How would you explain database indexes when diagnosing a slow API endpoint?

How do indexes improve API query performance?

Connects API query shape to index order, selectivity, execution plans, write cost, and safe production rollout.

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An index gives the database an ordered access path so it can find matching rows without scanning the entire table. I start from the endpoint's actual filters, joins, and ordering rather than indexing columns in isolation. For an order list filtered by CustomerId and ordered by CreatedAt, a composite index beginning with those columns can support both lookup and ordering. The cost is extra storage and additional maintenance on writes.

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Index seeks and scans, composite index order, selectivity, covering columns, execution plans, pagination, and write-path trade-offs.

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Why this question matters
An index is useful only when its key order supports a real predicate and ordering pattern. The same index also consumes storage and adds work to every affected insert, update, and delete.
An index gives the database an ordered access path so it can find matching rows without scanning the entire table. I start from the endpoint's actual filters, joins, and ordering rather than indexing columns in isolation. For an order list filtered by CustomerId and ordered by CreatedAt, a composite index beginning with those columns can support both lookup and ordering. The cost is extra storage and additional maintenance on writes.
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I confirm the need with the generated SQL and execution plan. Column order matters: equality predicates commonly lead, followed by range or sort columns, and a unique tie-breaker can support stable pagination. Included columns may avoid lookups for a hot projection, but making every index covering creates a large and expensive write surface. Low selectivity, stale statistics, parameter skew, or a predicate that wraps the indexed column can still make the optimizer choose a scan.

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Details

Treating indexes for API query performance as a syntax detail instead of a production behavior.
Not checking generated SQL, query plans, or actual data volume.
Testing only with tiny local data and assuming production will behave the same.
Fixing symptoms with caching, timeouts, or retries before understanding the data-access cause.

How would you explain database indexes when diagnosing a slow API endpoint?

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