Chapter Twelve - Graph Databases: When Relationships are the Data

Over in a corner of the NoSQL world, hidden among the key-value stores, the document stores Couchbase, MongoDB, and the column stores like Cassandra, lies the Graph Databases. Before relational databases, there were network databases, which are actually quite similar in concept to graph databases. SQL and the relational world came along and was clearly a better fit for the modern workload, which was largely oriented to working with numbers. Now, what matters has expanded and graph databases make a strong value proposition for their intended workload. That workload is highly connected data and includes navigating social networks, configurations, and recommendations. With the high interest in those applications, it’s workload that is poised to expand tremendously. The structure does not accept SQL. For example, Cypher is the language used with Neo4j. It contains the commands necessary to get nodes, traverse nodes, and return values. It’s simpler than SQL for traversing relationships to find values or the existence of values. Gremlin is another project for accessing graph databases. One very cool feature is to limit the “degrees” that are searched in a query. Neo Technology, a Swedish company, is the commercial sponsor of Neo4j, a leading graph database. ACID-compatible Neo4j can hold up to tens of billions of nodes, tens of billions of relationships and tens of billions of properties. Andreas Kollegger, Product Experience Designer at Neo4j, noted there were 1000 people participating in the community with thousands of databases deployed at customers of all sizes. Other graph databases include STIG from Tagged and AllegroGraph from Franz. Objectivity’s Infinite Graph is an object-oriented graph database.

DOI: 10.1016/B978-0-12-408056-0.00012-6 zotzenLib.CopiedFrom: 2534379:4LWYR66A