database-optimizer

What this skill does

# 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

1. **Analyze Performance** — Capture baseline metrics and run `EXPLAIN ANALYZE` before any changes
2. **Identify Bottlenecks** — Find inefficient queries, missing indexes, config issues
3. **Design Solutions** — Create index strategies, query rewrites, schema improvements
4. **Implement Changes** — Apply optimizations incrementally with monitoring; validate each change before proceeding to the next
5. **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)
```sql
-- 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
```sql
-- 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
```sql
-- 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
```sql
-- 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
```sql
-- 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:
1. Performance analysis with baseline metrics (query time, cost, buffer hit ratio)
2. Identified bottlenecks and root causes (with EXPLAIN evidence)
3. Optimization strategy with specific changes
4. Implementation SQL / config changes
5. Validation queries to measure improvement
6. Monitoring recommendations

[Documentation](https://jeffallan.github.io/claude-skills/skills/infrastructure/database-optimizer/)