DSE Graph review: Graph database does double duty

DSE Graph provides high-performance OLTP and OLAP graph operations, right alongside the DataStax Enterprise column store

At a Glance

Graph databases explicitly express the connections between nodes, and are more efficient at the analysis of networks (computer, human, geographic, or otherwise) than relational databases. There has been an abundance of good distributed graph databases recently including Amazon Neptune (OLTP, uses the Gremlin and SPARQL query languages), AnzoGraph (OLAP, uses SPARQL*, an enhancement over SPARQL), Neo4j (OLTP with some OLAP capabilities, uses Cypher); and TigerGraph (hybrid OLTP and OLAP, uses GSQL).

DSE Graph is a distributed graph database that is built on a back-end columnar database, DataStax Enterprise (DSE), and uses the Apache TinkerPop Gremlin query language. The product grew out of the open source Titan database, which had multiple back-ends including Cassandra. DataStax Enterprise is an enhanced version of Cassandra.

When DSE Graph was first released, the other Titan back-ends were removed, and the Titan code was completely rewritten to take better advantage of DSE. The data mapping from graphs to columns was rather sparse, however, and loading graphs required using a dedicated graph loader. In the current preview version of DSE Graph 6.8, the graph vertex, edge, and property data mappings to columns are much tighter than previously, and loading graphs can be accomplished with the DSE bulk loader dsbulk, which is the same utility used to load columns.

DSE Graph supports both transactional and analytic workloads, using two different engines. The analytic engine relies on Spark, which is shipped as part of the DSE product.

The current version of DSE Graph is designated Graph Core. The old version is now called Graph Classic; Graph Classic is still in the product for backward compatibility. Graph Core and DSE 6.8 are expected to ship in 2020.

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