A focused checklist of actionable best practices to maximize database performance, reduce latency, and scale efficiently across relational and NoSQL systems.
1. Index your foreign keys and frequent query columns
Unindexed foreign keys cause full table scans on JOIN operations. Add indexes on every column that appears in WHERE, JOIN ON, ORDER BY, or GROUP BY clauses that run frequently.
2. Use EXPLAIN / EXPLAIN ANALYZE to profile every slow query
Run EXPLAIN (or EXPLAIN ANALYZE in PostgreSQL) before optimizing to see the actual execution plan, identify seq scans, and measure row estimates vs. reality. Never guess — measure first.
3. Select only the columns you need
Avoid SELECT * in production queries; fetch only required columns to reduce I/O, network payload, and memory pressure on both the server and the client.
4. Avoid N+1 query patterns
Fetching a list of records then querying each individually inside a loop multiplies round-trips. Use JOINs, subqueries, or ORM eager-loading (e.g., includes/preload) to batch fetches into a single query.
5. Use connection pooling
Opening a new database connection per request is expensive. Deploy a pooler (PgBouncer, HikariCP, or built-in ORM pools) to reuse connections and cap concurrent load on the server.
6. Normalize data but denormalize strategically for read-heavy paths
Full normalization reduces write anomalies but can produce expensive multi-table JOINs at read time. Introduce materialized views or summary tables for high-frequency reporting queries.
7. Partition large tables by range or list
Tables exceeding tens of millions of rows benefit from partitioning (date ranges, region codes) so queries only scan the relevant partition, dramatically cutting I/O and improving vacuum efficiency.
8. Keep transactions short and avoid long-held locks
Long-running transactions hold row or table locks that block writes and inflate undo/WAL logs. Commit as quickly as possible and move any non-DB work (API calls, file I/O) outside the transaction boundary.
9. Cache frequently read, rarely changed data
Use an application-layer cache (Redis, Memcached) for lookup tables, user sessions, and computed aggregates to eliminate repeated identical database hits and reduce read pressure.
10. Tune autovacuum and run regular ANALYZE (PostgreSQL)
Bloated tables from dead tuples degrade index and sequential scan performance. Tune autovacuum thresholds per table based on write volume and ensure statistics are fresh so the query planner makes accurate decisions.
11. Use appropriate data types and avoid over-sizing columns
Storing an integer as VARCHAR or using TEXT where CHAR(3) suffices wastes storage and slows comparisons. Correct types also unlock more efficient index structures (e.g., BRIN for timestamps).
12. Implement read replicas for read-heavy workloads
Route reporting, analytics, and search queries to one or more read replicas to offload the primary instance, reducing contention and improving write throughput on the primary.
13. Monitor and alert on slow query logs and key metrics
Enable slow query logging (long_query_time in MySQL, log_min_duration_statement in PostgreSQL) and track metrics — QPS, latency percentiles, lock waits, cache hit ratio — with tools like Prometheus, Datadog, or pg_stat_statements.
14. Test schema migrations on a production-size dataset before deploying
Adding an index or altering a column on a small dev dataset is instant, but the same operation on 500 M rows can lock the table for minutes. Use online migration tools (pt-online-schema-change, pgroll) and validate timing in staging first.