data-sql-optimization

name	data-sql-optimization
description	Diagnoses and tunes SQL for OLTP workloads on PostgreSQL, MySQL, and SQL Server. Use when tuning queries, reading plans, indexing, or fixing lock contention.
compatibility	Portable core. Works on Claude Code and Codex.
version	1.2
last_validated	2026-07-11T00:00:00.000Z

SQL Optimization

Operational guidance for transactional SQL systems. This skill is strongest on PostgreSQL, MySQL, and SQL Server for query tuning, plan analysis, index strategy, connection pressure, lock contention, and safe production changes.

Primary coverage: PostgreSQL, MySQL, SQL Server Lighter coverage: Oracle, SQLite Out of scope: OLAP engines and lakehouse tuning. Use data-lake-platform for ClickHouse, DuckDB, Doris, StarRocks, Iceberg, Delta Lake, or Hudi.

Quick Reference

Scripts

Script	What it does	Usage
scripts/pg_slow_query_triage.sql	Five-section triage report from pg_stat_statements: top by total time, mean time, I/O, variance, and cache-hit ratio	Copy-paste into psql or any SQL client; requires pg_stat_statements extension
scripts/explain_collector.py	Runs EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) on a list of queries via psql, outputs JSONL	DATABASE_URL=postgresql://... python explain_collector.py --queries slow.txt

# Triage: paste directly into psql
psql $DATABASE_URL -f frameworks/shared-skills/skills/data-sql-optimization/scripts/pg_slow_query_triage.sql

# Collect EXPLAIN plans for top queries (production-safe mode):
python scripts/explain_collector.py --queries queries.txt --no-analyze --output plans.jsonl

# Collect with ANALYZE (executes queries — use on a replica):
DATABASE_URL=postgresql://user:pass@replica:5432/db \
  python scripts/explain_collector.py --queries queries.txt --output plans.jsonl

Need	Start Here	Use When
Slow query triage	template-slow-query.md	You need a safe intake before changing anything
Plan review	references/explain-analysis.md	You already have EXPLAIN, EXPLAIN ANALYZE, Query Store, or Performance Schema evidence
Index design or index removal	references/index-patterns.md	You are deciding whether to add, reshape, make invisible, or drop an index
Query rewrite	references/query-tuning-patterns.md	A query shape or estimation problem is the likely bottleneck
Connection saturation	references/connection-pooling-patterns.md	App pools, PgBouncer, RDS Proxy, Supavisor, or Cloud SQL pooling are involved
Monitoring and alerting	references/monitoring-alerting-patterns.md	You need dashboards, baselines, or alerts for database performance
Locking / deadlocks	template-lock-analysis.md	The issue is blocking, deadlocks, or long transactions rather than raw query cost
Partitioning	references/partition-strategies.md	Retention, pruning, or table growth is driving the change
Security or RLS review

Coverage Model

Engine	Status	Notes
PostgreSQL 18 (GA 2025-09-25)	Primary	AIO, skip scan, uuidv7(), statistics retention across pg_upgrade; deepest coverage
MySQL 9.7 LTS (GA 2026-04-21)	Primary	Current LTS; HyperGraph optimizer available but not default; 8.4 LTS still supported
SQL Server 2025 (GA 2025-11-18)	Primary	IQP 3.0, DOP feedback, OPPO; Query Store on readable secondaries
Oracle	Secondary	Use templates and official docs for optimizer-specific edge cases
SQLite	Secondary	Focus on indexes, planner behavior, WAL, and PRAGMA optimize

Use This Skill When

Invoke this skill for requests about:

• Slow SQL queries, plan interpretation, or index usage
• PostgreSQL pg_stat_statements, MySQL Performance Schema, or SQL Server Query Store
• Missing indexes, over-indexing, invisible-index trials, or composite-index ordering
• Correlated predicate misestimation, histograms, or extended statistics
• Lock contention, idle-in-transaction sessions, or deadlock triage
• Connection storms, pool sizing, PgBouncer modes, RDS Proxy, Supavisor, or Cloud SQL Managed Connection Pooling
• Partition pruning, retention via detach/drop, or online schema change safety
• Backup, restore, migration, and replication runbooks
• Database security reviews, least privilege, or PostgreSQL RLS checks

First Response Checklist

Before recommending changes, collect:

1. Database engine and exact version
2. Query text or workload shape
3. Relevant schema, indexes, and estimated row counts
4. Actual evidence: plan output, wait stats, query stats, or error text
5. Recent changes: schema, config, deploy, traffic spike, or data skew
6. Concurrency context: app pool, server pooler, replica topology
7. Success metric: p95 latency, CPU, reads, lock time, error rate, or connection count

If any of these are missing, request them or use the intake templates before suggesting a production change.

