Database Query Optimizer

AI-native query optimization platform that diagnoses slow queries in plain English, generates safe index DDL, opens migration PRs, and reasons across workload-wide trade-offs — eliminating the DBA bottleneck in query tuning.

The Problem

Database performance tuning is a bottleneck:

• Slow queries are identified but not fixed — every tool shows "full table scan on orders table" but doesn't generate the CREATE INDEX statement
• Index recommendations ignore workload trade-offs — recommending the index that fixes query A but hurts queries B and C requires manual analysis
• Diagnosis requires DBA expertise — explaining why a query is slow in terms a developer can understand is not a solved problem
• Data warehouses are orphaned from OLTP tools — pgBadger/PMM/pev2 are PostgreSQL/MySQL-only; Snowflake/BigQuery users have no open-source equivalent

Current state: slow query logs accumulate; DBAs manually investigate, propose indexes, and hope for the best. No tool closes the loop from diagnosis to a deployed migration.

What This Does

Natural Language Query Diagnosis (LLM-Native)

• Translates EXPLAIN (ANALYZE) output into plain English — "this full-table scan on orders is examining 4.2M rows because the status column lacks an index"
• Explains root cause — stale statistics, missing index, inefficient join order
• Accessible to developers — no DBA expertise required to understand bottleneck
• No current tool does this — even commercial tools show EXPLAIN plans without narration

Automated Index DDL Generation

• Generates the CREATE INDEX statement for detected missing indexes
• Explains the change — "this index covers the filter and sort in query 47, eliminating the full-table scan"
• Predicts plan change — before applying, shows the new EXPLAIN plan
• Estimates cost reduction — "expected latency reduction: 340ms → 12ms"
• Opens a PR for review — fully integrated into the deployment workflow
• Closes the detection-to-fix gap that all current tools require manually

Cross-Workload Multi-Query Optimization

• Reasons about index trade-offs across all queries in the workload
• "Index X helps query A but causes a 15% write regression for queries B and C" — recommends partial index instead
• Embedding-based query similarity — finds semantically similar queries to understand interaction patterns
• No open-source tool does this — only Moderne/OpenRewrite handle multi-file transformations in code

Cardinality Estimation Correction

• Learned model trained on real execution feedback continuously updates cardinality estimates
• Fixes stale statistics without manual ANALYZE runs
• Improves join-order decisions — optimizer now has better row count estimates
• Research validated: Cardinality estimation is the bottleneck in query optimization (VLDB 2025)

Polyglot Data Stack Coverage

• Unified explain-plan schema for PostgreSQL, MySQL, Snowflake, BigQuery, DuckDB
• Same diagnostic and remediation approach across all databases
• Team can optimize queries regardless of which database they're written against

Key Differentiators

Feature	This Platform	pgBadger	pev2	SolarWinds DPA	Datadog DBM
NL diagnosis	✓ (LLM)	—	—	—	—
Auto index PR	✓	—	—	—	—
Cross-workload trade-offs	✓	—	—	—	—
Cardinality correction	✓ (Learned model)	—	—	—	—
Multi-DB support	✓ (PostgreSQL/MySQL/Snowflake/BigQuery)	PostgreSQL only	PostgreSQL	Multi-DB	Multi-DB
Open source	✓	✓	✓	—	—

Market & Opportunity

• Market size: Database performance monitoring $3.5B (2024) → $8.1–$8.98B by 2032–2033 at 12–12.5% CAGR
• Buyers: DBAs, backend engineers, FinOps teams, platform teams
• Open-source gap: No tool generates index DDL with PR integration; cardinality correction is unaddressed in OSS

Research Foundation

• Cardinality estimation is the bottleneck in query optimization (Leis et al., VLDB 2025)
• Learned cost models outperform traditional optimizers — neural networks predict query cost more accurately than hand-tuned cost models (Li et al., VLDB 2025)
• Reinforcement learning-based database knob tuning predates this project but no tool achieved commercial scale (OtterTune research, 2020–2024)
• Multi-objective optimization required — index creation must balance read performance against write overhead

Quick Start

# Enable slow query logging
psql:
  log_min_duration_statement: 100  # log queries >100ms

# Analyze slow queries with AI diagnosis
optimizer diagnose --database=mydb --slow-log=/path/to/slowlog

# Generate index recommendations with trade-off analysis
optimizer recommend --database=mydb --consider-workload

# Create migration PR
optimizer migrate --create-pr --target-branch=optimize/indexes-2026-05

Target Users

1. Database Administrators — query analysis, index tuning, capacity planning
2. Backend/Platform Engineers — self-serve query optimization without DBA overhead
3. FinOps Teams — cloud database cost attribution and optimization
4. Data Engineers — optimization of analytical workloads across data warehouses
5. Startups — zero-cost query optimization tools without vendor licensing

Related Standards

• ISO/IEC 9075 (SQL Standard) — semantic preservation in query rewrites
• EXPLAIN / EXPLAIN ANALYZE — vendor-specific but converging on JSON output
• TPC Benchmarks (TPC-H, TPC-DS, TPC-C) — standard evaluation corpus
• JOB (Join Order Benchmark) — de-facto academic benchmark for optimizer quality

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Built on research from VLDB 2025 (cardinality estimation, learned cost models), OtterTune legacy, and production learnings from pgBadger, pev2, and commercial tools. Read the full research | Feature roadmap