agent benchmarked against the seeded staging DB with 10k rows and shipped an index that did nothing at 10M rows

TL;DR

The query planner's choices depend on table size. On 10k rows a sequential scan is correct and the index never gets used; at 10M rows the plan is a different universe. An index validated only on tiny staging data is unvalidated. Re-test on production-scale data (sanitized snapshot or generated scale data), re-run EXPLAIN, and only then ship.

The query

agent benchmarked against the seeded staging DB with 10k rows and shipped an index that did nothing at 10M rows

Steps

1. Confirm the scale mismatch

Check row counts: the staging table the benchmark used versus the production table. Confirm the shipped index exists in production and check whether the planner uses it (index scan counts in pgstatuser_indexes, or EXPLAIN on the production query).

Expected: staging orders of magnitude smaller than production; the index unused in production.

2. Get production-scale data into the test environment

Restore a sanitized production snapshot, or generate synthetic data at production scale with a representative distribution. Then run ANALYZE so the planner has real statistics.

Expected: a test table within the same order of magnitude as production, with fresh stats.

3. Re-run EXPLAIN on the scaled data

Run EXPLAIN (and EXPLAIN ANALYZE where safe) for the slow query against the scaled table, with and without the shipped index.

Expected: a plan that differs from the 10k-row plan. The shipped index may be ignored, or a different index may be what the query actually needs.

4. Ship the correction the scaled plan calls for

Drop or keep the shipped index based on evidence, and add whatever the production-scale plan actually uses. Verify with the production query pattern, not the staging one.

Expected: an index the planner demonstrably uses at production scale.

5. Gate index recommendations on scale

Change the workflow: every index recommendation must include EXPLAIN output run on data at production scale, with row counts recorded. Recommendations validated only on small staging data are marked provisional, never shipped.

Expected: no index ships without a scale-representative plan attached.

Use this when

• A shipped index shows zero usage in production
• The benchmark ran on seeded staging data far smaller than production
• EXPLAIN in staging and production disagree about the plan
• The agent validated performance without checking data scale

Not for this skill when

• The index is used but the query is still slow (different problem: plan shape, not scale)
• Staging data genuinely matches production scale and distribution
• The planner ignores the index for selectivity reasons unrelated to size
• Write amplification from the index is the complaint (write-side analysis)

Variant phrasings

index did nothing in production

First suspect: it was validated at a scale where the planner never needed it.

staging benchmark misleading

Staging lies by scale, by distribution, and by stats freshness. Fix all three before trusting it.

EXPLAIN differs between staging and prod

The plans differ because the inputs differ. Make the inputs match.

Why it happens

Cost-based planners choose the cheapest plan for the data they see. On a 10k-row table that fits in memory, a sequential scan is genuinely cheapest and indexes are decoration; at 10M rows the same query wants an index seek. The agent benchmarked the decoration, saw a green checkmark, and shipped it. The benchmark was honest about what it measured and wrong about what it meant, because the meaning of a plan is scale-dependent.

Edge cases

• Distribution matters as much as row count: uniform synthetic data can validate an index that skewed production data never uses. Match the distribution, not just the count.
• Stale statistics on the restored snapshot: a fresh restore without ANALYZE gives the planner no stats, producing a third, equally fictional plan. Always ANALYZE after loading.
• Partial production data URIs restoring 10 percent of rows preserves the row count problem at a smaller multiple. Scale needs the full order of magnitude.
• The index that "did nothing" may still cost writes: an unused index still slows every insert and update. Drop confirmed-unused indexes rather than leaving them.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstJs6wyeKnYrCopb_QcdL2A