for
data changing in real-time and for bulk data changes happening in the RDBMS.
A few strategies for synchronizing integrated data follows.
12.3.1. Event-based Synchronization
In this scenario, all data stores, both RDBMS and Neo4j, are fed with
domain-specific events via an event bus. Thus, the data held in the different
backends is not actually synchronized but rather replicated.
12.3.2. Periodic Synchronization
Another viable scenario is the periodic export of the latest changes in the
RDBMS to Neo4j via some form of SQL query. This allows a small amount of
latency in the synchronization, but has the advantage of using the RDBMS as the
master for all data purposes. The same process can be applied with Neo4j as the
master data source.
12.3.3. Periodic Full Export/Import of Data
Using the Batch Inserter tools for Neo4j, even large amounts of data can be
imported into the database in very short times. Thus, a full export from the
RDBMS and import into Neo4j becomes possible. If the propagation lag between
the RDBMS and Neo4j is not a big issue, this is a very viable solution.
12.4. Availability and Reliability
Most mission-critical systems require the database subsystem to be accessible
at all times. Neo4j ensures availability and reliability through a few
different strategies.
12.4.1. Operational Availability
In order not to create a single point of failure, Neo4j supports different
approaches which provide transparent fallback and/or recovery from failures.
12.4.1.1. Online backup (Cold spare)
In this approach, a single instance of the master database is used, with Online
Backup enabled. In case of a failure, the backup files can be mounted onto a
new Neo4j instance and reintegrated into the application.
12.4.1.2. Online Backup High Availability (Hot spare)
Here, a Neo4j "backup" instance listens to online transfers of changes from the
master. In the event of a failure of the master, the backup is already running
and can directly take over the load.
12.4.1.3. High Availability cluster
This approach uses a cluster of database instances, with one (read/write)
master and a number of (read-only) slaves. Failing slaves can simply be
restarted and brought back online. Alternatively, a new slave may be added by
cloning an existing one. Should the master instance fail, a new master will be
elected by the remaining cluster nodes.
12.4.2. Disaster Recovery/ Resiliency
In cases of a breakdown of major part of the IT infrastructure, there need to
be mechanisms in place that enable the fast recovery and regrouping of the
remaining services and servers. In Neo4j, there are different components that
are suitable to be part of a disaster recovery strategy.
12.4.2.1. Prevention
* Online Backup High Availability to other locations outside the current data
center.
* Online Backup to different file system locations: this is a simpler form of
backup, applying changes directly to backup files; it is thus more suited
for local backup scenarios.
* Neo4j High Availability cluster: a cluster of one write-master Neo4j server
and a number of read-slaves, getting transaction logs from the master.
Write-master failover is handled by quorum election among the read-slaves
for a new master.
12.4.2.2. Detection
* SNMP and JMX monitoring can be used for the Neo4j database.
12.4.2.3. Correction
* Online Backup: A new Neo4j server can be started directly on the backed-up
files and take over new requests.
* Neo4j High Availability cluster: A broken Neo4j read slave can be
reinserted into the cluster, getting the latest updates from the master.
Alternatively, a new server can be inserted by copying an existing server
and applying the latest updates to it.
12.5. Capacity
12.5.1. File Sizes
Neo4j relies on Java’s Non-blocking I/O subsystem for all file handling.
Furthermore, while the storage file layout is optimized for interconnected
data, Neo4j does not require raw devices. Thus, filesizes are only limited by
the underlying operating system’s capacity to handle large files. Physically,
there is no built-in limit of the file handling capacity in Neo4j.
Neo4j tries to memory-map as much of the underlying store files as possible. If
the available RAM is not sufficient to keep all data in RAM, Neo4j will use
buffers in some cases, reallocating the memory-mapped high-performance I/O
windows to the regions with the most I/O activity dynamically. Thus, ACID speed
degrades gracefully as RAM becomes the limiting factor.
12.5.2. Read speed
Enterprises want to optimize the use of hardware to deliver the maximum
business value from available resources. Neo4j’s approach to reading data
provides the best possible usage of all available hardware resources. Neo4j
does not block or lock any read operations; thus, there is no danger for
deadlocks in read operations and no need for read transactions. With a threaded
read access to the database, queries can be run simultaneously on as many
processors as may be available. This provides very good scale-up scenarios with
bigger servers.
12.5.3. Write speed
Write speed is a consideration for many enterprise applications. However, there
are two different scenarios:
1. sustained continuous operation and
2. bulk access (e.g., backup, initial or batch loading).
To support the disparate requirements of these scenarios, Neo4j supports two
modes of writing to the storage layer.
In transactional, ACID-compliant normal operation, isolation level is
maintained and read operations can occur at the same time as the writing
process. At every commit, the data is persisted to disk and can be recovered to
a consistent state upon system failures. This requires disk write access and a
real flushing of data. Thus, the write speed of Neo4j on a single server in
continuous mode is limited by the I/O capacity of the hardware. Consequently,
the use of fast SSDs is highly recommended for production scenarios.
Neo4j has a Batch Inserter that operates directly on the store files. This mode
does not provide transactional security, so it can only be used when there is a
single write thread. Because data is written sequentially, and never flushed to
the logical logs, huge performance boosts are achieved. The Batch Inserter is
optimized for non-transactional bulk import of large amounts of data.
12.5.4. Data size
In Neo4j, data size is mainly limited by the address space of the primary keys
for Nodes, Relationships, Properties and RelationshipTypes. Currently, the
address space is as follows:
* 2ˆ35 (~ 34 billion) nodes
* 2ˆ35 (~ 34 billion) relationships
* 2ˆ36 (~ 68 billion) properties
* 2ˆ15 (~ 32 000) relationship types
Chapter 13. Transaction Management
In order to fully maintain data integrity and ensure good transactional
behavior, Neo4j supports the ACID properties:
* atomicity - if any part of a transaction fails, the database state is left
unchanged
* consistency - any transaction will leave the database in a consistent state
* isolation - during a transaction, modified data cannot be accessed by other
operations
* durability - the DBMS can always recover the results of a committed
transaction
Specifically:
* All modifications to Neo4j data must be wrapped in transactions.
* The default isolation level is READ_COMMITTED.
* Data retrieved by traversals is not protected from modification by other
transactions.
* Non-repeatable reads may occur (i.e., only write locks are acquired and
held until the end of the transaction).
* One can manually acquire write locks on nodes and relationships to achieve
higher level of isolation (SERIALIZABLE).
* Locks are acquired at the Node and Relationship level.
* Deadlock detection is built into the core transaction management.
13.1. Interaction cycle
All write operations that work with the graph must be performed in a
transaction. Transactions are thread confined and can be nested as “flat nested
transactions”. Flat nested transactions means that all nested transactions are
added to the scope of the top level transaction. A nested transaction can mark
the top level transaction for rollback, meaning the entire transaction will be
rolled back. To only rollback changes made in a nested transaction is not
possible.
When working with transactions the interaction cycle looks like this:
1. Begin a transaction.
2. Operate on the graph performing write operations.
3. Mark the transaction as successful or not.
4. Finish the transaction.
It is very important to finish each transaction. The transaction will not
release the locks or memory it has acquired until it has been finished. The
idiomatic use of transactions in Neo4j is to use a try-finally block, starting
the transaction and then try to perform the write operations. The last
operation in the try block should mark the transaction as successful while the
finally block should finish the transaction. Finishing the transaction will
perform commit or rollback depending on the success status.
Caution
All modifications performed in a transaction are kept in memory. This means
that very large updates have to be split into several top level transactions to
avoid running out of memory. It must be a top level transaction since splitting
up the work in many nested transactions will just add all the work to the top
level transaction.
In an environment that makes use of thread pooling other errors may occur when
failing to finish a transaction properly. Consider a leaked transaction that
did not get finished properly. It will be tied to a thread and when that thread
gets scheduled to perform work starting a new (what looks to be a) top level
transaction it will actually be a nested transaction. If the leaked transaction
state is “marked for rollback” (which will happen if a deadlock was detected)
no more work can be performed on that transaction. Trying to do so will result
in error on each call to a write operation.
13.2. Isolation levels
By default a read operation will read the last committed value unless a local
modification within the current transaction exist. The default isolation level
is very similar to READ_COMMITTED: reads do not block or take any locks so
non-repeatable reads can occur. It is possible to achieve a stronger isolation
level (such as REPETABLE_READ and SERIALIZABLE) by manually acquiring read and
write locks.
13.3. Default locking behavior
* When adding, changing or removing a property on a node or relationship a
write lock will be taken on the specific node or relationship.
* When creating or deleting a node a write lock will be taken for the
specific node.
* When creating or deleting a relationship a write lock will be taken on the
specific relationship and both its nodes.
The locks will be added to the transaction and released when the transaction
finishes.
13.4. Deadlocks
Since locks are used it is possible for deadlocks to happen. Neo4j will however
detect any deadlock (caused by acquiring a lock) before they happen and throw
an exception. Before the exception is thrown the transaction is marked for
rollback. All locks acquired by the transaction are still being held but will
be released when the transaction is finished (in the finally block as pointed
out earlier). Once the locks are released other transactions that were waiting
for locks held by the transaction causing the deadlock can proceed. The work
performed by the transaction causing the deadlock can then be retried by the
user if needed.
Experiencing frequent deadlocks is an indication of concurrent write requests
happening in such a way that it is not possible to execute them while at the
same time live up to the intended isolation and consistency. The solution is to
make sure concurrent updates happen in a reasonable way. For example given two
specific nodes (A and B), adding or deleting relationships to both these nodes
in random order for each transaction will result in deadlocks when there are
two or more transactions doing that concurrently. One solution is to make sure
that updates always happens in the same order (first A then B). Another
solution is to make sure that each thread/transaction does not have any
conflicting writes to a node or relationship as some other concurrent
transaction. This can for example be achieved by letting a single thread do all
updates of a specific type.
Important
Deadlocks caused by the use of other synchronization than the locks managed by
Neo4j can still happen. Since all operations in the Neo4j API are thread safe
unless specified otherwise, there is no need for external synchronization.
Other code that requires synchronization should be synchronized in such a way
that it never performs any Neo4j operation in the synchronized block.
13.5. Delete semantics
When deleting a node or a relationship all properties for that entity will be
automatically removed but the relationships of a node will not be removed.
Caution
Neo4j enforces a constraint (upon commit) that all relationships must have a
valid start node and end node. In effect this means that trying to delete a
node that still has relationships attached to it will throw an exception upon
commit. It is however possible to choose in which order to delete the node and
the attached relationships as long as no relationships exist when the
transaction is committed.
The delete semantics can be summarized in the following bullets:
* All properties of a node or relationship will be removed when it is
deleted.
* A deleted node can not have any attached relationships when the transaction
commits.
* It is possible to acquire a reference to a deleted relationship or node
that has not yet been committed.
* Any write operation on a node or relationship after it has been deleted
(but not yet committed) will throw an exception
* After commit trying to acquire a new or work with an old reference to a
deleted node or relationship will throw an exception.
13.6. Creating unique nodes
In many use cases, a certain level of uniqueness is desired among entities. You
could for instance imagine that only one user with a certain e-mail address may
exist in a system. If multiple concurrent threads naively try to create the
user, duplicates will be created. There are three main strategies for ensuring
uniqueness, and they all work across HA and single-instance deployments.
13.6.1. Single thread
By using a single thread, no two threads will even try to create a particular
entity simultaneously. On HA, an external single-threaded client can perform
the operations on the cluster.
13.6.2. Get or create
By using put-if-absent functionality,
entity uniqueness can be guaranteed using an index. Here the index acts as the
lock and will only lock the smallest part needed to guaranteed uniqueness
across threads and transactions. To get the more high-level get-or-create
functionality make use of UniqueFactory as seen in the example
below.
Example code:
public Node getOrCreateUserWithUniqueFactory( String username, GraphDatabaseService graphDb )
{
UniqueFactory factory = new UniqueFactory.UniqueNodeFactory( graphDb, "users" )
{
@Override
protected void initialize( Node created, Map properties )
{
created.setProperty( "name", properties.get( "name" ) );
}
};
return factory.getOrCreate( "name", username );
}
13.6.3. Pessimistic locking
Important
While this is a working solution, please consider using the preferred
Section 13.6.2, “Get or create” instead.
By using explicit, pessimistic locking, unique creation of entities can be
achieved in a multi-threaded environment. It is most commonly done by locking
on a single or a set of common nodes.
One might be tempted to use Java synchronization for this, but it is dangerous.
By mixing locks in the Neo4j kernel and in the Java runtime, it is easy to
produce deadlocks that are not detectable by Neo4j. As long as all locking is
done by Neo4j, all deadlocks will be detected and avoided. Also, a solution
using manual synchronization doesn’t ensure uniqueness in an HA environment.
Example code:
public Node getOrCreateUserPessimistically( String username, GraphDatabaseService graphDb, Node lockNode )
{
Index usersIndex = graphDb.index().forNodes( "users" );
Node userNode = usersIndex.get( "name", username ).getSingle();
if ( userNode != null ) return userNode;
Transaction tx = graphDb.beginTx();
try
{
tx.acquireWriteLock( lockNode );
userNode = usersIndex.get( "name", username ).getSingle();
if ( userNode == null )
{
userNode = graphDb.createNode();
userNode.setProperty( "name", username );
usersIndex.add( userNode, "name", username );
}
tx.success();
return userNode;
}
finally
{
tx.finish();
}
}
13.7. Transaction events
Transaction event handlers can be registered to receive Neo4j Transaction
events. Once it has been registered at a GraphDatabaseService instance it will
receive events about what has happened in each transaction which is about to be
committed. Handlers won’t get notified about transactions which haven’t
performed any write operation or won’t be committed (either if Transaction#
success() hasn’t been called or the transaction has been marked as failed
Transaction#failure(). Right before a transaction is about to be committed the
beforeCommit method is called with the entire diff of modifications made in the
transaction. At this point the transaction is still running so changes can
still be made. However there’s no guarantee that other handlers will see such
changes since the order in which handlers are executed is undefined. This
method can also throw an exception and will, in such a case, prevent the
transaction from being committed (where a call to afterRollback will follow).
If beforeCommit is successfully executed the transaction will be committed and
the afterCommit method will be called with the same transaction data as well as
the object returned from beforeCommit. This assumes that all other handlers (if
more were registered) also executed beforeCommit successfully.
Chapter 14. Indexing
Indexing in Neo4j can be done in two different ways:
1. The database itself is a natural index consisting of its relationships of
different types between nodes. For example a tree structure can be layered
on top of the data and used for index lookups performed by a traverser.
2. Separate index engines can be used, with Apache Lucene being the default backend included
with Neo4j.
This chapter demonstrate how to use the second type of indexing, focusing on
Lucene.
14.1. Introduction
Indexing operations are part of the Neo4j index API .
Each index is tied to a unique, user-specified name (for example "first_name"
or "books") and can index either nodes or relationships .
The default index implementation is provided by the neo4j-lucene-index
component, which is included in the standard Neo4j download. It can also be
downloaded separately from http://repo1.maven.org/maven2/org/neo4j/
neo4j-lucene-index/ . For Maven users, the neo4j-lucene-index component has the coordinates
org.neo4j:neo4j-lucene-index and should be used with the same version of
org.neo4j:neo4j-kernel. Different versions of the index and kernel components
are not compatible in the general case. Both components are included
transitively by the org.neo4j:neo4j:pom artifact which makes it simple to keep
the versions in sync.
Note
All modifying index operations must be performed inside a transaction, as with
any mutating operation in Neo4j.
14.2. Create
An index is created if it doesn’t exist when you ask for it. Unless you give it
a custom configuration, it will be created with default configuration and
backend.
To set the stage for our examples, let’s create some indexes to begin with:
IndexManager index = graphDb.index();
Index actors = index.forNodes( "actors" );
Index movies = index.forNodes( "movies" );
RelationshipIndex roles = index.forRelationships( "roles" );
This will create two node indexes and one relationship index with default
configuration. See Section 14.8, “Relationship indexes” for more information
specific to relationship indexes.
See Section 14.10, “Configuration and fulltext indexes” for how to create
fulltext indexes.
You can also check if an index exists like this:
IndexManager index = graphDb.index();
boolean indexExists = index.existsForNodes( "actors" );
14.3. Delete
Indexes can be deleted. When deleting, the entire contents of the index will be
removed as well as its associated configuration. A new index can be created
with the same name at a later point in time.
IndexManager index = graphDb.index();
Index actors = index.forNodes( "actors" );
actors.delete();
Note that the actual deletion of the index is made during the commit of the
surrounding transaction. Calls made to such an index instance after delete()
has been called are invalid inside that transaction as
well as outside (if the transaction is successful), but will become valid again
if the transaction is rolled back.
14.4. Add
Each index supports associating any number of key-value pairs with any number
of entities (nodes or relationships), where each association between entity and
key-value pair is performed individually. To begin with, let’s add a few nodes
to the indexes:
// Actors
Node reeves = graphDb.createNode();
actors.add( reeves, "name", "Keanu Reeves" );
Node bellucci = graphDb.createNode();
actors.add( bellucci, "name", "Monica Bellucci" );
// multiple values for a field
actors.add( bellucci, "name", "La Bellucci" );
// Movies
Node theMatrix = graphDb.createNode();
movies.add( theMatrix, "title", "The Matrix" );
movies.add( theMatrix, "year", 1999 );
Node theMatrixReloaded = graphDb.createNode();
movies.add( theMatrixReloaded, "title", "The Matrix Reloaded" );
movies.add( theMatrixReloaded, "year", 2003 );
Node malena = graphDb.createNode();
movies.add( malena, "title", "Malèna" );
movies.add( malena, "year", 2000 );
Note that there can be multiple values associated with the same entity and key.
Next up, we’ll create relationships and index them as well:
// we need a relationship type
DynamicRelationshipType ACTS_IN = DynamicRelationshipType.withName( "ACTS_IN" );
// create relationships
Relationship role1 = reeves.createRelationshipTo( theMatrix, ACTS_IN );
roles.add( role1, "name", "Neo" );
Relationship role2 = reeves.createRelationshipTo( theMatrixReloaded, ACTS_IN );
roles.add( role2, "name", "Neo" );
Relationship role3 = bellucci.createRelationshipTo( theMatrixReloaded, ACTS_IN );
roles.add( role3, "name", "Persephone" );
Relationship role4 = bellucci.createRelationshipTo( malena, ACTS_IN );
roles.add( role4, "name", "Malèna Scordia" );
Assuming we set the same key-value pairs as properties as well, our example
graph looks like this:
Indexing example node space
14.5. Remove
Removing from an
index is similar to adding, but can be done by supplying one of the following
combinations of arguments:
* entity
* entity, key
* entity, key, value
// completely remove bellucci from the actors index
actors.remove( bellucci );
// remove any "name" entry of bellucci from the actors index
actors.remove( bellucci, "name" );
// remove the "name" -> "La Bellucci" entry of bellucci
actors.remove( bellucci, "name", "La Bellucci" );
14.6. Update
Important
To update an index entry, old one must be removed and a new one added.
Remember that a node or relationship can be associated with any number of
key-value pairs in an index, which means that you can index a node or
relationship with many key-value pairs that have the same key. In the case
where a property value changes and you’d like to update the index, it’s not
enough to just index the new value - you’ll have to remove the old value as
well.
Here’s a code example for that demonstrates how it’s done:
// create a node with a property
Node fishburn = graphDb.createNode();
fishburn.setProperty( "name", "Fishburn" );
// index it
actors.add( fishburn, "name", fishburn.getProperty( "name" ) );
// update the index entry
actors.remove( fishburn, "name", fishburn.getProperty( "name" ) );
fishburn.setProperty( "name", "Laurence Fishburn" );
actors.add( fishburn, "name", fishburn.getProperty( "name" ) );
14.7. Search
An index can be searched in two ways, get and query . The get method will return
exact matches to the given key-value pair, whereas query exposes querying
capabilities directly from the backend used by the index. For example the
Lucene query syntax can be used directly with the default indexing backend.
14.7.1. Get
This is how to search for a single exact match:
IndexHits hits = actors.get( "name", "Keanu Reeves" );
Node reeves = hits.getSingle();
IndexHits is an Iterable with some additional useful methods. For
example getSingle() returns the first and only item
from the result iterator, or null if there isn’t any hit.
Here’s how to get a single relationship by exact matching and retrieve its
start and end nodes:
Relationship persephone = roles.get( "name", "Persephone" ).getSingle();
Node actor = persephone.getStartNode();
Node movie = persephone.getEndNode();
Finally, we can iterate over all exact matches from a relationship index:
for ( Relationship role : roles.get( "name", "Neo" ) )
{
// this will give us Reeves twice
Node reeves = role.getStartNode();
}
Important
In you don’t iterate through all the hits, IndexHits.close() must be called explicitly.
14.7.2. Query
There are two query methods, one which uses a key-value signature where the
value represents a query for values with the given key only. The other method
is more generic and supports querying for more than one key-value pair in the
same query.
Here’s an example using the key-query option:
for ( Node actor : actors.query( "name", "*e*" ) )
{
// This will return Reeves and Bellucci
}
In the following example the query uses multiple keys:
for ( Node movie : movies.query( "title:*Matrix* AND year:1999" ) )
{
// This will return "The Matrix" from 1999 only.
}
Note
Beginning a wildcard search with "*" or "?" is discouraged by Lucene, but will
nevertheless work.
Caution
You can’t have any whitespace in the search term with this syntax. See
Section 14.11.3, “Querying with Lucene Query objects” for how to do that.
14.8. Relationship indexes
An index for relationships is just like an index for nodes, extended by
providing support to constrain a search to relationships with a specific start
and/or end nodes These extra methods reside in the RelationshipIndex interface which extends Index .
Example of querying a relationship index:
// find relationships filtering on start node
// using exact matches
IndexHits reevesAsNeoHits;
reevesAsNeoHits = roles.get( "name", "Neo", reeves, null );
Relationship reevesAsNeo = reevesAsNeoHits.iterator().next();
reevesAsNeoHits.close();
// find relationships filtering on end node
// using a query
IndexHits matrixNeoHits;
matrixNeoHits = roles.query( "name", "*eo", null, theMatrix );
Relationship matrixNeo = matrixNeoHits.iterator().next();
matrixNeoHits.close();
And here’s an example for the special case of searching for a specific
relationship type:
// find relationships filtering on end node
// using a relationship type.
// this is how to add it to the index:
roles.add( reevesAsNeo, "type", reevesAsNeo.getType().name() );
// Note that to use a compound query, we can't combine committed
// and uncommitted index entries, so we'll commit before querying:
tx.success();
tx.finish();
// and now we can search for it:
IndexHits typeHits;
typeHits = roles.query( "type:ACTS_IN AND name:Neo", null, theMatrix );
Relationship typeNeo = typeHits.iterator().next();
typeHits.close();
Such an index can be useful if your domain has nodes with a very large number
of relationships between them, since it reduces the search time for a
relationship between two nodes. A good example where this approach pays
dividends is in time series data, where we have readings represented as a
relationship per occurrence.
14.9. Scores
The IndexHits interface exposes scoring so that the
index can communicate scores for the hits. Note that the result is not sorted
by the score unless you explicitly specify that. See Section 14.11.2, “Sorting”
for how to sort by score.
IndexHits hits = movies.query( "title", "The*" );
for ( Node movie : hits )
{
System.out.println( movie.getProperty( "title" ) + " " + hits.currentScore() );
}
14.10. Configuration and fulltext indexes
At the time of creation extra configuration can be specified to control the
behavior of the index and which backend to use. For example to create a Lucene
fulltext index:
IndexManager index = graphDb.index();
Index fulltextMovies = index.forNodes( "movies-fulltext",
MapUtil.stringMap( IndexManager.PROVIDER, "lucene", "type", "fulltext" ) );
fulltextMovies.add( theMatrix, "title", "The Matrix" );
fulltextMovies.add( theMatrixReloaded, "title", "The Matrix Reloaded" );
// search in the fulltext index
Node found = fulltextMovies.query( "title", "reloAdEd" ).getSingle();
Tip
In order to search for tokenized words, the query method has to be used. The
get method will only match the full string value, not the tokens.
The configuration of the index is persisted once the index has been created.
The provider configuration key is interpreted by Neo4j, but any other
configuration is passed onto the backend index (e.g. Lucene) to interpret.
Table 14.1. Lucene indexing configuration parameters
Parameter Possible values Effect
type exact, fulltext exact is the default and uses a
Lucene keyword analyzer . fulltext uses
a white-space tokenizer in its
analyzer.
to_lower_case true, false This parameter goes together with
type: fulltext and converts values to
lower case during both additions and
querying, making the index case
insensitive. Defaults to true.
analyzer the full class name of an Overrides the type so that a custom
Analyzer indexed tokens, string queries will
not match as expected.
14.11. Extra features for Lucene indexes
14.11.1. Numeric ranges
Lucene supports smart indexing of numbers, querying for ranges and sorting such
results, and so does its backend for Neo4j. To mark a value so that it is
indexed as a numeric value, we can make use of the ValueContext class, like this:
movies.add( theMatrix, "year-numeric", new ValueContext( 1999 ).indexNumeric() );
movies.add( theMatrixReloaded, "year-numeric", new ValueContext( 2003 ).indexNumeric() );
movies.add( malena, "year-numeric", new ValueContext( 2000 ).indexNumeric() );
int from = 1997;
int to = 1999;
hits = movies.query( QueryContext.numericRange( "year-numeric", from, to ) );
Note
The same type must be used for indexing and querying. That is, you can’t index
a value as a Long and then query the index using an Integer.
By giving null as from/to argument, an open ended query is created. In the
following example we are doing that, and have added sorting to the query as
well:
hits = movies.query(
QueryContext.numericRange( "year-numeric", from, null )
.sortNumeric( "year-numeric", false ) );
From/to in the ranges defaults to be inclusive, but you can change this
behavior by using two extra parameters:
movies.add( theMatrix, "score", new ValueContext( 8.7 ).indexNumeric() );
movies.add( theMatrixReloaded, "score", new ValueContext( 7.1 ).indexNumeric() );
movies.add( malena, "score", new ValueContext( 7.4 ).indexNumeric() );
// include 8.0, exclude 9.0
hits = movies.query( QueryContext.numericRange( "score", 8.0, 9.0, true, false ) );
14.11.2. Sorting
Lucene performs sorting very well, and that is also exposed in the index
backend, through the QueryContext class:
hits = movies.query( "title", new QueryContext( "*" ).sort( "title" ) );
for ( Node hit : hits )
{
// all movies with a title in the index, ordered by title
}
// or
hits = movies.query( new QueryContext( "title:*" ).sort( "year", "title" ) );
for ( Node hit : hits )
{
// all movies with a title in the index, ordered by year, then title
}
We sort the results by relevance (score) like this:
hits = movies.query( "title", new QueryContext( "The*" ).sortByScore() );
for ( Node movie : hits )
{
// hits sorted by relevance (score)
}
14.11.3. Querying with Lucene Query objects
Instead of passing in Lucene query syntax queries, you can instantiate such
queries programmatically and pass in as argument, for example:
// a TermQuery will give exact matches
Node actor = actors.query( new TermQuery( new Term( "name", "Keanu Reeves" ) ) ).getSingle();
Note that the TermQuery is basically the same thing as using the
get method on the index.
This is how to perform wildcard searches using Lucene Query Objects:
hits = movies.query( new WildcardQuery( new Term( "title", "The Matrix*" ) ) );
for ( Node movie : hits )
{
System.out.println( movie.getProperty( "title" ) );
}
Note that this allows for whitespace in the search string.
14.11.4. Compound queries
Lucene supports querying for multiple terms in the same query, like so:
hits = movies.query( "title:*Matrix* AND year:1999" );
Caution
Compound queries can’t search across committed index entries and those who
haven’t got committed yet at the same time.
14.11.5. Default operator
The default operator (that is whether AND or OR is used in between different
terms) in a query is OR. Changing that behavior is also done via the
QueryContext class:
QueryContext query = new QueryContext( "title:*Matrix* year:1999" ).defaultOperator( Operator.AND );
hits = movies.query( query );
14.11.6. Caching
If your index lookups becomes a performance bottle neck, caching can be enabled
for certain keys in certain indexes (key locations) to speed up get requests.
The caching is implemented with an LRU cache so that only the most recently
accessed results are cached (with "results" meaning a query result of a get
request, not a single entity). You can control the size of the cache (the
maximum number of results) per index key.
Index index = graphDb.index().forNodes( "actors" );
( (LuceneIndex) index ).setCacheCapacity( "name", 300000 );
Caution
This setting is not persisted after shutting down the database. This means: set
this value after each startup of the database if you want to keep it.
14.12. Batch insertion
Neo4j has a batch insertion mode intended for initial imports, which must run
in a single thread and bypasses transactions and other checks in favor of
performance. Indexing during batch insertion is done using BatchInserterIndex
which are provided via BatchInserterIndexProvider
. An example:
BatchInserter inserter = new BatchInserterImpl( "target/neo4jdb-batchinsert" );
BatchInserterIndexProvider indexProvider = new LuceneBatchInserterIndexProvider( inserter );
BatchInserterIndex actors = indexProvider.nodeIndex( "actors", MapUtil.stringMap( "type", "exact" ) );
actors.setCacheCapacity( "name", 100000 );
Map properties = MapUtil.map( "name", "Keanu Reeves" );
long node = inserter.createNode( properties );
actors.add( node, properties );
//make the changes visible for reading, use this sparsely, requires IO!
actors.flush();
// Make sure to shut down the index provider
indexProvider.shutdown();
inserter.shutdown();
The configuration parameters are the same as mentioned in Section 14.10,
“Configuration and fulltext indexes”.
14.12.1. Best practices
Here are some pointers to get the most performance out of BatchInserterIndex:
* Try to avoid flushing too often because
each flush will result in all additions (since last flush) to be visible to
the querying methods, and publishing those changes can be a performance
penalty.
* Have (as big as possible) phases where one phase is either only writes or
only reads, and don’t forget to flush after a write phase so that those
changes becomes visible to the querying methods.
* Enable caching for keys you know you’re
going to do lookups for later on to increase performance significantly
(though insertion performance may degrade slightly).
Note
Changes to the index are available for reading first after they are flushed to
disk. Thus, for optimal performance, read and lookup operations should be kept
to a minimum during batchinsertion since they involve IO and impact speed
negatively.
14.13. Automatic Indexing
Neo4j provides a single index for nodes and one for relationships in each
database that automatically follow property values as they are added, deleted
and changed on database primitives. This functionality is called auto indexing
and is controlled both from the database configuration Map and through its own
API.
Caution
This is an experimental feature. Expect changes in the API and do not rely on
it for production data handling.
14.13.1. Configuration
By default Auto Indexing is off for both Nodes and Relationships. To enable it
on database startup set the configuration options Config.NODE_AUTO_INDEXING and
Config.RELATIONSHIP_AUTO_INDEXING to the string "true".
If you just enable auto indexing as above, then still no property will be auto
indexed. To define which property names you want the auto indexer to monitor as
a configuration parameter, set the Config.{NODE,RELATIONSHIP}_KEYS_INDEXABLE
option to a String that is a comma separated concatenation of the property
names you want auto indexed.
/*
* Creating the configuration, adding nodeProp1 and nodeProp2 as
* auto indexed properties for Nodes and relProp1 and relProp2 as
* auto indexed properties for Relationships. Only those will be
* indexed. We also have to enable auto indexing for both these
* primitives explicitly.
*/
Map config = new HashMap();
config.put( Config.NODE_KEYS_INDEXABLE, "nodeProp1, nodeProp2" );
config.put( Config.RELATIONSHIP_KEYS_INDEXABLE, "relProp1, relProp2" );
config.put( Config.NODE_AUTO_INDEXING, "true" );
config.put( Config.RELATIONSHIP_AUTO_INDEXING, "true" );
EmbeddedGraphDatabase graphDb = new EmbeddedGraphDatabase(
getStoreDir( "testConfig" ), config );
Transaction tx = graphDb.beginTx();
Node node1 = null, node2 = null;
Relationship rel = null;
try
{
// Create the primitives
node1 = graphDb.createNode();
node2 = graphDb.createNode();
rel = node1.createRelationshipTo( node2,
DynamicRelationshipType.withName( "DYNAMIC" ) );
// Add indexable and non-indexable properties
node1.setProperty( "nodeProp1", "nodeProp1Value" );
node2.setProperty( "nodeProp2", "nodeProp2Value" );
node1.setProperty( "nonIndexed", "nodeProp2NonIndexedValue" );
rel.setProperty( "relProp1", "relProp1Value" );
rel.setProperty( "relPropNonIndexed", "relPropValueNonIndexed" );
// Make things persistent
tx.success();
}
catch ( Exception e )
{
tx.failure();
}
finally
{
tx.finish();
}
14.13.2. Search
The usefulness of the auto indexing functionality comes of course from the
ability to actually query the index and retrieve results. To that end, you can
acquire a ReadableIndex object from the AutoIndexer that exposes all the query
and get methods of a full Index with exactly the same functionality.
Continuing from the previous example, accessing the index is done like this:
// Get the Node auto index
ReadableIndex autoNodeIndex = graphDb.index().getNodeAutoIndexer().getAutoIndex();
// node1 and node2 both had auto indexed properties, get them
assertEquals( node1,
autoNodeIndex.get( "nodeProp1", "nodeProp1Value" ).getSingle() );
assertEquals( node2,
autoNodeIndex.get( "nodeProp2", "nodeProp2Value" ).getSingle() );
// node2 also had a property that should be ignored.
assertFalse( autoNodeIndex.get( "nonIndexed",
"nodeProp2NonIndexedValue" ).hasNext() );
// Get the relationship auto index
ReadableIndex autoRelIndex = graphDb.index().getRelationshipAutoIndexer().getAutoIndex();
// One property was set for auto indexing
assertEquals( rel,
autoRelIndex.get( "relProp1", "relProp1Value" ).getSingle() );
// The rest should be ignored
assertFalse( autoRelIndex.get( "relPropNonIndexed",
"relPropValueNonIndexed" ).hasNext() );
14.13.3. Runtime Configuration
The same options that are available during database creation via the
configuration can also be set during runtime via the AutoIndexer API.
Gaining access to the AutoIndexer API and adding two Node and one +Relationship
properties to auto index is done like so:
// Start without any configuration
EmbeddedGraphDatabase graphDb = new EmbeddedGraphDatabase(
getStoreDir( "testAPI" ) );
// Get the Node AutoIndexer, set nodeProp1 and nodeProp2 as auto
// indexed.
AutoIndexer nodeAutoIndexer = graphDb.index().getNodeAutoIndexer();
nodeAutoIndexer.startAutoIndexingProperty( "nodeProp1" );
nodeAutoIndexer.startAutoIndexingProperty( "nodeProp2" );
// Get the Relationship AutoIndexer
AutoIndexer relAutoIndexer = graphDb.index().getRelationshipAutoIndexer();
relAutoIndexer.startAutoIndexingProperty( "relProp1" );
// None of the AutoIndexers are enabled so far. Do that now
nodeAutoIndexer.setEnabled( true );
relAutoIndexer.setEnabled( true );
Parameters to the AutoIndexers passed through the Configuration and settings
made through the API are cumulative. So you can set some beforehand known
settings, do runtime checks to augment the initial configuration and then
enable the desired auto indexers - the final configuration is the same
regardless of the method used to reach it.
14.13.4. Updating the Automatic Index
Updates to the auto indexed properties happen of course automatically as you
update them. Removal of properties from the auto index happens for two reasons.
One is that you actually removed the property. The other is that you stopped
autoindexing on a property. When the latter happens, any primitive you touch
and it has that property, it is removed from the auto index, regardless of any
operations on the property. When you start or stop auto indexing on a property,
no auto update operation happens currently. If you need to change the set of
auto indexed properties and have them re-indexed, you currently have to do this
by hand. An example will illustrate the above better:
/*
* Creating the configuration
*/
Map config = new HashMap();
config.put( Config.NODE_KEYS_INDEXABLE, "nodeProp1, nodeProp2" );
config.put( Config.NODE_AUTO_INDEXING, "true" );
EmbeddedGraphDatabase graphDb = new EmbeddedGraphDatabase(
getStoreDir( "mutations" ), config );
Transaction tx = graphDb.beginTx();
Node node1 = null, node2 = null, node3 = null, node4 = null;
try
{
// Create the primitives
node1 = graphDb.createNode();
node2 = graphDb.createNode();
node3 = graphDb.createNode();
node4 = graphDb.createNode();
// Add indexable and non-indexable properties
node1.setProperty( "nodeProp1", "nodeProp1Value" );
node2.setProperty( "nodeProp2", "nodeProp2Value" );
node3.setProperty( "nodeProp1", "nodeProp3Value" );
node4.setProperty( "nodeProp2", "nodeProp4Value" );
// Make things persistent
tx.success();
}
catch ( Exception e )
{
tx.failure();
}
finally
{
tx.finish();
}
/*
* Here both nodes are indexed. To demonstrate removal, we stop
* autoindexing nodeProp1.
*/
AutoIndexer nodeAutoIndexer = graphDb.index().getNodeAutoIndexer();
nodeAutoIndexer.stopAutoIndexingProperty( "nodeProp1" );
tx = graphDb.beginTx();
try
{
/*
* nodeProp1 is no longer auto indexed. It will be
* removed regardless. Note that node3 will remain.
*/
node1.setProperty( "nodeProp1", "nodeProp1Value2" );
/*
* node2 will be auto updated
*/
node2.setProperty( "nodeProp2", "nodeProp2Value2" );
/*
* remove node4 property nodeProp2 from index.
*/
node4.removeProperty( "nodeProp2" );
// Make things persistent
tx.success();
}
catch ( Exception e )
{
tx.failure();
}
finally
{
tx.finish();
}
// Verify
ReadableIndex nodeAutoIndex = nodeAutoIndexer.getAutoIndex();
// node1 is completely gone
assertFalse( nodeAutoIndex.get( "nodeProp1", "nodeProp1Value" ).hasNext() );
assertFalse( nodeAutoIndex.get( "nodeProp1", "nodeProp1Value2" ).hasNext() );
// node2 is updated
assertFalse( nodeAutoIndex.get( "nodeProp2", "nodeProp2Value" ).hasNext() );
assertEquals( node2,
nodeAutoIndex.get( "nodeProp2", "nodeProp2Value2" ).getSingle() );
/*
* node3 is still there, despite its nodeProp1 property not being monitored
* any more because it was not touched, in contrast with node1.
*/
assertEquals( node3,
nodeAutoIndex.get( "nodeProp1", "nodeProp3Value" ).getSingle() );
// Finally, node4 is removed because the property was removed.
assertFalse( nodeAutoIndex.get( "nodeProp2", "nodeProp4Value" ).hasNext() );
Caution
If you start the database with auto indexing enabled but different auto indexed
properties than the last run, then already auto-indexed entities will be
deleted as you work with them. Make sure that the monitored set is what you
want before enabling the functionality.
