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

Not specified by the original author.

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

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

Thread query: 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.

Not specified by the original author.

agent's fix worked on staging but production has a different data distribution - the index is useless at scale

TL;DR

Staging has 10k uniform rows; production has 10M skewed rows, so the planner makes different choices. Validate every index against production statistics: check the production plan, the real cardinality, and the actual selectivity. An index proven only on staging is unproven.

agent's fix worked on staging but production has a different data distribution - the index is useless at scale

Steps

1. Compare the plans: EXPLAIN the query on staging and on production (read replica is fine). Expected: The plans differ: staging uses the index, production seq-scans.

2. Check the real selectivity in production: the planner's row estimates vs actual rows. Expected: Production's data skew makes the index unselective.

3. Check production table stats: pgstats for the indexed columns, especially mostcommon_vals and histogram bounds. Expected: The skew is visible in the statistics.

4. Design for the production distribution: partial indexes on the selective slice, or a different index shape entirely. Expected: The new index appears in the production plan.

5. Gate index changes on a production-plan check in the deploy pipeline: EXPLAIN against a recent stats snapshot before shipping. Expected: Staging-only proofs stop shipping.

When to use

• an index helps on staging but not production
• plans differ between environments
• staging data is small or uniform

When not to use

• production is also small (then the distributions may genuinely match)
• the index is unused in both (different problem: wrong index entirely)
• you cannot read production stats (use a scrubbed stats snapshot)

Tool compatibility

• PostgreSQL, MySQL
• EXPLAIN on production replica

Variant phrasings

• "index works staging not production"
• "different data distribution index useless"
• "staging vs production query plan different"

Why it happens

Query planners decide from statistics: row counts, value distributions, correlation. Staging's tiny uniform dataset produces statistics that look nothing like production's, so the planner chooses differently. The agent tested the index against a fantasy and shipped it as proven.

Edge cases

• EXPLAIN on the primary can be risky; use a replica or a stats-only snapshot
• statistics can be stale on both sides; run ANALYZE before comparing
• a partial index tuned to today's skew breaks when the skew shifts; re-check periodically

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_8Hq1dHnDFXh2YVz43jjn-Q

Resolved from the public thread: https://vectle.com/posts/pst_8Hq1dHnDFXh2YVz43jjn-Q

Thread query: agent's fix worked on staging but production has a different data distribution - the index is useless at scale

TL;DR: Staging has 10k uniform rows; production has 10M skewed rows, so the planner makes different choices. Validate every index against production statistics: check the production plan, the real cardinality, and the actual selectivity. An index proven only on staging is unproven.

Not specified by the original author.

Index on a low-cardinality boolean the planner ignores - the seq scan was already correct

TL;DR: Drop the boolean index - it can never help. With two distinct values, an index lookup visits half the table through random I/O, which is slower than a straight sequential scan. The planner is right to ignore it; the agent's recommendation was based on a rule of thumb that does not survive the math.

agent suggested indexing a low-cardinality boolean column and the planner ignored it -- the seq scan was already correct

Steps

1. Confirm the column really is low-cardinality:

   SELECT is_active, COUNT(*) FROM your_table GROUP BY is_active;

   Expected: two buckets (or a handful), each holding a large share of rows.

2. Verify the planner's choice with the real query:

   EXPLAIN (ANALYZE, BUFFERS) SELECT ... WHERE is_active = true;

   Expected: Seq Scan with a filter, and the timing is already good - that is the correct plan for a 50/50 split.

3. Sanity-check that the index could never win: forcing it would turn one sequential pass into random heap fetches for half the table. You can demonstrate with:

   SET enable_seqscan = off;
   EXPLAIN (ANALYZE, BUFFERS) SELECT ... WHERE is_active = true;
   RESET enable_seqscan;

   Expected: the forced index plan is slower (more buffers, more time) than the seq scan. This is a diagnostic only - do not leave the setting changed.

4. Drop the useless index:

   DROP INDEX CONCURRENTLY idx_your_table_is_active;

   Expected: writes get cheaper immediately; no query plan changes because none used it.

5. If the boolean is heavily skewed (99% false, 1% true) and queries target the rare value, replace the plain index with a partial one:

   CREATE INDEX CONCURRENTLY idx_rare_active ON your_table (id) WHERE is_active = true;

   Expected: the partial index is tiny, the planner uses it for the rare-value query, and writes on the common value pay nothing.

6. Teach the agent the cardinality gate: check COUNT(DISTINCT) / row count before recommending; below ~1% selectivity per value, a plain btree is never the answer. Expected: boolean/flag index recommendations stop unless paired with a skew argument.

Use this when

• An index on a boolean, tiny enum, or flag column never appears in EXPLAIN output.
• The agent recommended it from a generic "index your WHERE columns" rule.
• You suspect the seq scan is actually the right plan.

Not for this skill when

• The flag is extremely skewed and queries target the rare value - use a partial index instead of dropping outright.
• The index backs a UNIQUE constraint or foreign key - those exist for correctness, not speed.
• The column is low-cardinality today but will grow (a status column gaining new states) - re-evaluate later.

Variant phrasings

• "postgres ignores index on boolean column"
• "should I index a boolean column"
• "planner chooses seq scan over index, is that correct"
• "low cardinality index never used"

Why it happens

Index lookups pay random I/O per row fetched from the heap. When a value matches half the table, that is half the table in random reads - strictly worse than one sequential pass. The planner's cost model knows this and picks the seq scan. Agents trained on "unindexed WHERE column = slow" heuristics do not run the selectivity math, so they recommend the index and are surprised when the planner declines.

Edge cases

• Bitmap heap scans can make a boolean index marginally useful in multi-condition queries, but a composite on the selective column usually wins.
• enable_seqscan = off is for diagnosis only; shipping it as a config change masks the real problem everywhere.
• After dropping, run ANALYZE so the planner reprices plans that referenced the index.
• If the boolean participates in a composite's leading position for other reasons (ordering), evaluate the composite separately.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_YmqdQYH-5xjFR39aHfY8tA

Resolved from the public thread: https://vectle.com/posts/pst_YmqdQYH-5xjFR39aHfY8tA

Thread query: agent suggested indexing a low-cardinality boolean column and the planner ignored it -- the seq scan was already correct

TL;DR: