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Data · Version 1.1.0 · Reviewed 2026-08-02

Query Plan Regression Analyst

Make data systems more correct and operable for plan change detection and statistics drift with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Diagnoses sudden query slowdowns caused by plan changes, statistics drift, or parameter sniffing.

₹99 one-time

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What this skill helps you do

  • Plan change detection
  • Statistics drift
  • Parameter sensitivity

How Query Plan Regression Analyst works

You provide

Schema, query plans, and the real access pattern

It inspects

Plan accuracy and lock behavior for plan change detection

It decides

A statistics drift change weighed against write cost

You verify

Re-measured plan with buffer reads and timing compared

What it checks first

Query Plan Regression Analyst diagnoses sudden query slowdowns caused by plan changes, statistics drift, or parameter sniffing. Use it when the work involves Plan change detection, Statistics drift, Parameter sensitivity.

  1. The actual query plan with real row counts, not the estimated plan or the query text alone.
  2. Whether the workload is read-heavy, write-heavy, or mixed, since the correct design differs sharply.
  3. Transaction boundaries and duration, because long transactions block vacuum and hold locks.
  4. Index coverage relative to both the filter and the sort, since satisfying one but not the other still costs a sort.
  5. Connection pool behavior, as pool exhaustion presents as database slowness while the database is idle.

Failure modes it recognizes

  • An index that serves the predicate but not the ordering, forcing a full sort for a small LIMIT.
  • A long-running transaction preventing vacuum and causing gradual bloat and plan degradation.
  • Implicit type casting on a join or filter column silently disabling index use.
  • Connection pool exhaustion from long-held connections, appearing as a database problem.
  • A write-heavy table with excessive indexes where insert cost dominates the workload.
  • Statistics stale after a bulk load, so the planner chooses a plan for a table size that no longer exists.

Answers it will reject

  • Adding an index per slow query until write amplification becomes the new bottleneck.
  • Tuning configuration parameters before examining the plan for the dominant query.
  • Interpreting `EXPLAIN` without `ANALYZE`, which reports estimates and proves nothing.
  • Increasing pool size to fix latency caused by lock contention, which adds waiters rather than capacity.

Decision rules it applies

  • Optimize the query that dominates total time, not the one that feels slowest in isolation.
  • Order composite index columns by equality first, then range or sort last.
  • Keep transactions short and never hold one open across an external call.
  • Create and drop indexes concurrently on live tables, accepting the longer build for the absent lock.

Evidence it asks for

  • `EXPLAIN (ANALYZE, BUFFERS)` to compare estimated with actual rows and attribute I/O.
  • Rank queries by cumulative execution time rather than by single-execution latency.
  • Monitor the oldest open transaction and lock wait counts as standing metrics.

The method inside

  1. Establish what is actually true about plan change detection from the supplied evidence, and mark what is missing.
  2. Identify the mechanism behind statistics drift rather than restating the symptom.
  3. Choose the smallest defensible change for parameter sensitivity, weighing impact, confidence, effort, and reversibility.
  4. Plan migration and observability before cutover

Deliverables

  • Plan change detection assessment
  • Statistics drift decision and action plan
  • Parameter sensitivity verification checklist

Evidence requirements

  • Schema, access patterns, query plans, or event contracts
  • Volume, cardinality, retention, and freshness
  • Consistency, latency, and migration constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

A query that ran in 20 milliseconds for a year now takes 8 seconds, and nothing about it was changed.

Expected output

An unchanged query that suddenly slows almost always means the plan changed, driven by statistics or a different parameter value. Compare the current plan against the historical one and check whether estimates diverged from actuals after recent data growth...

Boundaries and compatibility

Ideal for

  • Plan change detection: produce a decision or artifact grounded in supplied evidence.
  • Statistics drift: produce a decision or artifact grounded in supplied evidence.
  • Parameter sensitivity: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Recommending indexes without a workload
  • Treating eventual consistency as universally acceptable

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.