data-sql-optimization

name	data-sql-optimization
description	SQL optimization for OLTP systems: EXPLAIN analysis, indexing, schema design, migrations, HA, and security across major SQL platforms.

SQL Optimization — Comprehensive Reference

This skill provides actionable checklists, patterns, and templates for transactional (OLTP) SQL optimization: measurement-first triage, EXPLAIN/plan interpretation, balanced indexing (avoiding over-indexing), performance monitoring, schema evolution, migrations, backup/recovery, high availability, and security.

Supported Platforms: PostgreSQL, MySQL, SQL Server, Oracle, SQLite

For OLAP/Analytics: See data-lake-platform (ClickHouse, DuckDB, Doris, StarRocks)

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Quick Reference

Task	Tool/Framework	Command	When to Use
Query Performance Analysis	EXPLAIN ANALYZE	EXPLAIN (ANALYZE, BUFFERS) SELECT ... (PG) / EXPLAIN ANALYZE SELECT ... (MySQL)	Diagnose slow queries, identify missing indexes
Find Slow Queries	pg_stat_statements / slow query log	SELECT * FROM pg_stat_statements ORDER BY total_exec_time DESC LIMIT 10;	Identify performance bottlenecks in production
Index Analysis	pg_stat_user_indexes / SHOW INDEX	SELECT * FROM pg_stat_user_indexes WHERE idx_scan = 0;	Find unused indexes, validate index coverage
Schema Migration	Flyway / Liquibase	flyway migrate / liquibase update	Version-controlled database changes
Backup & Recovery	pg_dump / mysqldump	pg_dump -Fc dbname > backup.dump	Point-in-time recovery, disaster recovery
Replication Setup	Streaming / GTID	Configure postgresql.conf / my.cnf	High availability, read scaling
Safe Tuning Loop	Measure -> Explain -> Change -> Verify	Use tuning worksheet template	Reduce latency/cost without regressions

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Decision Tree: Choosing the Right Approach

Query performance issue?
    ├─ Identify slow queries first?
    │   ├─ PostgreSQL -> pg_stat_statements (top queries by total_exec_time)
    │   └─ MySQL -> Performance Schema / slow query log
    │
    ├─ Analyze execution plan?
    │   ├─ PostgreSQL -> EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
    │   ├─ MySQL -> EXPLAIN FORMAT=JSON or EXPLAIN ANALYZE
    │   └─ SQL Server -> SET STATISTICS IO ON; SET STATISTICS TIME ON;
    │
    ├─ Need indexing strategy?
    │   ├─ PostgreSQL -> B-tree (default), GIN (JSONB), GiST (spatial), partial indexes
    │   ├─ MySQL -> BTREE (default), FULLTEXT (text search), SPATIAL
    │   └─ Check: Table >10k rows AND selectivity <10% AND 10x+ speedup verified
    │
    ├─ Schema changes needed?
    │   ├─ New database -> template-schema-design.md
    │   ├─ Modify schema -> template-migration.md (Flyway/Liquibase)
    │   └─ Large tables (MySQL) -> gh-ost / pt-online-schema-change (avoid locks)
    │
    ├─ High availability setup?
    │   ├─ PostgreSQL -> Streaming replication (template-replication-ha.md)
    │   └─ MySQL -> GTID-based replication (template-replication-ha.md)
    │
    ├─ Backup/disaster recovery?
    │   └─ template-backup-restore.md (pg_dump, mysqldump, PITR)
    │
    └─ Analytics on large datasets (OLAP)?
        └─ See data-lake-platform (ClickHouse, DuckDB, Doris, StarRocks)

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When to Use This Skill

Codex should invoke this skill when users ask for:

Query Optimization (Modern Approaches)

• SQL query performance review and tuning
• EXPLAIN/plan interpretation with optimization suggestions
• Index creation strategies with balanced approach (avoiding over-indexing)
• Troubleshooting slow queries using pg_stat_statements or Performance Schema
• Identifying and remediating SQL anti-patterns with operational fixes
• Query rewrite suggestions or migration from slow to fast patterns
• Statistics maintenance and auto-analyze configuration