Chapter 15. Cypher Query Language
A new query language, code-named “Cypher”, has been added to Neo4j. It allows
for expressive and efficient querying of the graph store without having to
write traversers in code. Cypher is still growing and maturing, and that means
that there probably will be breaking syntax changes. It also means that it has
not undergone the same rigorous performance testing as the other components.
Cypher is designed to be a humane query language, suitable for both developers
and (importantly, we think) operations professionals who want to make ad-hoc
queries on the database. Its constructs are based on English prose and neat
iconography, which helps to make it (somewhat) self-explanatory.
Cypher is inspired by a number of different approaches and builds upon
established practices for expressive querying. Most of the keywords like WHERE
and ORDER BY are inspired by SQL . Pattern
matching borrows expression approaches from SPARQL . Regular expression matching is implemented using the Scala
programming language .
Cypher is a declarative language. It focuses on the clarity of expressing what
to retrieve from a graph, not how to do it, in contrast to imperative languages
like Java, and scripting languages like Gremlin
(supported via the Section 18.16, “Gremlin Plugin”) and the JRuby Neo4j
bindings . This makes the concern of how to
optimize queries in implementation detail not exposed to the user.
The query language is comprised of several distinct parts.
* START: Starting points in the graph, obtained by element IDs or via index
lookups
* MATCH: The graph pattern to match, bound to the starting points in START
* WHERE: Filtering criteria
* RETURN: What to return
Let’s see three of them in action:
Imagine an example graph like
Figure 15.1. Example Graph
Example-Graph-cypher-intro.svg
For example, here is a query which finds a user called John in an index and
then traverses the graph looking for friends of Johns friends (though not his
direct friends) before returning both John and any friends-of-friends that are
found.
START john=node:node_auto_index(name = 'John')
MATCH john-[:friend]->()-[:friend]->fof
RETURN john, fof
Resulting in
+--------------------------------------------+
|john |fof |
|--------------------------------------------|
|2 rows, 2 ms |
|--------------------------------------------|
|Node[4]{name->"John"}|Node[2]{name->"Maria"}|
|---------------------+----------------------|
|Node[4]{name->"John"}|Node[3]{name->"Steve"}|
+--------------------------------------------+
Next up we will add filtering to set all four parts in motion:
In this next example, we take a list of users (by node ID) and traverse the
graph looking for those other users that have an outgoing friend relationship,
returning only those followed users who have a name property starting with S.
START user=node(5,4,1,2,3)
MATCH user-[:friend]->follower
WHERE follower.name =~ /S.*/
RETURN user, follower.name
Resulting in
+-----------------------------------+
|user |follower.name|
|-----------------------------------|
|2 rows, 2 ms |
|-----------------------------------|
|Node[5]{name->"Joe"} |"Steve" |
|---------------------+-------------|
|Node[4]{name->"John"}|"Sara" |
+-----------------------------------+
To use Cypher from Java, see Section 4.8, “Cypher Queries”.
15.1. Compatibility
Cypher is still changing rather rapidly. Parts of the changes are internal - we
add new pattern matchers, aggregators and other optimizations, which hopefully
makes your queries run faster.
Other changes are directly visible to our users - the syntax is still changing.
New concepts are being added and old ones changed to fit into new
possibilities. To guard you from having to keep up with our syntax changes,
Cypher allows you to use an older parser, but still gain the speed from new
optimizations.
There are two ways you can select which parser to use. You can configure your
database with the configuration parameter cypher_parser_version, and enter
which parser you’d like to use (1.5 and 1.6 are supported now). Any Cypher
query that doesn’t explicitly say anything else, will get the parser you have
configured.
The other way is on a query by query basis. By simply pre-pending your query
with "CYPHER 1.5", that particular query will be parsed with the 1.5 version of
the parser. Example:
CYPHER 1.5 START n=node(0)
WHERE n.foo = "bar"
RETURN n
15.2. Parameters
Cypher supports querying with parameters. This allows developers to not to have
to do string building to create a query, and it also makes caching of execution
plans much easier for Cypher.
Parameters can be used for literals in the WHERE clause, for the index key and
index value in the START clause, index queries, and finally for node/
relationship ids.
Accepted names for parameter are letters and number, and any combination of
these.
Here follows a few examples of how you can use parameters from Java.
Parameter for node id
Map params = new HashMap();
params.put( "id", 0 );
ExecutionResult result = engine.execute( "start n=node({id}) return n.name", params );
Parameter for node object
Map params = new HashMap();
params.put( "node", andreasNode );
ExecutionResult result = engine.execute( "start n=node({node}) return n.name", params );
Parameter for multiple node ids
Map params = new HashMap();
params.put( "id", Arrays.asList( 0, 1, 2 ) );
ExecutionResult result = engine.execute( "start n=node({id}) return n.name", params );
Parameter for string literal
Map params = new HashMap();
params.put( "name", "Johan" );
ExecutionResult result = engine.execute( "start n=node(0,1,2) where n.name = {name} return n", params );
Parameter for index key and value
Map params = new HashMap();
params.put( "key", "name" );
params.put( "value", "Michaela" );
ExecutionResult result = engine.execute( "start n=node:people({key} = {value}) return n", params );
Parameter for index query
Map params = new HashMap();
params.put( "query", "name:Andreas" );
ExecutionResult result = engine.execute( "start n=node:people({query}) return n", params );
* Numeric parameters for SKIP and LIMIT *
Map params = new HashMap();
params.put( "s", 1 );
params.put( "l", 1 );
ExecutionResult result = engine.execute( "start n=node(0,1,2) return n.name skip {s} limit {l}", params );
* Parameter for regular expression *
Map params = new HashMap();
params.put( "regex", ".*h.*" );
ExecutionResult result = engine.execute( "start n=node(0,1,2) where n.name =~ {regex} return n.name", params );
15.3. Identifiers
When you reference parts of the pattern, you do so by naming them. The names
you give the different parts are calles identifiers.
In this example: START n=node(1) MATCH n-→b RETURN b
The identifiers are n and b.
Identifiers can be lower or upper case, and may contain underscore. If other
characters are needed, you can use the ` sign. The same rules apply to property
names.
"start n=node(0) return n.NOT_EXISTING, n.`property with spaces in it`"
15.4. Start
Every query describes a pattern, and in that pattern one can have multiple
bound points. A bound point is a relationship or a node that form the starting
points for a pattern match. You can either bind points by id, or by index
lookups.
Graph
cypher-start-graph.svg
15.4.1. Node by id
Binding a node as a start point is done with the node(*) function .
Query
START n=node(1)
RETURN n
The reference node is returned
Table 15.1. Result
+------------------+
|n |
|------------------|
|1 rows, 0 ms |
|------------------|
|Node[1]{name->"A"}|
+------------------+
15.4.2. Relationship by id
Binding a relationship as a start point is done with the relationship()
function, which can also be abbreviated rel().
Query
START r=relationship(0)
RETURN r
The relationship with id 0 is returned
Table 15.2. Result
+------------+
|r |
|------------|
|1 rows, 0 ms|
|------------|
|:KNOWS[0] {}|
+------------+
15.4.3. Multiple nodes by id
Multiple nodes are selected by listing them separated by commas.
Query
START n=node(1, 2, 3)
RETURN n
The nodes listed in the START statement.
Table 15.3. Result
+------------------+
|n |
|------------------|
|3 rows, 0 ms |
|------------------|
|Node[1]{name->"A"}|
|------------------|
|Node[2]{name->"B"}|
|------------------|
|Node[3]{name->"C"}|
+------------------+
15.4.4. Node by index lookup
If the start point can be found by index lookups, it can be done like this:
node:index-name(key = "value"). In this example, there exists a node index
named nodes.
Query
START n=node:nodes(name = "A")
RETURN n
The node indexed with name "A" is returned
Table 15.4. Result
+------------------+
|n |
|------------------|
|1 rows, 1 ms |
|------------------|
|Node[1]{name->"A"}|
+------------------+
15.4.5. Relationship by index lookup
If the start point can be found by index lookups, it can be done like this:
relationship:index-name(key = "value"].
Query
START r=relationship:rels(property = "some_value")
RETURN r
The relationship indexed with property "some_value" is returned
Table 15.5. Result
+----------------------------------+
|r |
|----------------------------------|
|1 rows, 1 ms |
|----------------------------------|
|:KNOWS[0] {property->"some_value"}|
+----------------------------------+
15.4.6. Node by index query
If the start point can be found by index more complex lucene queries:
node:index-name("query").This allows you to write more advanced index queries
Query
START n=node:nodes("name:A")
RETURN n
The node indexed with name "A" is returned
Table 15.6. Result
+------------------+
|n |
|------------------|
|1 rows, 1 ms |
|------------------|
|Node[1]{name->"A"}|
+------------------+
15.4.7. Multiple start points
Sometimes you want to bind multiple start points. Just list them separated by
commas.
Query
START a=node(1), b=node(2)
RETURN a,b
Both the A and the B node are returned
Table 15.7. Result
+-------------------------------------+
|a |b |
|-------------------------------------|
|1 rows, 0 ms |
|-------------------------------------|
|Node[1]{name->"A"}|Node[2]{name->"B"}|
+-------------------------------------+
15.5. Match
In the match part of a query, the pattern is described. The description of the
pattern is made up of one or more paths, separated by commas.
Node identifiers can be used with or without surrounding parenthesis. These two
match clauses are semantically identical:
MATCH (a)-->(b)
and
MATCH a-->b
All parts of the pattern must be directly or indirectly bound to a start point.
The optional relationship is a way to describe parts of the pattern that can
evaluate to null if it can not be matched to the real graph. It’s the
equivalent of SQL outer join - if Cypher finds one or more matches, they will
be returned. If no matches are found, Cypher will return a null.
Optionality travels - if a part of the pattern can only be reached from a bound
point through an optional relationship, that part is also optional. Also, named
paths that contain optional parts are also optional - if any part of the path
is null, the whole path is null.
In these example, b and p are all optional and can contain null:
START a=node(1) MATCH p = a-[?]->b
START a=node(1) MATCH p = a-[*?]->b
START a=node(1) MATCH p = a-[?]->x-->b
START a=node(1), x=node(100) MATCH p = shortestPath( a-[*?]->x )
Graph
cypher-match-graph.svg
15.5.1. Related nodes
The symbol -- means related to, without regard to type or direction.
Query
START n=node(3)
MATCH (n)--(x)
RETURN x
All nodes related to A are returned
Table 15.8. Result
+------------------------+
|x |
|------------------------|
|3 rows, 1 ms |
|------------------------|
|Node[4]{name->"Bossman"}|
|------------------------|
|Node[1]{name->"David"} |
|------------------------|
|Node[5]{name->"Cesar"} |
+------------------------+
15.5.2. Outgoing relationships
When the direction of a relationship is interesting, it is shown by using -->
or <--, like this:
Query
START n=node(3)
MATCH (n)-->(x)
RETURN x
All nodes that A has outgoing relationships to.
Table 15.9. Result
+------------------------+
|x |
|------------------------|
|2 rows, 0 ms |
|------------------------|
|Node[4]{name->"Bossman"}|
|------------------------|
|Node[5]{name->"Cesar"} |
+------------------------+
15.5.3. Directed relationships and identifier
If an identifier is needed, either for filtering on properties of the
relationship, or to return the relationship, this is how you introduce the
identifier.
Query
START n=node(3)
MATCH (n)-[r]->()
RETURN r
All outgoing relationships from node A.
Table 15.10. Result
+-------------+
|r |
|-------------|
|2 rows, 1 ms |
|-------------|
|:KNOWS[0] {} |
|-------------|
|:BLOCKS[1] {}|
+-------------+
15.5.4. Match by relationship type
When you know the relationship type you want to match on, you can specify it by
using a colon.
Query
START n=node(3)
MATCH (n)-[:BLOCKS]->(x)
RETURN x
All nodes that are BLOCKed by A.
Table 15.11. Result
+----------------------+
|x |
|----------------------|
|1 rows, 0 ms |
|----------------------|
|Node[5]{name->"Cesar"}|
+----------------------+
15.5.5. Match by relationship type and use an identifier
If you both want to introduce an identifier to hold the relationship, and
specify the relationship type you want, just add them both, like this.
Query
START n=node(3)
MATCH (n)-[r:BLOCKS]->()
RETURN r
All BLOCKS relationship going out from A.
Table 15.12. Result
+-------------+
|r |
|-------------|
|1 rows, 0 ms |
|-------------|
|:BLOCKS[1] {}|
+-------------+
15.5.6. Relationship types with uncommon characters
Sometime your database will have types with non-letter characters, or with
spaces in them. Use ` to escape these.
Query
START n=node(3)
MATCH (n)-[r:`TYPE WITH SPACE IN IT`]->()
RETURN r
This returns a relationship of a type with spaces in it.
Table 15.13. Result
+----------------------------+
|r |
|----------------------------|
|1 rows, 0 ms |
|----------------------------|
|:TYPE WITH SPACE IN IT[6] {}|
+----------------------------+
15.5.7. Multiple relationships
Relationships can be expressed by using multiple statements in the form of ()--
(), or they can be strung together, like this:
Query
START a=node(3)
MATCH (a)-[:KNOWS]->(b)-[:KNOWS]->(c)
RETURN a,b,c
The three nodes in the path.
Table 15.14. Result
+----------------------------------------------------------------------+
|a |b |c |
|----------------------------------------------------------------------|
|1 rows, 1 ms |
|----------------------------------------------------------------------|
|Node[3]{name->"Anders"}|Node[4]{name->"Bossman"}|Node[2]{name->"Emil"}|
+----------------------------------------------------------------------+
15.5.8. Variable length relationships
Nodes that are variable number of relationship→node hops can be found using -
[:TYPE*minHops..maxHops]->.
Query
START a=node(3), x=node(2, 4)
MATCH a-[:KNOWS*1..3]->x
RETURN a,x
Returns the start and end point, if there is a path between 1 and 3
relationships away
Table 15.15. Result
+------------------------------------------------+
|a |x |
|------------------------------------------------|
|2 rows, 1 ms |
|------------------------------------------------|
|Node[3]{name->"Anders"}|Node[2]{name->"Emil"} |
|-----------------------+------------------------|
|Node[3]{name->"Anders"}|Node[4]{name->"Bossman"}|
+------------------------------------------------+
15.5.9. Relationship identifier in variable length relationships
When the connection between two nodes is of variable length, a relationship
identifier becomes an iterable of relationships.
Query
START a=node(3), x=node(2, 4)
MATCH a-[r:KNOWS*1..3]->x
RETURN r
Returns the start and end point, if there is a path between 1 and 3
relationships away
Table 15.16. Result
+--------------------------------------+
|r |
|--------------------------------------|
|2 rows, 1 ms |
|--------------------------------------|
|List(Relationship[0], Relationship[3])|
|--------------------------------------|
|List(Relationship[0]) |
+--------------------------------------+
15.5.10. Zero length paths
When using variable length paths that have the lower bound zero, it means that
two identifiers can point to the same node. If the distance between two nodes
is zero, they are, by definition, the same node.
Query
START a=node(3)
MATCH p1=a-[:KNOWS*0..1]->b, p2=b-[:BLOCKS*0..1]->c
RETURN a,b,c, length(p1), length(p2)
This query will return four paths, some of them with length zero.
Table 15.17. Result
+-----------------------------------------------------------------------------+
|a |b |c |LENGTH |LENGTH |
| | | |(p1) |(p2) |
|-----------------------------------------------------------------------------|
|4 rows, 3 ms |
|-----------------------------------------------------------------------------|
|Node[3]{name-> |Node[3]{name-> |Node[3]{name-> |0 |0 |
|"Anders"} |"Anders"} |"Anders"} | | |
|-------------------+-------------------+-------------------+--------+--------|
|Node[3]{name-> |Node[3]{name-> |Node[5]{name-> |0 |1 |
|"Anders"} |"Anders"} |"Cesar"} | | |
|-------------------+-------------------+-------------------+--------+--------|
|Node[3]{name-> |Node[4]{name-> |Node[4]{name-> |1 |0 |
|"Anders"} |"Bossman"} |"Bossman"} | | |
|-------------------+-------------------+-------------------+--------+--------|
|Node[3]{name-> |Node[4]{name-> |Node[1]{name-> |1 |1 |
|"Anders"} |"Bossman"} |"David"} | | |
+-----------------------------------------------------------------------------+
15.5.11. Optional relationship
If a relationship is optional, it can be marked with a question mark. This
similar to how a SQL outer join works, if the relationship is there, it is
returned. If it’s not, null is returned in it’s place. Remember that anything
hanging of an optional relation, is in turn optional, unless it is connected
with a bound node some other path.
Query
START a=node(2)
MATCH a-[?]->x
RETURN a,x
A node, and null, since the node has no relationships.
Table 15.18. Result
+-----------------------------+
|a |x |
|-----------------------------|
|1 rows, 0 ms |
|-----------------------------|
|Node[2]{name->"Emil"} ||
+-----------------------------+
15.5.12. Optional typed and named relationship
Just as with a normal relationship, you can decide which identifier it goes
into, and what relationship type you need.
Query
START a=node(3)
MATCH a-[r?:LOVES]->()
RETURN a,r
A node, and null, since the node has no relationships.
Table 15.19. Result
+-------------------------------+
|a |r |
|-------------------------------|
|1 rows, 0 ms |
|-------------------------------|
|Node[3]{name->"Anders"} ||
+-------------------------------+
15.5.13. Properties on optional elements
Returning a property from an optional element that is null will also return
null.
Query
START a=node(2)
MATCH a-[?]->x
RETURN x, x.name
The element x (null in this query), and null as it’s name.
Table 15.20. Result
+-------------+
|x |x.name|
|-------------|
|1 rows, 0 ms |
|-------------|
|||
+-------------+
15.5.14. Complex matching
Using Cypher, you can also express more complex patterns to match on, like a
diamond shape pattern.
Query
START a=node(3)
MATCH (a)-[:KNOWS]->(b)-[:KNOWS]->(c), (a)-[:BLOCKS]-(d)-[:KNOWS]-(c)
RETURN a,b,c,d
The four nodes in the path.
Table 15.21. Result
+-----------------------------------------------------------------------------+
|a |b |c |d |
|-----------------------------------------------------------------------------|
|1 rows, 2 ms |
|-----------------------------------------------------------------------------|
|Node[3]{name-> |Node[4]{name-> |Node[2]{name-> |Node[5]{name-> |
|"Anders"} |"Bossman"} |"Emil"} |"Cesar"} |
+-----------------------------------------------------------------------------+
15.5.15. Shortest path
Finding a single shortest path between two nodes is as easy as using the
shortestPath-function, like this.
Query
START d=node(1), e=node(2)
MATCH p = shortestPath( d-[*..15]->e )
RETURN p
This means: find a single shortest path between two nodes, as long as the path
is max 15 relationships long. Inside of the parenthesis you write a single link
of a path - the starting node, the connecting relationship and the end node.
Characteristics describing the relationship like relationship type, max hops
and direction are all used when finding the shortest path. You can also mark
the path as optional.
Table 15.22. Result
+------------------------------------------------------+
|p |
|------------------------------------------------------|
|1 rows, 1 ms |
|------------------------------------------------------|
|(1)--[KNOWS,2]-->(3)--[KNOWS,0]-->(4)--[KNOWS,3]-->(2)|
+------------------------------------------------------+
15.5.16. All shortest paths
Finds all the shortest paths between two nodes.
Query
START d=node(1), e=node(2)
MATCH p = allShortestPaths( d-[*..15]->e )
RETURN p
This will find the two directed paths between David and Emil.
Table 15.23. Result
+-------------------------------------------------------+
|p |
|-------------------------------------------------------|
|2 rows, 0 ms |
|-------------------------------------------------------|
|(1)--[KNOWS,2]-->(3)--[KNOWS,0]-->(4)--[KNOWS,3]-->(2) |
|-------------------------------------------------------|
|(1)--[KNOWS,2]-->(3)--[BLOCKS,1]-->(5)--[KNOWS,4]-->(2)|
+-------------------------------------------------------+
15.5.17. Named path
If you want to return or filter on a path in your pattern graph, you can a
introduce a named path.
Query
START a=node(3)
MATCH p = a-->b
RETURN p
The two paths starting from the first node.
Table 15.24. Result
+---------------------+
|p |
|---------------------|
|2 rows, 0 ms |
|---------------------|
|(3)--[KNOWS,0]-->(4) |
|---------------------|
|(3)--[BLOCKS,1]-->(5)|
+---------------------+
15.5.18. Matching on a bound relationship
When your pattern contains a bound relationship, and that relationship pattern
doesn specify direction, Cypher will try to match the relationship where the
connected nodes switch sides.
Query
START r=rel(0)
MATCH a-[r]-b
RETURN a,b
This returns the two connected nodes, once as the start node, and once as the
end node
Table 15.25. Result
+-------------------------------------------------+
|a |b |
|-------------------------------------------------|
|2 rows, 1 ms |
|-------------------------------------------------|
|Node[3]{name->"Anders"} |Node[4]{name->"Bossman"}|
|------------------------+------------------------|
|Node[4]{name->"Bossman"}|Node[3]{name->"Anders"} |
+-------------------------------------------------+
15.6. Where
If you need filtering apart from the pattern of the data that you are looking
for, you can add clauses in the where part of the query.
Graph
cypher-where-graph.svg
15.6.1. Boolean operations
You can use the expected boolean operators AND and OR, and also the boolean
function NOT().
Query
START n=node(3, 1)
WHERE (n.age < 30 and n.name = "Tobias") or not(n.name = "Tobias")
RETURN n
The node.
Table 15.26. Result
+---------------------------------------------+
|n |
|---------------------------------------------|
|2 rows, 0 ms |
|---------------------------------------------|
|Node[3]{name->"Andres",age->36,belt->"white"}|
|---------------------------------------------|
|Node[1]{name->"Tobias",age->25} |
+---------------------------------------------+
15.6.2. Filter on node property
To filter on a property, write your clause after the WHERE keyword.
Query
START n=node(3, 1)
WHERE n.age < 30
RETURN n
The node.
Table 15.27. Result
+-------------------------------+
|n |
|-------------------------------|
|1 rows, 0 ms |
|-------------------------------|
|Node[1]{name->"Tobias",age->25}|
+-------------------------------+
15.6.3. Regular expressions
You can match on regular expressions by using =~ /regexp/, like this:
Query
START n=node(3, 1)
WHERE n.name =~ /Tob.*/
RETURN n
The node named Tobias.
Table 15.28. Result
+-------------------------------+
|n |
|-------------------------------|
|1 rows, 1 ms |
|-------------------------------|
|Node[1]{name->"Tobias",age->25}|
+-------------------------------+
15.6.4. Escaping in regular expressions
If you need a forward slash inside of your regular expression, escape it just
like you expect to.
Query
START n=node(3, 1)
WHERE n.name =~ /Some\/thing/
RETURN n
No nodes match this regular expression.
Table 15.29. Result
+--------------+
|n |
|--------------|
|0 rows, 0 ms |
|--------------|
|(empty result)|
+--------------+
15.6.5. Case insensitive regular expressions
By pre-pending a regular expression with (?i), the whole expression becomes
case insensitive.
Query
START n=node(3, 1)
WHERE n.name =~ /(?i)ANDR.*/
RETURN n
The node with name Andres is returned.
Table 15.30. Result
+---------------------------------------------+
|n |
|---------------------------------------------|
|1 rows, 0 ms |
|---------------------------------------------|
|Node[3]{name->"Andres",age->36,belt->"white"}|
+---------------------------------------------+
15.6.6. Filtering on relationship type
You can put the exact relationship type in the MATCH pattern, but sometimes you
want to be able to do more advanced filtering on the type. You can use the
special property TYPE to compare the type with something else. In this example,
the query does a regular expression comparison with the name of the
relationship type.
Query
START n=node(3)
MATCH (n)-[r]->()
WHERE type(r) =~ /K.*/
RETURN r
The relationship that has a type whose name starts with K.
Table 15.31. Result
+------------+
|r |
|------------|
|2 rows, 0 ms|
|------------|
|:KNOWS[0] {}|
|------------|
|:KNOWS[1] {}|
+------------+
15.6.7. Property exists
To only include nodes/relationships that have a property, just write out the
identifier and the property you expect it to have.
Query
START n=node(3, 1)
WHERE n.belt
RETURN n
The node named Andres.
Table 15.32. Result
+---------------------------------------------+
|n |
|---------------------------------------------|
|1 rows, 0 ms |
|---------------------------------------------|
|Node[3]{name->"Andres",age->36,belt->"white"}|
+---------------------------------------------+
15.6.8. Compare if property exists
If you want to compare a property on a graph element, but only if it exists,
use the nullable property syntax. It is the property with the dot notation,
followed by a question mark
Query
START n=node(3, 1)
WHERE n.belt? = 'white'
RETURN n
All nodes, even those without the belt property
Table 15.33. Result
+---------------------------------------------+
|n |
|---------------------------------------------|
|2 rows, 0 ms |
|---------------------------------------------|
|Node[3]{name->"Andres",age->36,belt->"white"}|
|---------------------------------------------|
|Node[1]{name->"Tobias",age->25} |
+---------------------------------------------+
15.6.9. Filter on null values
Sometimes you might want to test if a value or an identifier is null. This is
done just like SQL does it, with IS NULL. Also like SQL, the negative is IS NOT
NULL, althought NOT(IS NULL x) also works.
Query
START a=node(1), b=node(3, 2)
MATCH a<-[r?]-b
WHERE r is null
RETURN b
Nodes that Tobias is not connected to
Table 15.34. Result
+------------------------------+
|b |
|------------------------------|
|1 rows, 1 ms |
|------------------------------|
|Node[2]{name->"Peter",age->34}|
+------------------------------+
15.6.10. Filter on relationships
To filter out subgraphs based on relationships between nodes, you use a limited
part of the iconigraphy in the match clause. You can only describe the
relationship with direction and eventual type. These are all valid expressions:
WHERE a-→b WHERE a←-b WHERE a←[:KNOWS]-b WHERE a-[:KNOWS]-b
Not that you can not introduce new identifiers here. Although it might look very similar to the MATCH clause, the
WHERE clause is all about eliminating matched subgraphs. MATCH a-->b is very different from WHERE a-->b; the first will
produce a subgraph for every relationship between a and b, and the latter will eliminate any matched subgraphs where a and b
do not have a relationship between them.
Query
START a=node(1), b=node(3, 2)
WHERE a<--b
RETURN b
Nodes that Tobias is not connected to
Table 15.35. Result
+---------------------------------------------+
|b |
|---------------------------------------------|
|1 rows, 0 ms |
|---------------------------------------------|
|Node[3]{name->"Andres",age->36,belt->"white"}|
+---------------------------------------------+
15.7. Return
In the return part of your query, you define which parts of the pattern you are
interested in. It can be nodes, relationships, or properties on these.
Graph
cypher-return-graph.svg
15.7.1. Return nodes
To return a node, list it in the return statemenet.
Query
START n=node(2)
RETURN n
The node.
Table 15.36. Result
+------------------+
|n |
|------------------|
|1 rows, 0 ms |
|------------------|
|Node[2]{name->"B"}|
+------------------+
15.7.2. Return relationships
To return a relationship, just include it in the return list.
Query
START n=node(1)
MATCH (n)-[r:KNOWS]->(c)
RETURN r
The relationship.
Table 15.37. Result
+------------+
|r |
|------------|
|1 rows, 1 ms|
|------------|
|:KNOWS[0] {}|
+------------+
15.7.3. Return property
To return a property, use the dot separator, like this:
Query
START n=node(1)
RETURN n.name
The the value of the property name.
Table 15.38. Result
+------------+
|n.name |
|------------|
|1 rows, 0 ms|
|------------|
|"A" |
+------------+
15.7.4. Identifier with uncommon characters
To introduce a placeholder that is made up of characters that are outside of
the english alphabet, you can use the ` to enclose the identifier, like this:
Query
START `This isn't a common identifier`=node(1)
RETURN `This isn't a common identifier`.`<>`
The node indexed with name "A" is returned
Table 15.39. Result
+-----------------------------------------+
|This isn't a common identifier.<>|
|-----------------------------------------|
|1 rows, 0 ms |
|-----------------------------------------|
|"Yes!" |
+-----------------------------------------+
15.7.5. Column alias
If the name of the column should be different from the expression used, you can
rename it by using AS .
Query
START a=node(1)
RETURN a.age AS SomethingTotallyDifferent
Returns the age property of a node, but renames the column.
Table 15.40. Result
+-------------------------+
|SomethingTotallyDifferent|
|-------------------------|
|1 rows, 0 ms |
|-------------------------|
|55 |
+-------------------------+
15.7.6. Optional properties
If a property might or might not be there, you can select it optionally by
adding a questionmark to the identifier, like this:
Query
START n=node(1, 2)
RETURN n.age?
The age when the node has that property, or null if the property is not there.
Table 15.41. Result
+------------+
|n.age |
|------------|
|2 rows, 0 ms|
|------------|
|55 |
|------------|
| |
+------------+
15.7.7. Unique results
DISTINCT retrieves only unique rows depending on the columns that have been
selected to output.
Query
START a=node(1)
MATCH (a)-->(b)
RETURN distinct b
The node named B, but only once.
Table 15.42. Result
+------------------+
|b |
|------------------|
|1 rows, 1 ms |
|------------------|
|Node[2]{name->"B"}|
+------------------+
15.8. Aggregation
To calculate aggregated data, Cypher offers aggregation, much like SQL’s GROUP
BY. If any aggregation functions are found in the RETURN statement, all the
columns without aggregating functions are used as the grouping key.
Graph
cypher-aggregation-graph.svg
15.8.1. COUNT
COUNT is used to count the number of rows. COUNT can be used in two forms -
COUNT(*) which just counts the number of matching rows, and COUNT
(), which counts the number of non-null values in .
15.8.2. Count nodes
To count the number of nodes, for example the number of nodes connected to one
node, you can use count(*).
Query
START n=node(2)
MATCH (n)-->(x)
RETURN n, count(*)
The start node and the count of related nodes.
Table 15.43. Result
+----------------------------------------+
|n |count(*)|
|----------------------------------------|
|1 rows, 1 ms |
|----------------------------------------|
|Node[2]{name->"A",property->13}|3 |
+----------------------------------------+
15.8.3. Group Count Relationship Types
To count the groups of relationship types, return the types and count them with
count(*).
Query
START n=node(2)
MATCH (n)-[r]->()
RETURN type(r), count(*)
The relationship types and their group count.
Table 15.44. Result
+-----------------+
|TYPE(r)|count(*) |
|-----------------|
|1 rows, 1 ms |
|-----------------|
|"KNOWS"|3 |
+-----------------+
15.8.4. Count entities
Instead of counting the number of results with count(*), it might be more
expressive to include the name of the identifier you care about.
Query
START n=node(2)
MATCH (n)-->(x)
RETURN count(x)
The number of connected nodes from the start node.
Table 15.45. Result
+------------+
|count(x) |
|------------|
|1 rows, 0 ms|
|------------|
|3 |
+------------+
15.8.5. Count non null values
You can count the non-null values by using count().
Query
START n=node(2,3,4,1)
RETURN count(n.property?)
The count of related nodes.
Table 15.46. Result
+-----------------+
|count(n.property)|
|-----------------|
|1 rows, 0 ms |
|-----------------|
|3 |
+-----------------+
15.8.6. SUM
The SUM aggregation function simply sums all the numeric values it encounters.
Null values are silently dropped. This is an example of how you can use SUM.
Query
START n=node(2,3,4)
RETURN sum(n.property)
The sum of all the values in the property property.
Table 15.47. Result
+---------------+
|sum(n.property)|
|---------------|
|1 rows, 0 ms |
|---------------|
|90 |
+---------------+
15.8.7. AVG
AVG calculates the average of a numeric column.
Query
START n=node(2,3,4)
RETURN avg(n.property)
The average of all the values in the property property.
Table 15.48. Result
+---------------+
|avg(n.property)|
|---------------|
|1 rows, 0 ms |
|---------------|
|30.0 |
+---------------+
15.8.8. MAX
MAX find the largets value in a numeric column.
Query
START n=node(2,3,4)
RETURN max(n.property)
The largest of all the values in the property property.
Table 15.49. Result
+---------------+
|max(n.property)|
|---------------|
|1 rows, 1 ms |
|---------------|
|44 |
+---------------+
15.8.9. MIN
MIN takes a numeric property as input, and returns the smallest value in that
column.
Query
START n=node(2,3,4)
RETURN min(n.property)
The smallest of all the values in the property property.
Table 15.50. Result
+---------------+
|min(n.property)|
|---------------|
|1 rows, 1 ms |
|---------------|
|13 |
+---------------+
15.8.10. COLLECT
COLLECT collects all the values into a list.
Query
START n=node(2,3,4)
RETURN collect(n.property)
Returns a single row, with all the values collected.
Table 15.51. Result
+-------------------+
|collect(n.property)|
|-------------------|
|1 rows, 0 ms |
|-------------------|
|List(13, 33, 44) |
+-------------------+
15.8.11. DISTINCT
All aggregation functions also take DISTINCT modifier, which removes duplicates
from the values. So, to count the number of unique eye colours from nodes
related to a, this query can be used:
Query
START a=node(2)
MATCH a-->b
RETURN count(distinct b.eyes)
Returns the number of eye colours.
Table 15.52. Result
+----------------------+
|count(distinct b.eyes)|
|----------------------|
|1 rows, 1 ms |
|----------------------|
|2 |
+----------------------+
15.9. Order by
To sort the output, use the ORDER BY clause. Note that you can not sort on
nodes or relationships, just on properties on these.
Graph
cypher-orderby-graph.svg
15.9.1. Order nodes by property
ORDER BY is used to sort the output
Query
START n=node(3,1,2)
RETURN n
ORDER BY n.name
The nodes, sorted by their name.
Table 15.53. Result
+--------------------------------------+
|n |
|--------------------------------------|
|3 rows, 0 ms |
|--------------------------------------|
|Node[1]{name->"A",age->34,length->170}|
|--------------------------------------|
|Node[2]{name->"B",age->34} |
|--------------------------------------|
|Node[3]{name->"C",age->32,length->185}|
+--------------------------------------+
15.9.2. Order nodes by multiple properties
You can order by multiple properties by stating each identifier in the ORDER BY
statement. Cypher will sort the result by the first identifier listed, and for
equals values, go to the next property in the order by, and so on.
Query
START n=node(3,1,2)
RETURN n
ORDER BY n.age, n.name
The nodes, sorted first by their age, and then by their name.
Table 15.54. Result
+--------------------------------------+
|n |
|--------------------------------------|
|3 rows, 1 ms |
|--------------------------------------|
|Node[3]{name->"C",age->32,length->185}|
|--------------------------------------|
|Node[1]{name->"A",age->34,length->170}|
|--------------------------------------|
|Node[2]{name->"B",age->34} |
+--------------------------------------+
15.9.3. Order nodes in descending order
By adding DESC[ENDING] after the identifier to sort on, the sort will be done
in reverse order.
Query
START n=node(3,1,2)
RETURN n
ORDER BY n.name DESC
The nodes, sorted by their name reversely.
Table 15.55. Result
+--------------------------------------+
|n |
|--------------------------------------|
|3 rows, 0 ms |
|--------------------------------------|
|Node[3]{name->"C",age->32,length->185}|
|--------------------------------------|
|Node[2]{name->"B",age->34} |
|--------------------------------------|
|Node[1]{name->"A",age->34,length->170}|
+--------------------------------------+
15.9.4. Ordering null
When sorting the result set, null will always come at the end of the result set
for ascending sorting, and first when doing descending sort.
Query
START n=node(3,1,2)
RETURN n.length?, n
ORDER BY n.length?
The nodes sorted by the length property, with a node without that property
last.
Table 15.56. Result
+-----------------------------------------------+
|n.length|n |
|-----------------------------------------------|
|3 rows, 0 ms |
|-----------------------------------------------|
|170 |Node[1]{name->"A",age->34,length->170}|
|--------+--------------------------------------|
|185 |Node[3]{name->"C",age->32,length->185}|
|--------+--------------------------------------|
| |Node[2]{name->"B",age->34} |
+-----------------------------------------------+
15.10. Skip
SKIP enables the return of only subsets of the total result. By using SKIP, the
result set will trimmed from the top. Please note that no guarantees are made
on the order of the result unless the query specifies the ORDER BY clause.
Graph
cypher-skip-graph.svg
15.10.1. Skip first three
To return a subset of the result, starting from third result, use this syntax:
Query
START n=node(3, 4, 5, 1, 2)
RETURN n
ORDER BY n.name
SKIP 3
The first three nodes are skipped, and only the last two are returned.
Table 15.57. Result
+------------------+
|n |
|------------------|
|2 rows, 0 ms |
|------------------|
|Node[1]{name->"D"}|
|------------------|
|Node[2]{name->"E"}|
+------------------+
15.10.2. Return middle two
To return a subset of the result, starting from somewhere in the middle, use
this syntax:
Query
START n=node(3, 4, 5, 1, 2)
RETURN n
ORDER BY n.name
SKIP 1
LIMIT 2
Two nodes from the middle are returned
Table 15.58. Result
+------------------+
|n |
|------------------|
|2 rows, 0 ms |
|------------------|
|Node[4]{name->"B"}|
|------------------|
|Node[5]{name->"C"}|
+------------------+
15.11. Limit
LIMIT enables the return of only subsets of the total result.
Graph
cypher-limit-graph.svg
15.11.1. Return first part
To return a subset of the result, starting from the top, use this syntax:
Query
START n=node(3, 4, 5, 1, 2)
RETURN n
LIMIT 3
The top three items are returned
Table 15.59. Result
+------------------+
|n |
|------------------|
|3 rows, 0 ms |
|------------------|
|Node[3]{name->"A"}|
|------------------|
|Node[4]{name->"B"}|
|------------------|
|Node[5]{name->"C"}|
+------------------+
15.12. Functions
Here is a list of the functions in Cypher, seperated into three different
sections: Predicates, Scalar functions and Aggregated functions
Graph
cypher-functions-graph.svg
15.12.1. Predicates
Predicates are boolean functions that return true or false for a given set of
input. They are most commonly used to filter out subgraphs in the WHERE part of
a query.
15.12.2. ALL
Tests whether a predicate holds for all element of this iterable collection.
Syntax: ALL(identifier in iterable WHERE predicate)
Arguments:
* iterable: An array property, or an iterable symbol, or an iterable
function.
* identifier: This is the identifier that can be used from the predicate.
* predicate: A predicate that is tested against all items in iterable
Query
START a=node(3), b=node(1)
MATCH p=a-[*1..3]->b
WHERE all(x in nodes(p)
WHERE x.age > 30)
RETURN p
All nodes in the path.
Table 15.60. Result
+-------------------------------------+
|p |
|-------------------------------------|
|1 rows, 2 ms |
|-------------------------------------|
|(3)--[KNOWS,1]-->(5)--[KNOWS,3]-->(1)|
+-------------------------------------+
15.12.3. ANY
Tests whether a predicate holds for at least one element of this iterable
collection.
