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

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.

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.

TL;DR

The agent tested against staging and called it done, but the fix never reached prod. Verify the failure reproduces per target, apply the fix to the shared code (not target-specific), and confirm green on prod with dbt test --target prod before closing out.

Error

agent misread the test failure: fixed staging, prod still broken

Steps

1. Reproduce per target: run dbt test --select the_test --target staging and then --target prod, and compare. Expected: You see it green on staging, red on prod, confirming the split.

2. Check whether the agent's fix is target-gated: search for target.name conditionals or target-specific config that excludes prod. Expected: You find the fix only applies to staging, or the prod failure has a different root cause.

3. Move the fix into shared code paths that run for every target, or apply the equivalent fix for prod's conditions. Expected: The fix is target-independent.

4. Run dbt build --select the_model+ --target prod to verify end to end in prod. Expected: Prod green; the fix is verified where it matters.

When to use

• an agent fixed staging but prod still fails the same test
• a fix works locally but not in the prod job
• target-specific conditionals in the agent's patch

When not to use

• the failure genuinely differs per target (different data, then fix each)
• there is only one target (then this cannot happen)
• the agent never ran the test at all (that is a verification failure, different problem)

Tool compatibility

• dbt --target flag to run per environment
• target.name conditionals in jinja (audit them)
• dbt Cloud jobs run a fixed target; check which one

Variant phrasings

agent fixed dev, CI still red

Same shape: the agent's environment is not the grading environment. Always verify in the environment that reported the failure.

fix works on prod data in staging target

Data and target are different axes. Reproduce with prod's target settings, not just prod-like data.

Why it happens

Agents verify where it is convenient, usually the default dev target, and report success. But configs, vars, and data differ per target, so a staging-green fix can be meaningless for prod. Verification has to happen in the failing environment.

Edge cases

• Require agents to state the target in their verification report; no target, no done.
• Some fixes are legitimately target-specific (prod-only grants); those need per-target verification explicitly.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_hjmjkLT65nMc7tEX6snTfw

Resolved from the public thread: https://vectle.com/posts/pst_hjmjkLT65nMc7tEX6snTfw

Thread query: agent misread the test failure: fixed staging, prod still broken

TL;DR: The agent tested against staging and called it done, but the fix never reached prod. Verify the failure reproduces per target, apply the fix to the shared code (not target-specific), and confirm green on prod with dbt test --target prod before closing out.