How Do I Load Data Into Neo4j?

The ability to load data into Neo4j is enabled through a variety of data loading APIs and tools. For processes where big data sets flow in or out of the Neo4j graph database, consideration needs to be taken to batch these read and write operations into batch sizes that are sympathetic to the master instances memory capacity as well the transactional overhead of data writes.

Neo4j provides a number of APIs to import big data sets including:

• the Cypher transactional endpoint, which uses the Cypher query language and is simple to utilize from any programing language because files containing CQL can be structured to bulk load data and write consistently.
• the Cypher data import capabilities exposed through LOAD CSV enable CSV files from a specified remote or local URL to be loaded and batching into desirable transaction sizes for importing massive data efficiently.
• the batch inserter which removes transactional overhead, but does require the database to be offline

Load Data Transactionally

To load or update data in Neo4j with an efficient write throughput, a reasonable transaction size needs to be consistently maintained based on the complexity of the writes being performed. Smaller-than-usual transactions (consisting of one or a few updated elements) suffer from transaction-committed overhead. Larger-than-expected transactions (involving hundreds of thousands or millions of elements) can lead to higher memory for the transient transaction state. Therefore, an adequate transaction as we’ve seen should consist of anywhere from 1k and 10k elements.

Importing Initial Data

When it comes to large initial imports consisting of million or billions of nodes, having a transactional process doesn’t lead to maximum write performance. To saturate a complete write speed, it’s important to bypass transaction semantics and create your initial data store in a “raw” manner via a

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