Syntax: ANY(identifier in iterable WHERE predicate)
Arguments:
* iterable: An array property, or an iterable symbol, or an iterable
function.
* identifier: This is the identifier that can be used from the predicate.
* predicate: A predicate that is tested against all items in iterable
Query
START a=node(2)
WHERE any(x in a.array
WHERE x = "one")
RETURN a
All nodes in the path.
Table 15.61. Result
+---------------------------------------------------------------------------+
|a |
|---------------------------------------------------------------------------|
|1 rows, 1 ms |
|---------------------------------------------------------------------------|
|Node[2]{name->"E",age->41,eyes->"blue",array->[Ljava.lang.String;@42101da9}|
+---------------------------------------------------------------------------+
15.12.4. NONE
Returns true if the predicate holds for no element in the iterable.
Syntax: NONE(identifier in iterable WHERE predicate)
Arguments:
* iterable: An array property, or an iterable symbol, or an iterable
function.
* identifier: This is the identifier that can be used from the predicate.
* predicate: A predicate that is tested against all items in iterable
Query
START n=node(3)
MATCH p=n-[*1..3]->b
WHERE NONE(x in nodes(p)
WHERE x.age = 25)
RETURN p
All nodes in the path.
Table 15.62. Result
+-------------------------------------+
|p |
|-------------------------------------|
|2 rows, 2 ms |
|-------------------------------------|
|(3)--[KNOWS,1]-->(5) |
|-------------------------------------|
|(3)--[KNOWS,1]-->(5)--[KNOWS,3]-->(1)|
+-------------------------------------+
15.12.5. SINGLE
Returns true if the predicate holds for exactly one of the elements in the
iterable.
Syntax: SINGLE(identifier in iterable WHERE predicate)
Arguments:
* iterable: An array property, or an iterable symbol, or an iterable
function.
* identifier: This is the identifier that can be used from the predicate.
* predicate: A predicate that is tested against all items in iterable
Query
START n=node(3)
MATCH p=n-->b
WHERE SINGLE(var in nodes(p)
WHERE var.eyes = "blue")
RETURN p
All nodes in the path.
Table 15.63. Result
+--------------------+
|p |
|--------------------|
|1 rows, 1 ms |
|--------------------|
|(3)--[KNOWS,0]-->(4)|
+--------------------+
15.12.6. Scalar functions
Scalar functions return a single value.
15.12.7. LENGTH
To return or filter on the length of a path, use the special property LENGTH
Syntax: LENGTH( iterable )
Arguments:
* iterable: An iterable, value or function call
Query
START a=node(3)
MATCH p=a-->b-->c
RETURN length(p)
The length of the path p.
Table 15.64. Result
+------------+
|LENGTH(p) |
|------------|
|3 rows, 3 ms|
|------------|
|2 |
|------------|
|2 |
|------------|
|2 |
+------------+
15.12.8. TYPE
Returns a string representation of the relationship type.
Syntax: TYPE( relationship )
Arguments:
* relationship: A relationship
Query
START n=node(3)
MATCH (n)-[r]->()
RETURN type(r)
The relationship type of r.
Table 15.65. Result
+------------+
|TYPE(r) |
|------------|
|2 rows, 1 ms|
|------------|
|"KNOWS" |
|------------|
|"KNOWS" |
+------------+
15.12.9. ID
Returns the id of the relationship or node
Syntax: ID( property-container )
Arguments:
* property-container: A node or a relationship
Query
START a=node(3, 4, 5)
RETURN ID(a)
The node id for three nodes.
Table 15.66. Result
+------------+
|ID(a) |
|------------|
|3 rows, 0 ms|
|------------|
|3 |
|------------|
|4 |
|------------|
|5 |
+------------+
15.12.10. COALESCE
Returns the first non-null value in the list of expressions passed to it.
Syntax: COALESCE( expression [, expression]* )
Arguments:
* expression: The expression that might return null
Query
START a=node(3)
RETURN coalesce(a.hairColour?, a.eyes?)
Table 15.67. Result
+-----------------------------+
|COALESCE(a.hairColour,a.eyes)|
|-----------------------------|
|1 rows, 0 ms |
|-----------------------------|
|"brown" |
+-----------------------------+
15.12.11. Iterable functions
Iterable functions return an iterable of things - nodes in a path, and so on.
15.12.12. NODES
Returns all nodes in a path
Syntax: NODES( path )
Arguments:
* path: A path
Query
START a=node(3), c=node(2)
MATCH p=a-->b-->c
RETURN NODES(p)
All the nodes in the path p.
Table 15.68. Result
+-------------------------------+
|NODES(p) |
|-------------------------------|
|1 rows, 2 ms |
|-------------------------------|
|List(Node[3], Node[4], Node[2])|
+-------------------------------+
15.12.13. RELATIONSHIPS
Returns all relationships in a path
Syntax: RELATIONSHIPS( path )
Arguments:
* path: A path
Query
START a=node(3), c=node(2)
MATCH p=a-->b-->c
RETURN RELATIONSHIPS(p)
All the nodes in the path p.
Table 15.69. Result
+--------------------------------------+
|RELATIONSHIPS(p) |
|--------------------------------------|
|1 rows, 2 ms |
|--------------------------------------|
|List(Relationship[0], Relationship[4])|
+--------------------------------------+
15.12.14. EXTRACT
To return a single property, or the value of a function from an iterable of
nodes or relationships, you can use EXTRACT. It will go through all enitities
in the iterable, and run an expression, and return the results in an iterable
with these values. It works like the map method in functional languages such as
Lisp and Scala.
Syntax: EXTRACT( identifier in iterable : expression )
Arguments:
* iterable: An array property, or an iterable identifier, or an iterable
function.
* identifier: The closure will have an identifier introduced in it’s context.
Here you decide which identifier to use.
* expression: This expression will run once per value in the iterable, and
produces the result iterable.
Query
START a=node(3), b=node(4), c=node(1)
MATCH p=a-->b-->c
RETURN extract(n in nodes(p) : n.age)
The age property of all nodes in the path.
Table 15.70. Result
+------------------------------+
|extract(n in NODES(p) : n.age)|
|------------------------------|
|1 rows, 2 ms |
|------------------------------|
|List(38, 25, 54) |
+------------------------------+
Chapter 16. Graph Algorithms
Neo4j graph algorithms is a component that contains Neo4j implementations of
some common algorithms for graphs. It includes algorithms like:
* Shortest paths,
* all paths,
* all simple paths,
* Dijkstra and
* A*.
16.1. Introduction
The graph algorithms are found in the neo4j-graph-algo component, which is
included in the standard Neo4j download.
* Javadocs
* Download
* Source code
For information on how to use neo4j-graph-algo as a dependency with Maven and
other dependency management tools, see org.neo4j:neo4j-graph-algo Note
that it should be used with the same version of org.neo4j:neo4j-kernel .
Different versions of the graph-algo and kernel components are not compatible
in the general case. Both components are included transitively by the
org.neo4j:neo4j artifact which
makes it simple to keep the versions in sync.
The starting point to find and use graph algorithms is GraphAlgoFactory .
For examples, see Section 4.6, “Graph Algorithm examples” (embedded database)
and Section 18.13, “Built-in Graph Algorithms” (REST API).
Chapter 17. Neo4j Server
17.1. Server Installation
Neo4j can be installed as a server, running either as a headless application or
system service.
1. Download the latest release from http://neo4j.org/download
* select the appropriate version for your platform
2. Extract the contents of the archive
* refer to the top-level extracted directory as NEO4J_HOME
3. Use the scripts in the bin directory
* for Linux/MacOS, run $NEO4J_HOME/bin/neo4j start
* for Windows, double-click on %NEO4J_HOME%\bin\Neo4j.bat
4. Refer to the packaged information in the doc directory for details
17.1.1. As a Windows service
With administrative rights, Neo4j can be installed as a Windows service.
1. Click Start → All Programs → Accessories
2. Right click Command Prompt → Run as Administrator
3. Provide authorization and/or the Administrator password
4. Navigate to %NEO4J_HOME%
5. Run bin\Neo4j.bat install
To uninstall, run bin\Neo4j.bat remove as Administrator.
To query the status of the service, run bin\Neo4j.bat query
To start/stop the service from the command prompt, run bin\Neo4j.bat +action+
Note
Some users have reported problems on Windows when using the ZoneAlarm firewall.
If you are having problems getting large responses from the server, or if
Webadmin does not work, try disabling ZoneAlarm. Contact ZoneAlarm support to
get information on how to resolve this.
17.1.2. Linux Service
Neo4j can participate in the normal system startup and shutdown process. The
following procedure should work on most popular Linux distributions:
1. cd $NEO4J_HOME
2. sudo ./bin/neo4j install
if asked, enter your password to gain super-user privileges
3. service neo4j-service status
should indicate that the server is not running
4. service neo4j-service start
will start the server
During installation you will be given the option to select the user Neo4j
will run as. You will be asked to supply a username (defaulting to neo4j)
and if that user is not present on the system it will be created as a
system account and the $NEO4J_HOME/data directory will be chown'ed to that
user.
You are encouraged to create a dedicated user for running the service and
for that reason it is suggested that you unpack the distribution package
under /opt or your site specific optional packages directory.
After installation you may have to do some platform specific configuration
and performance tuning. For that, refer to Section 21.9, “Linux specific
notes”.
Finally, note that if you chose to create a new user account, on uninstall
you will be prompted to remove it from the system.
17.1.3. Mac OSX
17.1.3.1. via Homebrew
Using Homebrew , to install the latest stabel
version of Neo4j Server, do
brew install neo4j && neo4j start
and symlink the database directory if desired.
17.1.3.2. as a Service
Neo4j can be installed as a Mac launchd job:
1. cd $NEO4J_HOME
2. ./bin/neo4j install
3. launchctl list | grep neo
should reveal the launchd "org.neo4j.server.7474" job for running the Neo4j
Server
4. ./bin/neo4j status
should indicate that the server is running
5. launchctl stop org.neo4j.server.7474
should stop the server.
6. launchctl start org.neo4j.server.7474
should start the server again.
17.1.4. Multiple Server instances on one machine
Neo4j can be set up to run as several instances on one machine, providing for
instance several databases for development. To configure, install two instances
of the Neo4j Server in two different directories following the steps outlined
below.
17.1.4.1. First instance
First, create a directory to hold both database instances, and unpack the
development instance:
1. cd $INSTANCE_ROOT
2. mkdir -p neo4j
3. cd neo4j
4. tar -xvzf /path/to/neo4j-community.tar.gz
5. mv neo4j-community dev
Next, configure the instance by changing the following values in dev/conf/
neo4j-server.properties, see even Section 24.1, “Securing access to the Neo4j
Server”:
org.neo4j.server.webserver.port=7474
# Uncomment the following if the instance will be accessed from a host other than localhost.
org.neo4j.server.webserver.address=0.0.0.0
Before running the Windows install or startup, change in dev/conf/
neo4j-wrapper.properties
# Name of the service for the first instance
wrapper.name=neo4j_1
Start the instance:
dev/bin/neo4j start
Check that instance is available by browsing to http://localhost:7474/webadmin/
#
17.1.4.2. Second instance (testing, development)
In many cases during application development, it is desirable to have one
development database set up, and another against which to run unit tests. For
the following example, we are assuming that both databases will run on the same
host.
Now create the unit testing second instance:
1. cd $INSTANCE_ROOT/neo4j
2. tar -xvzf /path/to/neo4j-community.tar.gz
3. mv neo4j-community test
It’s good practice to reset the unit testing database before each test run.
This capability is not built into Neo4j server, so install a server plugin that
does this:
1. wget http://github.com/downloads/jexp/neo4j-clean-remote-db-addon/
test-delete-db-extension-1.4.jar
2. mv test-delete-db-extension-1.4.jar test/plugins
Next, configure the instance by changing the following values in test/conf/
neo4j-server.properties to
* change the server port to 7475
* activate the clean-database server extension for remote cleaning of the
database via REST
# Note the different port number from the development instance
org.neo4j.server.webserver.port=7475
# Uncomment the following if the instance will be accessed from a host other than localhost
org.neo4j.server.webserver.address=0.0.0.0
# Add the following lines to the JAXRS section at the bottom of the file
org.neo4j.server.thirdparty_jaxrs_classes=org.neo4j.server.extension.test.delete=/db/data/cleandb
org.neo4j.server.thirdparty.delete.key=secret-key
Differentiate the instance from the development instance by modifying test/conf
/neo4j-wrapper.properties.
wrapper.name=neo4j-test
Start the instance:
test/bin/neo4j start
Check that instance is available by browsing to http://localhost:7475/webadmin/
#
Test the remote clean plugin by switching to the webadmin "Console" tab,
selecting "HTTP" and entering the following:
DELETE /db/data/cleandb/secret-key
If this returns a "200 OK" response, the plugin is configured correctly.
17.1.5. High Availability Mode
For information on High Availability, please refer to Chapter 22, High
Availability.
17.2. Server Configuration
Quick info
* The server’s primary configuration file is found under conf/
neo4j-server.properties
* The conf/log4j.properties file contains the default server logging
configuration
* Low-level performance tuning parameters are found in conf/neo4j.properties
* Configuraion of the deamonizing wrapper are found in conf/
neo4j-wrapper.properties
17.2.1. Important server configurations parameters
The main configuration file for the server can be found at conf/
neo4j-server.properties. This file contains several important settings, and
although the defaults are sensible administrators might choose to make changes
(especially to the port settings).
Set the location on disk of the database directory like this:
org.neo4j.server.database.location=data/graph.db
Note
On Windows systems, absolute locations including drive letters need to read "c:
/data/db".
Specify the HTTP server port supporting data, administrative, and UI access:
org.neo4j.server.webserver.port=7474
Specify the client accept pattern for the webserver (default is 127.0.0.1,
localhost only):
#allow any client to connect
org.neo4j.server.webserver.address=0.0.0.0
For securing the Neo4j Server, see also Section 24.1, “Securing access to the
Neo4j Server”
Set the location of the round-robin database directory which gathers metrics on
the running server instance:
org.neo4j.server.webadmin.rrdb.location=data/graph.db/../rrd
Set the URI path for the REST data API through which the database is accessed.
This should be a relative path.
org.neo4j.server.webadmin.data.uri=/db/data/
Setting the management URI for the administration API that the Webadmin tool
uses. This should be a relative path.
org.neo4j.server.webadmin.management.uri=/db/manage
Force the server to use IPv4 network addresses, in conf/neo4j-wrapper.conf
under the section Java Additional Parameters add a new paramter:
wrapper.java.additional.3=-Djava.net.preferIPv4Stack=true
Low-level performance tuning parameters can be explicitly set by referring to
the following property:
org.neo4j.server.db.tuning.properties=neo4j.properties
If this property isn’t set, the server will look for a file called
neo4j.properties in the same directory as the neo4j-server.properties file.
If this property isn’t set, and there is no neo4j.properties file in the
default configuration directory, then the server will log a warning.
Subsequently at runtime the database engine will attempt tune itself based on
the prevailing conditions.
17.2.2. Neo4j Database performance configuration
The fine-tuning of the low-level Neo4j graph database engine is specified in a
separate properties file, conf/neo4j.properties.
The graph database engine has a range of performance tuning options which are
enumerated in Section 17.5, “Server Performance Tuning”. Note that other
factors than Neo4j tuning should be considered when performance tuning a
server, including general server load, memory and file contention, and even
garbage collection penalties on the JVM, though such considerations are beyond
the scope of this configuration document.
17.2.3. Logging configuration
The logging framework in use by the Neo4j server is java.util.logging and
is configured in conf/logging.properties.
By default it is setup to print INFO level messages both on screen and in a
rolling file in data/log. Most deployments will choose to use their own
configuration here to meet local standards. During development, much useful
information can be found in the logs so some form of logging to disk is well
worth keeping. On the other hand, if you want to completely silence the console
output, set:
java.util.logging.ConsoleHandler.level=OFF
By default log files are rotated at approximately 10Mb and named consecutively
neo4j...log To change the naming scheme, rotation
frequency and backlog size modify
java.util.logging.FileHandler.pattern
java.util.logging.FileHandler.limit
java.util.logging.FileHandler.count
respectively to your needs. Details are available at the Javadoc for
java.util.logging.FileHandler .
Apart from log statements originating from the Neo4j server, other libraries
report their messages through various frameworks.
Zookeeper is hardwired to use the log4j logging framework. The bundled conf/
log4j.properties applies for this use only and uses a rolling appender and
outputs logs by default to the data/log directory.
17.2.4. Other configuration options
17.2.4.1. Enabling logging from the garbage collector
To get garbage collection logging output you have to pass the corresponding
option to the server JVM executable by setting in conf/neo4j-wrapper.conf the
value
wrapper.java.additional.3=-Xloggc:data/log/neo4j-gc.log
This line is already present and needs uncommenting. Note also that logging is
not directed to console ; You will find the logging statements in data/log/
ne4j-gc.log or whatever directory you set at the option.
17.3. Setup for remote debugging
In order to configure the Neo4j server for remote debugging sessions, the Java
debugging parameters need to be passed to the Java process through the
configuration. They live in the conf/neo4j-wrapper.properties file.
In order to specify the parameters, add a line for the additional Java
arguments like this:
# Java Additional Parameters
wrapper.java.additional.1=-Dorg.neo4j.server.properties=conf/neo4j-server.properties
wrapper.java.additional.2=-Dlog4j.configuration=file:conf/log4j.properties
wrapper.java.additional.3=-agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=5005 -Xdebug-Xnoagent-Djava.compiler=NONE-Xrunjdwp:transport=dt_socket,server=y,suspend=n,address=5005
This configuration will start a Neo4j server ready for remote debugging
attachement at localhost and port 5005. Use these parameters to attach to the
process from Eclipse, IntelliJ or your remote debugger of choice after starting
the server.
17.4. Using the server (including web administration) with an embedded database
Even if you are using the Neo4j Java API directly, for instance via
EmbeddedGraphDatabase or HighlyAvailableGraphDatabase, you can still use the
features the server provides.
17.4.1. Getting the libraries
17.4.1.1. From the Neo4j Server installation
To run the server all the libraries you need are in the system/lib/ directory
of the download package . For further instructions,
see Section 4.1, “Include Neo4j in your project”. The only difference to the
embedded setup is that system/lib/ should be added as well, not only the lib/
directory.
17.4.1.2. Via Maven
For users of dependency management, an example for Apache Maven follows. Note that the web resources are in a different
artifact.
Maven pom.xml snippet.
org.neo4j.app
neo4j-server
${neo4j-version}
org.neo4j.app
neo4j-server
static-web
${neo4j-version}
neo4j-release-repository
Neo4j Maven 2 release repository
http://m2.neo4j.org/releases
true
false
Where ${neo4j-version} is the intended version.
17.4.1.3. Via Scala SBT / Ivy
In order to pull in the dependencys with SBT and configure the underlying Ivy dependency
manager, you can use a setup like the following in your build.sbt:
organization := "your.org"
name := "your.name"
version := "your.version"
/** Deps for Embedding the Neo4j Admin server - works around: http://neo4j.org/forums/#nabble-td3477583 */
libraryDependencies ++= Seq(
"org.neo4j.app" % "neo4j-server" % "{neo4j-version}",
"org.neo4j.app" % "neo4j-server" % "{neo4j-version}" classifier "static-web"
from "http://m2.neo4j.org/releases/org/neo4j/app/neo4j-server/1.5/neo4j-server-1.5-static-web.jar", "com.sun.jersey" % "jersey-core" % "1.9"
)
/** Repos for Neo4j Admin server dep */
resolvers ++= Seq(
"tinkerprop" at "http://tinkerpop.com/maven2",
"neo4j-public-repository" at "http://m2.neo4j.org/releases"
)
Where ${neo4j-version} is the intended version.
17.4.2. Starting the Server from Java
The Neo4j server exposes a class called WrappingNeoServerBootstrapper , which is capable of starting a Neo4j
server in the same process as your application. It uses an
AbstractGraphDatabase instance that you provide.
This gives your application, among other things, the REST API, statistics
gathering and the web administration interface that comes with the server.
Usage example.
AbstractGraphDatabase graphdb = getGraphDb();
WrappingNeoServerBootstrapper srv;
srv = new WrappingNeoServerBootstrapper( graphdb );
srv.start();
// The server is now running
// until we stop it:
srv.stop();
Once you have the server up and running, see Chapter 26, Web Administration and
Chapter 18, REST API for how to use it!
17.4.3. Providing custom configuration
You can modify the server settings programmatically and, within reason, the
same settings are available to you here as those outlined in Section 17.2,
“Server Configuration”.
The settings that are not available (or rather, that are ignored) are those
that concern the underlying database, such as database location and database
configuration path.
Custom configuration example.
AbstractGraphDatabase graphdb = getGraphDb();
EmbeddedServerConfigurator config;
config = new EmbeddedServerConfigurator( graphdb );
config.configuration().setProperty(
Configurator.WEBSERVER_PORT_PROPERTY_KEY, 7575 );
WrappingNeoServerBootstrapper srv;
srv = new WrappingNeoServerBootstrapper( graphdb, config );
srv.start();
17.5. Server Performance Tuning
At the heart of the Neo4j server is a regular Neo4j storage engine instance.
That engine can be tuned in the same way as the other embedded configurations,
using the same file format. The only difference is that the server must be told
where to find the fine-tuning configuration.
Quick info
* The neo4j.properties file is a standard configuration file that databases
load in order to tune their memory use and caching strategies.
* See Section 21.3, “Caches in Neo4j” for more information.
17.5.1. Specifying Neo4j tuning properties
The conf/neo4j-server.properties file in the server distribution, is the main
configuration file for the server. In this file we can specify a second
properties file that contains the database tuning settings (that is, the
neo4j.properties file). This is done by setting a single property to point to a
valid neo4j.properties file:
org.neo4j.server.db.tuning.properties={neo4j.properties file}
On restarting the server the tuning enhancements specified in the
neo4j.properties file will be loaded and configured into the underlying
database engine.
17.5.2. Specifying JVM tuning properties
Tuning the standalone server is achieved by editing the neo4j-wrapper.conf file
in the conf directory of NEO4J_HOME.
Edit the following properties:
Table 17.1. neo4j-wrapper.conf JVM tuning properties
+-----------------------------------------------------------------------------+
|Property Name |Meaning |
|-------------------------+---------------------------------------------------|
|wrapper.java.initmemory |initial heap size (in MB) |
|-------------------------+---------------------------------------------------|
|wrapper.java.maxmemory |maximum heap size (in MB) |
|-------------------------+---------------------------------------------------|
|wrapper.java.additional.N|additional literal JVM parameter, where N is a |
| |number for each |
+-----------------------------------------------------------------------------+
For more information on the tuning properties, see Section 21.4, “JVM Settings”
.
17.6. Server Installation in the Cloud
Neo4j on various cloud services either by a user, or as a managed instance on
the Neo Technology cloud fabric. Below are instructions for some of these.
17.6.1. Heroku
For the basic setup, please see the Heroku Quickstart tutorial
To add Neo4j to your Heroku app, do:
heroku addons:add neo4j
Chapter 18. REST API
The Neo4j REST API is designed with discoverability in mind, so that you can
start with a GET on the Section 18.1, “Service root” and from there discover
URIs to perform other requests. The examples below uses URIs in the examples;
they are subject to change in the future, so for future-proofness discover URIs
where possible, instead of relying on the current layout. The default
representation is json , both for responses and for data
sent with POST/PUT requests.
Below follows a listing of ways to interact with the REST API.
To interact with the JSON interface you must explicitly set the request header
Accept:application/json for those requests that responds with data. You should
also set the header Content-Type:application/json if your request sends data,
for example when you’re creating a relationship. The examples include the
relevant request and response headers.
18.1. Service root
18.1.1. Get service root
The service root is your starting point to discover the REST API. It contains
the basic starting points for the databse, and some version and extension
information. The reference_node entry will only be present if there is a
reference node set and exists in the database.
Figure 18.1. Final Graph
Final-Graph-Get-service-root.svg
Example request
* GET http://localhost:7474/db/data/
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
{
"cypher" : "http://localhost:7474/db/data/cypher",
"relationship_index" : "http://localhost:7474/db/data/index/relationship",
"node" : "http://localhost:7474/db/data/node",
"relationship_types" : "http://localhost:7474/db/data/relationship/types",
"neo4j_version" : "1.6",
"batch" : "http://localhost:7474/db/data/batch",
"extensions_info" : "http://localhost:7474/db/data/ext",
"node_index" : "http://localhost:7474/db/data/index/node",
"reference_node" : "http://localhost:7474/db/data/node/118",
"extensions" : {
}
}
18.2. Nodes
18.2.1. Create Node
Figure 18.2. Final Graph
Final-Graph-create-Node.svg
Example request
* POST http://localhost:7474/db/data/node
* Accept: application/json
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/node/43
{
"outgoing_relationships" : "http://localhost:7474/db/data/node/43/relationships/out",
"data" : {
},
"traverse" : "http://localhost:7474/db/data/node/43/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/43/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/43/properties/{key}",
"self" : "http://localhost:7474/db/data/node/43",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/43/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/43/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/43/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/43/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/43/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/43/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/43/relationships/in/{-list|&|types}"
}
18.2.2. Create Node with properties
Figure 18.3. Final Graph
Final-Graph-create-Node-with-properties.svg
Example request
* POST http://localhost:7474/db/data/node
* Accept: application/json
* Content-Type: application/json
{"foo" : "bar"}
Example response
* 201: Created
* Content-Length: 1108
* Content-Type: application/json
* Location: http://localhost:7474/db/data/node/44
{
"outgoing_relationships" : "http://localhost:7474/db/data/node/44/relationships/out",
"data" : {
"foo" : "bar"
},
"traverse" : "http://localhost:7474/db/data/node/44/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/44/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/44/properties/{key}",
"self" : "http://localhost:7474/db/data/node/44",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/44/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/44/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/44/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/44/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/44/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/44/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/44/relationships/in/{-list|&|types}"
}
18.2.3. Get node
Note that the response contains URI/templates for the available operations
for getting properties and relationships.
Figure 18.4. Final Graph
Final-Graph-Get-node.svg
Example request
* GET http://localhost:7474/db/data/node/3
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
{
"outgoing_relationships" : "http://localhost:7474/db/data/node/3/relationships/out",
"data" : {
},
"traverse" : "http://localhost:7474/db/data/node/3/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/3/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/3/properties/{key}",
"self" : "http://localhost:7474/db/data/node/3",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/3/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/3/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/3/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/3/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/3/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/3/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/3/relationships/in/{-list|&|types}"
}
18.2.4. Get non-existent node
Figure 18.5. Final Graph
Final-Graph-Get-non-existent-node.svg
Example request
* GET http://localhost:7474/db/data/node/800000
* Accept: application/json
Example response
* 404: Not Found
* Content-Type: application/json
{
"message" : "Cannot find node with id [800000] in database.",
"exception" : "org.neo4j.server.rest.web.NodeNotFoundException: Cannot find node with id [800000] in database.",
"stacktrace" : [ "org.neo4j.server.rest.web.DatabaseActions.node(DatabaseActions.java:116)", "org.neo4j.server.rest.web.DatabaseActions.getNode(DatabaseActions.java:227)", "org.neo4j.server.rest.web.RestfulGraphDatabase.getNode(RestfulGraphDatabase.java:216)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "com.sun.jersey.spi.container.JavaMethodInvokerFactory$1.invoke(JavaMethodInvokerFactory.java:60)", "com.sun.jersey.server.impl.model.method.dispatch.AbstractResourceMethodDispatchProvider$ResponseOutInvoker._dispatch(AbstractResourceMethodDispatchProvider.java:205)", "com.sun.jersey.server.impl.model.method.dispatch.ResourceJavaMethodDispatcher.dispatch(ResourceJavaMethodDispatcher.java:75)", "com.sun.jersey.server.impl.uri.rules.HttpMethodRule.accept(HttpMethodRule.java:288)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.ResourceClassRule.accept(ResourceClassRule.java:108)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.RootResourceClassesRule.accept(RootResourceClassesRule.java:84)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1469)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1400)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1349)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1339)", "com.sun.jersey.spi.container.servlet.WebComponent.service(WebComponent.java:416)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:537)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:699)", "javax.servlet.http.HttpServlet.service(HttpServlet.java:820)", "org.mortbay.jetty.servlet.ServletHolder.handle(ServletHolder.java:511)", "org.mortbay.jetty.servlet.ServletHandler.handle(ServletHandler.java:390)", "org.mortbay.jetty.servlet.SessionHandler.handle(SessionHandler.java:182)", "org.mortbay.jetty.handler.ContextHandler.handle(ContextHandler.java:765)", "org.mortbay.jetty.handler.HandlerCollection.handle(HandlerCollection.java:114)", "org.mortbay.jetty.handler.HandlerWrapper.handle(HandlerWrapper.java:152)", "org.mortbay.jetty.Server.handle(Server.java:326)", "org.mortbay.jetty.HttpConnection.handleRequest(HttpConnection.java:542)", "org.mortbay.jetty.HttpConnection$RequestHandler.headerComplete(HttpConnection.java:926)", "org.mortbay.jetty.HttpParser.parseNext(HttpParser.java:549)", "org.mortbay.jetty.HttpParser.parseAvailable(HttpParser.java:212)", "org.mortbay.jetty.HttpConnection.handle(HttpConnection.java:404)", "org.mortbay.io.nio.SelectChannelEndPoint.run(SelectChannelEndPoint.java:410)", "org.mortbay.thread.QueuedThreadPool$PoolThread.run(QueuedThreadPool.java:582)" ]
}
18.2.5. Delete node
Figure 18.6. Final Graph
Final-Graph-Delete-node.svg
Example request
* DELETE http://localhost:7474/db/data/node/51
* Accept: application/json
Example response
* 204: No Content
18.2.6. Nodes with relationships can not be deleted
The relationships on a node has to be deleted before the node can be deleted.
Figure 18.7. Final Graph
Final-Graph-Nodes-with-relationships-can-not-be-deleted.svg
Example request
* DELETE http://localhost:7474/db/data/node/52
* Accept: application/json
Example response
* 409: Conflict
* Content-Type: application/json
{
"message" : "The node with id 52 cannot be deleted. Check that the node is orphaned before deletion.",
"exception" : "org.neo4j.server.rest.web.OperationFailureException: The node with id 52 cannot be deleted. Check that the node is orphaned before deletion.",
"stacktrace" : [ "org.neo4j.server.rest.web.DatabaseActions.deleteNode(DatabaseActions.java:248)", "org.neo4j.server.rest.web.RestfulGraphDatabase.deleteNode(RestfulGraphDatabase.java:230)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "com.sun.jersey.spi.container.JavaMethodInvokerFactory$1.invoke(JavaMethodInvokerFactory.java:60)", "com.sun.jersey.server.impl.model.method.dispatch.AbstractResourceMethodDispatchProvider$ResponseOutInvoker._dispatch(AbstractResourceMethodDispatchProvider.java:205)", "com.sun.jersey.server.impl.model.method.dispatch.ResourceJavaMethodDispatcher.dispatch(ResourceJavaMethodDispatcher.java:75)", "com.sun.jersey.server.impl.uri.rules.HttpMethodRule.accept(HttpMethodRule.java:288)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.ResourceClassRule.accept(ResourceClassRule.java:108)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.RootResourceClassesRule.accept(RootResourceClassesRule.java:84)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1469)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1400)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1349)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1339)", "com.sun.jersey.spi.container.servlet.WebComponent.service(WebComponent.java:416)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:537)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:699)", "javax.servlet.http.HttpServlet.service(HttpServlet.java:820)", "org.mortbay.jetty.servlet.ServletHolder.handle(ServletHolder.java:511)", "org.mortbay.jetty.servlet.ServletHandler.handle(ServletHandler.java:390)", "org.mortbay.jetty.servlet.SessionHandler.handle(SessionHandler.java:182)", "org.mortbay.jetty.handler.ContextHandler.handle(ContextHandler.java:765)", "org.mortbay.jetty.handler.HandlerCollection.handle(HandlerCollection.java:114)", "org.mortbay.jetty.handler.HandlerWrapper.handle(HandlerWrapper.java:152)", "org.mortbay.jetty.Server.handle(Server.java:326)", "org.mortbay.jetty.HttpConnection.handleRequest(HttpConnection.java:542)", "org.mortbay.jetty.HttpConnection$RequestHandler.headerComplete(HttpConnection.java:926)", "org.mortbay.jetty.HttpParser.parseNext(HttpParser.java:549)", "org.mortbay.jetty.HttpParser.parseAvailable(HttpParser.java:212)", "org.mortbay.jetty.HttpConnection.handle(HttpConnection.java:404)", "org.mortbay.io.nio.SelectChannelEndPoint.run(SelectChannelEndPoint.java:410)", "org.mortbay.thread.QueuedThreadPool$PoolThread.run(QueuedThreadPool.java:582)" ]
}
18.3. Relationships
Relationships are a first class citizen in the Neo4j REST API. They can be
accessed either stand-alone or through the nodes they are attached to.
The general pattern to get relationships from a node is:
GET http://localhost:7474/db/data/node/123/relationships/{dir}/{-list|&|types}
Where dir is one of all, in, out and types is an ampersand-separated list of
types. See the examples below for more information.
18.3.1. Get Relationship by ID
Figure 18.8. Final Graph
Final-Graph-get-Relationship-by-ID.svg
Example request
* GET http://localhost:7474/db/data/relationship/6
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
{
"start" : "http://localhost:7474/db/data/node/24",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/6",
"property" : "http://localhost:7474/db/data/relationship/6/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/6/properties",
"type" : "know",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/23"
}
18.3.2. Create relationship
Documentation not available
Figure 18.9. Final Graph
Final-Graph-create-relationship.svg
Example request
* POST http://localhost:7474/db/data/node/94/relationships
* Accept: application/json
* Content-Type: application/json
{"to" : "http://localhost:7474/db/data/node/93", "type" : "LOVES", "data" : {"foo" : "bar"}}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/relationship/32
{
"start" : "http://localhost:7474/db/data/node/94",
"data" : {
"foo" : "bar"
},
"self" : "http://localhost:7474/db/data/relationship/32",
"property" : "http://localhost:7474/db/data/relationship/32/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/32/properties",
"type" : "LOVES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/93"
}
18.3.3. Create a relationship with properties
Figure 18.10. Starting Graph
Starting-Graph-Add-relationship-with-properties-before.svg
Figure 18.11. Final Graph
Final-Graph-create-a-relationship-with-properties.svg
Example request
* POST http://localhost:7474/db/data/node/92/relationships
* Accept: application/json
* Content-Type: application/json
{"to" : "http://localhost:7474/db/data/node/91", "type" : "LOVES", "data" : {"foo" : "bar"}}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/relationship/30
{
"start" : "http://localhost:7474/db/data/node/92",
"data" : {
"foo" : "bar"
},
"self" : "http://localhost:7474/db/data/relationship/30",
"property" : "http://localhost:7474/db/data/relationship/30/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/30/properties",
"type" : "LOVES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/91"
}
18.3.4. Delete relationship
Figure 18.12. Starting Graph
Starting-Graph-Delete-relationship1.svg
Figure 18.13. Final Graph
Final-Graph-Delete-relationship.svg
Example request
* DELETE http://localhost:7474/db/data/relationship/11
* Accept: application/json
Example response
* 204: No Content
18.3.5. Get all properties on a relationship
Figure 18.14. Final Graph
Final-Graph-get-all-properties-on-a-relationship.svg
Example request
* GET http://localhost:7474/db/data/relationship/15/properties
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
{
"since" : "1day",
"cost" : "high"
}
18.3.6. Set all properties on a relationship
Figure 18.15. Starting Graph
Starting-Graph-Set-relationship-property1.svg
Figure 18.16. Final Graph
Final-Graph-set-all-properties-on-a-relationship.svg
Example request
* PUT http://localhost:7474/db/data/relationship/14/properties
* Accept: application/json
* Content-Type: application/json
{
"happy" : false
}
Example response
* 204: No Content
18.3.7. Get single property on a relationship
Figure 18.17. Final Graph
Final-Graph-get-single-property-on-a-relationship.svg
Example request
* GET http://localhost:7474/db/data/relationship/12/properties/cost
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
"high"
18.3.8. Set single property on a relationship
Figure 18.18. Starting Graph
Starting-Graph-Set-relationship-property1.svg
Figure 18.19. Final Graph
Final-Graph-set-single-property-on-a-relationship.svg
Example request
* PUT http://localhost:7474/db/data/relationship/13/properties/cost
* Accept: application/json
* Content-Type: application/json
"deadly"
Example response
* 204: No Content
18.3.9. Get all relationships
Figure 18.20. Final Graph
Final-Graph-Get-all-relationships.svg
Example request
* GET http://localhost:7474/db/data/node/134/relationships/all
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ {
"start" : "http://localhost:7474/db/data/node/134",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/48",
"property" : "http://localhost:7474/db/data/relationship/48/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/48/properties",
"type" : "LIKES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/135"
}, {
"start" : "http://localhost:7474/db/data/node/136",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/49",
"property" : "http://localhost:7474/db/data/relationship/49/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/49/properties",
"type" : "LIKES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/134"
}, {
"start" : "http://localhost:7474/db/data/node/134",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/50",
"property" : "http://localhost:7474/db/data/relationship/50/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/50/properties",
"type" : "HATES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/137"
} ]
18.3.10. Get incoming relationships
Figure 18.21. Final Graph
Final-Graph-Get-incoming-relationships.svg
Example request
* GET http://localhost:7474/db/data/node/139/relationships/in
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ {
"start" : "http://localhost:7474/db/data/node/141",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/52",
"property" : "http://localhost:7474/db/data/relationship/52/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/52/properties",
"type" : "LIKES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/139"
} ]
18.3.11. Get outgoing relationships
Figure 18.22. Final Graph
Final-Graph-Get-outgoing-relationships.svg
Example request
* GET http://localhost:7474/db/data/node/144/relationships/out
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ {
"start" : "http://localhost:7474/db/data/node/144",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/54",
"property" : "http://localhost:7474/db/data/relationship/54/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/54/properties",
"type" : "LIKES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/145"
}, {
"start" : "http://localhost:7474/db/data/node/144",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/56",
"property" : "http://localhost:7474/db/data/relationship/56/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/56/properties",
"type" : "HATES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/147"
} ]
18.3.12. Get typed relationships
Note that the "&" needs to be escaped for example when using cURL from the terminal.