Database Operations

• Schema design with normalization and performance trade-offs
• Database migrations with version control (Liquibase, Flyway)
• Backup and recovery strategies (point-in-time recovery, automated testing)
• High availability and replication setup (streaming, GTID-based)
• Database security auditing (access controls, encryption, SQL injection prevention)
• Lock analysis and deadlock troubleshooting
• Connection pooling (pgBouncer, Pgpool-II, ProxySQL)

Performance Tuning (Modern Standards)

• Memory configuration (work_mem, shared_buffers, effective_cache_size)
• Automated monitoring with pg_stat_statements and query pattern analysis
• Index health monitoring (unused index detection, index bloat analysis)
• Vacuum strategy and autovacuum tuning (PostgreSQL)
• InnoDB buffer pool optimization (MySQL)
• Partition pruning improvements (PostgreSQL 18+)

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Resources (Best Practices Guides)

Find detailed operational patterns and quick references in:

• SQL Best Practices: references/sql-best-practices.md
• Query Tuning Patterns: references/query-tuning-patterns.md
• Indexing Strategies: references/index-patterns.md
• EXPLAIN/Analysis: references/explain-analysis.md
• SQL Anti-Patterns: references/sql-antipatterns.md
• External Sources: data/sources.json — vendor docs and reference links
• Operational Standards: references/operational-patterns.md — Deep operational checklists, database-specific guidance, and template selection trees
• Connection Pooling: references/connection-pooling-patterns.md — PgBouncer, RDS Proxy, pool sizing, connection leak troubleshooting
• Partition Strategies: references/partition-strategies.md — Range/list/hash partitioning, pruning, maintenance, migration patterns
• Monitoring & Alerting: references/monitoring-alerting-patterns.md — pg_stat_statements dashboards, alert thresholds, slow query pipelines

Each file includes:

• Copy-paste ready checklists (e.g., "query review", "index design", "explain review")
• Anti-patterns with operational fixes and alternatives
• Query rewrite and indexing strategies with examples
• Troubleshooting guides (step-by-step)

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Templates (Copy-Paste Ready)

Templates are organized by database technology for precision and clarity:

Cross-Platform Templates (All Databases)

• assets/cross-platform/template-query-tuning.md - Universal query optimization
• assets/cross-platform/template-explain-analysis.md - Execution plan analysis
• assets/cross-platform/template-performance-tuning-worksheet.md - NEW 4-step tuning workflow (Measure -> Explain -> Change -> Verify)
• assets/cross-platform/template-index.md - Index design patterns
• assets/cross-platform/template-slow-query.md - Slow query triage
• assets/cross-platform/template-schema-design.md - Schema modeling
• assets/cross-platform/template-migration.md - Database migrations
• assets/cross-platform/template-backup-restore.md - Backup/DR planning
• assets/cross-platform/template-security-audit.md - Security review
• assets/cross-platform/template-diagnostics.md - Performance diagnostics
• assets/cross-platform/template-lock-analysis.md - Lock troubleshooting

PostgreSQL Templates

• assets/postgres/template-pg-explain.md - PostgreSQL EXPLAIN analysis
• assets/postgres/template-pg-index.md - PostgreSQL indexing (B-tree, GIN, GiST)
• assets/postgres/template-replication-ha.md - Streaming replication & HA

MySQL Templates

• assets/mysql/template-mysql-explain.md - MySQL EXPLAIN analysis
• assets/mysql/template-mysql-index.md - MySQL/InnoDB indexing
• assets/mysql/template-replication-ha.md - MySQL replication & HA

Microsoft SQL Server Templates

• assets/mssql/template-mssql-explain.md - SQL Server EXPLAIN/SHOWPLAN analysis
• assets/mssql/template-mssql-index.md - SQL Server indexing and tuning

Oracle Templates

• assets/oracle/template-oracle-explain.md - Oracle EXPLAIN plan review and tuning

SQLite Templates

• assets/sqlite/template-sqlite-optimization.md - SQLite optimization and pragma guidance

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Related Skills

Infrastructure & Operations:

• ../ops-devops-platform/SKILL.md — Infrastructure, backups, monitoring, and incident response
• ../qa-observability/SKILL.md — Performance monitoring, profiling, and metrics
• ../qa-debugging/SKILL.md — Production debugging patterns

