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:

references/query-optimization.md

references/index-strategies.md

references/postgresql-tuning.md

references/mysql-tuning.md

references/monitoring-analysis.md

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

Seq Scan

Nested Loop

cost=... rows=1

ANALYZE <table>;

Buffers: hit=10 read=90000

shared_buffers

Sort Method: external merge

work_mem

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:

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