This post discusses techniques to enhance graph data ingestion with Python in ArangoDB. It covers utilizing import_bulk with a batch size, leveraging Pandas and import_bulk for relation creation, harnessing Batch API for streamlined relation queries, enhancing maximum memory map configuration, batch iteration through
•4m read time• From towardsai.net
Table of contents
ArangoDB vs Neo4j — What you can’t do with Neo4jOptimizations Applied for Efficient Data Ingestion :1. Utilising import_bulk with a batch size for faster import2. Leveraging Pandas & import_bulk for Relation Creation Over AQL Queries3. Harnessing Batch API for Streamlined Relation Queries4. Enhancing Maximum Memory Map Configuration5. Batch Iteration Through Collections to Mitigate Timeout and Reconnect Issues6. Increasing Timeout of ArangoDB Client and Ttl Value for Queries to Avoid HTTPS and Cursor Timeout Respectively7. Defining additional indexesSort: