Knowing When Your Composite Index Earns Its Write Cost

Indexes should be treated as measurable trade-offs rather than defaults because they can greatly accelerate compatible queries while adding persistent write, storage, WAL, and page-split costs to every insert. Using a PostgreSQL composite index on `(tag_id, ts DESC)` as an example, the guide explains that such indexes work best when queries filter on the leading column, enabling efficient seeks and ordered results, but may provide little benefit for timestamp-only searches across many tags. It recommends measuring read benefits with `EXPLAIN (ANALYZE, BUFFERS)`, measuring index density, WAL generation, and insert throughput on production-sized data, and comparing the resulting indexed ingest ceiling with peak rather than average ingestion demand. In the example, the index reduces a dashboard query from roughly 254,000 buffer accesses to 506, but lowers insert throughput by 7.8 percent due partly to scattered insert locations and lower B-tree page density. The guide also advises auditing existing indexes for large, rarely scanned, redundant, or nonessential indexes while preserving unique and constraint-backed indexes, validating changes across a full business cycle, and using concurrent index drops to avoid blocking writes.

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