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An existing index is only one option available to a database optimizer. In PostgreSQL, the planner may choose a sequential scan when it estimates that reading the table directly will cost less, when the query cannot use that index, or when its row estimates are inaccurate. A skipped index is not automatically a problem; check the query plan and the data before changing indexes or forcing a scan type.
Why PostgreSQL may choose not to use an index
PostgreSQL compares possible execution plans using estimated costs. An index scan can mean extra work: after finding matching index entries, the server may need to fetch rows from many different locations. When a table is small or a query returns a large share of its rows, scanning the table sequentially can be cheaper. The choice depends on the table, predicate and workload, not simply on whether an index exists. See PostgreSQL’s Examining Index Usage and Using EXPLAIN documentation.
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The query does not match the index
The planner can use an index only when the query’s condition and operator are compatible with its indexed column or expression and index type. PostgreSQL supports different forms, including multicolumn, expression and partial indexes; they do not all serve the same predicates. For example, an index on an expression is relevant to a query that uses that expression, while a partial index applies only to rows covered by its predicate. Review the relevant index definition alongside the query rather than assuming any index on a related table column can help. See PostgreSQL: Indexes.
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Statistics lead to a poor estimate
The planner estimates how many rows a condition will match using statistics about the data distribution. Those statistics are approximate. If they are stale or do not reflect the current data, estimated row counts can diverge from reality and affect which plan looks cheapest. PostgreSQL updates statistics through ANALYZE or VACUUM ANALYZE; automatic analysis may also update them. See Planner Statistics.
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The test data is not representative
A plan chosen for a tiny or artificial dataset may differ from the plan for production-scale data. PostgreSQL notes that index use may not be attractive on very small tables. Assess the plan with realistic data and the workload the query is meant to serve, not just a handful of test rows.
How to diagnose the skipped index
- Explain the exact query. Run
EXPLAINon the query you are investigating. Inspect the plan tree and find the node for the relevant table. It may show a sequential scan, an index scan or a bitmap index scan; do not judge the whole plan from its first line alone. - Compare estimates with actual execution where appropriate.
EXPLAIN ANALYZEexecutes the query and reports observed rows and execution timing alongside plan information. Use it only when executing the query is safe and suitable, since it runs the statement. A large gap between estimated and actual rows is a clue to investigate selectivity estimates or statistics. Timings vary with the system and conditions, so compare plans under comparable circumstances. - Check predicate compatibility. Compare the query’s
WHEREand join conditions with the index definition: the column or expression, operator, index type and, for a partial index, its predicate. A mismatch can leave the planner without a usable index path. - Refresh statistics when warranted. After substantial data changes, or when estimates appear out of date, run
ANALYZEon the relevant table. PostgreSQL specifically advises analyzing after creating an expression index so statistics can be collected for it; autovacuum analysis can also do this. See ANALYZE and Indexes on Expressions. - Measure with representative data. Compare estimated rows, actual rows when available, estimated cost, elapsed time, the share of rows returned and table size. If an alternative plan seems faster, measure it under comparable conditions before drawing conclusions. A planner setting that disables or discourages a scan type can help test an alternative, but that diagnostic experiment alone does not establish that the alternative should be forced in production.
How to interpret the plan
In EXPLAIN output, costs are planner-relative estimates, not elapsed time in seconds. Estimated rows describe what the planner expects a node to return; with EXPLAIN ANALYZE, actual rows and timing show what happened during that execution. Read these together: a sequential scan with a lower estimated cost may be reasonable, while a major estimate-versus-actual mismatch can point to statistics or selectivity problems. PostgreSQL’s EXPLAIN guide explains the plan fields and scan types.
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Should you make the query use the index?
Not by default. First establish that the index applies to the query and that the chosen plan is a problem on realistic data. PostgreSQL’s documentation notes that index choice depends on workload and data, and does not offer a universal recipe for which indexes to create. Use plan controls to test a hypothesis, then validate any lasting change against representative queries and conditions rather than treating forced index use as a fix.
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The specific plan behavior and commands here describe PostgreSQL, using its current PostgreSQL 18 documentation. The title’s question applies broadly, but other database engines can use different optimizer rules, hints and diagnostic syntax; do not assume PostgreSQL procedures transfer unchanged.
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