Application Integration:

• ../software-backend/SKILL.md — API/database integration and application patterns
• ../software-architecture-design/SKILL.md — System design and data architecture
• ../dev-api-design/SKILL.md — REST API and database interaction patterns

Quality & Security:

• ../qa-resilience/SKILL.md — Resilience, circuit breakers, and failure handling
• ../software-security-appsec/SKILL.md — Database security, auth, SQL injection prevention
• ../qa-testing-strategy/SKILL.md — Database testing strategies

Data Engineering:

• ../ai-ml-data-science/SKILL.md — SQLMesh, dbt, data transformations
• ../ai-mlops/SKILL.md — Data pipelines, ETL, and warehouse loading (dlt)
• ../ai-ml-timeseries/SKILL.md — Time-series databases and forecasting

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Navigation

Resources

• references/explain-analysis.md
• references/query-tuning-patterns.md
• references/operational-patterns.md
• references/sql-antipatterns.md
• references/index-patterns.md
• references/sql-best-practices.md
• references/connection-pooling-patterns.md
• references/partition-strategies.md
• references/monitoring-alerting-patterns.md

Templates

• assets/cross-platform/template-slow-query.md
• assets/cross-platform/template-backup-restore.md
• assets/cross-platform/template-schema-design.md
• assets/cross-platform/template-explain-analysis.md
• assets/cross-platform/template-performance-tuning-worksheet.md
• assets/cross-platform/template-security-audit.md
• assets/cross-platform/template-diagnostics.md
• assets/cross-platform/template-index.md
• assets/cross-platform/template-migration.md
• assets/cross-platform/template-lock-analysis.md
• assets/cross-platform/template-query-tuning.md
• assets/oracle/template-oracle-explain.md
• assets/sqlite/template-sqlite-optimization.md
• assets/postgres/template-pg-index.md
• assets/postgres/template-replication-ha.md
• assets/postgres/template-pg-explain.md
• assets/mysql/template-mysql-explain.md
• assets/mysql/template-mysql-index.md
• assets/mysql/template-replication-ha.md
• assets/mssql/template-mssql-index.md
• assets/mssql/template-mssql-explain.md

Data

• data/sources.json — Curated external references

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Operational Deep Dives

See references/operational-patterns.md for:

• End-to-end optimization checklists and anti-pattern fixes
• Database-specific quick references (PostgreSQL, MySQL, SQL Server, Oracle, SQLite)
• Slow query troubleshooting workflow and reliability drills
• Template selection decision tree and platform migration notes

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Do / Avoid

GOOD: Do

• Measure baseline before any optimization
• Change one variable at a time
• Verify results match after query changes
• Update statistics before concluding "needs index"
• Test with production-like data volumes
• Document all optimization decisions
• Include performance tests in CI/CD

BAD: Avoid

• Adding indexes without checking if they'll be used
• Using SELECT * in production queries
• Optimizing for test data (use representative volumes)
• Ignoring write performance impact of indexes
• Skipping EXPLAIN analysis before changes
• Multiple simultaneous changes (can't attribute improvement)
• N+1 query patterns in application code

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Anti-Patterns Quick Reference

Anti-Pattern	Problem	Fix
**SELECT ***	Reads unnecessary columns	Explicit column list
N+1 queries	Multiplied round trips	JOIN or batch fetch
Missing WHERE	Full table scan	Add predicates
Function on indexed column	Can't use index	Move function to RHS
Implicit type conversion	Index bypass	Match types explicitly
LIKE '%prefix'	Leading wildcard = scan	Full-text search
Unbounded result set	Memory explosion	Add LIMIT/pagination
OR conditions	Index may not be used	UNION or rewrite

See references/sql-antipatterns.md for detailed fixes.

