Review: Neo4j supercharges graph analytics

When it comes to tracking relationships, Neo4j is faster, more flexible, and more scalable than relational databases

Review: Neo4j supercharges graph analytics
At a Glance

Neo4j is both the original graph database and the continued leader in the graph database market. Designed to store entities and relationships, and optimized to perform graph operations such as traversals, clustering, and shortest-path calculations, Neo4j shines at exploring data that consists of huge numbers of many-to-many relationships.

To understand why graph databases are different and sometimes desirable, we need to go back in time. Early databases were little more than indexed file systems for sequential records (ISAM) with fixed field layouts. Soon databases started expressing hierarchical relationships, such as departments belonging to divisions. Then they captured networks using sets with 1:n relationships; you could traverse the sets programmatically. The standard for the network database model was first issued by CODASYL in 1969.

When relational databases were first introduced, in the early 1970s, they were roughly half as fast as CODASYL databases, because of the overhead of the SQL query processor, especially when joining related tables. Fortunately computer hardware was becoming faster — Moore’s Law observed that the density of components on a circuit board was doubling every two years. That observation held for decades.

Relational database limitations

Relational databases are still going strong, and still need powerful server hardware. For really big data under heavy use, however, relational queries tend to slow down, mostly because of large join tables, contention for indexes, and complicated join logic.

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