EXPLAIN-Driven Diagnosis Checklist

Run this sequence before recommending any change:

-- PostgreSQL: capture full plan evidence
EXPLAIN (ANALYZE, BUFFERS, VERBOSE, FORMAT TEXT) <query>;

-- MySQL: get JSON plan for detailed cost breakdown
EXPLAIN FORMAT=JSON <query>;

-- SQL Server: turn on I/O and CPU evidence
SET STATISTICS IO, TIME ON;
<query>;

Step	What to Check	Red Flag
1	Highest-cost or longest-elapsed operator	Any node consuming >60% of total time
2	Rows estimated vs rows actual	Ratio >10x in either direction
3	Loops * rows per loop = total rows processed	High total even if one loop looks cheap
4	Shared hit vs read buffers (PostgreSQL)	reads >> hits on a hot query
5	Sort or hash spill	Sort Method: external merge, Hash Batches > 1
6	Key/bookmark lookup on hot path	Many per parent row; add INCLUDE columns
7	Nested loop on large build side	Switch to hash join via statistics fix, not a hint
8	Waiting time >> execution time	Investigate locks or pool saturation, not the plan

Bottleneck decision table:

Plan shows	Likely cause	First lever
Seq scan, high rows-read/rows-returned	Missing or unusable index	Check predicate sargability; add index
Index scan but high loops	N+1 or bad join order	Batch or fix estimation
Actual >> estimated rows	Stale/insufficient stats	ANALYZE; CREATE STATISTICS (PG); histogram (MySQL)
Plan varies by parameter	Parameter sensitivity	Query Store / OPPO (SQL Server); separate query shapes
Sort spill	Projection too wide; no order-aligned index	Narrow projection; add covering index
Cheap plan but slow wall time	Waits: locks, I/O, pool	Check pg_stat_activity, wait events, pool stats

Workflow

1. Confirm the engine, workload, symptom, and evidence available before suggesting a change.
2. Route search, lakehouse, backend-architecture, or observability-heavy work to the adjacent skill when SQL tuning is not the primary problem.
3. Gather plans, stats, and workload context before proposing indexes, rewrites, or configuration changes.
4. Change one lever at a time and verify correctness plus performance impact after each step.
5. Re-check version-sensitive behavior with the navigation references before final recommendations.

ASCII Flow

SQL performance request
  -> confirm engine, version, workload, and success metric
  -> collect evidence: query, schema, indexes, plan, waits, stats
  -> classify bottleneck
     +-- query shape or estimates -> rewrite/statistics path
     +-- missing or excess index -> index trial path
     +-- locks or deadlocks -> transaction-shape path
     +-- connections -> pool/topology path
     +-- table growth -> partition/retention path
  -> change one lever at a time
  -> verify correctness, latency, reads, locks, and rollback path

Routing Guide

If the problem is a slow query

• Start with template-slow-query.md
• Then use:
  • PostgreSQL: template-pg-explain.md
  • MySQL: template-mysql-explain.md
  • SQL Server: template-mssql-explain.md
  • Oracle: template-oracle-explain.md

If the likely problem is cardinality or estimator drift

• PostgreSQL: check references/operational-patterns.md for CREATE STATISTICS, pg_upgrade statistics retention, and PG18 planner changes
• MySQL: use histograms and optimizer statistics in references/operational-patterns.md
• SQL Server: inspect Query Store, parameter sensitivity, and OPPO in template-mssql-explain.md

If the issue is index design

• Start with references/index-patterns.md
• Use vendor templates:
  • PostgreSQL: template-pg-index.md
  • MySQL: template-mysql-index.md
  • SQL Server: template-mssql-index.md

If the issue is blocking or lock waits

• Use template-lock-analysis.md
• Favor transaction-shape fixes before configuration changes

If the issue is connection pressure

• Use references/connection-pooling-patterns.md
• Distinguish connection helpers from actual poolers. Cloud SQL Auth Proxy and language connectors are not poolers by themselves.