Figure 18.23. Final Graph
Final-Graph-Get-typed-relationships.svg
Example request
* GET http://localhost:7474/db/data/node/149/relationships/all/LIKES&HATES
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ {
"start" : "http://localhost:7474/db/data/node/149",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/57",
"property" : "http://localhost:7474/db/data/relationship/57/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/57/properties",
"type" : "LIKES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/150"
}, {
"start" : "http://localhost:7474/db/data/node/151",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/58",
"property" : "http://localhost:7474/db/data/relationship/58/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/58/properties",
"type" : "LIKES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/149"
}, {
"start" : "http://localhost:7474/db/data/node/149",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/59",
"property" : "http://localhost:7474/db/data/relationship/59/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/59/properties",
"type" : "HATES",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/152"
} ]
18.3.13. Get relationships on a node without relationships
Figure 18.24. Final Graph
Final-Graph-Get-relationships-on-a-node-without-relationships.svg
Example request
* GET http://localhost:7474/db/data/node/168/relationships/all
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ ]
18.4. Relationship types
18.4.1. Get relationship types
Figure 18.25. Final Graph
Final-Graph-Get-relationship-types.svg
Example request
* GET http://localhost:7474/db/data/relationship/types
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ "to", "knowledge", "foo", "know", "related-to", "has", "knows", "LOVES", "some type", "HATES", "tested-together", "likes", "KNOWS", "dislikes", "bar", "LIKES", "TYPE" ]
18.5. Node properties
18.5.1. Set property on node
Setting different properties will retain the existing ones for this node. Note
that a single value are submitted not as a map but just as a value (which is
valid JSON) like in the example below.
Figure 18.26. Final Graph
Final-Graph-Set-property-on-node.svg
Example request
* PUT http://localhost:7474/db/data/node/17/properties/foo
* Accept: application/json
* Content-Type: application/json
"bar"
Example response
* 204: No Content
18.5.2. Update node properties
This will replace all existing properties on the node with the new set of
attributes.
Figure 18.27. Final Graph
Final-Graph-Update-node-properties.svg
Example request
* PUT http://localhost:7474/db/data/node/9/properties
* Accept: application/json
* Content-Type: application/json
{
"age" : "18"
}
Example response
* 204: No Content
18.5.3. Get properties for node
Figure 18.28. Final Graph
Final-Graph-Get-properties-for-node.svg
Example request
* GET http://localhost:7474/db/data/node/332/properties
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
{
"foo" : "bar"
}
18.5.4. Property values can not be null
This example shows the response you get when trying to set a property to null.
Figure 18.29. Final Graph
Final-Graph-Property-values-can-not-be-null.svg
Example request
* POST http://localhost:7474/db/data/node
* Accept: application/json
* Content-Type: application/json
{"foo":null}
Example response
* 400: Bad Request
* Content-Type: application/json
{
"message" : "Could not set property \"foo\", unsupported type: null",
"exception" : "org.neo4j.server.rest.web.PropertyValueException: Could not set property \"foo\", unsupported type: null",
"stacktrace" : [ "org.neo4j.server.rest.web.DatabaseActions.set(DatabaseActions.java:148)", "org.neo4j.server.rest.web.DatabaseActions.createNode(DatabaseActions.java:206)", "org.neo4j.server.rest.web.RestfulGraphDatabase.createNode(RestfulGraphDatabase.java:190)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "com.sun.jersey.spi.container.JavaMethodInvokerFactory$1.invoke(JavaMethodInvokerFactory.java:60)", "com.sun.jersey.server.impl.model.method.dispatch.AbstractResourceMethodDispatchProvider$ResponseOutInvoker._dispatch(AbstractResourceMethodDispatchProvider.java:205)", "com.sun.jersey.server.impl.model.method.dispatch.ResourceJavaMethodDispatcher.dispatch(ResourceJavaMethodDispatcher.java:75)", "com.sun.jersey.server.impl.uri.rules.HttpMethodRule.accept(HttpMethodRule.java:288)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.ResourceClassRule.accept(ResourceClassRule.java:108)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.RootResourceClassesRule.accept(RootResourceClassesRule.java:84)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1469)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1400)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1349)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1339)", "com.sun.jersey.spi.container.servlet.WebComponent.service(WebComponent.java:416)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:537)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:699)", "javax.servlet.http.HttpServlet.service(HttpServlet.java:820)", "org.mortbay.jetty.servlet.ServletHolder.handle(ServletHolder.java:511)", "org.mortbay.jetty.servlet.ServletHandler.handle(ServletHandler.java:390)", "org.mortbay.jetty.servlet.SessionHandler.handle(SessionHandler.java:182)", "org.mortbay.jetty.handler.ContextHandler.handle(ContextHandler.java:765)", "org.mortbay.jetty.handler.HandlerCollection.handle(HandlerCollection.java:114)", "org.mortbay.jetty.handler.HandlerWrapper.handle(HandlerWrapper.java:152)", "org.mortbay.jetty.Server.handle(Server.java:326)", "org.mortbay.jetty.HttpConnection.handleRequest(HttpConnection.java:542)", "org.mortbay.jetty.HttpConnection$RequestHandler.content(HttpConnection.java:943)", "org.mortbay.jetty.HttpParser.parseNext(HttpParser.java:756)", "org.mortbay.jetty.HttpParser.parseAvailable(HttpParser.java:218)", "org.mortbay.jetty.HttpConnection.handle(HttpConnection.java:404)", "org.mortbay.io.nio.SelectChannelEndPoint.run(SelectChannelEndPoint.java:410)", "org.mortbay.thread.QueuedThreadPool$PoolThread.run(QueuedThreadPool.java:582)" ]
}
18.5.5. Property values can not be nested
Nesting properties is not supported. You could for example store the nested
json as a string instead.
Figure 18.30. Final Graph
Final-Graph-Property-values-can-not-be-nested.svg
Example request
* POST http://localhost:7474/db/data/node/
* Accept: application/json
* Content-Type: application/json
{"foo" : {"bar" : "baz"}}
Example response
* 400: Bad Request
* Content-Type: application/json
{
"message" : "Could not set property \"foo\", unsupported type: {bar=baz}",
"exception" : "org.neo4j.server.rest.web.PropertyValueException: Could not set property \"foo\", unsupported type: {bar=baz}",
"stacktrace" : [ "org.neo4j.server.rest.web.DatabaseActions.set(DatabaseActions.java:148)", "org.neo4j.server.rest.web.DatabaseActions.createNode(DatabaseActions.java:206)", "org.neo4j.server.rest.web.RestfulGraphDatabase.createNode(RestfulGraphDatabase.java:190)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "com.sun.jersey.spi.container.JavaMethodInvokerFactory$1.invoke(JavaMethodInvokerFactory.java:60)", "com.sun.jersey.server.impl.model.method.dispatch.AbstractResourceMethodDispatchProvider$ResponseOutInvoker._dispatch(AbstractResourceMethodDispatchProvider.java:205)", "com.sun.jersey.server.impl.model.method.dispatch.ResourceJavaMethodDispatcher.dispatch(ResourceJavaMethodDispatcher.java:75)", "com.sun.jersey.server.impl.uri.rules.HttpMethodRule.accept(HttpMethodRule.java:288)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.ResourceClassRule.accept(ResourceClassRule.java:108)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.RootResourceClassesRule.accept(RootResourceClassesRule.java:84)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1469)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1400)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1349)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1339)", "com.sun.jersey.spi.container.servlet.WebComponent.service(WebComponent.java:416)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:537)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:699)", "javax.servlet.http.HttpServlet.service(HttpServlet.java:820)", "org.mortbay.jetty.servlet.ServletHolder.handle(ServletHolder.java:511)", "org.mortbay.jetty.servlet.ServletHandler.handle(ServletHandler.java:390)", "org.mortbay.jetty.servlet.SessionHandler.handle(SessionHandler.java:182)", "org.mortbay.jetty.handler.ContextHandler.handle(ContextHandler.java:765)", "org.mortbay.jetty.handler.HandlerCollection.handle(HandlerCollection.java:114)", "org.mortbay.jetty.handler.HandlerWrapper.handle(HandlerWrapper.java:152)", "org.mortbay.jetty.Server.handle(Server.java:326)", "org.mortbay.jetty.HttpConnection.handleRequest(HttpConnection.java:542)", "org.mortbay.jetty.HttpConnection$RequestHandler.content(HttpConnection.java:943)", "org.mortbay.jetty.HttpParser.parseNext(HttpParser.java:756)", "org.mortbay.jetty.HttpParser.parseAvailable(HttpParser.java:218)", "org.mortbay.jetty.HttpConnection.handle(HttpConnection.java:404)", "org.mortbay.io.nio.SelectChannelEndPoint.run(SelectChannelEndPoint.java:410)", "org.mortbay.thread.QueuedThreadPool$PoolThread.run(QueuedThreadPool.java:582)" ]
}
18.5.6. Delete all properties from node
Figure 18.31. Final Graph
Final-Graph-Delete-all-properties-from-node.svg
Example request
* DELETE http://localhost:7474/db/data/node/72/properties
* Accept: application/json
Example response
* 204: No Content
18.6. Relationship properties
18.6.1. Update relationship properties
Figure 18.32. Final Graph
Final-Graph-Update-relationship-properties.svg
Example request
* PUT http://localhost:7474/db/data/relationship/192/properties
* Accept: application/json
* Content-Type: application/json
{
"jim" : "tobias"
}
Example response
* 204: No Content
inlcude::remove-properties-from-a-relationship.txt[]
18.6.2. Remove property from a relationship
Documentation not available
Figure 18.33. Starting Graph
Starting-Graph-Remove-property-from-a-relationship1.svg
Figure 18.34. Final Graph
Final-Graph-Remove-property-from-a-relationship.svg
Example request
* DELETE http://localhost:7474/db/data/relationship/7/properties/cost
* Accept: application/json
Example response
* 204: No Content
18.6.3. Remove non-existent property from a relationship
Documentation not available
Figure 18.35. Final Graph
Final-Graph-Remove-non-existent-property-from-a-relationship.svg
Example request
* DELETE http://localhost:7474/db/data/relationship/8/properties/non-existent
* Accept: application/json
Example response
* 404: Not Found
* Content-Type: application/json
{
"message" : "Relationship[8] does not have a property \"non-existent\"",
"exception" : "org.neo4j.server.rest.web.NoSuchPropertyException: Relationship[8] does not have a property \"non-existent\"",
"stacktrace" : [ "org.neo4j.server.rest.web.DatabaseActions.removeRelationshipProperty(DatabaseActions.java:671)", "org.neo4j.server.rest.web.RestfulGraphDatabase.deleteRelationshipProperty(RestfulGraphDatabase.java:586)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "com.sun.jersey.spi.container.JavaMethodInvokerFactory$1.invoke(JavaMethodInvokerFactory.java:60)", "com.sun.jersey.server.impl.model.method.dispatch.AbstractResourceMethodDispatchProvider$ResponseOutInvoker._dispatch(AbstractResourceMethodDispatchProvider.java:205)", "com.sun.jersey.server.impl.model.method.dispatch.ResourceJavaMethodDispatcher.dispatch(ResourceJavaMethodDispatcher.java:75)", "com.sun.jersey.server.impl.uri.rules.HttpMethodRule.accept(HttpMethodRule.java:288)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.ResourceClassRule.accept(ResourceClassRule.java:108)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.RootResourceClassesRule.accept(RootResourceClassesRule.java:84)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1469)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1400)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1349)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1339)", "com.sun.jersey.spi.container.servlet.WebComponent.service(WebComponent.java:416)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:537)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:699)", "javax.servlet.http.HttpServlet.service(HttpServlet.java:820)", "org.mortbay.jetty.servlet.ServletHolder.handle(ServletHolder.java:511)", "org.mortbay.jetty.servlet.ServletHandler.handle(ServletHandler.java:390)", "org.mortbay.jetty.servlet.SessionHandler.handle(SessionHandler.java:182)", "org.mortbay.jetty.handler.ContextHandler.handle(ContextHandler.java:765)", "org.mortbay.jetty.handler.HandlerCollection.handle(HandlerCollection.java:114)", "org.mortbay.jetty.handler.HandlerWrapper.handle(HandlerWrapper.java:152)", "org.mortbay.jetty.Server.handle(Server.java:326)", "org.mortbay.jetty.HttpConnection.handleRequest(HttpConnection.java:542)", "org.mortbay.jetty.HttpConnection$RequestHandler.headerComplete(HttpConnection.java:926)", "org.mortbay.jetty.HttpParser.parseNext(HttpParser.java:549)", "org.mortbay.jetty.HttpParser.parseAvailable(HttpParser.java:212)", "org.mortbay.jetty.HttpConnection.handle(HttpConnection.java:404)", "org.mortbay.io.nio.SelectChannelEndPoint.run(SelectChannelEndPoint.java:410)", "org.mortbay.thread.QueuedThreadPool$PoolThread.run(QueuedThreadPool.java:582)" ]
}
18.6.4. Remove properties from a non-existing relationship
Documentation not available
Figure 18.36. Final Graph
Final-Graph-Remove-properties-from-a-non-existing-relationship.svg
Example request
* DELETE http://localhost:7474/db/data/relationship/1234/properties
* Accept: application/json
Example response
* 404: Not Found
* Content-Type: application/json
{
"exception" : "org.neo4j.server.rest.web.RelationshipNotFoundException",
"stacktrace" : [ "org.neo4j.server.rest.web.DatabaseActions.relationship(DatabaseActions.java:130)", "org.neo4j.server.rest.web.DatabaseActions.removeAllRelationshipProperties(DatabaseActions.java:649)", "org.neo4j.server.rest.web.RestfulGraphDatabase.deleteAllRelationshipProperties(RestfulGraphDatabase.java:570)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "com.sun.jersey.spi.container.JavaMethodInvokerFactory$1.invoke(JavaMethodInvokerFactory.java:60)", "com.sun.jersey.server.impl.model.method.dispatch.AbstractResourceMethodDispatchProvider$ResponseOutInvoker._dispatch(AbstractResourceMethodDispatchProvider.java:205)", "com.sun.jersey.server.impl.model.method.dispatch.ResourceJavaMethodDispatcher.dispatch(ResourceJavaMethodDispatcher.java:75)", "com.sun.jersey.server.impl.uri.rules.HttpMethodRule.accept(HttpMethodRule.java:288)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.ResourceClassRule.accept(ResourceClassRule.java:108)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.RootResourceClassesRule.accept(RootResourceClassesRule.java:84)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1469)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1400)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1349)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1339)", "com.sun.jersey.spi.container.servlet.WebComponent.service(WebComponent.java:416)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:537)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:699)", "javax.servlet.http.HttpServlet.service(HttpServlet.java:820)", "org.mortbay.jetty.servlet.ServletHolder.handle(ServletHolder.java:511)", "org.mortbay.jetty.servlet.ServletHandler.handle(ServletHandler.java:390)", "org.mortbay.jetty.servlet.SessionHandler.handle(SessionHandler.java:182)", "org.mortbay.jetty.handler.ContextHandler.handle(ContextHandler.java:765)", "org.mortbay.jetty.handler.HandlerCollection.handle(HandlerCollection.java:114)", "org.mortbay.jetty.handler.HandlerWrapper.handle(HandlerWrapper.java:152)", "org.mortbay.jetty.Server.handle(Server.java:326)", "org.mortbay.jetty.HttpConnection.handleRequest(HttpConnection.java:542)", "org.mortbay.jetty.HttpConnection$RequestHandler.headerComplete(HttpConnection.java:926)", "org.mortbay.jetty.HttpParser.parseNext(HttpParser.java:549)", "org.mortbay.jetty.HttpParser.parseAvailable(HttpParser.java:212)", "org.mortbay.jetty.HttpConnection.handle(HttpConnection.java:404)", "org.mortbay.io.nio.SelectChannelEndPoint.run(SelectChannelEndPoint.java:410)", "org.mortbay.thread.QueuedThreadPool$PoolThread.run(QueuedThreadPool.java:582)" ]
}
18.6.5. Remove property from a non-existing relationship
Documentation not available
Figure 18.37. Final Graph
Final-Graph-Remove-property-from-a-non-existing-relationship.svg
Example request
* DELETE http://localhost:7474/db/data/relationship/1234/properties/cost
* Accept: application/json
Example response
* 404: Not Found
* Content-Type: application/json
{
"exception" : "org.neo4j.server.rest.web.RelationshipNotFoundException",
"stacktrace" : [ "org.neo4j.server.rest.web.DatabaseActions.relationship(DatabaseActions.java:130)", "org.neo4j.server.rest.web.DatabaseActions.removeRelationshipProperty(DatabaseActions.java:665)", "org.neo4j.server.rest.web.RestfulGraphDatabase.deleteRelationshipProperty(RestfulGraphDatabase.java:586)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "com.sun.jersey.spi.container.JavaMethodInvokerFactory$1.invoke(JavaMethodInvokerFactory.java:60)", "com.sun.jersey.server.impl.model.method.dispatch.AbstractResourceMethodDispatchProvider$ResponseOutInvoker._dispatch(AbstractResourceMethodDispatchProvider.java:205)", "com.sun.jersey.server.impl.model.method.dispatch.ResourceJavaMethodDispatcher.dispatch(ResourceJavaMethodDispatcher.java:75)", "com.sun.jersey.server.impl.uri.rules.HttpMethodRule.accept(HttpMethodRule.java:288)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.ResourceClassRule.accept(ResourceClassRule.java:108)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.RootResourceClassesRule.accept(RootResourceClassesRule.java:84)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1469)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1400)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1349)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1339)", "com.sun.jersey.spi.container.servlet.WebComponent.service(WebComponent.java:416)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:537)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:699)", "javax.servlet.http.HttpServlet.service(HttpServlet.java:820)", "org.mortbay.jetty.servlet.ServletHolder.handle(ServletHolder.java:511)", "org.mortbay.jetty.servlet.ServletHandler.handle(ServletHandler.java:390)", "org.mortbay.jetty.servlet.SessionHandler.handle(SessionHandler.java:182)", "org.mortbay.jetty.handler.ContextHandler.handle(ContextHandler.java:765)", "org.mortbay.jetty.handler.HandlerCollection.handle(HandlerCollection.java:114)", "org.mortbay.jetty.handler.HandlerWrapper.handle(HandlerWrapper.java:152)", "org.mortbay.jetty.Server.handle(Server.java:326)", "org.mortbay.jetty.HttpConnection.handleRequest(HttpConnection.java:542)", "org.mortbay.jetty.HttpConnection$RequestHandler.headerComplete(HttpConnection.java:926)", "org.mortbay.jetty.HttpParser.parseNext(HttpParser.java:549)", "org.mortbay.jetty.HttpParser.parseAvailable(HttpParser.java:212)", "org.mortbay.jetty.HttpConnection.handle(HttpConnection.java:404)", "org.mortbay.io.nio.SelectChannelEndPoint.run(SelectChannelEndPoint.java:410)", "org.mortbay.thread.QueuedThreadPool$PoolThread.run(QueuedThreadPool.java:582)" ]
}
18.7. Indexes
An index can contain either nodes or relationships.
Note
To create an index with default configuration, simply start using it by adding
nodes/relationships to it. It will then be automatically created for you.
What default configuration means depends on how you have configured your
database. If you haven’t changed any indexing configuration, it means the
indexes will be using a Lucene-based backend.
All the examples below show you how to do operations on node indexes, but all
of them are just as applicable to relationship indexes. Simple change the
"node" part of the URL to "relationship".
If you want to customize the index settings, see Section 18.7.2, “Create node
index with configuration”.
18.7.1. Create node index
Note
Instead of creating the index this way, you can simply start to use it, and it
will be created automatically with default configuration.
Figure 18.38. Final Graph
Final-Graph-Create-node-index.svg
Example request
* POST http://localhost:7474/db/data/index/node/
* Accept: application/json
* Content-Type: application/json
{
"name" : "favorites"
}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/index/node/favorites/
{
"template" : "http://localhost:7474/db/data/index/node/favorites/{key}/{value}"
}
18.7.2. Create node index with configuration
This request is only necessary if you want to customize the index settings. If
you are happy with the defaults, you can just start indexing nodes/
relationships, as non-existent indexes will automatically be created as you do.
See Section 14.10, “Configuration and fulltext indexes” for more information on
index configuration.
Figure 18.39. Final Graph
Final-Graph-Create-node-index-with-configuration.svg
Example request
* POST http://localhost:7474/db/data/index/node/
* Accept: application/json
* Content-Type: application/json
{"name":"fulltext", "config":{"type":"fulltext","provider":"lucene"}}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/index/node/fulltext/
{
"template" : "http://localhost:7474/db/data/index/node/fulltext/{key}/{value}",
"provider" : "lucene",
"type" : "fulltext"
}
18.7.3. Delete node index
Figure 18.40. Final Graph
Final-Graph-Delete-node-index.svg
Example request
* DELETE http://localhost:7474/db/data/index/node/kvnode
* Accept: application/json
Example response
* 204: No Content
18.7.4. List node indexes
Figure 18.41. Final Graph
Final-Graph-List-node-indexes.svg
Example request
* GET http://localhost:7474/db/data/index/node/
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
{
"favorites" : {
"template" : "http://localhost:7474/db/data/index/node/favorites/{key}/{value}",
"provider" : "lucene",
"type" : "exact"
}
}
18.7.5. Add node to index
Associates a node with the given key/value pair in the given index.
Note
Spaces in the URI have to be escaped.
Caution
This does not overwrite previous entries. If you index the same key/value/item
combination twice, two index entries are created. To do update-type operations,
you need to delete the old entry before adding a new one.
Figure 18.42. Final Graph
Final-Graph-Add-node-to-index.svg
Example request
* POST http://localhost:7474/db/data/index/node/favorites
* Accept: application/json
* Content-Type: application/json
{
"value" : "some value",
"uri" : "http://localhost:7474/db/data/node/104",
"key" : "some-key"
}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/index/node/favorites/some-key/
some%20value/104
{
"indexed" : "http://localhost:7474/db/data/index/node/favorites/some-key/some%20value/104",
"outgoing_relationships" : "http://localhost:7474/db/data/node/104/relationships/out",
"data" : {
},
"traverse" : "http://localhost:7474/db/data/node/104/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/104/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/104/properties/{key}",
"self" : "http://localhost:7474/db/data/node/104",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/104/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/104/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/104/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/104/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/104/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/104/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/104/relationships/in/{-list|&|types}"
}
18.7.6. Remove all entries with a given node from an index
Figure 18.43. Final Graph
Final-Graph-Remove-all-entries-with-a-given-node-from-an-index.svg
Example request
* DELETE http://localhost:7474/db/data/index/node/kvnode/111
* Accept: application/json
Example response
* 204: No Content
18.7.7. Remove all entries with a given node and key from an index
Figure 18.44. Final Graph
Final-Graph-Remove-all-entries-with-a-given-node-and-key-from-an-index.svg
Example request
* DELETE http://localhost:7474/db/data/index/node/kvnode/kvkey2/112
* Accept: application/json
Example response
* 204: No Content
18.7.8. Remove all entries with a given node, key and value from an index
Figure 18.45. Final Graph
Final-Graph-Remove-all-entries-with-a-given-node,-key-and-value-from-an-index.svg
Example request
* DELETE http://localhost:7474/db/data/index/node/kvnode/kvkey1/value1/113
* Accept: application/json
Example response
* 204: No Content
18.7.9. Find node by exact match
Note
Spaces in the URI have to be escaped.
Figure 18.46. Final Graph
Final-Graph-Find-node-by-exact-match.svg
Example request
* GET http://localhost:7474/db/data/index/node/favorites/key/the%2520value
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ {
"indexed" : "http://localhost:7474/db/data/index/node/favorites/key/the%2520value/105",
"outgoing_relationships" : "http://localhost:7474/db/data/node/105/relationships/out",
"data" : {
},
"traverse" : "http://localhost:7474/db/data/node/105/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/105/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/105/properties/{key}",
"self" : "http://localhost:7474/db/data/node/105",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/105/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/105/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/105/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/105/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/105/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/105/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/105/relationships/in/{-list|&|types}"
} ]
18.7.10. Find node by query
The query language used here depends on what type of index you are querying.
The default index type is Lucene, in which case you should use the Lucene query
language here. Below an example of a fuzzy search over multiple keys.
See: http://lucene.apache.org/java/3_5_0/queryparsersyntax.html
Figure 18.47. Final Graph
Final-Graph-Find-node-by-query.svg
Example request
* GET http://localhost:7474/db/data/index/node/bobTheIndex?query=
Name:Build~0.1%20AND%20Gender:Male
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/106/relationships/out",
"data" : {
"Name" : "Builder"
},
"traverse" : "http://localhost:7474/db/data/node/106/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/106/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/106/properties/{key}",
"self" : "http://localhost:7474/db/data/node/106",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/106/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/106/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/106/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/106/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/106/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/106/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/106/relationships/in/{-list|&|types}"
} ]
18.8. Unique Indexes
For more information, see Section 13.6, “Creating unique nodes”.
18.8.1. Create a unique node in an index
Figure 18.48. Final Graph
Final-Graph-Create-a-unique-node-in-an-index.svg
Example request
* POST http://localhost:7474/db/data/index/node/people?unique
* Accept: application/json
* Content-Type: application/json
{"key": "name", "value": "Tobias", "properties": {"name": "Tobias", "sequence": 1}}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/index/node/people/name/Tobias/114
{
"indexed" : "http://localhost:7474/db/data/index/node/people/name/Tobias/114",
"outgoing_relationships" : "http://localhost:7474/db/data/node/114/relationships/out",
"data" : {
"sequence" : 1,
"name" : "Tobias"
},
"traverse" : "http://localhost:7474/db/data/node/114/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/114/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/114/properties/{key}",
"self" : "http://localhost:7474/db/data/node/114",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/114/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/114/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/114/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/114/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/114/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/114/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/114/relationships/in/{-list|&|types}"
}
18.8.2. Create a unique node in an index (the case where it exists)
Figure 18.49. Final Graph
Final-Graph-Create-a-unique-node-in-an-index-(the-case-where-it-exists).svg
Example request
* POST http://localhost:7474/db/data/index/node/people?unique
* Accept: application/json
* Content-Type: application/json
{"key": "name", "value": "Peter", "properties": {"name": "Peter", "sequence": 2}}
Example response
* 200: OK
* Content-Type: application/json
{
"indexed" : "http://localhost:7474/db/data/index/node/people/name/Peter/115",
"outgoing_relationships" : "http://localhost:7474/db/data/node/115/relationships/out",
"data" : {
"sequence" : 1,
"name" : "Peter"
},
"traverse" : "http://localhost:7474/db/data/node/115/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/115/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/115/properties/{key}",
"self" : "http://localhost:7474/db/data/node/115",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/115/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/115/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/115/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/115/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/115/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/115/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/115/relationships/in/{-list|&|types}"
}
18.8.3. Add a node to an index unless a node already exists for the given
mapping
Figure 18.50. Final Graph
Final-Graph-Add-a-node-to-an-index-unless-a-node-already-exists-for-the-given-mapping.svg
Example request
* POST http://localhost:7474/db/data/index/node/people?unique
* Accept: application/json
* Content-Type: application/json
{"key": "name", "value": "Mattias", "uri":"http://localhost:7474/db/data/node/116"}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/index/node/people/name/Mattias/116
{
"indexed" : "http://localhost:7474/db/data/index/node/people/name/Mattias/116",
"outgoing_relationships" : "http://localhost:7474/db/data/node/116/relationships/out",
"data" : {
},
"traverse" : "http://localhost:7474/db/data/node/116/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/116/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/116/properties/{key}",
"self" : "http://localhost:7474/db/data/node/116",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/116/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/116/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/116/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/116/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/116/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/116/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/116/relationships/in/{-list|&|types}"
}
18.8.4. Create a unique relationship in an index
Figure 18.51. Final Graph
Final-Graph-Create-a-unique-relationship-in-an-index.svg
Example request
* POST http://localhost:7474/db/data/index/relationship/knowledge/?unique
* Accept: application/json
* Content-Type: application/json
{"key": "name", "value":"Tobias", "start": "http://localhost:7474/db/data/node/67", "end": "http://localhost:7474/db/data/node/68", "type": "knowledge"}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/index/relationship/knowledge/name/
Tobias/23
{
"indexed" : "http://localhost:7474/db/data/index/relationship/knowledge/name/Tobias/23",
"start" : "http://localhost:7474/db/data/node/67",
"data" : {
"name" : "Tobias"
},
"self" : "http://localhost:7474/db/data/relationship/23",
"property" : "http://localhost:7474/db/data/relationship/23/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/23/properties",
"type" : "knowledge",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/68"
}
18.8.5. Add a relationship to an index unless a relationship already exists for
the given mapping
Figure 18.52. Final Graph
Final-Graph-Add-a-relationship-to-an-index-unless-a-relationship-already-exists-for-the-given-mapping.svg
Example request
* POST http://localhost:7474/db/data/index/relationship/knowledge/?unique
* Accept: application/json
* Content-Type: application/json
{"key": "name", "value":"Mattias", "uri": "http://localhost:7474/db/data/relationship/24"}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/index/relationship/knowledge/name/
Mattias/24
{
"indexed" : "http://localhost:7474/db/data/index/relationship/knowledge/name/Mattias/24",
"start" : "http://localhost:7474/db/data/node/69",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/24",
"property" : "http://localhost:7474/db/data/relationship/24/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/24/properties",
"type" : "knowledge",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/70"
}
18.9. Automatic Indexes
18.9.1. Find node by exact match from an automatic index
Automatic index nodes can be found via exact lookups with normal Index REST
syntax.
Figure 18.53. Final Graph
Final-Graph-find-node-by-exact-match-from-an-automatic-index.svg
Example request
* GET http://localhost:7474/db/data/index/auto/node/name/I
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/317/relationships/out",
"data" : {
"name" : "I"
},
"traverse" : "http://localhost:7474/db/data/node/317/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/317/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/317/properties/{key}",
"self" : "http://localhost:7474/db/data/node/317",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/317/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/317/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/317/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/317/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/317/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/317/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/317/relationships/in/{-list|&|types}"
} ]
18.9.2. Find node by query from an automatic index
See Find node by query for the actual query syntax.
Figure 18.54. Final Graph
Final-Graph-Find-node-by-query-from-an-automatic-index.svg
Example request
* GET http://localhost:7474/db/data/index/auto/node/?query=name:I
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/316/relationships/out",
"data" : {
"name" : "I"
},
"traverse" : "http://localhost:7474/db/data/node/316/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/316/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/316/properties/{key}",
"self" : "http://localhost:7474/db/data/node/316",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/316/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/316/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/316/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/316/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/316/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/316/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/316/relationships/in/{-list|&|types}"
} ]
18.10. Configurable Automatic Indexing
Out of the box auto-indexing supports exact matches since they are created with
the default configuration (http://docs.neo4j.org/chunked/snapshot/
indexing-create.html ) the first time you access them. However it is possible
to intervene in the lifecycle of the server before any auto indexes are created
to change their configuration.
This approach cannot be used on databases that already have auto-indexes
established. To change the auto-index configuration existing indexes would have
to be deleted first, so be careful!
Caution
This technique works, but it is not particularly pleasant. Future versions of
Neo4j may remove this loophole in favour of a better structured feature for
managing auto-indexing configurations.
Auto-indexing must be enabled through configuration before we can create or
configure them. Firstly ensure that you’ve added some config like this into
your server’s neo4j.properties file:
node_auto_indexing=true
relationship_auto_indexing=true
node_keys_indexable=name,phone
relationship_keys_indexable=since
The node_auto_indexing and relationship_auto_indexing turn auto-indexing on for
nodes and relationships respectively. The node_keys_indexable key allows you to
specify a comma-separated list of node property keys to be indexed. The
relationship_keys_indexable does the same for relationship property keys.
Next start the server as usual by invoking the start script as described in
Section 17.1, “Server Installation”.
Next we have to pre-empt the creation of an auto-index, by telling the server
to create an apparently manual index which has the same name as the node (or
relationship) auto-index. For example, in this case we’ll create a node auto
index whose name is node_auto_index, like so:
18.10.1. Create an auto index for nodes with specific configuration
Example request
* POST http://localhost:7474/db/data/index/node/
* Accept: application/json
* Content-Type: application/json
{"name":"node_auto_index", "config":{"type":"fulltext","provider":"lucene"}}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/index/node/node_auto_index/
{
"template" : "http://localhost:7474/db/data/index/node/node_auto_index/{key}/{value}",
"provider" : "lucene",
"type" : "fulltext"
}
If you require configured auto-indexes for relationships, the approach is
similar:
18.10.2. Create an auto index for relationships with specific configuration
Example request
* POST http://localhost:7474/db/data/index/relationship/
* Accept: application/json
* Content-Type: application/json
{"name":"relationship_auto_index", "config":{"type":"fulltext","provider":"lucene"}}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/index/relationship/
relationship_auto_index/
{
"template" : "http://localhost:7474/db/data/index/relationship/relationship_auto_index/{key}/{value}",
"provider" : "lucene",
"type" : "fulltext"
}
In case you’re curious how this works, on the server side it triggers the
creation of an index which happens to have the same name as the auto index that
the database would create for itself. Now when we interact with the database,
the index thinks the index is already is created so the state machine skips
over that step and just gets on with normal day-to-day auto-indexing.
Caution
You have to do this early in your server lifecycle, before any normal auto
indexes are created.
18.11. Traversals
Traversals are performed from a start node. The traversal is controlled by the
URI and the body sent with the request.
returnType
The kind of objects in the response is determined by traverse/{returnType}
in the URL. returnType can have one of these values:
* node
* relationship
* path - contains full representations of start and end node, the rest
are URIs
* fullpath - contains full representations of all nodes and relationships
To decide how the graph should be traversed you can use these parameters in the
request body:
order
Decides in which order to visit nodes. Possible values:
* breadth_first - see Breadth-first search
* depth_first - see Depth-first search
relationships
Decides which relationship types and directions should be followed. The
direction can be one of:
* all
* in
* out
uniqueness
Decides how uniqueness should be calculated. For details on different
uniqueness values see the Java API on Uniqueness .
Possible values:
* node_global
* none
* relationship_global
* node_path
* relationship_path
prune_evaluator
Decides whether the traverser should continue down that path or if it
should be pruned so that the traverser won’t continue down that path. You
can write your own prune evaluator as (see Section 18.11.1, “Traversal
using a return filter” or use the built-in none prune evaluator.
return_filter
Decides whether the current position should be included in the result. You
can provide your own code for this (see Section 18.11.1, “Traversal using a
return filter”), or use one of the built-in filters:
* all
* all_but_start_node
max_depth
Is a short-hand way of specifying a prune evaluator which prunes after a
certain depth. If not specified a max depth of 1 is used and if a
prune_evaluator is specified instead of a max_depth, no max depth limit is
set.
The position object in the body of the return_filter and prune_evaluator is a
Path object representing the path from the start node to the current
traversal position.
Out of the box, the REST API supports JavaScript code in filters and
evaluators. The script body will be executed in a Java context which has access
to the full Neo4j Java API .
See the examples for the exact syntax of the request.
18.11.1. Traversal using a return filter
In this example, the none prune evaluator is used and a return filter is
supplied in order to return all names containing "t". The result is to be
returned as nodes and the max depth is set to 3.
Figure 18.55. Final Graph
Final-Graph-Traversal-using-a-return-filter.svg
Example request
* POST http://localhost:7474/db/data/node/17/traverse/node
* Accept: application/json
* Content-Type: application/json
{
"order" : "breadth_first",
"return_filter" : {
"body" : "position.endNode().getProperty('name').toLowerCase().contains('t')",
"language" : "javascript"
},
"prune_evaluator" : {
"body" : "position.length() > 10",
"language" : "javascript"
},
"uniqueness" : "node_global",
"relationships" : [ {
"direction" : "all",
"type" : "knows"
}, {
"direction" : "all",
"type" : "loves"
} ],
"max_depth" : 3
}
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/17/relationships/out",
"data" : {
"name" : "Root"
},
"traverse" : "http://localhost:7474/db/data/node/17/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/17/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/17/properties/{key}",
"self" : "http://localhost:7474/db/data/node/17",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/17/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/17/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/17/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/17/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/17/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/17/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/17/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/20/relationships/out",
"data" : {
"name" : "Mattias"
},
"traverse" : "http://localhost:7474/db/data/node/20/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/20/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/20/properties/{key}",
"self" : "http://localhost:7474/db/data/node/20",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/20/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/20/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/20/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/20/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/20/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/20/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/20/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/19/relationships/out",
"data" : {
"name" : "Peter"
},
"traverse" : "http://localhost:7474/db/data/node/19/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/19/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/19/properties/{key}",
"self" : "http://localhost:7474/db/data/node/19",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/19/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/19/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/19/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/19/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/19/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/19/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/19/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/18/relationships/out",
"data" : {
"name" : "Tobias"
},
"traverse" : "http://localhost:7474/db/data/node/18/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/18/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/18/properties/{key}",
"self" : "http://localhost:7474/db/data/node/18",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/18/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/18/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/18/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/18/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/18/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/18/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/18/relationships/in/{-list|&|types}"
} ]
18.11.2. Return relationships from a traversal
Figure 18.56. Final Graph
Final-Graph-return-relationships-from-a-traversal.svg
Example request
* POST http://localhost:7474/db/data/node/8/traverse/relationship
* Accept: application/json
* Content-Type: application/json
{"order":"breadth_first","uniqueness":"none","return_filter":{"language":"builtin","name":"all"}}
Example response
* 200: OK
* Content-Type: application/json
[ {
"start" : "http://localhost:7474/db/data/node/8",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/2",
"property" : "http://localhost:7474/db/data/relationship/2/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/2/properties",
"type" : "know",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/7"
}, {
"start" : "http://localhost:7474/db/data/node/8",
"data" : {
},
"self" : "http://localhost:7474/db/data/relationship/3",
"property" : "http://localhost:7474/db/data/relationship/3/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/3/properties",
"type" : "own",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/6"
} ]
18.11.3. Return paths from a traversal
Figure 18.57. Final Graph
Final-Graph-return-paths-from-a-traversal.svg
Example request
* POST http://localhost:7474/db/data/node/11/traverse/path
* Accept: application/json
* Content-Type: application/json
{"order":"breadth_first","uniqueness":"none","return_filter":{"language":"builtin","name":"all"}}
Example response
* 200: OK
* Content-Type: application/json
[ {
"start" : "http://localhost:7474/db/data/node/11",
"nodes" : [ "http://localhost:7474/db/data/node/11" ],
"length" : 0,
"relationships" : [ ],
"end" : "http://localhost:7474/db/data/node/11"
}, {
"start" : "http://localhost:7474/db/data/node/11",
"nodes" : [ "http://localhost:7474/db/data/node/11", "http://localhost:7474/db/data/node/10" ],
"length" : 1,
"relationships" : [ "http://localhost:7474/db/data/relationship/4" ],
"end" : "http://localhost:7474/db/data/node/10"
}, {
"start" : "http://localhost:7474/db/data/node/11",
"nodes" : [ "http://localhost:7474/db/data/node/11", "http://localhost:7474/db/data/node/9" ],
"length" : 1,
"relationships" : [ "http://localhost:7474/db/data/relationship/5" ],
"end" : "http://localhost:7474/db/data/node/9"
} ]
18.11.4. Traversal returning nodes below a certain depth
Here, all nodes at a traversal depth below 3 are returned.
