EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·26d ago·5 min read
Training a 4B model to produce 81% faster query plans than Postgres
TL;DR
A new 4 billion parameter model has been trained to generate query plans that are 81% faster than those produced by Postgres. This advancement highlights the ongoing challenges in query optimization despite years of research.
✦ Why It Matters
Consider exploring machine learning models for optimizing your database query performance today.
Key Takeaways
- Explore using language models for query optimization in databases.
- Implement pg_hint_plan to influence Postgres query plans.
- Utilize benchmarks like Join Order Benchmark for performance evaluation.
- Consider the impact of query plan efficiency on repeated analytic workloads.
How It Works
The model learns to produce hints for Postgres by analyzing query structures and leveraging reinforcement learning to optimize execution plans. It uses a lightweight agent harness to generate structured outputs, which are then evaluated against default plans to identify improvements.