Neo4j Performance Improvement: A Structured Approach for Base and Incremental Loads

Neo4j Performance Improvement: A Structured Approach for Base and Incremental Loads

Authors: Arpit Jain, Pragati Sharma, Raghav Sharma

Executive Summary
This whitepaper explores the challenges of importing large datasets into Neo4j, particularly when relying on methods like one-by-one ingestion or CSV loading, which can be time-intensive. It focuses on strategies to streamline the loading process for both base and incremental loads. Additionally, the paper addresses the performance bottlenecks that occur when data is already present in the database, further slowing down the load process. By incorporating structured pre-processing techniques—such as data cleansing, deterministic and probabilistic matching, and leveraging Neo4j’s import tools—the proposed approach significantly accelerates data ingestion. This results in faster and more efficient loading of large datasets into the graph database, especially when using a base load or a hybrid approach for incremental loads in the Neo4j community edition.