Figure 18.58. Final Graph
Final-Graph-Traversal-returning-nodes-below-a-certain-depth.svg
Example request
* POST http://localhost:7474/db/data/node/24/traverse/node
* Accept: application/json
* Content-Type: application/json
{
"return_filter" : {
"body" : "position.length()<3;",
"language" : "javascript"
},
"prune_evaluator" : {
"name" : "none",
"language" : "builtin"
}
}
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/24/relationships/out",
"data" : {
"name" : "Root"
},
"traverse" : "http://localhost:7474/db/data/node/24/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/24/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/24/properties/{key}",
"self" : "http://localhost:7474/db/data/node/24",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/24/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/24/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/24/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/24/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/24/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/24/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/24/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/27/relationships/out",
"data" : {
"name" : "Mattias"
},
"traverse" : "http://localhost:7474/db/data/node/27/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/27/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/27/properties/{key}",
"self" : "http://localhost:7474/db/data/node/27",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/27/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/27/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/27/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/27/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/27/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/27/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/27/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/22/relationships/out",
"data" : {
"name" : "Johan"
},
"traverse" : "http://localhost:7474/db/data/node/22/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/22/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/22/properties/{key}",
"self" : "http://localhost:7474/db/data/node/22",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/22/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/22/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/22/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/22/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/22/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/22/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/22/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/23/relationships/out",
"data" : {
"name" : "Emil"
},
"traverse" : "http://localhost:7474/db/data/node/23/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/23/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/23/properties/{key}",
"self" : "http://localhost:7474/db/data/node/23",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/23/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/23/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/23/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/23/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/23/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/23/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/23/relationships/in/{-list|&|types}"
} ]
18.11.5. Creating a paged traverser
Paged traversers are created by POST-ing a traversal description to the link
identified by the paged_traverser key in a node representation. When creating a
paged traverser, the same options apply as for a regular traverser, meaning
that node, path, or fullpath, can be targeted.
Example request
* POST http://localhost:7474/db/data/node/34/paged/traverse/node
* Accept: application/json
* Content-Type: application/json
{
"prune_evaluator" : {
"language" : "builtin",
"name" : "none"
},
"return_filter" : {
"language" : "javascript",
"body" : "position.endNode().getProperty('name').contains('1');"
},
"order" : "depth_first",
"relationships" : {
"type" : "NEXT",
"direction" : "out"
}
}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/node/34/paged/traverse/node/
df8ef332894c4717a0d89d7be19453e8
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/35/relationships/out",
"data" : {
"name" : "1"
},
"traverse" : "http://localhost:7474/db/data/node/35/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/35/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/35/properties/{key}",
"self" : "http://localhost:7474/db/data/node/35",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/35/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/35/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/35/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/35/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/35/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/35/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/35/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/44/relationships/out",
"data" : {
"name" : "10"
},
"traverse" : "http://localhost:7474/db/data/node/44/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/44/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/44/properties/{key}",
"self" : "http://localhost:7474/db/data/node/44",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/44/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/44/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/44/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/44/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/44/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/44/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/44/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/45/relationships/out",
"data" : {
"name" : "11"
},
"traverse" : "http://localhost:7474/db/data/node/45/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/45/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/45/properties/{key}",
"self" : "http://localhost:7474/db/data/node/45",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/45/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/45/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/45/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/45/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/45/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/45/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/45/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/46/relationships/out",
"data" : {
"name" : "12"
},
"traverse" : "http://localhost:7474/db/data/node/46/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/46/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/46/properties/{key}",
"self" : "http://localhost:7474/db/data/node/46",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/46/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/46/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/46/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/46/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/46/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/46/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/46/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/47/relationships/out",
"data" : {
"name" : "13"
},
"traverse" : "http://localhost:7474/db/data/node/47/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/47/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/47/properties/{key}",
"self" : "http://localhost:7474/db/data/node/47",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/47/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/47/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/47/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/47/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/47/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/47/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/47/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/48/relationships/out",
"data" : {
"name" : "14"
},
"traverse" : "http://localhost:7474/db/data/node/48/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/48/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/48/properties/{key}",
"self" : "http://localhost:7474/db/data/node/48",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/48/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/48/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/48/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/48/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/48/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/48/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/48/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/49/relationships/out",
"data" : {
"name" : "15"
},
"traverse" : "http://localhost:7474/db/data/node/49/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/49/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/49/properties/{key}",
"self" : "http://localhost:7474/db/data/node/49",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/49/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/49/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/49/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/49/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/49/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/49/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/49/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/50/relationships/out",
"data" : {
"name" : "16"
},
"traverse" : "http://localhost:7474/db/data/node/50/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/50/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/50/properties/{key}",
"self" : "http://localhost:7474/db/data/node/50",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/50/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/50/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/50/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/50/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/50/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/50/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/50/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/51/relationships/out",
"data" : {
"name" : "17"
},
"traverse" : "http://localhost:7474/db/data/node/51/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/51/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/51/properties/{key}",
"self" : "http://localhost:7474/db/data/node/51",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/51/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/51/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/51/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/51/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/51/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/51/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/51/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/52/relationships/out",
"data" : {
"name" : "18"
},
"traverse" : "http://localhost:7474/db/data/node/52/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/52/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/52/properties/{key}",
"self" : "http://localhost:7474/db/data/node/52",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/52/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/52/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/52/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/52/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/52/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/52/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/52/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/53/relationships/out",
"data" : {
"name" : "19"
},
"traverse" : "http://localhost:7474/db/data/node/53/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/53/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/53/properties/{key}",
"self" : "http://localhost:7474/db/data/node/53",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/53/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/53/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/53/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/53/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/53/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/53/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/53/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/55/relationships/out",
"data" : {
"name" : "21"
},
"traverse" : "http://localhost:7474/db/data/node/55/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/55/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/55/properties/{key}",
"self" : "http://localhost:7474/db/data/node/55",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/55/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/55/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/55/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/55/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/55/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/55/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/55/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/65/relationships/out",
"data" : {
"name" : "31"
},
"traverse" : "http://localhost:7474/db/data/node/65/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/65/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/65/properties/{key}",
"self" : "http://localhost:7474/db/data/node/65",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/65/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/65/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/65/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/65/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/65/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/65/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/65/relationships/in/{-list|&|types}"
} ]
18.11.6. Paging through the results of a paged traverser
Paged traversers hold state on the server, and allow clients to page through
the results of a traversal. To progress to the next page of traversal results,
the client issues a HTTP GET request on the paged traversal URI which causes
the traversal to fill the next page (or partially fill it if insufficient
results are available).
Note that if a traverser expires through inactivity it will cause a 404
response on the next GET request. Traversers' leases are renewed on every
successful access for the same amount of time as originally specified.
When the paged traverser reaches the end of its results, the client can expect
a 404 response as the traverser is disposed by the server.
Example request
* GET http://localhost:7474/db/data/node/67/paged/traverse/node/
1aaeb9712f974e7791e44a26ccb2d45c
* Accept: application/json
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/398/relationships/out",
"data" : {
"name" : "331"
},
"traverse" : "http://localhost:7474/db/data/node/398/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/398/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/398/properties/{key}",
"self" : "http://localhost:7474/db/data/node/398",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/398/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/398/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/398/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/398/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/398/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/398/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/398/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/408/relationships/out",
"data" : {
"name" : "341"
},
"traverse" : "http://localhost:7474/db/data/node/408/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/408/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/408/properties/{key}",
"self" : "http://localhost:7474/db/data/node/408",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/408/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/408/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/408/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/408/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/408/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/408/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/408/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/418/relationships/out",
"data" : {
"name" : "351"
},
"traverse" : "http://localhost:7474/db/data/node/418/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/418/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/418/properties/{key}",
"self" : "http://localhost:7474/db/data/node/418",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/418/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/418/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/418/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/418/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/418/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/418/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/418/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/428/relationships/out",
"data" : {
"name" : "361"
},
"traverse" : "http://localhost:7474/db/data/node/428/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/428/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/428/properties/{key}",
"self" : "http://localhost:7474/db/data/node/428",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/428/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/428/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/428/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/428/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/428/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/428/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/428/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/438/relationships/out",
"data" : {
"name" : "371"
},
"traverse" : "http://localhost:7474/db/data/node/438/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/438/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/438/properties/{key}",
"self" : "http://localhost:7474/db/data/node/438",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/438/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/438/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/438/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/438/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/438/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/438/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/438/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/448/relationships/out",
"data" : {
"name" : "381"
},
"traverse" : "http://localhost:7474/db/data/node/448/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/448/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/448/properties/{key}",
"self" : "http://localhost:7474/db/data/node/448",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/448/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/448/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/448/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/448/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/448/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/448/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/448/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/458/relationships/out",
"data" : {
"name" : "391"
},
"traverse" : "http://localhost:7474/db/data/node/458/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/458/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/458/properties/{key}",
"self" : "http://localhost:7474/db/data/node/458",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/458/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/458/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/458/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/458/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/458/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/458/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/458/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/468/relationships/out",
"data" : {
"name" : "401"
},
"traverse" : "http://localhost:7474/db/data/node/468/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/468/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/468/properties/{key}",
"self" : "http://localhost:7474/db/data/node/468",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/468/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/468/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/468/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/468/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/468/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/468/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/468/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/477/relationships/out",
"data" : {
"name" : "410"
},
"traverse" : "http://localhost:7474/db/data/node/477/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/477/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/477/properties/{key}",
"self" : "http://localhost:7474/db/data/node/477",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/477/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/477/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/477/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/477/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/477/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/477/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/477/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/478/relationships/out",
"data" : {
"name" : "411"
},
"traverse" : "http://localhost:7474/db/data/node/478/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/478/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/478/properties/{key}",
"self" : "http://localhost:7474/db/data/node/478",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/478/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/478/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/478/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/478/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/478/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/478/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/478/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/479/relationships/out",
"data" : {
"name" : "412"
},
"traverse" : "http://localhost:7474/db/data/node/479/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/479/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/479/properties/{key}",
"self" : "http://localhost:7474/db/data/node/479",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/479/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/479/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/479/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/479/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/479/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/479/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/479/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/480/relationships/out",
"data" : {
"name" : "413"
},
"traverse" : "http://localhost:7474/db/data/node/480/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/480/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/480/properties/{key}",
"self" : "http://localhost:7474/db/data/node/480",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/480/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/480/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/480/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/480/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/480/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/480/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/480/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/481/relationships/out",
"data" : {
"name" : "414"
},
"traverse" : "http://localhost:7474/db/data/node/481/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/481/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/481/properties/{key}",
"self" : "http://localhost:7474/db/data/node/481",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/481/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/481/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/481/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/481/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/481/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/481/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/481/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/482/relationships/out",
"data" : {
"name" : "415"
},
"traverse" : "http://localhost:7474/db/data/node/482/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/482/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/482/properties/{key}",
"self" : "http://localhost:7474/db/data/node/482",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/482/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/482/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/482/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/482/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/482/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/482/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/482/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/483/relationships/out",
"data" : {
"name" : "416"
},
"traverse" : "http://localhost:7474/db/data/node/483/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/483/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/483/properties/{key}",
"self" : "http://localhost:7474/db/data/node/483",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/483/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/483/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/483/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/483/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/483/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/483/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/483/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/484/relationships/out",
"data" : {
"name" : "417"
},
"traverse" : "http://localhost:7474/db/data/node/484/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/484/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/484/properties/{key}",
"self" : "http://localhost:7474/db/data/node/484",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/484/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/484/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/484/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/484/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/484/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/484/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/484/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/485/relationships/out",
"data" : {
"name" : "418"
},
"traverse" : "http://localhost:7474/db/data/node/485/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/485/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/485/properties/{key}",
"self" : "http://localhost:7474/db/data/node/485",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/485/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/485/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/485/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/485/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/485/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/485/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/485/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/486/relationships/out",
"data" : {
"name" : "419"
},
"traverse" : "http://localhost:7474/db/data/node/486/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/486/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/486/properties/{key}",
"self" : "http://localhost:7474/db/data/node/486",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/486/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/486/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/486/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/486/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/486/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/486/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/486/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/488/relationships/out",
"data" : {
"name" : "421"
},
"traverse" : "http://localhost:7474/db/data/node/488/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/488/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/488/properties/{key}",
"self" : "http://localhost:7474/db/data/node/488",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/488/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/488/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/488/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/488/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/488/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/488/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/488/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/498/relationships/out",
"data" : {
"name" : "431"
},
"traverse" : "http://localhost:7474/db/data/node/498/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/498/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/498/properties/{key}",
"self" : "http://localhost:7474/db/data/node/498",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/498/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/498/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/498/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/498/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/498/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/498/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/498/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/508/relationships/out",
"data" : {
"name" : "441"
},
"traverse" : "http://localhost:7474/db/data/node/508/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/508/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/508/properties/{key}",
"self" : "http://localhost:7474/db/data/node/508",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/508/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/508/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/508/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/508/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/508/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/508/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/508/relationships/in/{-list|&|types}"
} ]
18.11.7. Paged traverser page size
The default page size is 50 items, but depending on the application larger or
smaller pages sizes might be appropriate. This can be set by adding a pageSize
query parameter.
Example request
* POST http://localhost:7474/db/data/node/544/paged/traverse/node?pageSize=1
* Accept: application/json
* Content-Type: application/json
{
"prune_evaluator" : {
"language" : "builtin",
"name" : "none"
},
"return_filter" : {
"language" : "javascript",
"body" : "position.endNode().getProperty('name').contains('1');"
},
"order" : "depth_first",
"relationships" : {
"type" : "NEXT",
"direction" : "out"
}
}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/node/544/paged/traverse/node/
e184d56d31704f05ae2239626473f505
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/545/relationships/out",
"data" : {
"name" : "1"
},
"traverse" : "http://localhost:7474/db/data/node/545/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/545/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/545/properties/{key}",
"self" : "http://localhost:7474/db/data/node/545",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/545/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/545/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/545/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/545/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/545/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/545/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/545/relationships/in/{-list|&|types}"
} ]
18.11.8. Paged traverser timeout
The default timeout for a paged traverser is 60 seconds, but depending on the
application larger or smaller timeouts might be appropriate. This can be set by
adding a leaseTime query parameter with the number of seconds the paged
traverser should last.
Example request
* POST http://localhost:7474/db/data/node/577/paged/traverse/node?leaseTime=
10
* Accept: application/json
* Content-Type: application/json
{
"prune_evaluator" : {
"language" : "builtin",
"name" : "none"
},
"return_filter" : {
"language" : "javascript",
"body" : "position.endNode().getProperty('name').contains('1');"
},
"order" : "depth_first",
"relationships" : {
"type" : "NEXT",
"direction" : "out"
}
}
Example response
* 201: Created
* Content-Type: application/json
* Location: http://localhost:7474/db/data/node/577/paged/traverse/node/
ccb6c40a67c54f448220400fe3c8ee65
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/578/relationships/out",
"data" : {
"name" : "1"
},
"traverse" : "http://localhost:7474/db/data/node/578/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/578/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/578/properties/{key}",
"self" : "http://localhost:7474/db/data/node/578",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/578/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/578/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/578/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/578/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/578/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/578/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/578/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/587/relationships/out",
"data" : {
"name" : "10"
},
"traverse" : "http://localhost:7474/db/data/node/587/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/587/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/587/properties/{key}",
"self" : "http://localhost:7474/db/data/node/587",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/587/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/587/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/587/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/587/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/587/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/587/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/587/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/588/relationships/out",
"data" : {
"name" : "11"
},
"traverse" : "http://localhost:7474/db/data/node/588/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/588/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/588/properties/{key}",
"self" : "http://localhost:7474/db/data/node/588",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/588/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/588/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/588/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/588/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/588/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/588/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/588/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/589/relationships/out",
"data" : {
"name" : "12"
},
"traverse" : "http://localhost:7474/db/data/node/589/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/589/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/589/properties/{key}",
"self" : "http://localhost:7474/db/data/node/589",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/589/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/589/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/589/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/589/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/589/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/589/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/589/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/590/relationships/out",
"data" : {
"name" : "13"
},
"traverse" : "http://localhost:7474/db/data/node/590/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/590/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/590/properties/{key}",
"self" : "http://localhost:7474/db/data/node/590",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/590/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/590/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/590/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/590/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/590/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/590/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/590/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/591/relationships/out",
"data" : {
"name" : "14"
},
"traverse" : "http://localhost:7474/db/data/node/591/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/591/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/591/properties/{key}",
"self" : "http://localhost:7474/db/data/node/591",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/591/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/591/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/591/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/591/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/591/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/591/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/591/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/592/relationships/out",
"data" : {
"name" : "15"
},
"traverse" : "http://localhost:7474/db/data/node/592/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/592/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/592/properties/{key}",
"self" : "http://localhost:7474/db/data/node/592",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/592/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/592/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/592/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/592/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/592/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/592/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/592/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/593/relationships/out",
"data" : {
"name" : "16"
},
"traverse" : "http://localhost:7474/db/data/node/593/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/593/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/593/properties/{key}",
"self" : "http://localhost:7474/db/data/node/593",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/593/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/593/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/593/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/593/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/593/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/593/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/593/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/594/relationships/out",
"data" : {
"name" : "17"
},
"traverse" : "http://localhost:7474/db/data/node/594/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/594/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/594/properties/{key}",
"self" : "http://localhost:7474/db/data/node/594",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/594/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/594/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/594/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/594/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/594/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/594/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/594/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/595/relationships/out",
"data" : {
"name" : "18"
},
"traverse" : "http://localhost:7474/db/data/node/595/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/595/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/595/properties/{key}",
"self" : "http://localhost:7474/db/data/node/595",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/595/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/595/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/595/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/595/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/595/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/595/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/595/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/596/relationships/out",
"data" : {
"name" : "19"
},
"traverse" : "http://localhost:7474/db/data/node/596/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/596/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/596/properties/{key}",
"self" : "http://localhost:7474/db/data/node/596",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/596/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/596/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/596/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/596/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/596/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/596/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/596/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/598/relationships/out",
"data" : {
"name" : "21"
},
"traverse" : "http://localhost:7474/db/data/node/598/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/598/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/598/properties/{key}",
"self" : "http://localhost:7474/db/data/node/598",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/598/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/598/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/598/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/598/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/598/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/598/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/598/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/608/relationships/out",
"data" : {
"name" : "31"
},
"traverse" : "http://localhost:7474/db/data/node/608/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/608/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/608/properties/{key}",
"self" : "http://localhost:7474/db/data/node/608",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/608/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/608/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/608/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/608/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/608/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/608/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/608/relationships/in/{-list|&|types}"
} ]
18.12. Cypher queries
The Neo4j REST API allows querying with the Chapter 15, Cypher Query Language.
The results are returned as a list of string headers (columns), and a data
part, consisting of a list of all rows, every row consisting of a list of REST
representations of the field value - Node, Relationship, Path or any simple
value like String.
18.12.1. Send a Query
A simple query returning all nodes connected to node 1, returning the node and
the name property, if it exists, otherwise null:
START x = node(205)
MATCH x -[r]-> n
RETURN type(r), n.name?, n.age?
Figure 18.59. Final Graph
Final-Graph-Send-a-Query.svg
Example request
* POST http://localhost:7474/db/data/cypher
* Accept: application/json
* Content-Type: application/json
{"query": "start x = node(205) match x -[r]-> n return type(r), n.name?, n.age?","params": {}},
Example response
* 200: OK
* Content-Type: application/json
{
"data" : [ [ "know", "him", 25 ], [ "know", "you", null ] ],
"columns" : [ "TYPE(r)", "n.name", "n.age" ]
}
18.12.2. Return paths
Paths can be returned together with other return types by just specifying
returns.
START x = node(%I%)
MATCH path = (x--friend)
RETURN path, friend.name
Figure 18.60. Final Graph
Final-Graph-return-paths.svg
Example request
* POST http://localhost:7474/db/data/cypher
* Accept: application/json
* Content-Type: application/json
{"query": "start x = node(209) match path = (x--friend) return path, friend.name","params": {}},
Example response
* 200: OK
* Content-Type: application/json
{
"data" : [ [ {
"start" : "http://localhost:7474/db/data/node/209",
"nodes" : [ "http://localhost:7474/db/data/node/209", "http://localhost:7474/db/data/node/208" ],
"length" : 1,
"relationships" : [ "http://localhost:7474/db/data/relationship/92" ],
"end" : "http://localhost:7474/db/data/node/208"
}, "you" ] ],
"columns" : [ "path", "friend.name" ]
}
18.12.3. Send queries with parameters
Cypher supports queries with parameters which are submitted as a JSON map.
START x = node:node_auto_index(name={STARTName})
MATCH path = (x-[r]-friend)
WHERE friend.name = {name}
RETURN TYPE(r)
Figure 18.61. Final Graph
Final-Graph-send-queries-with-parameters.svg
Example request
* POST http://localhost:7474/db/data/cypher
* Accept: application/json
* Content-Type: application/json
{"query": "start x = node:node_auto_index(name={startName}) match path = (x-[r]-friend) where friend.name = {name} return TYPE(r)","params": {"startName":"I","name":"you"}},
Example response
* 200: OK
* Content-Type: application/json
{
"data" : [ [ "know" ] ],
"columns" : [ "TYPE(r)" ]
}
18.12.4. Nested results
When sending queries that return nested results like list and maps, these will
get serialized into nested JSON representations according to their types.
START n = node(%I%,%you%)
RETURN collect(n.name), collect(n)
Figure 18.62. Final Graph
Final-Graph-nested-results.svg
Example request
* POST http://localhost:7474/db/data/cypher
* Accept: application/json
* Content-Type: application/json
{"query": "start n = node(215,214) return collect(n.name), collect(n)","params": {}},
Example response
* 200: OK
* Content-Type: application/json
{
"data" : [ [ [ "I", "you" ], [ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/215/relationships/out",
"data" : {
"name" : "I"
},
"traverse" : "http://localhost:7474/db/data/node/215/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/215/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/215/properties/{key}",
"self" : "http://localhost:7474/db/data/node/215",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/215/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/215/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/215/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/215/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/215/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/215/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/215/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/214/relationships/out",
"data" : {
"name" : "you"
},
"traverse" : "http://localhost:7474/db/data/node/214/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/214/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/214/properties/{key}",
"self" : "http://localhost:7474/db/data/node/214",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/214/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/214/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/214/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/214/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/214/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/214/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/214/relationships/in/{-list|&|types}"
} ] ] ],
"columns" : [ "collect(n.name)", "collect(n)" ]
}
18.12.5. Server errors
Errors on the server will be reported as a JSON-formatted stacktrace and
message.
START x = node(%I%)
RETURN x.dummy
Figure 18.63. Final Graph
Final-Graph-Server-errors.svg
Example request
* POST http://localhost:7474/db/data/cypher
* Accept: application/json
* Content-Type: application/json
{"query": "start x = node(207) return x.dummy","params": {}},
Example response
* 400: Bad Request
* Content-Type: application/json
{
"message" : "The property 'dummy' does not exist on Node[207]",
"exception" : "org.neo4j.cypher.EntityNotFoundException: The property 'dummy' does not exist on Node[207]",
"stacktrace" : [ "org.neo4j.cypher.commands.Property.apply(Expression.scala:77)", "org.neo4j.cypher.commands.Property.apply(Expression.scala:70)", "org.neo4j.cypher.commands.ExpressionReturnItem.apply(ReturnItem.scala:37)", "org.neo4j.cypher.commands.ExpressionReturnItem.apply(ReturnItem.scala:35)", "org.neo4j.cypher.internal.pipes.ExtractPipe$$anonfun$createResults$1$$anonfun$2.apply(ExtractPipe.scala:42)", "org.neo4j.cypher.internal.pipes.ExtractPipe$$anonfun$createResults$1$$anonfun$2.apply(ExtractPipe.scala:42)", "scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:194)", "scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:194)", "scala.collection.LinearSeqOptimized$class.foreach(LinearSeqOptimized.scala:59)", "scala.collection.immutable.List.foreach(List.scala:45)", "scala.collection.TraversableLike$class.map(TraversableLike.scala:194)", "scala.collection.immutable.List.map(List.scala:45)", "org.neo4j.cypher.internal.pipes.ExtractPipe$$anonfun$createResults$1.apply(ExtractPipe.scala:42)", "org.neo4j.cypher.internal.pipes.ExtractPipe$$anonfun$createResults$1.apply(ExtractPipe.scala:41)", "scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:194)", "scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:194)", "scala.collection.LinearSeqOptimized$class.foreach(LinearSeqOptimized.scala:59)", "scala.collection.immutable.List.foreach(List.scala:45)", "scala.collection.TraversableLike$class.map(TraversableLike.scala:194)", "scala.collection.immutable.List.map(List.scala:45)", "org.neo4j.cypher.internal.pipes.ExtractPipe.createResults(ExtractPipe.scala:41)", "org.neo4j.cypher.internal.pipes.ColumnFilterPipe.createResults(ColumnFilterPipe.scala:33)", "org.neo4j.cypher.internal.ExecutionPlanImpl$$anonfun$6.apply(ExecutionPlanImpl.scala:120)", "org.neo4j.cypher.internal.ExecutionPlanImpl$$anonfun$6.apply(ExecutionPlanImpl.scala:118)", "org.neo4j.cypher.internal.ExecutionPlanImpl.execute(ExecutionPlanImpl.scala:34)", "org.neo4j.cypher.ExecutionEngine.execute(ExecutionEngine.scala:72)", "org.neo4j.cypher.ExecutionEngine.execute(ExecutionEngine.scala:68)", "org.neo4j.cypher.javacompat.ExecutionEngine.execute(ExecutionEngine.java:73)", "org.neo4j.server.rest.web.CypherService.cypher(CypherService.java:71)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "com.sun.jersey.spi.container.JavaMethodInvokerFactory$1.invoke(JavaMethodInvokerFactory.java:60)", "com.sun.jersey.server.impl.model.method.dispatch.AbstractResourceMethodDispatchProvider$ResponseOutInvoker._dispatch(AbstractResourceMethodDispatchProvider.java:205)", "com.sun.jersey.server.impl.model.method.dispatch.ResourceJavaMethodDispatcher.dispatch(ResourceJavaMethodDispatcher.java:75)", "com.sun.jersey.server.impl.uri.rules.HttpMethodRule.accept(HttpMethodRule.java:288)", "com.sun.jersey.server.impl.uri.rules.ResourceClassRule.accept(ResourceClassRule.java:108)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.RootResourceClassesRule.accept(RootResourceClassesRule.java:84)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1469)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1400)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1349)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1339)", "com.sun.jersey.spi.container.servlet.WebComponent.service(WebComponent.java:416)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:537)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:699)", "javax.servlet.http.HttpServlet.service(HttpServlet.java:820)", "org.mortbay.jetty.servlet.ServletHolder.handle(ServletHolder.java:511)", "org.mortbay.jetty.servlet.ServletHandler.handle(ServletHandler.java:390)", "org.mortbay.jetty.servlet.SessionHandler.handle(SessionHandler.java:182)", "org.mortbay.jetty.handler.ContextHandler.handle(ContextHandler.java:765)", "org.mortbay.jetty.handler.HandlerCollection.handle(HandlerCollection.java:114)", "org.mortbay.jetty.handler.HandlerWrapper.handle(HandlerWrapper.java:152)", "org.mortbay.jetty.Server.handle(Server.java:326)", "org.mortbay.jetty.HttpConnection.handleRequest(HttpConnection.java:542)", "org.mortbay.jetty.HttpConnection$RequestHandler.content(HttpConnection.java:943)", "org.mortbay.jetty.HttpParser.parseNext(HttpParser.java:756)", "org.mortbay.jetty.HttpParser.parseAvailable(HttpParser.java:212)", "org.mortbay.jetty.HttpConnection.handle(HttpConnection.java:404)", "org.mortbay.io.nio.SelectChannelEndPoint.run(SelectChannelEndPoint.java:410)", "org.mortbay.thread.QueuedThreadPool$PoolThread.run(QueuedThreadPool.java:582)" ]
}
18.13. Built-in Graph Algorithms
Neo4j comes with a number of built-in graph algorithms. They are performed from
a start node. The traversal is controlled by the URI and the body sent with the
request.
algorithm
The algorithm to choose. If not set, default is shortestPath. algorithm can
have one of these values:
* shortestPath
* allSimplePaths
* allPaths
* dijkstra (optional with cost_property and default_cost parameters)
max_depth
The maximum depth as an integer for the algorithms like ShortestPath, where
applicable. Default is 1.
18.13.1. Find all shortest paths
The shortestPath algorithm can find multiple paths between the same nodes, like
in this example.
Figure 18.64. Final Graph
Final-Graph-Find-all-shortest-paths.svg
Example request
* POST http://localhost:7474/db/data/node/243/paths
* Accept: application/json
* Content-Type: application/json
{
"to" : "http://localhost:7474/db/data/node/238",
"max_depth" : 3,
"relationships" : {
"type" : "to",
"direction" : "out"
},
"algorithm" : "shortestPath"
}
Example response
* 200: OK
* Content-Type: application/json
[ {
"start" : "http://localhost:7474/db/data/node/243",
"nodes" : [ "http://localhost:7474/db/data/node/243", "http://localhost:7474/db/data/node/239", "http://localhost:7474/db/data/node/238" ],
"length" : 2,
"relationships" : [ "http://localhost:7474/db/data/relationship/125", "http://localhost:7474/db/data/relationship/131" ],
"end" : "http://localhost:7474/db/data/node/238"
}, {
"start" : "http://localhost:7474/db/data/node/243",
"nodes" : [ "http://localhost:7474/db/data/node/243", "http://localhost:7474/db/data/node/242", "http://localhost:7474/db/data/node/238" ],
"length" : 2,
"relationships" : [ "http://localhost:7474/db/data/relationship/124", "http://localhost:7474/db/data/relationship/133" ],
"end" : "http://localhost:7474/db/data/node/238"
} ]
18.13.2. Find one of the shortest paths between nodes
If no path algorithm is specified, a ShortestPath algorithm with a max depth of
1 will be chosen. In this example, the max_depth is set to 3 in order to find
the shortest path between 3 linked nodes.
Figure 18.65. Final Graph
Final-Graph-Find-one-of-the-shortest-paths-between-nodes.svg
Example request
* POST http://localhost:7474/db/data/node/250/path
* Accept: application/json
* Content-Type: application/json
{
"to" : "http://localhost:7474/db/data/node/245",
"max_depth" : 3,
"relationships" : {
"type" : "to",
"direction" : "out"
},
"algorithm" : "shortestPath"
}
Example response
* 200: OK
* Content-Type: application/json
{
"start" : "http://localhost:7474/db/data/node/250",
"nodes" : [ "http://localhost:7474/db/data/node/250", "http://localhost:7474/db/data/node/246", "http://localhost:7474/db/data/node/245" ],
"length" : 2,
"relationships" : [ "http://localhost:7474/db/data/relationship/135", "http://localhost:7474/db/data/relationship/141" ],
"end" : "http://localhost:7474/db/data/node/245"
}
18.13.3. Execute a Dijkstra algorithm with similar weights on relationships
Figure 18.66. Final Graph
Final-Graph-Execute-a-Dijkstra-algorithm-with-similar-weights-on-relationships.svg
Example request
* POST http://localhost:7474/db/data/node/265/path
* Accept: application/json
* Content-Type: application/json
{
"to" : "http://localhost:7474/db/data/node/268",
"cost_property" : "cost",
"relationships" : {
"type" : "to",
"direction" : "out"
},
"algorithm" : "dijkstra"
}
Example response
* 200: OK
* Content-Type: application/json
{
"weight" : 2.0,
"start" : "http://localhost:7474/db/data/node/265",
"nodes" : [ "http://localhost:7474/db/data/node/265", "http://localhost:7474/db/data/node/266", "http://localhost:7474/db/data/node/268" ],
"length" : 2,
"relationships" : [ "http://localhost:7474/db/data/relationship/157", "http://localhost:7474/db/data/relationship/158" ],
"end" : "http://localhost:7474/db/data/node/268"
}
18.13.4. Execute a Dijkstra algorithm with weights on relationships
Figure 18.67. Final Graph
Final-Graph-Execute-a-Dijkstra-algorithm-with-weights-on-relationships.svg
Example request
* POST http://localhost:7474/db/data/node/256/path
* Accept: application/json
* Content-Type: application/json
{
"to" : "http://localhost:7474/db/data/node/259",
"cost_property" : "cost",
"relationships" : {
"type" : "to",
"direction" : "out"
},
"algorithm" : "dijkstra"
}
Example response
* 200: OK
* Content-Type: application/json
{
"weight" : 6.0,
"start" : "http://localhost:7474/db/data/node/256",
"nodes" : [ "http://localhost:7474/db/data/node/256", "http://localhost:7474/db/data/node/257", "http://localhost:7474/db/data/node/254", "http://localhost:7474/db/data/node/255", "http://localhost:7474/db/data/node/252", "http://localhost:7474/db/data/node/253", "http://localhost:7474/db/data/node/259" ],
"length" : 6,
"relationships" : [ "http://localhost:7474/db/data/relationship/144", "http://localhost:7474/db/data/relationship/146", "http://localhost:7474/db/data/relationship/148", "http://localhost:7474/db/data/relationship/151", "http://localhost:7474/db/data/relationship/153", "http://localhost:7474/db/data/relationship/154" ],
"end" : "http://localhost:7474/db/data/node/259"
}
18.14. Batch operations
Caution
Batch support is currently experimental. Expect this part of the API to change.
18.14.1. Execute multiple operations in batch
This lets you execute multiple API calls through a single HTTP call,
significantly improving performance for large insert and update operations.
The batch service expects an array of job descriptions as input, each job
description describing an action to be performed via the normal server API.
This service is transactional. If any of the operations performed fails
(returns a non-2xx HTTP status code), the transaction will be rolled back and
all changes will be undone.
Each job description should contain a path attribute, with a value relative to
the data API root (so http://localhost:7474/db/data/node becomes just /node), and a method attribute containing HTTP verb
to use.
Optionally you may provide a body attribute, and an id attribute to help you
keep track of responses, although responses are guaranteed to be returned in
the same order the job descriptions are received.
The following figure outlines the different parts of the job descriptions:
batch-request-api.png
Figure 18.68. Final Graph
Final-Graph-Execute-multiple-operations-in-batch.svg
Example request
* POST http://localhost:7474/db/data/batch
* Accept: application/json
* Content-Type: application/json
[ {
"method" : "PUT",
"to" : "/node/76/properties",
"body" : {
"age" : 1
},
"id" : 0
}, {
"method" : "GET",
"to" : "/node/76",
"id" : 1
}, {
"method" : "POST",
"to" : "/node",
"body" : {
"age" : 1
},
"id" : 2
}, {
"method" : "POST",
"to" : "/node",
"body" : {
"age" : 1
},
"id" : 3
} ]
Example response
* 200: OK
* Content-Type: application/json
[ {
"id" : 0,
"from" : "/node/76/properties"
}, {
"id" : 1,
"body" : {
"outgoing_relationships" : "http://localhost:7474/db/data/node/76/relationships/out",
"data" : {
"age" : 1
},
"traverse" : "http://localhost:7474/db/data/node/76/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/76/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/76/properties/{key}",
"self" : "http://localhost:7474/db/data/node/76",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/76/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/76/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/76/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/76/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/76/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/76/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/76/relationships/in/{-list|&|types}"
},
"from" : "/node/76"
}, {
"id" : 2,
"location" : "http://localhost:7474/db/data/node/77",
"body" : {
"outgoing_relationships" : "http://localhost:7474/db/data/node/77/relationships/out",
"data" : {
"age" : 1
},
"traverse" : "http://localhost:7474/db/data/node/77/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/77/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/77/properties/{key}",
"self" : "http://localhost:7474/db/data/node/77",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/77/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/77/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/77/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/77/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/77/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/77/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/77/relationships/in/{-list|&|types}"
},
"from" : "/node"
}, {
"id" : 3,
"location" : "http://localhost:7474/db/data/node/78",
"body" : {
"outgoing_relationships" : "http://localhost:7474/db/data/node/78/relationships/out",
"data" : {
"age" : 1
},
"traverse" : "http://localhost:7474/db/data/node/78/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/78/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/78/properties/{key}",
"self" : "http://localhost:7474/db/data/node/78",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/78/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/78/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/78/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/78/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/78/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/78/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/78/relationships/in/{-list|&|types}"
},
"from" : "/node"
} ]
18.14.2. Refer to items created earlier in the same batch job
The batch operation API allows you to refer to the URI returned from a created
resource in subsequent job descriptions, within the same batch call.
Use the {[JOB ID]} special syntax to inject URIs from created resources into
JSON strings in subsequent job descriptions.
Figure 18.69. Final Graph
Final-Graph-Refer-to-items-created-earlier-in-the-same-batch-job.svg
Example request
* POST http://localhost:7474/db/data/batch
* Accept: application/json
* Content-Type: application/json
[ {
"method" : "POST",
"to" : "/node",
"id" : 0,
"body" : {
"name" : "bob"
}
}, {
"method" : "POST",
"to" : "/node",
"id" : 1,
"body" : {
"age" : 12
}
}, {
"method" : "POST",
"to" : "{0}/relationships",
"id" : 3,
"body" : {
"to" : "{1}",
"data" : {
"since" : "2010"
},
"type" : "KNOWS"
}
}, {
"method" : "POST",
"to" : "/index/relationship/my_rels",
"id" : 4,
"body" : {
"key" : "since",
"value" : "2010",
"uri" : "{3}"
}
} ]
Example response
* 200: OK
* Content-Type: application/json
[ {
"id" : 0,
"location" : "http://localhost:7474/db/data/node/79",
"body" : {
"outgoing_relationships" : "http://localhost:7474/db/data/node/79/relationships/out",
"data" : {
"name" : "bob"
},
"traverse" : "http://localhost:7474/db/data/node/79/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/79/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/79/properties/{key}",
"self" : "http://localhost:7474/db/data/node/79",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/79/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/79/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/79/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/79/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/79/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/79/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/79/relationships/in/{-list|&|types}"
},
"from" : "/node"
}, {
"id" : 1,
"location" : "http://localhost:7474/db/data/node/80",
"body" : {
"outgoing_relationships" : "http://localhost:7474/db/data/node/80/relationships/out",
"data" : {
"age" : 12
},
"traverse" : "http://localhost:7474/db/data/node/80/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/80/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/80/properties/{key}",
"self" : "http://localhost:7474/db/data/node/80",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/80/relationships/out/{-list|&|types}",
"properties" : "http://localhost:7474/db/data/node/80/properties",
"incoming_relationships" : "http://localhost:7474/db/data/node/80/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/80/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/80/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/80/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/80/relationships/in/{-list|&|types}"
},
"from" : "/node"
}, {
"id" : 3,
"location" : "http://localhost:7474/db/data/relationship/26",
"body" : {
"start" : "http://localhost:7474/db/data/node/79",
"data" : {
"since" : "2010"
},
"self" : "http://localhost:7474/db/data/relationship/26",
"property" : "http://localhost:7474/db/data/relationship/26/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/26/properties",
"type" : "KNOWS",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/80"
},
"from" : "http://localhost:7474/db/data/node/79/relationships"
}, {
"id" : 4,
"location" : "http://localhost:7474/db/data/index/relationship/my_rels/since/2010/26",
"body" : {
"indexed" : "http://localhost:7474/db/data/index/relationship/my_rels/since/2010/26",
"start" : "http://localhost:7474/db/data/node/79",
"data" : {
"since" : "2010"
},
"self" : "http://localhost:7474/db/data/relationship/26",
"property" : "http://localhost:7474/db/data/relationship/26/properties/{key}",
"properties" : "http://localhost:7474/db/data/relationship/26/properties",
"type" : "KNOWS",
"extensions" : {
},
"end" : "http://localhost:7474/db/data/node/80"
},
"from" : "/index/relationship/my_rels"
} ]
18.15. Cypher Plugin
Warning
This functionality is now provided by the core REST API. The plugin will
continue to work for some time, but is as of Neo4j 1.6 deprecated. See
Section 18.12, “Cypher queries” for documentation on the built in cypher
support.
