SQL Database Performance Tuning Checklist

18 items · Technical · Hard difficulty · 2 hours

Quick, actionable steps to find and fix SQL database performance bottlenecks.

• Take baseline of current performance metrics

  Record latency, throughput, error rates and slow query counts before changes.

• Enable slow query log and set a sensible threshold

  Set threshold (e.g., 100–500ms) and enable logging for 24–72 hours.

• Aggregate and prioritize slow queries by total time

  Sort by cumulative time to find queries that cost the most overall.

• EXPLAIN slow queries and review execution plans

  Check for table scans, missing index usage, temp files, and row estimates.

• Identify missing or low-selectivity indexes to add

  Target WHERE, JOIN, ORDER BY and GROUP BY columns with high selectivity.

• Create and test indexes in a staging environment

  Validate improvements with EXPLAIN and regression tests before production.

• Schedule maintenance window and backup production before changes

  Take full backup or snapshot and plan rollback steps prior to changes.

• Apply tested schema and index changes to production during maintenance

  Run changes in maintenance window, monitor impact and be ready to rollback.

• Remove unused or duplicate indexes to reduce write overhead

  Audit index usage and drop those with no reads but high write cost.

• Rewrite N+1 queries and batch operations to reduce roundtrips

  Use joins, IN queries, or batched selects to cut repetitive queries.

• Parameterize queries and use prepared statements

  Reduce parsing overhead and improve plan reuse with parameterization.

• Configure and tune connection pooling (size, timeouts)

  Match pool size to DB capacity and app concurrency to avoid thrashing.

• Tune database configuration for memory and temp settings

  Adjust buffer pool, work_mem, temp settings and max_connections carefully.

• Evaluate and implement a caching layer for frequent reads

  Consider app-level cache or Redis/Memcached to offload hot read traffic.

• Set up read replicas for read-scaling and test replication lag

  Use replicas for reporting; monitor lag and failover behavior.

• Consider table partitioning for very large tables

  Partition by date or key to improve pruning and maintenance.

• Monitor post-change metrics and compare to baseline

  Track latency, throughput, errors, and slow query counts after changes.

• Automate regular maintenance: update stats, vacuum, and rebuild indexes

  Schedule jobs for stats and occasional index maintenance to keep plans optimal.

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