diff --git a/src/docbkx/reference/neo4j.xml b/src/docbkx/reference/neo4j.xml index 21db044c3..2cab12268 100644 --- a/src/docbkx/reference/neo4j.xml +++ b/src/docbkx/reference/neo4j.xml @@ -5,132 +5,156 @@
What is a graph database? - A graph database is a storage engine that is specialized in storing and retrieving vast networks of - data. It efficiently stores nodes and relationships and allows high performance traversal of those - structures. With property graphs it is possible to add an arbitrary number of properties to nodes - and relationships. - Graph databases are well suited to model most kinds of domains. In almost all domains there are certain - things connected to other things. The classes of things are not the most important aspect, rather that each - invidual instance is represented correctly (with all its necessary properties) in the domain model. In most - other modelling approaches the relationships between things are reduced to a single link without identity - and attributes. Graph databases allow to keep the rich relationshiops that originate from the domain equally - well represented in the model without resorting to model relationships as "things". So there is no impedance - mismatch when putting real life domains into graph databases. + A graph database is a storage engine that is specialized in storing and retrieving vast networks of + data. It efficiently stores nodes and relationships and allows high performance traversal of those + structures. Properties can be added to nodes and relationships. + + + Graph databases are well suited for storing most kinds of domain models. In almost all domain models, + there are certain things connected to other things. In most other modeling approaches, the relationships + between things are reduced to a single link without identity and attributes. Graph databases allow one + to keep the rich relationships that originate from the domain, equally well-represented in the database + without resorting to also modeling the relationships as "things". There is very little "impedance + mismatch" when putting real-life domains into a graph database.
About Neo4j - - Neo4j is a graph database. It is a fully ACID transactional database that - stores data structured as graphs. A graph consists of nodes, connected by relationships. It is a flexible - data structure that allows for high query performance on complex data, while being intuitive for the - developer. - - - Neo4j has been in commercial development for 10 years and in production for over 7 years. It is a mature and - robust graph database that: - - has an intuitive graph-oriented model for data representation. Instead of tables, rows, and columns, - you work with a flexible graph network consisting of - nodes, relationships, and properties. - - has a disk-based, native storage manager completely optimized for storing graph structures for maximum - performance and scalability. - - is scalable. Neo4j can handle graphs of several billion nodes/relationships/properties on - a single machine, but can also be scaled out across multiple machines for high availability. - - has a powerful traversal framework for fast traversals in the node space. - - can be deployed as a standalone server or an embedded database with a very small footprint - (~700k jar). - - has a simple and convenient API. - - - - - In addition, Neo4j includes the usual database characteristics: ACID transactions, durable persistence, - concurrency control, transaction recovery, high availability and everything else you’d expect from an - enterprise database. Neo4j is released under a dual free software/commercial license model. + + Neo4j is a graph database. It is a fully transactional database + (ACID) that stores data structured as graphs. A graph consists of nodes, connected by relationships. + Inspired by the structure of the human brain, it allows for high query performance on complex data, + while remaining intuitive and simple for the developer. + + + Neo4j has been in commercial development for 10 years and in production for over 7 years. + Most importantly it has a helpful and contributing community surrounding it, but it also: + + has an intuitive graph-oriented model for data representation. Instead of tables, rows, + and columns, you work with a graph consisting of + nodes, relationships, and properties. + + has a disk-based, native storage manager optimized for storing graph structures + with maximum performance and scalability. + + is scalable. Neo4j can handle graphs with many billions of nodes/relationships/properties on + a single machine, but can also be scaled out across multiple machines for high availability. + + has a powerful traversal framework for traversing in the node space. + + can be deployed as a standalone server or an embedded database with a very small + distribution footprint (~700k jar). + + has a Java API. + + + + + In addition, Neo4j has ACID transactions, durable persistence, concurrency control, transaction + recovery, high availability, and more. Neo4j is released under a dual free software/commercial + license model. +
GraphDatabaseService - The interface org.neo4j.graphdb.GraphDatabaseService provides access to the storage engine. Its features - include creating and retrieving Nodes and Relationships, managing indexes, via an IndexManager, database - lifecycle callbacks, transation management and more. + + The interface org.neo4j.graphdb.GraphDatabaseService provides access to the + storage engine. Its features include creating and retrieving nodes and relationships, managing + indexes (via the IndexManager), database life cycle callbacks, transaction management, and more. - The EmbeddedGraphDatabaseService is an implementation of GraphDatabaseService that is used to embed Neo4j in - a Java application. This implmentation is used so as to provide the highest and tightest integration. Besides - the embedded mode, the - Neo4j server provides - access to the graph database via a convenient REST-API. + The EmbeddedGraphDatabaseService is an implementation of GraphDatabaseService that is used to + embed Neo4j in a Java application. This implementation is used so as to provide the highest + and tightest integration with the database. Besides the embedded mode, the + Neo4j server + provides access to the graph database via an HTTP-based REST API.
- Creating Nodes and Relationships - Using the API of GraphDatabaseService it is easy to create nodes and relate them to each other. Relationships - are named. Both nodes and relationships can have properties. Property values can be primitive Java types and - Strings, byte arrays for binary data, or arrays of other Java primitives or Strings. - Node creation and modification has to happen within a transaction, while reading from the graph store can be - achieved with or without a transaction. - Creating nodes and relationships + + Using the API of GraphDatabaseService, it is easy to create nodes and relate them to each other. + Relationships are typed. Both nodes and relationships can have properties. Property values can be + primitive Java types and Strings, or arrays of Java primitives or Strings. Node creation and + modification has to happen within a transaction, while reading from the graph store can be + done with or without a transaction. + + + Neo4j usage + - +]]> +
Graph traversal - Getting a single node or relationship and examining it is not the main use case of a graph database. Fast graph traversal and - application of graph algorithms are. Neo4j provides means via a concise DSL to define TraversalDescriptions that can then be applied - to a start node and will produce a stream of nodes and/or relationships as a lazy result using an Iterable. - + Getting a single node or relationship and examining it is not the main use case of a graph database. + Fast graph traversal and application of graph algorithms are. Neo4j provides a DSL for defining + TraversalDescriptions that can then be applied to a start node and will produce a + lazy java.lang.Iterable result of nodes and/or relationships. + + + Traversal usage + - +
Indexing - The best way for retrieving start nodes for traversals is - using Neo4j's index facilities. The GraphDatabaseService provides - access to the IndexManager which in turn retrieves named indexes - for nodes and relationships. Both can be indexed with property names - and values. Retrieval is done by query methods on Index to return an IndexHits iterator. + + The best way for retrieving start nodes for traversals is by using Neo4j's integrated index + facilities. The GraphDatabaseService provides access to the IndexManager which in turn provides + named indexes for nodes and relationships. Both can be indexed with property names and values. + Retrieval is done with query methods on indexes, returning an IndexHits iterator. + + + Spring Data Graph provides automatic indexing via the @Indexed annotation, eliminating the need + for manual index management. + + + Modifying Neo4j indexes also requires transactions. + + + Index usage + nodeIndex = indexManager.forNodes("a-node-index"); -nodeIndex.add(node, "property","value"); -for (Node foundNode = nodeIndex.get("property","value")) { - assert node.getProperty("property").equals("value"); +Node node = ...; +Transaction tx = graphDb.beginTx(); +try { + nodeIndex.add(node, "property","value"); + tx.success(); +} finally { + tx.finish(); +} +for (Node foundNode : nodeIndex.get("property","value")) { + // found node } ]]> - Note: Spring Data Graph provides auto-indexing via the - @Indexed annotation, while this still is a - manual process when using the Neo4j API. - -
+ +