The Neo4j Cypher Plugin enables querying with the Chapter 15, Cypher Query
Language. The results are returned as a list of string headers (columns), and a
data part, consisting of a list of all rows, every row consisting of a list of
REST representations of the field value - Node, Relationship or any simple
value like String.
18.15.1. Send a Query
A simple query returning all nodes connected to node 1, returning the node and
the name property, if it exists, otherwise null:
START x = node(3)
MATCH x -[r]-> n
RETURN type(r), n.name?, n.age?
Figure 18.70. Final Graph
Final-Graph-Send-a-Query.svg
Example request
* POST http://localhost:7474/db/data/ext/CypherPlugin/graphdb/execute_query
* Accept: application/json
* Content-Type: application/json
{"query": "start x = node(3) match x -[r]-> n return type(r), n.name?, n.age?","params": {}},
Example response
* 200: OK
* Content-Type: application/json
{
"data" : [ [ "know", "him", 25 ], [ "know", "you", null ] ],
"columns" : [ "TYPE(r)", "n.name", "n.age" ]
}
18.15.2. Return paths
Paths can be returned together with other return types by just specifying
returns.
START x = node(%I%)
MATCH path = (x--friend)
RETURN path, friend.name
Figure 18.71. Final Graph
Final-Graph-return-paths.svg
Example request
* POST http://localhost:7474/db/data/ext/CypherPlugin/graphdb/execute_query
* Accept: application/json
* Content-Type: application/json
{"query": "start x = node(7) match path = (x--friend) return path, friend.name","params": {}},
Example response
* 200: OK
* Content-Type: application/json
{
"data" : [ [ {
"start" : "http://localhost:7474/db/data/node/7",
"nodes" : [ "http://localhost:7474/db/data/node/7", "http://localhost:7474/db/data/node/6" ],
"length" : 1,
"relationships" : [ "http://localhost:7474/db/data/relationship/3" ],
"end" : "http://localhost:7474/db/data/node/6"
}, "you" ] ],
"columns" : [ "path", "friend.name" ]
}
18.15.3. Send queries with parameters
Cypher supports queries with parameters which are submitted as a JSON map.
START x = node:node_auto_index(name={STARTName})
MATCH path = (x-[r]-friend)
WHERE friend.name = {name}
RETURN TYPE(r)
Figure 18.72. Final Graph
Final-Graph-send-queries-with-parameters.svg
Example request
* POST http://localhost:7474/db/data/ext/CypherPlugin/graphdb/execute_query
* Accept: application/json
* Content-Type: application/json
{"query": "start x = node:node_auto_index(name={startName}) match path = (x-[r]-friend) where friend.name = {name} return TYPE(r)","params": {"startName":"I","name":"you"}},
Example response
* 200: OK
* Content-Type: application/json
{
"data" : [ [ "know" ] ],
"columns" : [ "TYPE(r)" ]
}
18.15.4. Server errors
Errors on the server will be reported as a JSON-formatted stacktrace and
message.
START x = node(%I%)
RETURN x.dummy
Figure 18.73. Final Graph
Final-Graph-Server-errors.svg
Example request
* POST http://localhost:7474/db/data/ext/CypherPlugin/graphdb/execute_query
* Accept: application/json
* Content-Type: application/json
{"query": "start x = node(5) return x.dummy","params": {}},
Example response
* 400: Bad Request
* Content-Type: application/json
{
"message" : "The property 'dummy' does not exist on Node[5]",
"exception" : "org.neo4j.server.rest.repr.BadInputException: The property 'dummy' does not exist on Node[5]",
"stacktrace" : [ "org.neo4j.server.plugin.cypher.CypherPlugin.executeScript(CypherPlugin.java:81)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "org.neo4j.server.plugins.PluginMethod.invoke(PluginMethod.java:57)", "org.neo4j.server.plugins.PluginManager.invoke(PluginManager.java:168)", "org.neo4j.server.rest.web.ExtensionService.invokeGraphDatabaseExtension(ExtensionService.java:300)", "org.neo4j.server.rest.web.ExtensionService.invokeGraphDatabaseExtension(ExtensionService.java:122)", "sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)", "sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)", "sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)", "java.lang.reflect.Method.invoke(Method.java:597)", "com.sun.jersey.server.impl.model.method.dispatch.AbstractResourceMethodDispatchProvider$ResponseOutInvoker._dispatch(AbstractResourceMethodDispatchProvider.java:187)", "com.sun.jersey.server.impl.model.method.dispatch.ResourceJavaMethodDispatcher.dispatch(ResourceJavaMethodDispatcher.java:71)", "com.sun.jersey.server.impl.uri.rules.HttpMethodRule.accept(HttpMethodRule.java:280)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.ResourceClassRule.accept(ResourceClassRule.java:108)", "com.sun.jersey.server.impl.uri.rules.RightHandPathRule.accept(RightHandPathRule.java:147)", "com.sun.jersey.server.impl.uri.rules.RootResourceClassesRule.accept(RootResourceClassesRule.java:84)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1341)", "com.sun.jersey.server.impl.application.WebApplicationImpl._handleRequest(WebApplicationImpl.java:1273)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1223)", "com.sun.jersey.server.impl.application.WebApplicationImpl.handleRequest(WebApplicationImpl.java:1213)", "com.sun.jersey.spi.container.servlet.WebComponent.service(WebComponent.java:414)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:537)", "com.sun.jersey.spi.container.servlet.ServletContainer.service(ServletContainer.java:699)", "javax.servlet.http.HttpServlet.service(HttpServlet.java:820)", "org.mortbay.jetty.servlet.ServletHolder.handle(ServletHolder.java:511)", "org.mortbay.jetty.servlet.ServletHandler.handle(ServletHandler.java:390)", "org.mortbay.jetty.servlet.SessionHandler.handle(SessionHandler.java:182)", "org.mortbay.jetty.handler.ContextHandler.handle(ContextHandler.java:765)", "org.mortbay.jetty.handler.HandlerCollection.handle(HandlerCollection.java:114)", "org.mortbay.jetty.handler.HandlerWrapper.handle(HandlerWrapper.java:152)", "org.mortbay.jetty.Server.handle(Server.java:326)", "org.mortbay.jetty.HttpConnection.handleRequest(HttpConnection.java:542)", "org.mortbay.jetty.HttpConnection$RequestHandler.content(HttpConnection.java:943)", "org.mortbay.jetty.HttpParser.parseNext(HttpParser.java:756)", "org.mortbay.jetty.HttpParser.parseAvailable(HttpParser.java:218)", "org.mortbay.jetty.HttpConnection.handle(HttpConnection.java:404)", "org.mortbay.io.nio.SelectChannelEndPoint.run(SelectChannelEndPoint.java:410)", "org.mortbay.thread.QueuedThreadPool$PoolThread.run(QueuedThreadPool.java:582)" ]
}
18.16. Gremlin Plugin
gremlin-logo.png
Gremlin is a Groovy based Graph Traversal
Language. It provides a very expressive way of explicitly scripting traversals
through a Neo4j graph.
The Neo4j Gremlin Plugin provides an endpoint to send Gremlin scripts to the
Neo4j Server. The scripts are executed on the server database and the results
are returned as Neo4j Node and Relationship representations. This keeps the
types throughout the REST API consistent. The results are quite verbose when
returning Neo4j Node, Relationship or Graph representations. On the other hand,
just return properties like in the Section 18.16.4, “Send a Gremlin Script -
JSON encoded with table results” example for responses tailored to specific
needs.
18.16.1. Send a Gremlin Script - URL encoded
Scripts can be sent as URL-encoded In this example, the graph has been
autoindexed by Neo4j, so we can look up the name property on nodes.
Raw script source
g.idx('node_auto_index')[[name:'I']].out
Figure 18.74. Final Graph
Final-Graph-Send-a-Gremlin-Script---URL-encoded.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/x-www-form-urlencoded
script=g.idx%28%27node_auto_index%27%29%5B%5Bname%3A%27I%27%5D%5D.out
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/1/relationships/out",
"data" : {
"name" : "you"
},
"traverse" : "http://localhost:7474/db/data/node/1/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/1/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/1/properties/{key}",
"self" : "http://localhost:7474/db/data/node/1",
"properties" : "http://localhost:7474/db/data/node/1/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/1/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/1/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/1/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/1/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/1/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/1/relationships/in/{-list|&|types}"
} ]
18.16.2. Load a sample graph
Import a graph form a GraphML file can be
achieved through the Gremlin GraphMLReader. The following script imports a
small GraphML file from an URL into Neo4j, resulting in the depicted graph. It
then returns a list of all nodes in the graph.
Raw script source
g.loadGraphML('https://raw.github.com/neo4j/gremlin-plugin/master/src/data/graphml1.xml')
g.V
Figure 18.75. Final Graph
Final-Graph-Load-a-sample-graph.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "g.loadGraphML('https://raw.github.com/neo4j/gremlin-plugin/master/src/data/graphml1.xml');g.V;","params": {}},
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/7/relationships/out",
"data" : {
"name" : "I"
},
"traverse" : "http://localhost:7474/db/data/node/7/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/7/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/7/properties/{key}",
"self" : "http://localhost:7474/db/data/node/7",
"properties" : "http://localhost:7474/db/data/node/7/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/7/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/7/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/7/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/7/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/7/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/7/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/8/relationships/out",
"data" : {
"name" : "you"
},
"traverse" : "http://localhost:7474/db/data/node/8/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/8/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/8/properties/{key}",
"self" : "http://localhost:7474/db/data/node/8",
"properties" : "http://localhost:7474/db/data/node/8/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/8/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/8/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/8/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/8/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/8/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/8/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/9/relationships/out",
"data" : {
"name" : "him"
},
"traverse" : "http://localhost:7474/db/data/node/9/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/9/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/9/properties/{key}",
"self" : "http://localhost:7474/db/data/node/9",
"properties" : "http://localhost:7474/db/data/node/9/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/9/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/9/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/9/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/9/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/9/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/9/relationships/in/{-list|&|types}"
} ]
18.16.3. Sort a result using raw Groovy operations
The following script returns a sorted list of all nodes connected via outgoing
relationships to node 1, sorted by their name-property.
Raw script source
g.idx('node_auto_index')[[name:'I']].out.sort{it.name}
Figure 18.76. Final Graph
Final-Graph-Sort-a-result-using-raw-Groovy-operations.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "g.idx('node_auto_index')[[name:'I']].out.sort{it.name}","params": {}},
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/14/relationships/out",
"data" : {
"name" : "him"
},
"traverse" : "http://localhost:7474/db/data/node/14/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/14/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/14/properties/{key}",
"self" : "http://localhost:7474/db/data/node/14",
"properties" : "http://localhost:7474/db/data/node/14/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/14/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/14/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/14/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/14/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/14/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/14/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/13/relationships/out",
"data" : {
"name" : "you"
},
"traverse" : "http://localhost:7474/db/data/node/13/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/13/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/13/properties/{key}",
"self" : "http://localhost:7474/db/data/node/13",
"properties" : "http://localhost:7474/db/data/node/13/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/13/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/13/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/13/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/13/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/13/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/13/relationships/in/{-list|&|types}"
} ]
18.16.4. Send a Gremlin Script - JSON encoded with table results
To send a Script JSON encoded, set the payload Content-Type Header. In this
example, find all the things that my friends like, and return a table listing
my friends by their name, and the names of the things they like in a table with
two columns, ignoring the third named step variable I. Remember that everything
in Gremlin is an iterator - in order to populate the result table t, iterate
through the pipes with iterate().
Raw script source
t= new Table()
g.v(21).as('I').out('know').as('friend').out('like').as('likes').table(t,['friend','likes']){it.name}{it.name}.iterate()
t
Figure 18.77. Final Graph
Final-Graph-Send-a-Gremlin-Script---JSON-encoded-with-table-results.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "t= new Table();g.v(21).as('I').out('know').as('friend').out('like').as('likes').table(t,['friend','likes']){it.name}{it.name}.iterate();t;","params": {}},
Example response
* 200: OK
* Content-Type: application/json
{
"data" : [ [ "Joe", "cats" ], [ "Joe", "dogs" ] ],
"columns" : [ "friend", "likes" ]
}
18.16.5. Returning nested pipes
Raw script source
g.v(25).as('I').out('know').as('friend').out('like').as('likes').table(new Table()){it.name}{it.name}.cap
Figure 18.78. Final Graph
Final-Graph-returning-nested-pipes.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "g.v(25).as('I').out('know').as('friend').out('like').as('likes').table(new Table()){it.name}{it.name}.cap;","params": {}},
Example response
* 200: OK
* Content-Type: application/json
[ [ {
"data" : [ [ "I", "Joe", "cats" ], [ "I", "Joe", "dogs" ] ],
"columns" : [ "I", "friend", "likes" ]
} ] ]
18.16.6. Set script variables
To set variables in the bindings for the Gremlin Script Engine on the server,
you can include a params parameter with a String representing a JSON map of
variables to set to initial values. These can then be accessed as normal
variables within the script.
Raw script source
meaning_of_life
Figure 18.79. Final Graph
Final-Graph-Set-script-variables.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{
"script" : "meaning_of_life",
"params" : {
"meaning_of_life" : 42.0
}
}
Example response
* 200: OK
* Content-Type: application/json
42.0
18.16.7. Send a Gremlin Script with variables in a JSON Map
Send a Gremlin Script, as JSON payload and additional parameters
Raw script source
g.v(me).out
Figure 18.80. Final Graph
Final-Graph-Send-a-Gremlin-Script-with-variables-in-a-JSON-Map.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "g.v(me).out","params": {"me":"6"}},
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/5/relationships/out",
"data" : {
"name" : "you"
},
"traverse" : "http://localhost:7474/db/data/node/5/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/5/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/5/properties/{key}",
"self" : "http://localhost:7474/db/data/node/5",
"properties" : "http://localhost:7474/db/data/node/5/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/5/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/5/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/5/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/5/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/5/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/5/relationships/in/{-list|&|types}"
} ]
18.16.8. Return paths from a Gremlin script
The following script returns paths. Paths in Gremlin consist of the pipes that
make up the path from the starting pipes. The server is returning JSON
representations of their content as a nested list.
Raw script source
g.v(18).out.name.paths
Figure 18.81. Final Graph
Final-Graph-Return-paths-from-a-Gremlin-script.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "g.v(18).out.name.paths","params": {}},
Example response
* 200: OK
* Content-Type: application/json
[ [ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/18/relationships/out",
"data" : {
"name" : "I"
},
"traverse" : "http://localhost:7474/db/data/node/18/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/18/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/18/properties/{key}",
"self" : "http://localhost:7474/db/data/node/18",
"properties" : "http://localhost:7474/db/data/node/18/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/18/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/18/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/18/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/18/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/18/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/18/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/16/relationships/out",
"data" : {
"name" : "you"
},
"traverse" : "http://localhost:7474/db/data/node/16/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/16/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/16/properties/{key}",
"self" : "http://localhost:7474/db/data/node/16",
"properties" : "http://localhost:7474/db/data/node/16/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/16/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/16/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/16/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/16/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/16/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/16/relationships/in/{-list|&|types}"
}, "you" ], [ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/18/relationships/out",
"data" : {
"name" : "I"
},
"traverse" : "http://localhost:7474/db/data/node/18/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/18/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/18/properties/{key}",
"self" : "http://localhost:7474/db/data/node/18",
"properties" : "http://localhost:7474/db/data/node/18/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/18/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/18/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/18/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/18/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/18/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/18/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/17/relationships/out",
"data" : {
"name" : "him"
},
"traverse" : "http://localhost:7474/db/data/node/17/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/17/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/17/properties/{key}",
"self" : "http://localhost:7474/db/data/node/17",
"properties" : "http://localhost:7474/db/data/node/17/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/17/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/17/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/17/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/17/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/17/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/17/relationships/in/{-list|&|types}"
}, "him" ] ]
18.16.9. Send an arbitrary Groovy script - Lucene sorting
This example demonstrates that you via the Groovy runtime embedded with the
server have full access to all of the servers Java APIs. The below example
creates Nodes in the database both via the Blueprints and the Neo4j API indexes
the nodes via the native Neo4j Indexing API constructs a custom Lucene sorting
and searching returns a Neo4j IndexHits result iterator.
Raw script source
import org.neo4j.graphdb.index.*
import org.neo4j.index.lucene.*
import org.apache.lucene.search.*
neo4j = g.getRawGraph()
tx = neo4j.beginTx()
meVertex = g.addVertex([name:'me'])
meNode = meVertex.getRawVertex()
youNode = neo4j.createNode()
youNode.setProperty('name','you')
idxManager = neo4j.index()
personIndex = idxManager.forNodes('persons')
personIndex.add(meNode,'name',meVertex.name)
personIndex.add(youNode,'name',youNode.getProperty('name'))
tx.success()
tx.finish()
query = new QueryContext( 'name:*' ).sort( new Sort(new SortField( 'name',SortField.STRING, true ) ) )
results = personIndex.query( query )
Figure 18.82. Final Graph
Final-Graph-Send-an-arbitrary-Groovy-script---Lucene-sorting.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "import org.neo4j.graphdb.index.*;import org.neo4j.index.lucene.*;import org.apache.lucene.search.*;neo4j = g.getRawGraph();tx = neo4j.beginTx();meVertex = g.addVertex([name:'me']);meNode = meVertex.getRawVertex();youNode = neo4j.createNode();youNode.setProperty('name','you');idxManager = neo4j.index();personIndex = idxManager.forNodes('persons');personIndex.add(meNode,'name',meVertex.name);personIndex.add(youNode,'name',youNode.getProperty('name'));tx.success();tx.finish();query = new QueryContext( 'name:*' ).sort( new Sort(new SortField( 'name',SortField.STRING, true ) ) );results = personIndex.query( query );","params": {}},
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/32/relationships/out",
"data" : {
"name" : "you"
},
"traverse" : "http://localhost:7474/db/data/node/32/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/32/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/32/properties/{key}",
"self" : "http://localhost:7474/db/data/node/32",
"properties" : "http://localhost:7474/db/data/node/32/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/32/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/32/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/32/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/32/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/32/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/32/relationships/in/{-list|&|types}"
}, {
"outgoing_relationships" : "http://localhost:7474/db/data/node/31/relationships/out",
"data" : {
"name" : "me"
},
"traverse" : "http://localhost:7474/db/data/node/31/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/31/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/31/properties/{key}",
"self" : "http://localhost:7474/db/data/node/31",
"properties" : "http://localhost:7474/db/data/node/31/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/31/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/31/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/31/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/31/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/31/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/31/relationships/in/{-list|&|types}"
} ]
18.16.10. Emit a sample graph
Exporting a graph can be done by simple emitting the appropriate String.
Raw script source
writer = new GraphMLWriter(g)
out = new java.io.ByteArrayOutputStream()
writer.outputGraph(out)
result = out.toString()
Figure 18.83. Final Graph
Final-Graph-Emit-a-sample-graph.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "writer = new GraphMLWriter(g);out = new java.io.ByteArrayOutputStream();writer.outputGraph(out);result = out.toString();","params": {}},
Example response
* 200: OK
* Content-Type: application/json
"youhimI"
18.16.11. HyperEdges - find user roles in groups
Imagine a user being part of different groups. A group can have different
roles, and a user can be part of different groups. He also can have different
roles in different groups apart from the membership. The association of a User,
a Group and a Role can be referred to as a HyperEdge. However, it can be easily
modeled in a property graph as a node that captures this n-ary relationship, as
depicted below in the U1G2R1 node.
To find out in what roles a user is for a particular groups (here Group2), the
following script can traverse this HyperEdge node and provide answers.
Raw script source
g.v(39).out('hasRoleInGroup').as('hyperedge').out('hasGroup').filter{it.name=='Group2'}.back('hyperedge').out('hasRole').name
Figure 18.84. Final Graph
Final-Graph-HyperEdges---find-user-roles-in-groups.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "g.v(39).out('hasRoleInGroup').as('hyperedge').out('hasGroup').filter{it.name=='Group2'}.back('hyperedge').out('hasRole').name","params": {}},
Example response
* 200: OK
* Content-Type: application/json
[ "Role1" ]
18.16.12. Group count
This example is showing a group count in Germlin, for instance the counting of
the different relationship types connected to some the start node. The result
is collected into a variable that then is returned.
Raw script source
m = [:]
g.v(43).bothE().label.groupCount(m).iterate()
m
Figure 18.85. Final Graph
Final-Graph-group-count.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "m = [:];g.v(43).bothE().label.groupCount(m).iterate();m","params": {}},
Example response
* 200: OK
* Content-Type: application/json
"{knows=2, likes=1}"
18.16.13. Collect multiple traversal results
Multiple traversals can be combined into a single result, using splitting and
merging pipes in a lazy fashion.
Raw script source
g.idx('node_auto_index')[[name:'Peter']].copySplit(_().out('knows'), _().in('likes')).fairMerge.name
Figure 18.86. Final Graph
Final-Graph-collect-multiple-traversal-results.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "g.idx('node_auto_index')[[name:'Peter']].copySplit(_().out('knows'), _().in('likes')).fairMerge.name","params": {}},
Example response
* 200: OK
* Content-Type: application/json
[ "Ian", "Marie" ]
18.16.14. Collaborative filtering
This example demonstrates basic collaborative filtering - ordering a traversal
after occurence counts and substracting objects that are not interesting in the
final result.
Here, we are finding Friends-of-Friends that are not Joes friends already. The
same can be applied to graphs of users that LIKE things and others.
Raw script source
x=[]
fof=[:]
g.v(65).out('knows').aggregate(x).out('knows').except(x).groupCount(fof).iterate()
fof.sort{a,b -> b.value <=> a.value}
Figure 18.87. Final Graph
Final-Graph-collaborative-filtering.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "x=[];fof=[:];g.v(65).out('knows').aggregate(x).out('knows').except(x).groupCount(fof).iterate();fof.sort{a,b -> b.value <=> a.value}","params": {}},
Example response
* 200: OK
* Content-Type: application/json
"{v[63]=2, v[62]=1, v[64]=1}"
18.16.15. Chunking and offsetting in Gremlin
Raw script source
g.v(53).out('knows').filter{it.name == 'Sara'}[0..100]
Figure 18.88. Final Graph
Final-Graph-chunking-and-offsetting-in-Gremlin.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "g.v(53).out('knows').filter{it.name == 'Sara'}[0..100]","params": {}},
Example response
* 200: OK
* Content-Type: application/json
[ {
"outgoing_relationships" : "http://localhost:7474/db/data/node/51/relationships/out",
"data" : {
"name" : "Sara"
},
"traverse" : "http://localhost:7474/db/data/node/51/traverse/{returnType}",
"all_typed_relationships" : "http://localhost:7474/db/data/node/51/relationships/all/{-list|&|types}",
"property" : "http://localhost:7474/db/data/node/51/properties/{key}",
"self" : "http://localhost:7474/db/data/node/51",
"properties" : "http://localhost:7474/db/data/node/51/properties",
"outgoing_typed_relationships" : "http://localhost:7474/db/data/node/51/relationships/out/{-list|&|types}",
"incoming_relationships" : "http://localhost:7474/db/data/node/51/relationships/in",
"extensions" : {
},
"create_relationship" : "http://localhost:7474/db/data/node/51/relationships",
"paged_traverse" : "http://localhost:7474/db/data/node/51/paged/traverse/{returnType}{?pageSize,leaseTime}",
"all_relationships" : "http://localhost:7474/db/data/node/51/relationships/all",
"incoming_typed_relationships" : "http://localhost:7474/db/data/node/51/relationships/in/{-list|&|types}"
} ]
18.16.16. Modify the graph while traversing
This example is showing a group count in Germlin, for instance the counting of
the different relationship types connected to some the start node. The result
is collected into a variable that then is returned.
Figure 18.89. Starting Graph
Starting-Graph-starting_graphmodify-the-graph-while-traversing.svg
Raw script source
g.v(46).bothE.each{g.removeEdge(it)}
Figure 18.90. Final Graph
Final-Graph-modify-the-graph-while-traversing.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "g.v(46).bothE.each{g.removeEdge(it)}","params": {}},
Example response
* 200: OK
* Content-Type: application/json
[ ]
18.16.17. Flow algorithms with Gremlin
This is a basic stub example for implementing flow algorithms in for instance
Flow Networks with a pipes-based
approach and scripting, here between source and sink using the capacity
property on relationships as the base for the flow function and modifying the
graph during calculation.
Figure 18.91. Starting Graph
Starting-Graph-starting_graphflow-algorithms-with-Gremlin.svg
Raw script source
source=g.v(66)
sink=g.v(67)
maxFlow = 0
source.outE.inV.loop(2){!it.object.equals(sink)}.paths.each{flow = it.capacity.min()
maxFlow += flow
it.findAll{it.capacity}.each{it.capacity -= flow}}
maxFlow
Figure 18.92. Final Graph
Final-Graph-flow-algorithms-with-Gremlin.svg
Example request
* POST http://localhost:7474/db/data/ext/GremlinPlugin/graphdb/execute_script
* Accept: application/json
* Content-Type: application/json
{"script": "source=g.v(66);sink=g.v(67);maxFlow = 0;source.outE.inV.loop(2){!it.object.equals(sink)}.paths.each{flow = it.capacity.min(); maxFlow += flow;it.findAll{it.capacity}.each{it.capacity -= flow}};maxFlow","params": {}},
Example response
* 200: OK
* Content-Type: application/json
4
Chapter 19. Python embedded bindings
This describes neo4j-embedded, a Python library that lets you use the embedded
Neo4j database in Python.
Apart from the reference documentation and installation instructions in this
section, you may also want to take a look at Chapter 11, Using Neo4j embedded
in Python applications.
The source code for this project lives on GitHub: https://github.com/neo4j/
python-embedded
19.1. Installation
Note
The Neo4j database itself (from the Community Edition) is included in the
neo4j-embedded distribution.
19.1.1. Installation on OSX/Linux
19.1.1.1. Prerequisites
Caution
Make sure that the entire stack used is either 64bit or 32bit (no mixing, that
is). That means the JVM, Python and JPype.
First, install JPype:
1. Download the latest version of JPype from http://sourceforge.net/projects/
jpype/files/JPype/ .
2. Unzip the file.
3. Open a console and navigate into the unzipped folder.
4. Run sudo python setup.py install
JPype is also available in the Debian repos:
sudo apt-get install python-jpype
Then, make sure the JAVA_HOME environment variable is set to your jre or jdk
folder, so that JPype can find the JVM.
Note
Installation can be problematic on OSX. See the following Stack Overflow
discussion for help: http://stackoverflow.com/questions/8525193/
cannot-install-jpype-on-os-x-lion-to-use-with-neo4j
19.1.1.2. Installing neo4j-embedded
You can install neo4j-embedded with your python package manager of choice:
sudo pip install neo4j-embedded
sudo easy_install neo4j-embedded
Or install manually:
1. Download the latest appropriate version of JPype from http://
sourceforge.net/projects/jpype/files/JPype/ for 32bit or from http://www.lfd.uci.edu/
~gohlke/pythonlibs/ for 64bit.
2. Unzip the file.
3. Open a console and navigate into the unzipped folder.
4. Run sudo python setup.py install
19.1.2. Installation on Windows
19.1.2.1. Prerequisites
Warning
It is imperative that the entire stack used is either 64bit or 32bit (no
mixing, that is). That means the JVM, Python, JPype and all extra DLLs (see
below).
First, install JPype:
Note
Notice that JPype only works with Python 2.6 and 2.7. Also note that there are
different downloads depending on which version you use.
1. Download the latest appropriate version of JPype from http://
sourceforge.net/projects/jpype/files/JPype/ for 32bit or from http://www.lfd.uci.edu/
~gohlke/pythonlibs/ for 64bit.
2. Run the installer.
Then, make sure the JAVA_HOME environment variable is set to your jre or jdk
folder. There is a description of how to set environment variables in
Section 19.1.2.3, “Solving problems with missing DLL files”.
Note
There may be DLL files missing from your system that are required by JPype. See
Section 19.1.2.3, “Solving problems with missing DLL files” for instructions
for how to fix this.
19.1.2.2. Installing neo4j-embedded
1. Download the latest version from http://pypi.python.org/pypi/neo4j-embedded
/ .
2. Run the installer.
19.1.2.3. Solving problems with missing DLL files
Certain versions of Windows ship without DLL files needed to programmatically
launch a JVM. You will need to make IEShims.dll and certain debugging dlls
available on Windows.
IEShims.dll is normally included with Internet Explorer installs. To make
windows find this file globally, you need to add the IE install folder to your
PATH.
1. Right click on "My Computer" or "Computer".
2. Select "Properties".
3. Click on "Advanced" or "Advanced system settings".
4. Click the "Environment variables" button.
5. Find the path varible, and add C:\Program Files\Internet Explorer to it (or
the install location of IE, if you have installed it somewhere else).
Required debugging dlls are bundled with Microsoft Visual C++ Redistributable
libraries.
* 32bit Windows: http://www.microsoft.com/download/en/details.aspx?
displaylang=en&id=5555
* 64bit Windows: http://www.microsoft.com/download/en/details.aspx?
displaylang=en&id=14632
If you are still getting errors about missing DLL files, you can use http://
www.dependencywalker.com/ to open your
jvm.dll (located in JAVA_HOME/bin/client/ or JAVA_HOME/bin/server/), and it
will tell you if there are other missing dlls.
19.2. Core API
This section describes how get get up and running, and how to do basic
operations.
19.2.1. Getting started
19.2.1.1. Creating a database
from neo4j import GraphDatabase
# Create db
db = GraphDatabase(folder_to_put_db_in)
# Always shut down your database
db.shutdown()
19.2.1.2. Creating a database, with configuration
Please see Chapter 21, Configuration & Performance for what options you can use
here.
from neo4j import GraphDatabase
# Example configuration parameters
db = GraphDatabase(folder_to_put_db_in, string_block_size=200, array_block_size=240)
db.shutdown()
19.2.1.3. JPype JVM configuration
You can set extra arguments to be passed to the JVM using the
NEO4J_PYTHON_JVMARGS environment variable. This can be used to, for instance,
increase the max memory for the database.
Note that you must set this before you import the neo4j package, either by
setting it before you start python, or by setting it programatically in your
app.
import os
os.environ['NEO4J_PYTHON_JVMARGS'] = '-Xms128M -Xmx512M'
import neo4j
You can also override the classpath used by neo4j-embedded, by setting the
NEO4J_PYTHON_CLASSPATH environment variable.
19.2.2. Transactions
All write operations to the database need to be performed from within
transactions. This ensures that your database never ends up in an inconsistent
state.
See Chapter 13, Transaction Management for details on how Neo4j handles
transactions.
We use the python with statement to define a transaction context. If you are
using an older version of Python, you may have to import the with statement:
from __future__ import with_statement
Either way, this is how you get into a transaction:
# Start a transaction
with db.transaction:
# This is inside the transactional
# context. All work done here
# will either entirely succeed,
# or no changes will be applied at all.
# Create a node
node = db.node()
# Give it a name
node['name'] = 'Cat Stevens'
# The transaction is automatically
# commited when you exit the with
# block.
19.2.3. Nodes
This describes operations that are specific to node objects. For documentation
on how to handle properties on both relationships and nodes, see
Section 19.2.5, “Properties”.
19.2.3.1. Creating a node
with db.transaction:
# Create a node
thomas = db.node(name='Thomas Anderson', age=42)
19.2.3.2. Fetching a node by id
# You don't have to be in a transaction
# to do read operations.
a_node = db.node[some_node_id]
# Ids on nodes and relationships are available via the "id"
# property, eg.:
node_id = a_node.id
19.2.3.3. Fetching the reference node
reference = db.reference_node
19.2.3.4. Removing a node
with db.transaction:
node = db.node()
node.delete()
Tip
See also Section 13.5, “Delete semantics”.
19.2.3.5. Removing a node by id
with db.transaction:
del db.node[some_node_id]
19.2.3.6. Accessing relationships from a node
For details on what you can do with the relationship objects, see
Section 19.2.4, “Relationships”.
# All relationships on a node
for rel in a_node.relationships:
pass
# Incoming relationships
for rel in a_node.relationships.incoming:
pass
# Outgoing relationships
for rel in a_node.relationships.outgoing:
pass
# Relationships of a specific type
for rel in a_node.mayor_of:
pass
# Incoming relationships of a specific type
for rel in a_node.mayor_of.incoming:
pass
# Outgoing relationships of a specific type
for rel in a_node.mayor_of.outgoing:
pass
19.2.3.7. Getting and/or counting all nodes
Use this with care, it will become extremely slow in large datasets.
for node in db.nodes:
pass
# Shorthand for iterating through
# and counting all nodes
number_of_nodes = len(db.nodes)
19.2.4. Relationships
This describes operations that are specific to relationship objects. For
documentation on how to handle properties on both relationships and nodes, see
Section 19.2.5, “Properties”.
19.2.4.1. Creating a relationship
with db.transaction:
# Nodes to create a relationship between
steven = self.graphdb.node(name='Steve Brook')
poplar_bluff = self.graphdb.node(name='Poplar Bluff')
# Create a relationship of type "mayor_of"
relationship = steven.mayor_of(poplar_bluff, since="12th of July 2012")
# Or, to create relationship types with names
# that would not be possible with the above
# method.
steven.relationships.create('mayor_of', poplar_bluff, since="12th of July 2012")
19.2.4.2. Fetching a relationship by id
the_relationship = db.relationship[a_relationship_id]
19.2.4.3. Removing a relationship
with db.transaction:
# Create a relationship
source = db.node()
target = db.node()
rel = source.Knows(target)
# Delete it
rel.delete()
Tip
See also Section 13.5, “Delete semantics”.
19.2.4.4. Removing a relationship by id
with db.transaction:
del db.relationship[some_relationship_id]
19.2.4.5. Relationship start node, end node and type
relationship_type = relationship.type
start_node = relationship.start
end_node = relationship.end
19.2.4.6. Getting and/or counting all relationships
Use this with care, it will become extremely slow in large datasets.
for rel in db.relationships:
pass
# Shorthand for iterating through
# and counting all relationships
number_of_rels = len(db.relationships)
19.2.5. Properties
Both nodes and relationships can have properties, so this section applies
equally to both node and relationship objects. Allowed property values include
strings, numbers, booleans, as well as arrays of those primitives. Within each
array, all values must be of the same type.
19.2.5.1. Setting properties
with db.transaction:
node_or_rel['name'] = 'Thomas Anderson'
node_or_rel['age'] = 42
node_or_rel['favourite_numbers'] = [1,2,3]
node_or_rel['favourite_words'] = ['banana','blue']
19.2.5.2. Getting properties
numbers = node_or_rel['favourite_numbers']
19.2.5.3. Removing properties
with db.transaction:
del node_or_rel['favourite_numbers']
19.2.5.4. Looping through properties
# Loop key and value at the same time
for key, value in node_or_rel.items():
pass
# Loop property keys
for key in node_or_rel.keys():
pass
# Loop property values
for value in node_or_rel.values():
pass
19.2.6. Paths
A path object represents a path between two nodes in the graph. Paths thus
contain at least two nodes and one relationship, but can reach arbitrary
length. It is used in various parts of the API, most notably in traversals.
19.2.6.1. Accessing the start and end nodes
start_node = path.start
end_node = path.end
19.2.6.2. Accessing the last relationship
last_relationship = path.last_relationship
19.2.6.3. Looping through the entire path
You can loop through all elements of a path directly, or you can choose to only
loop through nodes or relationships. When you loop through all elements, the
first item will be the start node, the second will be the first relationship,
the third the node that the relationship led to and so on.
for item in path:
# Item is either a Relationship,
# or a Node
pass
for nodes in path.nodes:
# All nodes in a path
pass
for nodes in path.relationships:
# All relationships in a path
pass
19.3. Indexes
In order to rapidly find nodes or relationship based on properties, Neo4j
supports indexing. This is commonly used to find start nodes for traversals.
By default, the underlying index is powered by Apache Lucene , but it is also possible to use Neo4j
with other index implementations.
You can create an arbitrary number of named indexes. Each index handles either
nodes or relationships, and each index works by indexing key/value/object
triplets, object being either a node or a relationship, depending on the index
type.
19.3.1. Index management
Just like the rest of the API, all write operations to the index must be
performed from within a transaction.
19.3.1.1. Creating an index
Create a new index, with optional configuration.
with db.transaction:
# Create a relationship index
rel_idx = db.relationship.indexes.create('my_rels')
# Create a node index, passing optional
# arguments to the index provider.
# In this case, enable full-text indexing.
node_idx = db.node.indexes.create('my_nodes', type='fulltext')
19.3.1.2. Retrieving a pre-existing index
with db.transaction:
node_idx = db.node.indexes.get('my_nodes')
rel_idx = db.relationship.indexes.get('my_rels')
19.3.1.3. Deleting indexes
with db.transaction:
node_idx = db.node.indexes.get('my_nodes')
node_idx.delete()
rel_idx = db.relationship.indexes.get('my_rels')
rel_idx.delete()
19.3.1.4. Checking if an index exists
exists = db.node.indexes.exists('my_nodes')
19.3.2. Indexing things
19.3.2.1. Adding nodes or relationships to an index
with db.transaction:
# Indexing nodes
a_node = db.node()
node_idx = db.node.indexes.create('my_nodes')
# Add the node to the index
node_idx['akey']['avalue'] = a_node
# Indexing relationships
a_relationship = a_node.knows(db.node())
rel_idx = db.relationship.indexes.create('my_rels')
# Add the relationship to the index
rel_idx['akey']['avalue'] = a_relationship
19.3.2.2. Removing indexed items
Removing items from an index can be done at several levels of granularity. See
the example below.
# Remove specific key/value/item triplet
del idx['akey']['avalue'][item]
# Remove all instances under a certain
# key
del idx['akey'][item]
# Remove all instances all together
del idx[item]
19.3.3. Searching the index
You can retrieve indexed items in two ways. Either you do a direct lookup, or
you perform a query. The direct lookup is the same across different index
providers while the query syntax depends on what index provider you use. As
mentioned previously, Lucene is the default and by far most common index
provider. For querying Lucene you will want to use the Lucene query language
.
