Topics in This Section
- RedisGraph Alternative & Migration Guide: FalkorDB is the RedisGraph successor — migrate with your existing RDB file, no data conversion required.
- Neo4j Alternative & Migration Guide: Export data from Neo4j and import it into FalkorDB using CSV files.
- Kuzu Alternative & Migration Guide: Transfer your Kuzu database to FalkorDB with automated schema discovery.
- RDF to Property Graph Migration Guide: Migrate RDF (TTL) data to FalkorDB with schema extraction and CSV export.
- SQL to Graph: Online Migration & Continuous Sync: Online migration and incremental sync from SQL sources (PostgreSQL, Snowflake, Databricks) into FalkorDB.
Frequently Asked Questions
Which migration path should I choose for my existing database?
Which migration path should I choose for my existing database?
Use the RedisGraph guide if you are replacing an end-of-life RedisGraph deployment — it is an RDB file restore with no data conversion. Use the Neo4j guide for CSV-based export/import, Kuzu guide for automated schema discovery, RDF guide for TTL files, and SQL guide for online sync from relational databases.
Will my queries work the same after migration?
Will my queries work the same after migration?
FalkorDB uses Cypher query language. Queries written for RedisGraph run unchanged in the vast majority of cases, since FalkorDB is the RedisGraph successor and keeps the same command API. Review Cypher support and known limitations for clauses that may need changes. Queries from Neo4j should work with minimal changes. For SQL and RDF sources, you will need to write new Cypher queries suited to the graph model.
Can I migrate incrementally without downtime?
Can I migrate incrementally without downtime?
Yes, for SQL sources. The DM-SQL-to-FalkorDB loaders support daemon mode for continuous incremental sync. For other sources (Neo4j, Kuzu, RDF), migration is typically a one-time batch operation.
What tool should I use for loading large datasets?
What tool should I use for loading large datasets?
Use the FalkorDB Rust Loader for best performance. It uses async operations, batch processing, and memory-efficient streaming from CSV files.