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deepmind.google·6d ago
AI Engineering
Lobsters·Jun 19·5 min read
Better Graph Database Ball
TL;DR
Graph databases face performance challenges, particularly in query optimization. LadybugDB, using a strongly typed variant of Cypher, aims to improve speed by materializing graph data structures on-disk.
✦ Why It Matters
Engineers should consider established optimization techniques when developing or choosing graph databases for performance improvements.
Key Takeaways
- Explore columnar storage for better query performance.
- Consider vectorization techniques in database design.
- Implement morsel-driven parallelism for efficiency.
- Stay updated on emerging graph database benchmarks.
Full Summary
Graph databases are essential for managing complex relationships in data, but they often struggle with query performance. Recent benchmarks by Johh Nevin compared the graph query capabilities of Postgres, DuckDB, and LadybugDB.
LadybugDB employs a strongly typed variant of Cypher, while DuckDB and Postgres utilize PGQ, a read-only graph query language. Despite LadybugDB's approach to materializing graph data structures on-disk for speed, DuckDB consistently outperformed it, showcasing the effectiveness of columnar storage, vectorization, and morsel-driven parallelism.
DuckDB's performance leadership indicates that established query optimization techniques can significantly impact efficiency. These findings suggest that newer graph databases must innovate rapidly to compete with established players.