There is a python library for programatically generating Lucene queries,
available at GitHub .
Important
Unless you loop through the entire index result, you have to close the result
when you are done with it. If you do not, the database does not know when it
can release the resources the result is taking up.
19.3.3.1. Direct lookups
hits = idx['akey']['avalue']
for item in hits:
pass
# Always close index results when you are
# done, to free up resources.
hits.close()
19.3.3.2. Querying
hits = idx.query('akey:avalue')
for item in hits:
pass
# Always close index results when you are
# done, to free up resources.
hits.close()
19.4. Cypher Queries
You can use the Cypher query language from neo4j-embedded. Read more about
cypher syntax and cool stuff you can with it here: Chapter 15, Cypher Query
Language.
19.4.1. Querying and reading the result
19.4.1.1. Basic query
To execute a plain text cypher query, do this:
result = db.query("START n=node(0) RETURN n")
19.4.1.2. Retrieve query result
Cypher returns a tabular result. You can either loop through the table
row-by-row, or you can loop through the values in a given column. Here is how
to loop row-by-row:
root_node = "START n=node(0) RETURN n"
# Iterate through all result rows
for row in db.query(root_node):
node = row['n']
# We know it's a single result,
# so we could have done this as well
node = db.query(root_node).single['n']
Here is how to loop through the values of a given column:
root_node = "START n=node(0) RETURN n"
# Fetch an iterator for the "n" column
column = db.query(root_node)['n']
for cell in column:
node = cell
# Coumns support "single":
column = db.query(root_node)['n']
node = column.single
19.4.1.3. List the result columns
You can get a list of the column names in the result like this:
result = db.query("START n=node(0) RETURN n,count(n)")
# Get a list of the column names
columns = result.keys()
19.4.2. Parameterized and prepared queries
19.4.2.1. Parameterized queries
Cypher supports parameterized queries, see Section 15.2, “Parameters”. This is
how you use them in neo4j-embedded.
result = db.query("START n=node({id}) RETURN n",id=0)
node = result.single['n']
19.4.2.2. Prepared queries
Prepared queries, where you could retrieve a pre-parsed version of a cypher
query to be used later, is deprecated. Cypher will recognize if it has
previously parsed a given query, and won’t parse the same string twice.
So, in effect, all cypher queries are prepared queries, if you use them more
than once. Use parameterized queries to gain the full power of this - then a
generic query can be pre-parsed, and modified with parameters each time it is
executed.
19.5. Traversals
Traversing is a high-performance way to search your database. The traversal API
used here is essentially the same as the one used in the Java API, with a few
modifications.
Traversals start at a given node and uses a set of rules to move through the
graph and to decide what parts of the graph to return.
You may also want to look at Chapter 15, Cypher Query Language, which is a high
level query language for your graph.
19.5.1. Basic traversals
19.5.1.1. Following a relationship
The most basic traversals simply follow certain relationship types, and return
everything they encounter. By default, each node is visited only once, so there
is no risk of infinite loops.
traverser = db.traversal()\
.relationships('related_to')\
.traverse(start_node)
# The graph is traversed as
# you loop through the result.
for node in traverser.nodes:
pass
19.5.1.2. Following a relationship in a specific direction
You can tell the traverser to only follow relationships in some specific
direction.
from neo4j import OUTGOING, INCOMING, ANY
traverser = db.traversal()\
.relationships('related_to', OUTGOING)\
.traverse(start_node)
19.5.1.3. Following multiple relationship types
You can specify an arbitrary number of relationship types and directions to
follow.
from neo4j import OUTGOING, INCOMING, ANY
traverser = db.traversal()\
.relationships('related_to', INCOMING)\
.relationships('likes')\
.traverse(start_node)
19.5.2. Traversal results
A traversal can give you one of three different result types: nodes,
relationships or paths.
Traversals are performed lazily, which means that the graph is traversed as you
loop through the result.
traverser = db.traversal()\
.relationships('related_to')\
.traverse(start_node)
# Get each possible path
for path in traverser:
pass
# Get each node
for node in traverser.nodes:
pass
# Get each relationship
for relationship in traverser.relationships:
pass
19.5.3. Uniqueness
To avoid infinite loops, it’s important to define what parts of the graph can
be re-visited during a traversal. By default, uniqueness is set to NODE_GLOBAL,
which means that each node is only visited once.
Here are the other options that are available.
from neo4j import Uniqueness
# Available options are:
Uniqueness.NONE
# Any position in the graph may be revisited.
Uniqueness.NODE_GLOBAL
# Default option
# No node in the entire graph may be visited
# more than once. This could potentially
# consume a lot of memory since it requires
# keeping an in-memory data structure
# remembering all the visited nodes.
Uniqueness.RELATIONSHIP_GLOBAL
# No relationship in the entire graph may be
# visited more than once. For the same
# reasons as NODE_GLOBAL uniqueness, this
# could use up a lot of memory. But since
# graphs typically have a larger number of
# relationships than nodes, the memory
# overhead of this uniqueness level could
# grow even quicker.
Uniqueness.NODE_PATH
# A node may not occur previously in the
# path reaching up to it.
Uniqueness.RELATIONSHIP_PATH
# A relationship may not occur previously in
# the path reaching up to it.
Uniqueness.NODE_RECENT
# Similar to NODE_GLOBAL uniqueness in that
# there is a global collection of visited
# nodes each position is checked against.
# This uniqueness level does however have a
# cap on how much memory it may consume in
# the form of a collection that only
# contains the most recently visited nodes.
# The size of this collection can be
# specified by providing a number as the
# second argument to the
# uniqueness()-method along with the
# uniqueness level.
Uniqueness.RELATIONSHIP_RECENT
# works like NODE_RECENT uniqueness, but
# with relationships instead of nodes.
traverser = db.traversal()\
.uniqueness(Uniqueness.NODE_PATH)\
.traverse(start_node)
19.5.4. Ordering
You can traverse either depth first, or breadth first. Depth first is the
default, because it has lower memory overhead.
# Depth first traversal, this
# is the default.
traverser = db.traversal()\
.depthFirst()\
.traverse(self.source)
# Breadth first traversal
traverser = db.traversal()\
.breadthFirst()\
.traverse(start_node)
19.5.5. Evaluators - advanced filtering
In order to traverse based on other critera, such as node properties, or more
complex things like neighboring nodes or patterns, we use evaluators. An
evaluator is a normal Python method that takes a path as an argument, and
returns a description of what to do next.
The path argument is the current position the traverser is at, and the
description of what to do can be one of four things, as seen in the example
below.
from neo4j import Evaluation
# Evaluation contains the four
# options that an evaluator can
# return. They are:
Evaluation.INCLUDE_AND_CONTINUE
# Include this node in the result and
# continue the traversal
Evaluation.INCLUDE_AND_PRUNE
# Include this node in the result, but don't
# continue the traversal
Evaluation.EXCLUDE_AND_CONTINUE
# Exclude this node from the result, but
# continue the traversal
Evaluation.EXCLUDE_AND_PRUNE
# Exclude this node from the result and
# don't continue the traversal
# An evaluator
def my_evaluator(path):
# Filter on end node property
if path.end['message'] == 'world':
return Evaluation.INCLUDE_AND_CONTINUE
# Filter on last relationship type
if path.last_relationship.type.name() == 'related_to':
return Evaluation.INCLUDE_AND_PRUNE
# You can do even more complex things here, like subtraversals.
return Evaluation.EXCLUDE_AND_CONTINUE
# Use the evaluator
traverser = db.traversal()\
.evaluator(my_evaluator)\
.traverse(start_node)
Part IV. Operations
This part describes how to install and maintain a Neo4j installation. This
includes topics such as backing up the database and monitoring the health of
the database as well as diagnosing issues.
Table of Contents
20. Installation & Deployment
20.1. Deployment Scenarios
20.1.1. Server
20.1.2. Embedded
20.2. System Requirements
20.2.1. CPU
20.2.2. Memory
20.2.3. Disk
20.2.4. Filesystem
20.2.5. Software
20.2.6. JDK Version
20.3. Installation
20.3.1. Embedded Installation
20.3.2. Server Installation
20.4. Upgrading
20.4.1. Automatic Upgrade
20.4.2. Explicit Upgrade
20.4.3. Upgrade 1.4 → 1.5
20.4.4. Upgrade 1.5 → 1.6
20.5. Usage Data Collector
20.5.1. Technical Information
20.5.2. How to disable UDC
21. Configuration & Performance
21.1. Introduction
21.1.1. How to add configuration settings
21.2. Performance Guide
21.2.1. Try this first
21.2.2. Neo4j primitives' lifecycle
21.2.3. Configuring Neo4j
21.3. Caches in Neo4j
21.3.1. File buffer cache
21.3.2. Object cache
21.4. JVM Settings
21.4.1. Configuring heap size and GC
21.5. Compressed storage of short strings
21.6. Compressed storage of short arrays
21.7. Memory mapped IO settings
21.7.1. Optimizing for traversal speed example
21.7.2. Batch insert example
21.8. Linux Performance Guide
21.8.1. Setup
21.8.2. Running the benchmark
21.8.3. Fixing the problem
21.9. Linux specific notes
21.9.1. File system tuning for high IO
21.9.2. Setting the number of open files
22. High Availability
22.1. Architecture
22.2. Setup and configuration
22.2.1. Small
22.2.2. Medium
22.2.3. Large
22.2.4. Installation Notes
22.3. How Neo4j HA operates
22.4. High Availability setup tutorial
22.4.1. Set up a Coordinator cluster
22.5. Setting up HAProxy as a load balancer
22.5.1. Installing HAProxy
22.5.2. Configuring HAProxy
22.5.3. Configuring separate sets for master and slaves
22.5.4. Cache-based sharding with HAProxy
23. Backup
23.1. Embedded and Server
23.2. Online Backup from Java
23.3. High Availability
23.4. Restoring Your Data
24. Security
24.1. Securing access to the Neo4j Server
24.1.1. Secure the port and remote client connection accepts
24.1.2. Server Authorization Rules
24.1.3. Hosted Scripting
24.1.4. Security in Depth
24.1.5. Rewriting URLs with a Proxy installation
25. Monitoring
25.1. JMX
25.1.1. Adjusting remote JMX access to the Neo4j Server
25.1.2. How to connect to a Neo4j instance using JMX and JConsole
25.1.3. How to connect to the JMX monitoring programmatically
25.1.4. Reference of supported JMX MBeans
Chapter 20. Installation & Deployment
20.1. Deployment Scenarios
Neo4j can be embedded into your application, run as a standalone server or
deployed on several machines to provide high availability.
Table 20.1. Neo4j deployment options
+--------------------------------------------------------------------+
| |Single Instance |Multiple Instances |
|----------+---------------------+-----------------------------------|
|Embedded |EmbeddedGraphDatabase|HighlyAvailableGraphDatabase |
|----------+---------------------+-----------------------------------|
|Standalone|Neo4j Server |Neo4j Server high availability mode|
+--------------------------------------------------------------------+
20.1.1. Server
Neo4j is normally accessed as a standalone server, either directly through a
REST interface or through a language-specific driver. More information about
Neo4j server is found in Chapter 17, Neo4j Server. For running the server and
embedded installations in high availability mode, see Chapter 22, High
Availability.
20.1.2. Embedded
Neo4j can be embedded directly in a server application by including the
appropriate Java libraries. When programming, you can refer to the
GraphDatabaseService API. To switch from a single instance to
multiple highly available instances, simply switch from the concrete
EmbeddedGraphDatabase to the HighlyAvailableGraphDatabase .
20.2. System Requirements
Memory constrains graph size, disk I/O constrains read/write performance, as
always.
20.2.1. CPU
Performance is generally memory or I/O bound for large graphs, and compute
bound for graphs which fit in memory.
Minimum
Intel 486
Recommended
Intel Core i7
20.2.2. Memory
More memory allows even larger graphs, but runs the risk of inducing larger
Garbage Collection operations.
Minimum
1GB
Recommended
4-8GB
20.2.3. Disk
Aside from capacity, the performance characteristics of the disk are the most
important when selecting storage.
Minimum
SCSI, EIDE
Recommended
SSD w/ SATA
20.2.4. Filesystem
For proper ACID behavior, the filesystem must support flush (fsync, fdatasync).
Minimum
ext3 (or similar)
Recommended
ext4, ZFS
20.2.5. Software
Neo4j is Java-based.
Java
1.6+
Operating Systems
Linux, Windows XP, Mac OS X
20.2.6. JDK Version
The Neo4j runtime is continuously tested with
* Oracle Java Runtime Environment JRE 1.6
20.3. Installation
Neo4j can be installed as a server, running either as a headless application or
system service. For Java developers, it is also possible to use Neo4j as a
library, embedded in your application.
For information on installing Neo4j as a server, see Section 17.1, “Server
Installation”.
The following table outlines the available editions and their names for use
with dependency management tools.
Tip
Follow the links in the table for details on dependency configuration with
Apache Maven, Apache Buildr, Apache Ivy and Groovy Grape!
Table 20.2. Neo4j editions
+----------------------------------------------------------------------------------------+
|Edition |Dependency |Description |License|
|----------+-------------------------------------------------------+-------------+-------|
|Community |org.neo4j:neo4j |performance, | |
| | |fully ACID | |
| | |transactional| |
| | |graph | |
| | |database | |
|----------+-------------------------------------------------------+-------------+-------|
|Advanced |org.neo4j:neo4j-advanced |monitoring | |
|----------+-------------------------------------------------------+-------------+-------|
|Enterprise|org.neo4j:neo4j-enterprise |High | |
| | |Availability | |
| | |clustering | |
+----------------------------------------------------------------------------------------+
Note
The listed dependencies do not contain the implementation, but pulls it in
transitively.
For more information regarding licensing, see the Licensing Guide .
20.3.1. Embedded Installation
The latest release is always available from http://neo4j.org/download , included as part of the Neo4j download packages. After
selecting the appropriate version for your platform, embed Neo4j in your Java
application by including the Neo4j library jars in your build. Either take the
jar files from the lib/ directory of the download, or directly use the
artifacts available from Maven Central Repository ^[1]. Stable and milestone
releases are available there.
For information on how to use Neo4j as a dependency with Maven and other
dependency management tools, see the following table:
Note
The listed dependencies do not contain the implementation, but pulls it in
transitively.
Maven dependency.
...
org.neo4j
neo4j
${neo4j-version}
...
...
Where ${neo4j-version} is the intended version and the artifactId is one of
neo4j, neo4j-advanced, neo4j-enterprise.
20.3.2. Server Installation
please refer to Chapter 17, Neo4j Server and Section 17.1, “Server
Installation”.
20.4. Upgrading
A database can be upgraded from a minor version to the next, e.g. 1.1 → 1.2,
and 1.2 → 1.3, but you can not jump directly from 1.1 → 1.3. The upgrade
process is a one way step; databases cannot be downgraded.
For most upgrades, only small changes are required to the database store, and
these changes proceed automatically when you start up the database using the
newer version of Neo4J.
However, some upgrades require more significant changes to the database store.
In these cases, Neo4j will refuse to start without explicit configuration to
allow the upgrade.
The table below lists recent Neo4J versions, and the type of upgrade required.
Table 20.3. Upgrade process for Neo4J version
+--------------------------------------+
|From Version |To Version|Upgrade Type |
|-------------+----------+-------------|
|1.3 |1.4 |Automatic |
|-------------+----------+-------------|
|1.4 |1.5 |Explicit |
|-------------+----------+-------------|
|1.5 |1.6 |Explicit |
+--------------------------------------+
20.4.1. Automatic Upgrade
To perform a normal upgrade (for minor changes to the database store):
1. download the newer version of Neo4j
2. cleanly shutdown the database to upgrade, if it is running
3. startup the database with the newer version of Neo4j
20.4.2. Explicit Upgrade
To perform a special upgrade (for significant changes to the database store):
1. make sure the database you are upgrading has been cleanly shut down
2. set the Neo4j configuration parameter "allow_store_upgrade=true" in your
neo4j.properties or embedded configuration
3. start the database
4. the upgrade will happen during startup and the process is done when the
database has been successfully started
5. "allow_store_upgrade=true" configuration parameter should be removed, set
to "false" or commented out
20.4.3. Upgrade 1.4 → 1.5
This upgrade includes a significant change to the layout of property store
files, which reduces their size on disk, and improves IO performance. To
achieve this layout change, the upgrade process takes some time to process the
whole of the existing database. You should budget for several minutes per
gigabyte of data as part of your upgrade planning.
20.4.4. Upgrade 1.5 → 1.6
This upgrade changes lucene version from 3.1 to 3.5. The upgrade itself is done
by Lucene by loading an index.
In an HA environment these steps need to be performed:
1. shut down all the databases in the cluster
2. shut down the zoo keeper cluster and clear the version-2 directories on all
the zoo keeper instances
3. start the zoo keeper cluster again
4. remove the databases except the master and start the master database with
1.5, where migration will happen
5. start up the other databases so that they get a copy from the master
Warning
The upgrade process for this upgrade temporarily requires additional disk
space, for the period while the upgrade is in progress. Before starting the
upgrade to Neo4J 1.5, you should ensure that the machine performing the upgrade
has free space equal to the current size of of the database on disk. You can
find the current space occupied by the database by inspecting the store file
directory (data/graph.db is the default location in Neo4J server). Once the
upgrade is complete, this additional space is no longer required.
20.5. Usage Data Collector
The Neo4j Usage Data Collector is a sub-system that gathers usage data,
reporting it to the UDC-server at udc.neo4j.org. It is easy to disable, and
does not collect any data that is confidential. For more information about what
is being sent, see below.
The Neo4j team uses this information as a form of automatic, effortless
feedback from the Neo4j community. We want to verify that we are doing the
right thing by matching download statistics with usage statistics. After each
release, we can see if there is a larger retention span of the server software.
The data collected is clearly stated here. If any future versions of this
system collect additional data, we will clearly announce those changes.
The Neo4j team is very concerned about your privacy. We do not disclose any
personally identifiable information.
20.5.1. Technical Information
To gather good statistics about Neo4j usage, UDC collects this information:
* Kernel version - the build number, and if there are any modifications to
the kernel.
* Store id - it is a randomized globally unique id created at the same time a
database is created.
* Ping count - UDC holds an internal counter which is incremented for every
ping, and reset for every restart of the kernel.
* Source - this is either "neo4j" or "maven". If you downloaded Neo4j from
the Neo4j website, it’s "neo4j", if you are using Maven to get Neo4j, it
will be "maven".
* Java version - the referrer string shows which version of Java is being
used.
After startup, UDC waits for ten minutes before sending the first ping. It does
this for two reasons; first, we don’t want the startup to be slower because of
UDC, and secondly, we want to keep pings from automatic tests to a minimum. The
ping to the UDC servers is done with a HTTP GET.
20.5.2. How to disable UDC
We’ve tried to make it extremely easy to disable UDC. In fact, the code for UDC
is not even included in the kernel jar but as a completely separate component.
There are three ways you can disable UDC:
1. The easiest way is to just remove the neo4j-udc-*.jar file. By doing this,
the kernel will not load UDC, and no pings will be sent.
2. If you are using Maven, and want to make sure that UDC is never installed
in your system, a dependency element like this will do that:
org.neo4j
neo4j
${neo4j-version}
org.neo4j
neo4j-udc
Where ${neo4j-version} is the Neo4j version in use.
3. Lastly, if you are using a packaged version of Neo4j, and do not want to
make any change to the jars, a system property setting like this will also
make sure that UDC is never activated: -Dneo4j.ext.udc.disable=true.
--------------
^[1] http://repo1.maven.org/maven2/org/neo4j/
Chapter 21. Configuration & Performance
In order to get optimum performance out of Neo4j for your application there are
a few parameters that can be tweaked. The two main components that can be
configured are the Neo4j caches and the JVM that Neo4j runs in. The following
sections describe how to tune these.
21.1. Introduction
To gain good performance, these are the things to look into first:
* Make sure the JVM is not spending too much time performing garbage
collection. Monitoring heap usage on an application that uses Neo4j can be
a bit confusing since Neo4j will increase the size of caches if there is
available memory and decrease if the heap is getting full. The goal is to
have a large enough heap to make sure that heavy/peak load will not result
in so called GC trashing (performance can drop as much as two orders of
magnitude when GC trashing happens).
* Start the JVM with the -server flag and a good sized heap (see
Section 21.4, “JVM Settings”). Having too large heap may also hurt
performance so you may have to try some different heap sizes.
* Use the parallel/concurrent garbage collector (we found that
-XX:+UseConcMarkSweepGC works well in most use-cases)
21.1.1. How to add configuration settings
When creating the embedded Neo4j instance it is possible to pass in parameters
contained in a map where keys and values are strings.
Map config = new HashMap();
config.put( "neostore.nodestore.db.mapped_memory", "10M" );
config.put( "string_block_size", "60" );
config.put( "array_block_size", "300" );
EmbeddedGraphDatabase db = new EmbeddedGraphDatabase( "target/mydb", config );
If no configuration is provided, the Database Kernel will try to determine
suitable settings from the information available via the JVM settings and the
underlying operating system.
The JVM is configured by passing command line flags when starting the JVM. The
most important configuration parameters for Neo4j are the ones that control the
memory and garbage collector, but some of the parameters for configuring the
Just In Time compiler are also of interest.
This is an example of starting up your applications main class using 64-bit
server VM mode and a heap space of 1GB:
java -d64 -server -Xmx1024m -cp /path/to/neo4j-kernel.jar:/path/to/jta.jar:/path/to/your-application.jar com.example.yourapp.MainClass
Looking at the example above you will also notice one of the most basic command
line parameters: the one for specifying the classpath. The classpath is the
path in which the JVM searches for your classes. It is usually a list of
jar-files. Specifying the classpath is done by specifying the flag -cp (or
-classpath) and then the value of the classpath. For Neo4j applications this
should at least include the path to the Neo4j neo4j-kernel.jar and the Java
Transaction API (jta.jar) as well as the path where the classes for your
application are located.
Tip
On Linux, Unix and Mac OS X each element in the path list are separated by a
colon symbol (:), on Windows the path elements are separated by a semicolon
(;).
When using the Neo4j REST server, see Section 17.2, “Server Configuration” for
how to add configuration settings for the database to the server.
21.2. Performance Guide
This is the Neo4j performance guide. It will attempt to guide you in how to use
Neo4j to achieve maximum performance.
21.2.1. Try this first
The first thing is to make sure the JVM is running well and not spending to
much time in garbage collection. Monitoring heap usage on an application that
uses Neo4j can be a bit confusing since Neo4j will increase the size of caches
if there is available memory and decrease if the heap is getting full. The goal
is to have a large enough heap so heavy/peak load will not result in so called
GC trashing (performance can drop as much as two orders of magnitude when this
happens).
Start the JVM with -server flag and -Xmx (f.ex. -Xmx512M for
512Mb memory or -Xmx3G for 3Gb memory). Having too large heap may also hurt
performance so you may have to try some different heap sizes. Make sure
parallel/concurrent garbage collector is running (-XX:+UseConcMarkSweepGC works
well in most use-cases).
Finally make sure the OS has some memory to manage proper file system caches
meaning if your server has 8GB of RAM don’t use all of that RAM for heap
(unless you turned off memory mapped buffers) but leave a good size of it to
the OS. For more information on this see Chapter 21, Configuration &
Performance.
For Linux specific tweaks, see Section 21.8, “Linux Performance Guide”.
21.2.2. Neo4j primitives' lifecycle
Neo4j manages its primitives (nodes, relationships and properties) different
depending on how you use Neo4j. For example if you never get a property from a
certain node or relationship that node or relationship will not have its
properties loaded into memory. Another example is a node that you never request
any relationships from, that node will then not load any of its relationships
into memory.
Nodes and relationships are cached using LRU caches. If you (for some strange
reason) only work with nodes the relationship cache will become smaller and
smaller while the node cache is allowed to grow (if needed). Working with many
relationships and few nodes results in bigger relationship cache and smaller
node cache.
The Neo4j API specification does not say anything about order regarding
relationships so invoking
Node.getRelationships()
may return the relationships in a different order than the previous invocation.
This allows us to make even heavier optimizations returning the relationships
that are most commonly traversed.
All in all Neo4j has been designed to be very adaptive depending on how it is
used. The (unachievable) overall goal is to be able to handle any incoming
operation without having to go down and work with the file/disk I/O layer.
21.2.3. Configuring Neo4j
In Chapter 21, Configuration & Performance page there’s information on how to
configure Neo4j and the JVM. These settings have a lot impact on performance.
21.2.3.1. Disks, RAM and other tips
As always, as with any persistence solution, performance is very much depending
on the persistence media used. Better disks equals better performance.
If you have multiple disks or persistence media available it may be a good idea
to split the store files and transaction logs across those disks. Having the
store files running on disks with low seek time can do wonders for non cached
read operations. Today a typical mechanical drive has an average seek time of
about 5ms, this can cause a query or traversal to be very slow when available
RAM is too low or caches and memory mapped settings are badly configured. A new
good SATA enabled SSD has an average seek time of <100 microseconds meaning
those scenarios will execute at least 50 times faster.
To avoid hitting disk you need more RAM. On a standard mechanical drive you can
handle graphs with a few tens of millions of primitives with 1-2GB of RAM.
4-8GB of RAM can handle graphs with hundreds of millions of primitives while
you need a good server with 16-32GB to handle billions of primitives. However,
if you invest in a good SSD you will be able to handle much larger graphs on
less RAM.
Neo4j likes Java 1.6 JVMs and running in server mode so consider upgrading to
that if you haven’t yet (or at least give the -server flag). Use tools like
vmstat or equivalent to gather info when your application is running. If you
have high I/O waits and not that many blocks going out/in to disks when running
write/read transactions its a sign that you need to tweak your Java heap, Neo4j
cache and memory mapped settings (maybe even get more RAM or better disks).
21.2.3.2. Write performance
If you are experiencing poor write performance after writing some data
(initially fast, then massive slowdown) it may be the operating system writing
out dirty pages from the memory mapped regions of the store files. These
regions do not need to be written out to maintain consistency so to achieve
highest possible write speed that type of behavior should be avoided.
Another source of writes slow down can be the transaction size. Many small
transactions result in a lot of I/O writes to disc and should be avoided. Too
big transactions can result in OutOfMemory errors, since the uncommitted
transaction data is held on the Java Heap in memory. On details about
transaction management in Neo4j, please read the Chapter 13, Transaction
Management guidelines.
The Neo4j kernel makes use of several store files and a logical log file to
store the graph on disk. The store files contain the actual graph and the log
contains mutating operations. All writes to the logical log are append-only and
when a transaction is committed changes to the logical log will be forced
(fdatasync) down to disk. The store files are however not flushed to disk and
writes to them are not append-only either. They will be written to in a more or
less random pattern (depending on graph layout) and writes will not be forced
to disk until the log is rotated or the Neo4j kernel is shut down. It may be a
good idea to increase the logical log target size for rotation or turn off log
rotation if you experience problems with writes that can be linked to the
actual rotation of the log. Here is some example code demonstrating how to
change log rotation settings at runtime:
GraphDatabaseService graphDb; // ...
// get the XaDataSource for the native store
TxModule txModule = ((EmbeddedGraphDatabase) graphDb).getConfig().getTxModule();
XaDataSourceManager xaDsMgr = txModule.getXaDataSourceManager();
XaDataSource xaDs = xaDsMgr.getXaDataSource( "nioneodb" );
// turn off log rotation
xaDs.setAutoRotate( false );
// or to increase log target size to 100MB (default 10MB)
xaDs.setLogicalLogTargetSize( 100 * 1024 * 1024L );
Since random writes to memory mapped regions for the store files may happen it
is very important that the data does not get written out to disk unless needed.
Some operating systems have very aggressive settings regarding when to write
out these dirty pages to disk. If the OS decides to start writing out dirty
pages of these memory mapped regions, write access to disk will stop being
sequential and become random. That hurts performance a lot, so to get maximum
write performance when using Neo4j make sure the OS is configured not to write
out any of the dirty pages caused by writes to the memory mapped regions of the
store files. As an example, if the machine has 8GB of RAM and the total size of
the store files is 4GB (fully memory mapped) the OS has to be configured to
accept at least 50% dirty pages in virtual memory to make sure we do not get
random disk writes.
Note: make sure to read the Section 21.8, “Linux Performance Guide” as well for
more specific information.
21.2.3.3. Second level caching
While normally building applications and "always assume the graph is in
memory", sometimes it is necessary to optimize certain performance critical
sections. Neo4j adds a small overhead even if the node, relationship or
property in question is cached when you compare to in memory data structures.
If this becomes an issue use a profiler to find these hot spots and then add
your own second-level caching. We believe second-level caching should be
avoided to greatest extend possible since it will force you to take care of
invalidation which sometimes can be hard. But when everything else fails you
have to use it so here is an example of how it can be done.
We have some POJO that wrapps a node holding its state. In this particular POJO
we’ve overridden the equals implementation.
public boolean equals( Object obj )
{
return underlyingNode.getProperty( "some_property" ).equals( obj );
}
public int hashCode()
{
return underlyingNode.getProperty( "some_property" ).hashCode();
}
This works fine in most scenarios but in this particular scenario many
instances of that POJO is being worked with in nested loops adding/removing/
getting/finding to collection classes. Profiling the applications will show
that the equals implementation is being called many times and can be viewed as
a hot spot. Adding second-level caching for the equals override will in this
particular scenario increase performance.
private Object cachedProperty = null;
public boolean equals( Object obj )
{
if ( cachedProperty == null )
{
cachedProperty = underlyingNode.getProperty( "some_property" );
}
return cachedProperty.equals( obj );
}
public int hashCode()
{
if ( cachedPropety == null )
{
cachedProperty = underlyingNode.getProperty( "some_property" );
}
return cachedProperty.hashCode();
}
The problem now is that we need to invalidate the cached property whenever the
some_property is changed (may not be a problem in this scenario since the state
picked for equals and hash code computation often won’t change).
Tip
To sum up, avoid second-level caching if possible and only add it when you
really need it.
21.3. Caches in Neo4j
For how to provide custom configuration to Neo4j, see Section 21.1,
“Introduction”.
Neo4j utilizes two different types of caches: A file buffer cache and an object
cache. The file buffer cache caches the storage file data in the same format as
it is stored on the durable storage media. The object cache caches the nodes,
relationships and properties in a format that is optimized for high traversal
speeds and transactional mutation.
21.3.1. File buffer cache
Quick info
* The file buffer cache is sometimes called low level cache or file system
cache.
* It caches the Neo4j data as stored on the durable media.
* It uses the operating system memory mapping features when possible.
* Neo4j will configure the cache automatically as long as the heap size of
the JVM is configured properly.
The file buffer cache caches the Neo4j data in the same format as it is
represented on the durable storage media. The purpose of this cache layer is to
improve both read and write performance. The file buffer cache improves write
performance by writing to the cache and deferring durable write until the
logical log is rotated. This behavior is safe since all transactions are always
durably written to the logical log, which can be used to recover the store
files in the event of a crash.
Since the operation of the cache is tightly related to the data it stores, a
short description of the Neo4j durable representation format is necessary
background. Neo4j stores data in multiple files and relies on the underlying
file system to handle this efficiently. Each Neo4j storage file contains
uniform fixed size records of a particular type:
Store file Record Contents
size
nodestore 9 B Nodes
relstore 33 B Relationships
propstore 41 B Properties for nodes
and relationships
stringstore 128 B Values of string
properties
arraystore 128 B Values of array
properties
For strings and arrays, where data can be of variable length, data is stored in
one or more 120B chunks, with 8B record overhead. The sizes of these blocks can
actually be configured when the store is created using the string_block_size
and array_block_size parameters. The size of each record type can also be used
to calculate the storage requirements of a Neo4j graph or the appropriate cache
size for each file buffer cache. Note that some strings and arrays can be
stored without using the string store or the array store respectively, see
Section 21.5, “Compressed storage of short strings” and Section 21.6,
“Compressed storage of short arrays”.
Neo4j uses multiple file buffer caches, one for each different storage file.
Each file buffer cache divides its storage file into a number of equally sized
windows. Each cache window contains an even number of storage records. The
cache holds the most active cache windows in memory and tracks hit vs. miss
ratio for the windows. When the hit ratio of an uncached window gets higher
than the miss ratio of a cached window, the cached window gets evicted and the
previously uncached window is cached instead.
Important
Note that the block sizes can only be configured at store creation time.
21.3.1.1. Configuration
Parameter Possible Effect
values
use_memory_mapped_buffers true or If set to true
false Neo4j will use the
operating systems
memory mapping
functionality for
the file buffer
cache windows. If
set to false Neo4j
will use its own
buffer
implementation. In
this case the
buffers will reside
in the JVM heap
which needs to be
increased
accordingly. The
default value for
this parameter is
true, except on
Windows.
neostore.nodestore.db.mapped_memory The maximum amount
of memory to use
for the file buffer
cache of the node
storage file.
neostore.relationshipstore.db.mapped_memory The maximum amount
of memory to use
for the file buffer
cache of the
relationship store
The file.
maximum
neostore.propertystore.db.index.keys.mapped_memory amount of The maximum amount
memory to of memory to use
use for for the file buffer
memory cache of the
mapped something-something
buffers file.
for this
neostore.propertystore.db.index.mapped_memory file The maximum amount
buffer of memory to use
cache. for the file buffer
The cache of the
default something-something
unit is file.
MiB, for
neostore.propertystore.db.mapped_memory other The maximum amount
units use of memory to use
any of for the file buffer
the cache of the
following property storage
suffixes: file.
B, k, M
neostore.propertystore.db.strings.mapped_memory or G. The maximum amount
of memory to use
for the file buffer
cache of the string
property storage
file.
neostore.propertystore.db.arrays.mapped_memory The maximum amount
of memory to use
for the file buffer
cache of the array
property storage
file.
string_block_size Specifies the block
size for storing
strings. This
parameter is only
honored when the
store is created,
otherwise it is
ignored. Note that
each character in a
string occupies two
bytes, meaning that
a block size of 120
(the default size)
will hold a 60
character long
string before
overflowing into a
second block. Also
note that each
The block carries an
number of overhead of 8
bytes per bytes. This means
block. that if the block
size is 120, the
size of the stored
records will be 128
bytes.
array_block_size Specifies the block
size for storing
arrays. This
parameter is only
honored when the
store is created,
otherwise it is
ignored. The
default block size
is 120 bytes, and
the overhead of
each block is the
same as for string
blocks, i.e., 8
bytes.
dump_configuration true or If set to true the
false current
configuration
settings will be
written to the
default system
output, mostly the
console or the
logfiles.
When memory mapped buffers are used (use_memory_mapped_buffers = true) the heap
size of the JVM must be smaller than the total available memory of the
computer, minus the total amount of memory used for the buffers. When heap
buffers are used (use_memory_mapped_buffers = false) the heap size of the JVM
must be large enough to contain all the buffers, plus the runtime heap memory
requirements of the application and the object cache.
When reading the configuration parameters on startup Neo4j will automatically
configure the parameters that are not specified. The cache sizes will be
configured based on the available memory on the computer, how much is used by
the JVM heap, and how large the storage files are.
21.3.2. Object cache
Quick info
* The object cache is sometimes called high level cache.
* It caches the Neo4j data in a form optimized for fast traversal.
The object cache caches individual nodes and relationships and their properties
in a form that is optimized for fast traversal of the graph. The content of
this cache are objects with a representation geared towards supporting the
Neo4j object API and graph traversals. Reading from this cache is 5 to 10 times
faster than reading from the file buffer cache. This cache is contained in the
heap of the JVM and the size is adapted to the current amount of available heap
memory.
Nodes and relationships are added to the object cache as soon as they are
accessed. The cached objects are however populated lazily. The properties for a
node or relationship are not loaded until properties are accessed for that node
or relationship. String (and array) properties are not loaded until that
particular property is accessed. The relationships for a particular node is
also not loaded until the relationships are accessed for that node. Eviction
from the cache happens in an LRU manner when the memory is needed.
21.3.2.1. Configuration
The main configuration parameter for the object cache is the cache_type
parameter. This specifies which cache implementation to use for the object
cache. The available cache types are:
cache_type Description
none Do not use a high level cache. No objects will be cached.
soft Provides optimal utilization of the available memory. Suitable for
high performance traversal. May run into GC issues under high load
if the frequently accessed parts of the graph does not fit in the
cache.
This is the default cache implementation.
weak Provides short life span for cached objects. Suitable for high
throughput applications where a larger portion of the graph than
what can fit into memory is frequently accessed.
strong This cache will cache all data in the entire graph. It will never
release memory held by the cache. Provides optimal performance if
your graph is small enough to fit in memory.
You can read about references and relevant JVM settings for Sun HotSpot here:
* Understanding soft/weak references
* How Hotspot Decides to Clear SoftReferences
* HotSpot FAQ
21.3.2.2. Heap memory usage
This table can be used to calculate how much memory the data in the object
cache will occupy on a 64bit JVM:
Object Size Comment
Node 344 Size for each node (not counting its relationships or
B properties).
48 B Object overhead.
136 Property storage (ArrayMap 48B, HashMap 88B).
B
136 Relationship storage (ArrayMap 48B, HashMap 88B).
B
24 B Location of first / next set of relationships.
Relationship 208 Size for each relationship (not counting its properties).
B
48 B Object overhead.
136 Property storage (ArrayMap 48B, HashMap 88B).
B
Property 116 Size for each property of a node or relationship.
B
32 B Data element - allows for transactional modification and
keeps track of on disk location.
48 B Entry in the hash table where it is stored.
12 B Space used in hash table, accounts for normal fill ratio.
24 B Property key index.
Relationships 108 Size for each relationship type for a node that has a
B relationship of that type.
48 B Collection of the relationships of this type.
48 B Entry in the hash table where it is stored.
12 B Space used in hash table, accounts for normal fill ratio.
Relationships 8 B Space used by each relationship related to a particular node
(both incoming and outgoing).
Primitive 24 B Size of a primitive property value.
String 64+B Size of a string property value. 64 + 2*len(string) B (64
bytes, plus two bytes for each character in the string).
21.4. JVM Settings
There are two main memory parameters for the JVM, one controls the heap space
and the other controls the stack space. The heap space parameter is the most
important one for Neo4j, since this governs how many objects you can allocate.
The stack space parameter governs the how deep the call stack of your
application is allowed to get.
When it comes to heap space the general rule is: the larger heap space you have
the better, but make sure the heap fits in the RAM memory of the computer. If
the heap is paged out to disk performance will degrade rapidly. Having a heap
that is much larger than what your application needs is not good either, since
this means that the JVM will accumulate a lot of dead objects before the
garbage collector is executed, this leads to long garbage collection pauses and
undesired performance behavior.
Having a larger heap space will mean that Neo4j can handle larger transactions
and more concurrent transactions. A large heap space will also make Neo4j run
faster since it means Neo4j can fit a larger portion of the graph in its
caches, meaning that the nodes and relationships your application uses
frequently are always available quickly. The default heap size for a 32bit JVM
is 64MB (and 30% larger for 64bit), which is too small for most real
applications.
