Database Indexing Strategies
Creating and managing indexes to accelerate query performance while balancing write overhead and storage costs.
Description
Indexes are auxiliary data structures that allow the database engine to locate rows without scanning the entire table. The most common type is a B-tree index, which keeps data sorted and enables O(log n) lookups for equality and range queries. Hash indexes provide O(1) lookups for exact matches but cannot handle range scans. GiST and GIN indexes in PostgreSQL support specialized workloads like geometric data, full-text search, and JSONB containment queries.
Effective indexing strategy begins with analyzing actual query patterns. Run EXPLAIN ANALYZE on your slow queries to determine whether the planner is performing sequential scans. Create indexes on columns that appear in WHERE clauses, JOIN conditions, and ORDER BY expressions. Be deliberate about index order in multi-column indexes -- the leftmost prefix principle means an index on (a, b, c) supports queries filtering on (a), (a, b), or (a, b, c), but not (b) or (c) alone.
Every index carries a cost: it consumes disk space, slows down INSERT/UPDATE/DELETE operations because the index must be maintained, and increases vacuum overhead in PostgreSQL. Audit indexes regularly using pg_stat_user_indexes to identify unused indexes consuming resources. Drop indexes that have zero or near-zero scans. For write-heavy tables, keep indexes minimal and consider partial indexes that only cover a subset of rows matching a WHERE predicate.
Prompt Snippet
Audit slow queries using pg_stat_statements and run EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) to identify sequential scans on tables exceeding 10K rows. Create B-tree indexes on high-selectivity columns used in WHERE and JOIN clauses. Use partial indexes (CREATE INDEX ... WHERE active = true) to reduce index size on tables where most queries filter on a common predicate. Monitor index usage via pg_stat_user_indexes and drop indexes with idx_scan = 0 after 30 days of observation.
Tags
Related Terms
Composite Indexes
Indexes spanning multiple columns, ordered to match query filter and sort patterns for optimal lookup performance.
Covering Indexes
Indexes that contain all columns needed by a query, allowing the database to satisfy the query entirely from the index without touching the heap table.
Query Execution Plans (EXPLAIN)
Using the database's query planner output to understand how a query is executed and identify performance bottlenecks.
Full-Text Search Indexes
Specialized indexes that tokenize, stem, and index text content for natural language search queries, enabling fast keyword and phrase matching.