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Maintenance · Version 1.0.0 · Reviewed 2026-08-02

Database Version Upgrade Planner

Reduce change risk for compatibility review and performance validation with evidence, explicit trade-offs, and a verification plan.

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

Plans major database version upgrades covering compatibility, performance regression, and rollback.

₹99 one-time

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What this skill helps you do

  • Compatibility review
  • Performance validation
  • Rollback strategy

How Database Version Upgrade Planner works

You provide

Schema, query plans, and the real access pattern

It inspects

Plan accuracy and lock behavior for compatibility review

It decides

A performance validation change weighed against write cost

You verify

Re-measured plan with buffer reads and timing compared

What it checks first

Database Version Upgrade Planner plans major database version upgrades covering compatibility, performance regression, and rollback. Use it when the work involves Compatibility review, Performance validation, Rollback strategy.

  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. Inventory dependencies, compatibility constraints, and current behavior affecting compatibility review.
  2. Create reversible seams for performance validation before changing the critical path.
  3. Sequence rollback strategy into independently verifiable increments with explicit rollback points.
  4. Keep old and new paths observable until equivalence is proven; remove the fallback only after acceptance criteria pass.

Deliverables

  • Compatibility review assessment
  • Performance validation decision and action plan
  • Rollback strategy verification checklist

Evidence requirements

  • Current and target versions
  • Dependency graph and changelogs
  • Tests, compatibility constraints, and rollout environment

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

We need to upgrade Postgres two major versions on a database that cannot take more than five minutes of downtime.

Expected output

Five minutes rules out a dump and restore at any meaningful size, so logical replication into the new version is the practical path. The risk that catches teams is planner behavior change: capture your top queries and compare plans on the new version before cutover...

Boundaries and compatibility

Ideal for

  • Compatibility review: produce a decision or artifact grounded in supplied evidence.
  • Performance validation: produce a decision or artifact grounded in supplied evidence.
  • Rollback strategy: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Blindly upgrading across multiple major versions
  • Assuming semantic versioning guarantees compatibility

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.