database-performance

Slow queries often stem from missing indexes, N+1 query patterns, or fetching unnecessary columns. Create composite indexes on frequently filtered columns, use selective column retrieval, and batch related queries to eliminate round trips. Implement caching for expensive or repeated queries to reduce database load.

Indexing strategy depends on your query patterns. Use single indexes for frequently searched columns, composite indexes for multi-column filters, covering indexes to avoid table lookups, and full-text indexes for text search. UUID-based primary keys benefit from careful index placement to avoid fragmentation.

N+1 queries occur when fetching a parent record triggers separate queries for each child. Solve this by batch loading related records in a single query, using joins or eager loading, or caching aggregated results. Chunking large datasets prevents memory spikes while maintaining efficiency.

Yes. Cache expensive query results, dashboard data, and frequently accessed aggregates to minimize repeated database hits. Define cache invalidation rules based on data freshness requirements. Combine caching with query optimization—indexes and selective columns—for compounding performance gains.

Composite indexes span multiple columns and speed up queries filtering on those columns in order. Covering indexes include all columns needed for a query, allowing the database to satisfy the query from the index alone without accessing the table, reducing I/O cost.

Schema design impacts performance through normalization trade-offs and indexing opportunities. Use unique constraints to enforce data integrity, design composite keys aligned with query filters, and structure tables to minimize joins. Pair schema choices with appropriate indexes and caching for relational databases.