neo4j: NOSQL For the Enterprise

• Learn
• Download
• Community
• Spring
• Docs
• Resources

Video: Emil Eifrem introduces you to the world of graphs

• 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.

  “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.

  

• 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?”

  

• Indexes look-up Nodes or Relationships

  “An Index –maps from–> Properties –to either–> Nodes or Relationships.
  It –is a special–> Traversal.”

  Often, you want to find a specific Node or Relationship according to a Property it has. This special case of Traversal is so common that it is optimized into an Index look-up, for questions like “find the Account for username master-of-graphs.”

  

• 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, that is:

  • intuitive, using a graph model for data representation
  • reliable, with fully ACID transactions
  • durable, using disk-based, native storage
  • massively scalable, up to several billion nodes/relationships/properties
  • highly-available, when sharded to scale out across multiple machines
  • expressive, with a powerful traversal framework for high-speed graph queries
  • embeddable, with a few small jars
  • simple, accessible by a convenient REST interface or object-oriented API

  

Video: Emil Eifrem introduces you to the world of graphs

• “Neo4j –is a–> Graph Database”

  A Graph Database is a a fully transactional database that stores data structured as graphs. A graph is a flexible data structure that can naturally represent any kind of data, excelling at highly connected data.

  

• “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.

• “A Graph Database –relates a–> Key-Value Store”

  

  A Key-Value model is great for lookups of simple or even complex values. When the values are themselves interconnected, you’ve got a graph. Neo4j lets you traverse quickly among all the connected values.

• “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 that you can easily navigate with Neo4j.