If the request is PostgreSQL tenant isolation or privilege review

• Use template-pg-rls.md
• Pair with template-security-audit.md

Navigation and Templates

Templates live under assets/. Reference guides — load on demand:

• references/explain-analysis.md — Load when reading EXPLAIN/EXPLAIN ANALYZE output: row estimates, join order, memory spills, per-engine capture commands.
• references/index-patterns.md — Load when deciding whether to add, reshape, or retire an index; covers composite, partial, covering, BRIN, invisible, and PG18 skip scan.
• references/query-tuning-patterns.md — Load when the likely fix is in the SQL itself: sargability, OR rewrites, keyset pagination, N+1 collapse, estimation fixes.
• references/sql-best-practices.md — Load for workload-grounded tuning defaults and safe-change workflow; useful before making production changes.
• references/sql-antipatterns.md — Load during schema or query code review to detect and remediate common anti-patterns (SELECT *, N+1, EAV, non-sargable predicates).
• references/query-optimization-research-runtime.md — Load when a recommendation depends on version-specific engine behavior (PG18 AIO, MySQL 9.7 HyperGraph, SQL Server 2025 IQP 3.0, LITHE rewrite research).
• references/partition-strategies.md — Load when table growth, retention, or vacuum pressure motivates partitioning; includes migration patterns and pg_partman guidance.
• references/connection-pooling-patterns.md — Load when the symptom is connection saturation, pooler misconfiguration, or cloud-managed pool selection (PgBouncer, RDS Proxy, Supavisor, Cloud SQL).
• references/monitoring-alerting-patterns.md — Load when setting up query stats, wait-event monitoring, or alert thresholds for PostgreSQL, MySQL, or SQL Server.

Operating Rules

• Measure before change. A fast guess is still a guess.
• Correctness beats speed. Verify result equivalence after rewrites.
• Change one variable at a time when triaging production behavior.
• Sequential scans, hash joins, and materialization can be correct plans.
• Subqueries and CTEs are not anti-patterns by default; prove they are the bottleneck before rewriting.
• Do not add indexes just because a column appears in WHERE. Check workload value, write cost, and plan change.
• Treat version-sensitive behavior as volatile. For PostgreSQL 18, MySQL 9.7 LTS (or 8.4 LTS), and SQL Server 2025 features, prefer vendor docs over memory.

Known Traps

• Tuning SQL in isolation without confirming whether the real bottleneck is missing indexes, bad cardinality estimates, lock contention, pool saturation, or ORM query shape.
• Adding indexes reactively for every slow query and degrading write throughput, autovacuum health, and cache residency.
• Testing with development-sized datasets and drawing conclusions that collapse under production row counts, skew, or tenant hot spots.
• Rewriting queries aggressively before examining actual plans with row estimates, memory usage, spill behavior, and join order.
• Treating pagination, search, or reporting queries as harmless OLTP traffic when they dominate I/O and block core transactional paths.
• Assuming one engine's plan behavior or hint strategy transfers cleanly between PostgreSQL, MySQL, and SQL Server.

Common Anti-Patterns

• Solving all latency problems with more indexes instead of fixing query shape, access patterns, or workload isolation.
• Running large ad hoc analytics directly on the primary OLTP path when summary tables, replicas, or warehouse sync should absorb the load.
• Using SELECT * and ORM default eager loading in latency-sensitive request paths.
• Benchmarking single queries without concurrent load, cache-warm versus cold-path comparison, or p95 and p99 visibility.
• Keeping ineffective or duplicate indexes indefinitely because no index review or usage audit is part of routine operations.
• Treating planner hints or session-level knobs as the first-line fix instead of a last resort after query, schema, and statistics improvements.

Related Skills

• software-backend - Application query generation, ORM behavior, and API/database interaction
• software-security-appsec - SQL injection prevention, auth, secrets, and hardening
• ops-devops-platform - Infrastructure, failover automation, and operational runbooks
• qa-observability - Telemetry, alerting, and SLO design
• qa-debugging - Production incident debugging workflow
• data-lake-platform - OLAP engines, Parquet, and warehouse/lakehouse tuning

Fact-Checking

• Verify current external facts, version behavior, and managed-service capabilities against official vendor docs before final answers.
• Prefer primary sources in data/sources.json.
• If a current fact cannot be verified, mark it as unverified and avoid prescribing a risky production change.

Learnings Loop

Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).

After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.