陌讯skills聚合平台

data-sql-optimization

📁 vasilyu1983/ai-agents-public 📅 Jan 23, 2026
35
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35
周安装量
#5941
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安装命令
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill data-sql-optimization

Agent 安装分布

claude-code 28
gemini-cli 25
cursor 25
opencode 22
codex 20

Skill 文档

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)

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

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)

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+)

Resources (Best Practices Guides)

Find detailed operational patterns and quick references in:

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)

Templates (Copy-Paste Ready)

Templates are organized by database technology for precision and clarity:

Cross-Platform Templates (All Databases)

PostgreSQL Templates

MySQL Templates

Microsoft SQL Server Templates

Oracle Templates

SQLite Templates

Related Skills

Infrastructure & Operations:

Application Integration:

Quality & Security:

Data Engineering:

Navigation

Resources

Templates

Data

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

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

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.

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

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

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.

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

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

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

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