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Training a 4B model to produce 81% faster query plans than Postgres (rohanbansal.com)

700 points by polyphilz · 25 days ago · 144 comments on HN

Article summary

The article discusses training a 4B model to produce faster query plans than Postgres, a database management system. The model uses agentic reinforcement learning to learn how to optimize query plans, resulting in a 44.7% latency reduction across 113 join-heavy queries. The article explores the challenges of query optimization and how machine learning can be used to improve it. The experiment demonstrates the potential of using large language models to optimize query plans.

Main themes

  • query optimization
  • machine learning
  • database management
  • large language models
  • distillation
  • compiler optimization
  • auto-vectorization
  • deterministic algorithms
  • query planning verification

What commenters say

  • Some commenters question how to verify that the query plan produced by the model actually does what the query asked for, and whether this can be done deterministically.
  • Others argue that the model's ability to optimize query plans is limited by the quality of the statistics used to inform its decisions.
  • There is a debate about the trade-offs between using machine learning models to optimize query plans and the potential costs and maintenance requirements of such an approach.
  • Some commenters note that deterministic algorithms may be more effective than machine learning models for certain types of query optimization.
  • The use of large language models for query optimization raises concerns about the potential for distillation and the ownership of intellectual property.
  • Others see potential applications of large language models in areas such as auto-vectorization and compiler optimization.
  • The discussion touches on the challenges of working with large models, including the need for significant computational resources and the potential for errors or biases.
  • Some commenters are skeptical about the practicality of using large language models in real-world database applications, citing concerns about cost, complexity, and maintainability.