Neo4j works fine with the default stack space configuration, but if your
application implements some recursive behavior it is a good idea to increment
the stack size. Note that the stack size is shared for all threads, so if you
application is running a lot of concurrent threads it is a good idea to
increase the stack size.
* The heap size is set by specifying the -Xmx???m parameter to hotspot, where
??? is the heap size in megabytes. Default heap size is 64MB for 32bit
JVMs, 30% larger (appr. 83MB) for 64bit JVMs.
* The stack size is set by specifying the -Xss???m parameter to hotspot,
where ??? is the stack size in megabytes. Default stack size is 512kB for
32bit JVMs on Solaris, 320kB for 32bit JVMs on Linux (and Windows), and
1024kB for 64bit JVMs.
Most modern CPUs implement a Non-Uniform Memory Access (NUMA) architecture
, where different parts
of the memory have different access speeds. Suns Hotspot JVM is able to
allocate objects with awareness of the NUMA structure as of version 1.6.0
update 18. When enabled this can give up to 40% performance improvements. To
enabled the NUMA awareness, specify the -XX:+UseNUMA parameter (works only when
using the Parallel Scavenger garbage collector (default or -XX:+UseParallelGC
not the concurrent mark and sweep one).
Properly configuring memory utilization of the JVM is crucial for optimal
performance. As an example, a poorly configured JVM could spend all CPU time
performing garbage collection (blocking all threads from performing any work).
Requirements such as latency, total throughput and available hardware have to
be considered to find the right setup. In production, Neo4j should run on a
multi core/CPU platform with the JVM in server mode.
21.4.1. Configuring heap size and GC
A large heap allows for larger node and relationship caches — which is a good
thing — but large heaps can also lead to latency problems caused by full
garbage collection. The different high level cache implementations available in
Neo4j together with a suitable JVM configuration of heap size and garbage
collection (GC) should be able to handle most workloads.
The default cache (soft reference based LRU cache) works best with a heap that
never gets full: a graph where the most used nodes and relationships can be
cached. If the heap gets too full there is a risk that a full GC will be
triggered; the larger the heap, the longer it can take to determine what soft
references should be cleared.
Using the strong reference cache means that all the nodes and relationships
being used must fit in the available heap. Otherwise there is a risk of getting
out-of-memory exceptions. The soft reference and strong reference caches are
well suited for applications were the overal throughput is important.
The weak reference cache basically needs enough heap to handle the peak load of
the application — peak load multiplied by the average memory required per
request. It is well suited for low latency requirements were GC interuptions
are not acceptable.
Important
When running Neo4j on Windows, keep in mind that the memory mapped buffers are
allocated on heap by default, so they need to be taken into account when
determining heap size.
Table 21.1. Guidelines for heap size
+---------------------------------------------------------+
|Number of |RAM size |Heap |Reserved RAM for |
|primitives | |configuration|the OS |
|--------------+----------+-------------+-----------------|
|10M |2GB |512MB |the rest |
|--------------+----------+-------------+-----------------|
|100M |8GB+ |1-4GB |1-2GB |
|--------------+----------+-------------+-----------------|
|1B+ |16GB-32GB+|4GB+ |1-2GB |
+---------------------------------------------------------+
Tip
The recommended garbage collector to use when running Neo4j in production is
the Concurrent Mark and Sweep Compactor turned on by supplying
-XX:+UseConcMarkSweepGC as a JVM parameter.
When having made sure that the heap size is well configured the second thing to
tune in order to tune the garbage collector for your application is to specify
the sizes of the different generations of the heap. The default settings are
well tuned for "normal" applications, and work quite well for most
applications, but if you have an application with either really high allocation
rate, or a lot of long lived objects you might want to consider tuning the
sizes of the heap generation. The ratio between the young and tenured
generation of the heap is specified by using the -XX:NewRatio=# command line
option (where # is replaced by a number). The default ratio is 1:12 for client
mode JVM, and 1:8 for server mode JVM. You can also specify the size of the
young generation explicitly using the -Xmn command line option, which works
just like the -Xmx option that specifies the total heap space.
+-----------------------------------------------------------------------------+
|GC shortname |Generation|Command line parameter |Comment |
|-------------------+----------+-----------------------+----------------------|
|Copy |Young |-XX:+UseSerialGC |The Copying collector |
|-------------------+----------+-----------------------+----------------------|
|MarkSweepCompact |Tenured |-XX:+UseSerialGC |The Mark and Sweep |
| | | |Compactor |
|-------------------+----------+-----------------------+----------------------|
|ConcurrentMarkSweep|Tenured |-XX:+UseConcMarkSweepGC|The Concurrent Mark |
| | | |and Sweep Compactor |
|-------------------+----------+-----------------------+----------------------|
|ParNew |Young |-XX:+UseParNewGC |The parallel Young |
| | | |Generation |
| | | |Collector — can only |
| | | |be used with the |
| | | |Concurrent mark and |
| | | |sweep compactor. |
|-------------------+----------+-----------------------+----------------------|
|PS Scavenge |Young |-XX:+UseParallelGC |The parallel object |
| | | |scavenger |
|-------------------+----------+-----------------------+----------------------|
|PS MarkSweep |Tenured |-XX:+UseParallelGC |The parallel mark and |
| | | |sweep collector |
+-----------------------------------------------------------------------------+
These are the default configurations on some platforms according to our
non-exhaustive research:
+-----------------------------------------------------------------------------+
|JVM |-d32 -client |-d32 -server |-d64 -client |-d64 |
| | | | |-server |
|----------+-------------------+----------------+-------------------+---------|
|Mac OS X |ParNew and |PS Scavenge and |ParNew and |PS |
|Snow |ConcurrentMarkSweep|PS MarkSweep |ConcurrentMarkSweep|Scavenge |
|Leopard, | | | |and PS |
|64-bit, | | | |MarkSweep|
|Hotspot | | | | |
|1.6.0_17 | | | | |
|----------+-------------------+----------------+-------------------+---------|
|Ubuntu, |Copy and |Copy and |N/A |N/A |
|32-bit, |MarkSweepCompact |MarkSweepCompact| | |
|Hotspot | | | | |
|1.6.0_16 | | | | |
+-----------------------------------------------------------------------------+
21.5. Compressed storage of short strings
Neo4j will try to classify your strings in a short string class and if it
manages that it will treat it accordingly. In that case, it will be stored
without indirection in the property store, inlining it instead in the property
record, meaning that the dynamic string store will not be involved in storing
that value, leading to reduced disk footprint. Additionally, when no string
record is needed to store the property, it can be read and written in a single
lookup, leading to performance improvements.
The various classes for short strings are:
* Numerical, consisting of digits 0..9 and the punctuation space, period,
dash, plus, comma and apostrophe.
* Date, consisting of digits 0..9 and the punctuation space dash, colon,
slash, plus and comma.
* Uppercase, consisting of uppercase letters A..Z, and the punctuation space,
underscore, period, dash, colon and slash.
* Lowercase, like upper but with lowercase letters a..z instead of uppercase
* E-mail, consisting of lowercase letters a..z and the punctuation comma,
underscore, period, dash, plus and the at sign (@)
* URI, consisting of lowercase letters a..z, digits 0..9 and most punctuation
available.
* Alphanumerical, consisting of both upper and lowercase letters a..zA..z,
digits 0..9 and punctuation space and underscore.
* Alphasymbolical, consisting of both upper and lowercase letters a..zA..Z
and the punctuation space, underscore, period, dash, colon, slash, plus,
comma, apostrophe, at sign, pipe and semicolon.
* European, consisting of most accented european characters and digits plus
punctuation space, dash, underscore and period - like latin1 but with less
punctuation
* Latin 1
* UTF-8
In addition to the string’s contents, the number of characters also determines
if the string can be inlined or not. Each class has its own character count
limits, which are
* For Numerical and Date, 54
* For Uppercase, Lowercase and E-mail, 43
* For URI, Alphanumerical and Alphasymbolical, 36
* For European, 31
* For Latin1, 27
* For UTF-8, 14
That means that the largest inline-able string is 54 characters long and must
be of the Numerical class and also that all Strings of size 14 or less will
always be inlined.
Also note that the above limits are for the default 41 byte PropertyRecord
layout - if that parameter is changed via editing the source and recompiling,
the above have to be recalculated.
21.6. Compressed storage of short arrays
Neo4j will try to store your primitive arrays in a compressed way, so as to
save disk space and possibly an I/O operation. To do that, it employs a
"bit-shaving" algorithm that tries to reduce the number of bits required for
storing the members of the array. In particular:
1. For each member of the array, it determines the position of leftmost set
bit.
2. Determines the largest such position among all members of the array
3. It reduces all members to that number of bits
4. Stores those values, prefixed by a small header.
That means that when even a single negative value is included in the array then
the natural size of the primitives will be used.
There is a possibility that the result can be inlined in the property record
if:
* It is less than 24 bytes after compression
* It has less than 64 members
For example, an array long[] {0L, 1L, 2L, 4L} will be inlined, as the largest
entry (4) will require 3 bits to store so the whole array will be stored in 4*3
=12 bits. The array long[] {-1L, 1L, 2L, 4L} however will require the whole 64
bits for the -1 entry so it needs 64*4 = 32 bytes and it will end up in the
dynamic store.
21.7. Memory mapped IO settings
Each file in the Neo4j store can use memory mapped I/O for reading/writing.
Best performance is achieved if the full file can be memory mapped but if there
isn’t enough memory for that Neo4j will try and make the best use of the memory
it gets (regions of the file that get accessed often will more likely be memory
mapped).
Important
Neo4j makes heavy use of the java.nio package. Native I/O will result in memory
being allocated outside the normal Java heap so that memory usage needs to be
taken into consideration. Other processes running on the OS will impact the
availability of such memory. Neo4j will require all of the heap memory of the
JVM plus the memory to be used for memory mapping to be available as physical
memory. Other processes may thus not use more than what is available after the
configured memory allocation is made for Neo4j.
A well configured OS with large disk caches will help a lot once we get cache
misses in the node and relationship caches. Therefore it is not a good idea to
use all available memory as Java heap.
If you look into the directory of your Neo4j database, you will find its store
files, all prefixed by neostore:
* nodestore stores information about nodes
* relationshipstore holds all the relationships
* propertystore stores information of properties and all simple properties
such as primitive types (both for relationships and nodes)
* propertystore strings stores all string properties
* propertystore arrays stores all array properties
There are other files there as well, but they are normally not interesting in
this context.
This is how the default memory mapping configuration looks:
neostore.nodestore.db.mapped_memory=25M
neostore.relationshipstore.db.mapped_memory=50M
neostore.propertystore.db.mapped_memory=90M
neostore.propertystore.db.strings.mapped_memory=130M
neostore.propertystore.db.arrays.mapped_memory=130M
21.7.1. Optimizing for traversal speed example
To tune the memory mapping settings start by investigating the size of the
different store files found in the directory of your Neo4j database. Here is an
example of some of the files and sizes in a Neo4j database:
14M neostore.nodestore.db
510M neostore.propertystore.db
1.2G neostore.propertystore.db.strings
304M neostore.relationshipstore.db
In this example the application is running on a machine with 4GB of RAM. We’ve
reserved about 2GB for the OS and other programs. The Java heap is set to
1.5GB, that leaves about 500MB of RAM that can be used for memory mapping.
Tip
If traversal speed is the highest priority it is good to memory map as much as
possible of the node- and relationship stores.
An example configuration on the example machine focusing on traversal speed
would then look something like:
neostore.nodestore.db.mapped_memory=15M
neostore.relationshipstore.db.mapped_memory=285M
neostore.propertystore.db.mapped_memory=100M
neostore.propertystore.db.strings.mapped_memory=100M
neostore.propertystore.db.arrays.mapped_memory=0M
21.7.2. Batch insert example
The configuration should suit the data set you are about to inject using
BatchInsert. Lets say we have a random-like graph with 10M nodes and 100M
relationships. Each node (and maybe some relationships) have different
properties of string and Java primitive types (but no arrays). The important
thing with a random graph will be to give lots of memory to the relationship
and node store:
neostore.nodestore.db.mapped_memory=90M
neostore.relationshipstore.db.mapped_memory=3G
neostore.propertystore.db.mapped_memory=50M
neostore.propertystore.db.strings.mapped_memory=100M
neostore.propertystore.db.arrays.mapped_memory=0M
The configuration above will fit the entire graph (with exception to
properties) in memory.
A rough formula to calculate the memory needed for the nodes:
number_of_nodes * 9 bytes
and for relationships:
number_of_relationships * 33 bytes
Properties will typically only be injected once and never read so a few
megabytes for the property store and string store is usually enough. If you
have very large strings or arrays you may want to increase the amount of memory
assigned to the string and array store files.
An important thing to remember is that the above configuration will need a Java
heap of 3.3G+ since in batch inserter mode normal Java buffers that gets
allocated on the heap will be used instead of memory mapped ones.
21.8. Linux Performance Guide
The key to achieve good performance on reads and writes is to have lots of RAM
since disks are so slow. This guide will focus on achieving good write
performance on a Linux kernel based operating system.
If you have not already read the information available in Chapter 21,
Configuration & Performance do that now to get some basic knowledge on memory
mapping and store files with Neo4j.
This section will guide you through how to set up a file system benchmark and
use it to configure your system in a better way.
21.8.1. Setup
Create a large file with random data. The file should fit in RAM so if your
machine has 4GB of RAM a 1-2GB file with random data will be enough. After the
file has been created we will read the file sequentially a few times to make
sure it is cached.
$ dd if=/dev/urandom of=store bs=1M count=1000
1000+0 records in
1000+0 records out
1048576000 bytes (1.0 GB) copied, 263.53 s, 4.0 MB/s
$
$ dd if=store of=/dev/null bs=100M
10+0 records in
10+0 records out
1048576000 bytes (1.0 GB) copied, 38.6809 s, 27.1 MB/s
$
$ dd if=store of=/dev/null bs=100M
10+0 records in
10+0 records out
1048576000 bytes (1.0 GB) copied, 1.52365 s, 688 MB/s
$ dd if=store of=/dev/null bs=100M
10+0 records in
10+0 records out
1048576000 bytes (1.0 GB) copied, 0.776044 s, 1.4 GB/s
If you have a standard hard drive in the machine you may know that it is not
capable of transfer speeds as high as 1.4GB/s. What is measured is how fast we
can read a file that is cached for us by the operating system.
Next we will use a small utility that simulates the Neo4j kernel behavior to
benchmark write speed of the system.
$ git clone git@github.com:neo4j/tooling.git
...
$ cd tooling/write-test/
$ mvn compile
[INFO] Scanning for projects...
...
$ ./run
Usage: <[record size] [min tx size] [max tx size] [tx count] <[--nosync | --nowritelog | --nowritestore | --noread | --nomemorymap]>>
The utility will be given a store file (large file we just created) and a name
of a log file. Then a record size in bytes, min tx size, max tx size and
transaction count must be set. When started the utility will map the large
store file entirely in memory and read (transaction size) records from it
randomly and then write them sequentially to the log file. The log file will
then force changes to disk and finally the records will be written back to the
store file.
21.8.2. Running the benchmark
Lets try to benchmark 100 transactions of size 100-500 with a record size of 33
bytes (same record size used by the relationship store).
$ ./run store logfile 33 100 500 100
tx_count[100] records[30759] fdatasyncs[100] read[0.96802425 MB] wrote[1.9360485 MB]
Time was: 4.973
20.108585 tx/s, 6185.2 records/s, 20.108585 fdatasyncs/s, 199.32773 kB/s on reads, 398.65546 kB/s on writes
We see that we get about 6185 record updates/s and 20 transactions/s with the
current transaction size. We can change the transaction size to be bigger, for
example writing 10 transactions of size 1000-5000 records:
$ ./run store logfile 33 1000 5000 10
tx_count[10] records[24511] fdatasyncs[10] read[0.77139187 MB] wrote[1.5427837 MB]
Time was: 0.792
12.626263 tx/s, 30948.232 records/s, 12.626263 fdatasyncs/s, 997.35516 kB/s on reads, 1994.7103 kB/s on writes
With larger transaction we will do fewer of them per second but record
throughput will increase. Lets see if it scales, 10 transactions in under 1s
then 100 of them should execute in about 10s:
$ ./run store logfile 33 1000 5000 100
tx_count[100] records[308814] fdatasyncs[100] read[9.718763 MB] wrote[19.437527 MB]
Time was: 65.115
1.5357445 tx/s, 4742.594 records/s, 1.5357445 fdatasyncs/s, 152.83751 kB/s on reads, 305.67502 kB/s on writes
This is not very linear scaling. We modified a bit more than 10x records in
total but the time jumped up almost 100x. Running the benchmark watching vmstat
output will reveal that something is not as it should be:
$ vmstat 3
procs -----------memory---------- ---swap-- -----io---- -system-- ----cpu----
r b swpd free buff cache si so bi bo in cs us sy id wa
0 1 47660 298884 136036 2650324 0 0 0 10239 1167 2268 5 7 46 42
0 1 47660 302728 136044 2646060 0 0 0 7389 1267 2627 6 7 47 40
0 1 47660 302408 136044 2646024 0 0 0 11707 1861 2016 8 5 48 39
0 2 47660 302472 136060 2646432 0 0 0 10011 1704 1878 4 7 49 40
0 1 47660 303420 136068 2645788 0 0 0 13807 1406 1601 4 5 44 47
There are a lot of blocks going out to IO, way more than expected for the write
speed we are seeing in the benchmark. Another observation that can be made is
that the Linux kernel has spawned a process called "flush-x:x" (run top) that
seems to be consuming a lot of resources.
The problem here is that the Linux kernel is trying to be smart and write out
dirty pages from the virtual memory. As the benchmark will memory map a 1GB
file and do random writes it is likely that this will result in 1/4 of the
memory pages available on the system to be marked as dirty. The Neo4j kernel is
not sending any system calls to the Linux kernel to write out these pages to
disk however the Linux kernel decided to start doing so and it is a very bad
decision. The result is that instead of doing sequential like writes down to
disk (the logical log file) we are now doing random writes writing regions of
the memory mapped file to disk.
It is possible to observe this behavior in more detail by looking at /proc/
vmstat "nr_dirty" and "nr_writeback" values. By default the Linux kernel will
start writing out pages at a very low ratio of dirty pages (10%).
$ sync
$ watch grep -A 1 dirty /proc/vmstat
...
nr_dirty 22
nr_writeback 0
The "sync" command will write out all data (that needs writing) from memory to
disk. The second command will watch the "nr_dirty" and "nr_writeback" count
from vmstat. Now start the benchmark again and observe the numbers:
nr_dirty 124947
nr_writeback 232
The "nr_dirty" pages will quickly start to rise and after a while the
"nr_writeback" will also increase meaning the Linux kernel is scheduling a lot
of pages to write out to disk.
21.8.3. Fixing the problem
As we have 4GB RAM on the machine and memory map a 1GB file that does not need
its content written to disk (until we tell it to do so because of logical log
rotation or Neo4j kernel shutdown) it should be possible to do endless random
writes to that memory with high throughput. All we have to do is to tell the
Linux kernel to stop trying to be smart. Edit the /etc/sysctl.conf (need root
access) and add the following lines:
vm.dirty_background_ratio = 50
vm.dirty_ratio = 80
Then (as root) execute:
# sysctl -p
The "vm.dirty_background_ratio" tells at what ratio should the linux kernel
start the background task of writing out dirty pages. We increased this from
the default 10% to 50% and that should cover the 1GB memory mapped file. The
"vm.dirty_ratio" tells at what ratio all IO writes become synchronous, meaning
that we can not do IO calls without waiting for the underlying device to
complete them (which is something you never want to happen).
Rerun the benchmark:
$ ./run store logfile 33 1000 5000 100
tx_count[100] records[265624] fdatasyncs[100] read[8.35952 MB] wrote[16.71904 MB]
Time was: 6.781
14.7470875 tx/s, 39171.805 records/s, 14.7470875 fdatasyncs/s, 1262.3726 kB/s on reads, 2524.745 kB/s on writes
Results are now more in line with what can be expected, 10x more records
modified results in 10x longer execution time. The vmstat utility will not
report any absurd amount of IO blocks going out (it reports the ones caused by
the fdatasync to the logical log) and Linux kernel will not spawn a "flush-x:x"
background process writing out dirty pages caused by writes to the memory
mapped store file.
21.9. Linux specific notes
21.9.1. File system tuning for high IO
In order to support the high IO load of small transactions from a database, the
underlying file system should be tuned. Symptoms for this are low CPU load with
high iowait. In this case, there are a couple of tweaks possible on Linux
systems:
* Disable access-time updates: noatime,nodiratime flags for disk mount
command or in the /etc/fstab for the database disk volume mount.
* Tune the IO scheduler for high disk IO on the database disk.
21.9.2. Setting the number of open files
Linux platforms impose an upper limit on the number of concurrent files a user
may have open. This number is reported for the current user and session with
the command
user@localhost:~$ ulimit -n
1024
The usual default of 1024 is often not enough, especially when many indexes are
used or a server installation sees too many connections (network sockets count
against that limit as well). Users are therefore encouraged to increase that
limit to a healthy value of 40000 or more, depending on usage patterns. Setting
this value via the ulimit command is possible only for the root user and that
for that session only. To set the value system wide you have to follow the
instructions for your platform.
What follows is the procedure to set the open file descriptor limit to 40k for
user neo4j under Ubuntu 10.04 and later. If you opted to run the neo4j service
as a different user, change the first field in step 2 accordingly.
1. Become root since all operations that follow require editing protected
system files.
user@localhost:~$ sudo su -
Password:
root@localhost:~$
2. Edit /etc/security/limits.conf and add these two lines:
neo4j soft nofile 40000
neo4j hard nofile 40000
3. Edit /etc/pam.d/su and uncomment or add the following line:
session required pam_limits.so
4. A restart is required for the settings to take effect.
After the above procedure, the neo4j user will have a limit of 40000
simultaneous open files. If you continue experiencing exceptions on Too
many open files or Could not stat() directory then you may have to raise
that limit further.
Chapter 22. High Availability
Note
The High Availability features are only available in the Neo4j Enterprise
Edition.
Neo4j High Availability or “Neo4j HA” provides the following two main features:
1. It enables a fault-tolerant database architecture, where several Neo4j
slave databases can be configured to be exact replicas of a single Neo4j
master database. This allows the end-user system to be fully functional and
both read and write to the database in the event of hardware failure.
2. It enables a horizontally scaling read-mostly architecture that enables the
system to handle more read load than a single Neo4j database instance can
handle.
22.1. Architecture
Neo4j HA has been designed to make the transition from single machine to multi
machine operation simple, by not having to change the already existing
application.
Consider an existing application with Neo4j embedded and running on a single
machine. To deploy such an application in a multi machine setup the only
required change is to switch the creation of the GraphDatabaseService from
EmbeddedGraphDatabase to HighlyAvailableGraphDatabase. Since both implement the
same interface, no additional changes are required.
Figure 22.1. Typical setup when running multiple Neo4j instances in HA mode
Neo4j Highly Available Cluster
When running Neo4j in HA mode there is always a single master and zero or more
slaves. Compared to other master-slave replication setups Neo4j HA can handle
writes on a slave so there is no need to redirect writes to the master.
A slave will handle writes by synchronizing with the master to preserve
consistency. Updates will however propagate from the master to other slaves
eventually so a write from one slave is not immediately visible on all other
slaves. This is the only difference between multiple machines running in HA
mode compared to single machine operation. All other ACID characteristics are
the same.
22.2. Setup and configuration
Neo4j HA can be set up to accommodate differing requirements for load, fault
tolerance and available hardware.
Within a cluster, Neo4j HA uses Apache ZooKeeper ^[2] for master election and
propagation of general cluster and machine status information. ZooKeeper can be
seen as a distributed coordination service. Neo4j HA requires a coordinator
service for initial master election, new master election (current master
failing) and to publish general status information about the current Neo4j HA
cluster (for example when a server joined or left the cluster). Read operations
through the GraphDatabaseService API will always work and even writes can
survive coordinator failures if a master is present.
ZooKeeper requires a majority of the ZooKeeper instances to be available to
operate properly. This means that the number of ZooKeeper instances should
always be an odd number since that will make best use of available hardware.
To further clarify the fault tolerance characteristics of Neo4j HA here are a
few example setups:
22.2.1. Small
* 3 physical (or virtual) machines
* 1 Coordinator running on each machine
* 1 Neo4j HA instance running on each machine
This setup is conservative in the use of hardware while being able to handle
moderate read load. It can fully operate when at least 2 of the coordinator
instances are running. Since the coordinator and Neo4j HA are running together
on each machine this will in most scenarios mean that only one server is
allowed to go down.
22.2.2. Medium
* 5-7+ machines
* Coordinator running on 3, 5 or 7 machines
* Neo4j HA can run on 5+ machines
This setup may mean that two different machine setups have to be managed (some
machines run both coordinator and Neo4j HA). The fault tolerance will depend on
how many machines there are that are running coordinators. With 3 coordinators
the cluster can survive one coordinator going down, with 5 it can survive 2 and
with 7 it can handle 3 coordinators failing. The number of Neo4j HA instances
that can fail for normal operations is theoretically all but 1 (but for each
required master election the coordinator must be available).
22.2.3. Large
* 8+ total machines
* 3+ Neo4j HA machines
* 5+ Coordinators, on separate dedicated machines
In this setup all coordinators are running on separate machines as a dedicated
service. The dedicated coordinator cluster can handle half of the instances,
minus 1, going down. The Neo4j HA cluster will be able to operate with at least
a single live machine. Adding more Neo4j HA instances is very easy in this
setup since the coordinator cluster is operating as a separate service.
22.2.4. Installation Notes
For installation instructions of a High Availability cluster see Section 22.4,
“High Availability setup tutorial”.
Note that while the HighlyAvailableGraphDatabase supports the same API as the
EmbeddedGraphDatabase, it does have additional configuration parameters.
Table 22.1. HighlyAvailableGraphDatabase configuration parameters
+---------------------------------------------------------------------------------------------------------+
|Parameter Name |Value |Example value |Required?|
|--------------------------------+-----------------+--------------------------------------------+---------|
|ha.server_id |integer >= 0 |1 |yes |
|--------------------------------+-----------------+--------------------------------------------+---------|
|ha.server |(auto-discovered)|my-domain.com:6001 |no |
| |host & port to | | |
| |bind when acting | | |
| |as master | | |
|--------------------------------+-----------------+--------------------------------------------+---------|
|ha.coordinators |comma delimited |localhost:2181,localhost:2182,localhost:2183|yes |
| |coordinator | | |
| |connections | | |
|--------------------------------+-----------------+--------------------------------------------+---------|
|ha.pull_interval |interval for |30 |no |
| |polling master | | |
| |from a slave, in | | |
| |seconds | | |
|--------------------------------+-----------------+--------------------------------------------+---------|
|ha.slave_coordinator_update_mode|creates a |none |no |
| |slave-only | | |
| |instance that | | |
| |will never become| | |
| |a master | | |
|--------------------------------+-----------------+--------------------------------------------+---------|
|ha.read_timeout |how long a slave |20 |no |
| |will wait for | | |
| |response from | | |
| |master before | | |
| |giving up | | |
| |(default 20) | | |
|--------------------------------+-----------------+--------------------------------------------+---------|
|ha.lock_read_timeout |how long a slave |40 |no |
| |lock acquisition | | |
| |request will wait| | |
| |for response from| | |
| |master before | | |
| |giving up | | |
| |(defaults to what| | |
| |ha.read_timeout | | |
| |is, or its | | |
| |default if | | |
| |absent) | | |
+---------------------------------------------------------------------------------------------------------+
Caution
Neo4j’s HA setup depends on ZooKeeper a.k.a. Coordinator which makes certain
assumptions about the state of the underlying operating system. In particular
ZooKeeper expects that the system time on each machine is set correctly,
synchronized with respect to each other. If this is not true, then Neo4j HA
will appear to misbehave, caused by seemingly random ZooKeeper hiccups.
Caution
Neo4j uses the Coordinator cluster to store information representative of the
deployment’s state, including key fields of the database itself. Since that
information is permanently stored in the cluster, you cannot reuse it for
multiple deployments of different databases. In particular, removing the Neo4j
servers, replacing the database and restarting them using the same coordinator
instances will result in errors mentioning the existing HA deployment. To reset
the Coordinator cluster to a clean state, you have to shutdown all instances,
remove the data/coordinator/version-2/* data files and restart the
Coordinators.
22.3. How Neo4j HA operates
A Neo4j HA cluster operates cooperatively, coordinating activity through
Zookeeper.
On startup a Neo4j HA instance will connect to the coordinator service
(ZooKeeper) to register itself and ask, "who is master?" If some other machine
is master, the new instance will start as slave and connect to that master. If
the machine starting up was the first to register — or should become master
according to the master election algorithm — it will start as master.
When performing a write transaction on a slave each write operation will be
synchronized with the master (locks will be acquired on both master and slave).
When the transaction commits it will first occur on the master. If the master
commit is successful the transaction will be committed on the slave as well. To
ensure consistency, a slave has to be up to date with the master before
performing a write operation. This is built into the communication protocol
between the slave and master, so that updates will happen automatically if
needed.
You can make a database instance permanently slave-only by including the
ha.slave_coordinator_update_mode=none configuration parameter in its
configuration. Such instances will never become a master during fail-over
elections though otherwise they behave identically to any other slaves,
including the ability to write-through permanent slaves to the master.
When performing a write on the master it will execute in the same way as
running in normal embedded mode. Currently the master will not push updates to
the slave. Instead, slaves can be configured to have a pull interval. Without
polling, updates will only happen on slaves whenever they synchronize a write
with the master.
Having all writes go through slaves has the benefit that the data will be
replicated on two machines. This is recommended to avoid rollbacks in case of a
master failure that could potentially happen when the new master is elected.
Whenever a server running a neo4j database becomes unavailable the coordinator
service will detect that and remove it from the cluster. If the master goes
down a new master will automatically be elected. Normally a new master is
elected and started within just a few seconds and during this time no writes
can take place (the write will throw an exception). A machine that becomes
available after being unavailable will automatically reconnect to the cluster.
The only time this is not true is when an old master had changes that did not
get replicated to any other machine. If the new master is elected and performs
changes before the old master recovers, there will be two different versions of
the data. The old master will move away the branched database and download a
full copy from the new master.
All this can be summarized as:
* Slaves can handle write transactions.
* Updates to slaves are eventual consistent.
* Neo4j HA is fault tolerant and (depending on ZooKeeper setup) can continue
to operate from X machines down to a single machine.
* Slaves will be automatically synchronized with the master on a write
operation.
* If the master fails a new master will be elected automatically.
* Machines will be reconnected automatically to the cluster whenever the
issue that caused the outage (network, maintenance) is resolved.
* Transactions are atomic, consistent and durable but eventually propagated
out to other slaves.
* If the master goes down any running write transaction will be rolled back
and during master election no write can take place.
* Reads are highly available.
22.4. High Availability setup tutorial
This is a guide to set up a Neo4j HA cluster and run embedded Neo4j or Neo4j
Server instances participating as cluster nodes.
22.4.1. Set up a Coordinator cluster
The members of the HA cluster (see Chapter 22, High Availability) use a
Coordinator cluster to manage themselves and coordinate lifecycle activity like
electing a master. When running an Neo4j HA cluster, a Coordinator cluster is
used for cluster collaboration and must be installed and configured before
working with the Neo4j database HA instances.
Tip
Neo4j Server (see Chapter 17, Neo4j Server) and Neo4j Embedded (see
Section 21.1, “Introduction”) can both be used as nodes in the same HA cluster.
This opens for scenarios where one application can insert and update data via a
Java or JVM language based application, and other instances can run Neo4j
Server and expose the data via the REST API (Chapter 18, REST API).
Below, there will be 3 coordinator instances set up on one local machine.
22.4.1.1. Download and unpack Neoj4 Enterprise
Download and unpack three installations of Neo4j Enterprise (called
$NEO4J_HOME1, $NEO4J_HOME2, $NEO4J_HOME3) from the Neo4j download site .
22.4.1.2. Setup and start the Coordinator cluster
Now, in the NEO4J_HOME1/conf/coord.cfg file, adjust the coordinator clientPort
and let the coordinator search for other coordinator cluster members at the
localhost port ranges:
#$NEO4J_HOME1/conf/coord.cfg
server.1=localhost:2888:3888
server.2=localhost:2889:3889
server.3=localhost:2890:3890
clientPort=2181
The other two config files in $NEO4J_HOME2 and $NEO4J_HOME3 will have a
different clienttPort set but the other parameters identical to the first one:
#$NEO4J_HOME2/conf/coord.cfg
...
server.1=localhost:2888:3888
server.2=localhost:2889:3889
server.3=localhost:2890:3890
...
clientPort=2182
#$NEO4J_HOME2/conf/coord.cfg
...
server.1=localhost:2888:3888
server.2=localhost:2889:3889
server.3=localhost:2890:3890
...
clientPort=2183
Next we need to create a file in each data directory called "myid" that
contains an id for each server equal to the number in server.1, server.2 and
server.3 from the configuration files.
neo4j_home1$ echo '1' > data/coordinator/myid
neo4j_home2$ echo '2' > data/coordinator/myid
neo4j_home3$ echo '3' > data/coordinator/myid
We are now ready to start the Coordinator instances:
neo4j_home1$ ./bin/neo4j-coordinator start
neo4j_home2$ ./bin/neo4j-coordinator start
neo4j_home3$ ./bin/neo4j-coordinator start
22.4.1.3. Start the Neo4j Servers in HA mode
In your conf/neo4j.properties file, enable HA by setting the necessary
parameters for all 3 installations, adjusting the ha.server_id for all
instances:
#$NEO4J_HOME1/conf/neo4j.properties
#unique server id for this graph database
#can not be negative id and must be unique
ha.server_id = 1
#ip and port for this instance to bind to
ha.server = localhost:6001
#connection information to the coordinator cluster client ports
ha.coordinators = localhost:2181,localhost:2182,localhost:2183
#$NEO4J_HOME2/conf/neo4j.properties
#unique server id for this graph database
#can not be negative id and must be unique
ha.server_id = 2
#ip and port for this instance to bind to
ha.server = localhost:6001
#connection information to the coordinator cluster client ports
ha.coordinators = localhost:2181,localhost:2182,localhost:2183
#$NEO4J_HOME3/conf/neo4j.properties
#unique server id for this graph database
#can not be negative id and must be unique
ha.server_id = 3
#ip and port for this instance to bind to
ha.server = localhost:6001
#connection information to the coordinator cluster client ports
ha.coordinators = localhost:2181,localhost:2182,localhost:2183
To avoid port clashes when starting the servers, adjust the ports for the REST
endpoints in all instances under conf/neo4j-server.properties and enable HA
mode:
#$NEO4J_HOME1/conf/neo4j-server.properties
...
# http port (for all data, administrative, and UI access)
org.neo4j.server.webserver.port=7474
...
# Allowed values:
# HA - High Availability
# SINGLE - Single mode, default.
# To run in High Availability mode, configure the coord.cfg file, and the
# neo4j.properties config file, then uncomment this line:
org.neo4j.server.database.mode=HA
#$NEO4J_HOME2/conf/neo4j-server.properties
...
# http port (for all data, administrative, and UI access)
org.neo4j.server.webserver.port=7475
...
# Allowed values:
# HA - High Availability
# SINGLE - Single mode, default.
# To run in High Availability mode, configure the coord.cfg file, and the
# neo4j.properties config file, then uncomment this line:
org.neo4j.server.database.mode=HA
#$NEO4J_HOME3/conf/neo4j-server.properties
...
# http port (for all data, administrative, and UI access)
org.neo4j.server.webserver.port=7476
...
# Allowed values:
# HA - High Availability
# SINGLE - Single mode, default.
# To run in High Availability mode, configure the coord.cfg file, and the
# neo4j.properties config file, then uncomment this line:
org.neo4j.server.database.mode=HA
To avoid JMX port clashes adjust the assigned ports for all instances under
conf/neo4j-wrapper.properties:
#$NEO4J_HOME1/conf/neo4j-wrapper.properties
...
# Remote JMX monitoring, adjust the following lines if needed.
# Also make sure to update the jmx.access and jmx.password files with appropriate permission roles and passwords,
# the shipped configuration contains only a read only role called 'monitor' with password 'Neo4j'.
# For more details, see: http://download.oracle.com/javase/6/docs/technotes/guides/management/agent.html
wrapper.java.additional.4=-Dcom.sun.management.jmxremote.port=3637
...
#$NEO4J_HOME2/conf/neo4j-wrapper.properties
...
# Remote JMX monitoring, adjust the following lines if needed.
# Also make sure to update the jmx.access and jmx.password files with appropriate permission roles and passwords,
# the shipped configuration contains only a read only role called 'monitor' with password 'Neo4j'.
# For more details, see: http://download.oracle.com/javase/6/docs/technotes/guides/management/agent.html
wrapper.java.additional.4=-Dcom.sun.management.jmxremote.port=3638
...
#$NEO4J_HOME3/conf/neo4j-server.properties
...
# Remote JMX monitoring, adjust the following lines if needed.
# Also make sure to update the jmx.access and jmx.password files with appropriate permission roles and passwords,
# the shipped configuration contains only a read only role called 'monitor' with password 'Neo4j'.
# For more details, see: http://download.oracle.com/javase/6/docs/technotes/guides/management/agent.html
wrapper.java.additional.4=-Dcom.sun.management.jmxremote.port=3639
...
Now, start all three server instances.
neo4j_home1$ ./bin/neo4j start
neo4j_home2$ ./bin/neo4j start
neo4j_home3$ ./bin/neo4j start
Now, you should be able to access the 3 servers (the first one being elected as
master since it was started first) at http://localhost:7474/webadmin/#/info/
org.neo4j/High%20Availability/ , http://localhost:7475/webadmin/#/info/org.neo4j/
High%20Availability/ and http://localhost:7476/webadmin/#/info/org.neo4j/
High%20Availability/ and check the status of the HA configuration.
Alternatively, the REST API is exposing JMX, so you can check the HA JMX bean
with e.g.
curl -H "Content-Type:application/json" -d '["org.neo4j:*"]' http://localhost:7474/db/manage/server/jmx/query
And find in the response
"description" : "Information about all instances in this cluster",
"name" : "InstancesInCluster",
"value" : [ {
"description" : "org.neo4j.management.InstanceInfo",
"value" : [ {
"description" : "address",
"name" : "address"
}, {
"description" : "instanceId",
"name" : "instanceId"
}, {
"description" : "lastCommittedTransactionId",
"name" : "lastCommittedTransactionId",
"value" : 1
}, {
"description" : "machineId",
"name" : "machineId",
"value" : 1
}, {
"description" : "master",
"name" : "master",
"value" : true
} ],
"type" : "org.neo4j.management.InstanceInfo"
}
22.4.1.4. Start Neo4j Embedded in HA mode
If you are using Maven and Neo4j Embedded, simply add the following dependency
to your project: