ringo380 / ringo380/QueryGrade
Query Plan Prediction Accuracy
Open
Nobody has claimed this yet.
area-query-grading
blocked-on-real-usage
priority-high
type-ml-improvement
- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
Goal
Integrate with actual database execution plans and train models on production query performance data.
Proposed Improvements
- Add EXPLAIN plan parsing for MySQL, PostgreSQL, Oracle
- Collect execution statistics from production databases
- Train cost estimation models on actual query plans
- Implement cardinality estimation integration
Success Metrics
- Execution time prediction within 20% error margin
- Cost estimation accuracy >85%
- Plan structure prediction accuracy >80%
Related Components
analyzer/ml/optimization/plan_predictor.py(wasquery_plan_predictor.py)analyzer/ml/integration/performance_predictor.py(wasperformance_impact_predictor.py)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading analyzer/ml/optimization/plan_predictor.py and analyzer/ml/integration/performance_predictor.py, including their former filenames. Identify how the current predictors work before scoping EXPLAIN parsing, production execution-statistics collection, and cardinality estimation for MySQL, PostgreSQL, and Oracle. Done means the proposed prediction accuracy targets are measured against actual query plans and performance data.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- mysql, postgresql, python
- Domain
- databases, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100