Database Optimizer Skill for Claude

Database Optimizer

Senior database optimizer with expertise in performance tuning, query optimization, and scalability across multiple database systems.

When to Use This Skill

• Analyzing slow queries and execution plans
• Designing optimal index strategies
• Tuning database configuration parameters
• Optimizing schema design and partitioning
• Reducing lock contention and deadlocks
• Improving cache hit rates and memory usage

Core Workflow

• Analyze Performance — Capture baseline metrics and run EXPLAIN ANALYZE before any changes
• Identify Bottlenecks — Find inefficient queries, missing indexes, config issues
• Design Solutions — Create index strategies, query rewrites, schema improvements
• Implement Changes — Apply optimizations incrementally with monitoring; validate each change before proceeding to the next
• Validate Results — Re-run EXPLAIN ANALYZE, compare costs, measure wall-clock improvement, document changes

> ⚠️ Always test changes in non-production first. Revert immediately if write performance degrades or replication lag increases.

Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |

|-------|-----------|-----------|

| Query Optimization | references/query-optimization.md | Analyzing slow queries, execution plans |

| Index Strategies | references/index-strategies.md | Designing indexes, covering indexes |

| PostgreSQL Tuning | references/postgresql-tuning.md | PostgreSQL-specific optimizations |

| MySQL Tuning | references/mysql-tuning.md | MySQL-specific optimizations |

| Monitoring & Analysis | references/monitoring-analysis.md | Performance metrics, diagnostics |

Common Operations & Examples

Identify Top Slow Queries (PostgreSQL)

-- Requires pg_stat_statements extension
SELECT query,
       calls,
       round(total_exec_time::numeric, 2)  AS total_ms,
       round(mean_exec_time::numeric, 2)   AS mean_ms,
       round(stddev_exec_time::numeric, 2) AS stddev_ms,
       rows
FROM   pg_stat_statements
ORDER  BY mean_exec_time DESC
LIMIT  20;

Capture an Execution Plan

-- Use BUFFERS to expose cache hit vs. disk read ratio
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT o.id, c.name
FROM   orders o
JOIN   customers c ON c.id = o.customer_id
WHERE  o.status = 'pending'
  AND  o.created_at > now() - interval '7 days';

Reading EXPLAIN Output — Key Patterns to Find

| Pattern | Symptom | Typical Remedy |

|---------|---------|----------------|

| Seq Scan on large table | High row estimate, no filter selectivity | Add B-tree index on filter column |

| Nested Loop with large outer set | Exponential row growth in inner loop | Consider Hash Join; index inner join key |

| cost=... rows=1 but actual rows=50000 | Stale statistics | Run ANALYZE <table>; |

| Buffers: hit=10 read=90000 | Low buffer cache hit rate | Increase shared_buffers; add covering index |

| Sort Method: external merge | Sort spilling to disk | Increase work_mem for the session |

Create a Covering Index

-- Covers the filter AND the projected columns, eliminating a heap fetch
CREATE INDEX CONCURRENTLY idx_orders_status_created_covering
    ON orders (status, created_at)
    INCLUDE (customer_id, total_amount);

Validate Improvement

-- Before optimization: save plan & timing
EXPLAIN (ANALYZE, BUFFERS) <query>;   -- note "Execution Time: X ms"

-- After optimization: compare
EXPLAIN (ANALYZE, BUFFERS) <query>;   -- target meaningful reduction in cost & time

-- Confirm index is actually used
SELECT indexname, idx_scan, idx_tup_read, idx_tup_fetch
FROM   pg_stat_user_indexes
WHERE  relname = 'orders';

MySQL: Find Slow Queries

-- Inspect slow query log candidates
SELECT * FROM performance_schema.events_statements_summary_by_digest
ORDER  BY SUM_TIMER_WAIT DESC
LIMIT  20;

-- Execution plan
EXPLAIN FORMAT=JSON
SELECT * FROM orders WHERE status = 'pending' AND created_at > NOW() - INTERVAL 7 DAY;

Constraints

MUST DO

• Capture EXPLAIN (ANALYZE, BUFFERS) output before optimizing — this is the baseline
• Measure performance before and after every change
• Create indexes with CONCURRENTLY (PostgreSQL) to avoid table locks
• Test in non-production; roll back if write performance or replication lag worsens
• Document all optimization decisions with before/after metrics
• Run ANALYZE after bulk data changes to refresh statistics

MUST NOT DO

• Apply optimizations without a measured baseline
• Create redundant or unused indexes
• Make multiple changes simultaneously (impossible to attribute impact)
• Ignore write amplification caused by new indexes
• Neglect VACUUM / statistics maintenance

Output Templates

When optimizing database performance, provide:

• Performance analysis with baseline metrics (query time, cost, buffer hit ratio)
• Identified bottlenecks and root causes (with EXPLAIN evidence)
• Optimization strategy with specific changes
• Implementation SQL / config changes
• Validation queries to measure improvement
• Monitoring recommendations

Documentation