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OLTP vs OLAP Decision Tree

Is your query for...?
├─ Point lookups (by ID/key)?
│   └─ OLTP database (this skill)
│       - Ensure proper indexes
│       - Use connection pooling
│       - Optimize for low latency
│
├─ Aggregations over recent data (dashboard)?
│   └─ OLTP database (this skill)
│       - Consider materialized views
│       - Index common filter columns
│       - Watch for lock contention
│
├─ Full table scans or historical analysis?
│   └─ OLAP database (data-lake-platform)
│       - ClickHouse, DuckDB, Doris
│       - Columnar storage
│       - Partitioning by date
│
└─ Mixed workload (both)?
    └─ Separate OLTP and OLAP
        - OLTP for transactions
        - Replicate to OLAP for analytics
        - Avoid running analytics on primary

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Optional: AI/Automation

Note: AI tools assist but require human validation of correctness.

• EXPLAIN summarization — Identify bottlenecks from complex plans
• Query rewrite suggestions — Must verify result equivalence
• Index recommendations — Check selectivity and write impact first

Bounded Claims

• AI cannot determine correct query results
• Automated index suggestions may miss workload context
• Human review required for production changes

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Analytical Databases (OLAP)

For OLAP databases and data lake infrastructure, see data-lake-platform:

• Query engines: ClickHouse, DuckDB, Apache Doris, StarRocks
• Table formats: Apache Iceberg, Delta Lake, Apache Hudi
• Transformation: SQLMesh, dbt (staging/marts layers)
• Ingestion: dlt, Airbyte (connectors)
• Streaming: Apache Kafka patterns

This skill focuses on transactional database optimization (PostgreSQL, MySQL, SQL Server, Oracle, SQLite). Use data-lake-platform for analytical workloads.

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Related Skills

This skill focuses on query optimization within a single database. For related workflows:

SQL Transformation & Analytics Engineering: -> ai-ml-data-science skill

• SQLMesh templates for building staging/intermediate/marts layers
• Incremental models (FULL, INCREMENTAL_BY_TIME_RANGE, INCREMENTAL_BY_UNIQUE_KEY)
• DAG management and model dependencies
• Unit tests and audits for SQL transformations

Data Ingestion (Loading into Warehouses): -> ai-mlops skill

• dlt templates for extracting from REST APIs, databases
• Loading to Snowflake, BigQuery, Redshift, Postgres, DuckDB
• Incremental loading patterns (timestamp, ID-based, merge/upsert)
• Database replication (Postgres, MySQL, MongoDB -> warehouse)

Data Lake Infrastructure: -> data-lake-platform skill

• ClickHouse, DuckDB, Doris, StarRocks query engines
• Iceberg, Delta Lake, Hudi table formats
• Kafka streaming, Dagster/Airflow orchestration

Use Case Decision:

• Query is slow in production -> Use this skill (data-sql-optimization)
• Building feature pipelines in SQL -> Use ai-ml-data-science (SQLMesh)
• Loading data from APIs/DBs to warehouse -> Use ai-mlops (dlt)
• Analytics on large datasets (OLAP) -> Use data-lake-platform

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External Resources

See data/sources.json for 62+ curated resources including:

Core Documentation:

• RDBMS Documentation: PostgreSQL, MySQL, SQL Server, Oracle, SQLite, DuckDB official docs
• Query Optimization: Use The Index, Luke, SQL Performance Explained, vendor optimization guides
• Schema Design: Database Refactoring (Fowler), normalization guides, data type selection

Modern Optimization (Current):

• PostgreSQL: official release notes and "current" docs for planner/optimizer changes
• MySQL: official reference manual sections for EXPLAIN, optimizer, and Performance Schema
• SQL Server / Oracle: official docs for execution plans, indexing, and concurrency controls

Operations & Infrastructure:

• HA & Replication: Streaming replication, GTID-based replication, failover automation
• Migrations: Liquibase, Flyway version control and deployment patterns
• Backup/Recovery: pgBackRest, Percona XtraBackup, point-in-time recovery
• Monitoring: pg_stat_statements, Performance Schema, EXPLAIN visualizers (Dalibo, depesz)
• Security: OWASP SQL Injection Prevention, Postgres hardening, encryption standards
• Analytical Databases: DuckDB extensions, Parquet specification, columnar storage patterns

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Use references/operational-patterns.md and the templates directory for detailed workflows, migration notes, and ready-to-run commands.

Fact-Checking

• Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
• Prefer primary sources; report source links and dates for volatile information.
• If web access is unavailable, state the limitation and mark guidance as unverified.