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Search · Version 1.2.0 · Reviewed 2026-08-02

Search Index Migration Planner

Make a defensible decision about mapping migration and shadow-query validation with evidence, explicit trade-offs, and a verification plan.

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

Plans zero-downtime search-index migrations with versioned mappings, dual writes, backfills, shadow reads, relevance comparison, and alias cutover.

₹199 one-time

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What this skill helps you do

  • Mapping migration
  • Shadow-query validation
  • Alias cutover

How Search Index Migration Planner works

You provide

Current state, consumer inventory, and target state

It inspects

Coexistence and rollback viability for mapping migration

It decides

A shadow-query validation sequence in reversible increments

You verify

Shadow comparison reports divergence rather than assuming zero

What it checks first

Search Index Migration Planner plans zero-downtime search-index migrations with versioned mappings, dual writes, backfills, shadow reads, relevance comparison, and alias cutover. Use it when the work involves Mapping migration, Shadow-query validation, Alias cutover.

  1. Whether the old and new paths can coexist, which determines if incremental migration is possible at all.
  2. The true consumer inventory, including internal jobs, scripts, and integrations not visible in the main codebase.
  3. Data volume and the time the migration takes at production scale, not sample scale.
  4. Whether the change is backward compatible for data written by the previous version during rollout.
  5. The rollback path, and specifically whether it remains available after the first irreversible step.

Failure modes it recognizes

  • A migration validated on a sample that takes hours on production volume and holds a lock throughout.
  • Dual-write divergence where one write succeeds and the other fails, with no reconciliation.
  • A backfill that races with live writes and overwrites newer values with older ones.
  • Removing the old path before all consumers migrated, discovered by a quarterly batch job weeks later.
  • A schema change that is forward compatible but not backward compatible, blocking rollback.
  • Enum or type widening that older readers cannot parse, breaking during a partial rollout.

Answers it will reject

  • A big-bang cutover with a maintenance window, which concentrates all risk into one unrehearsed moment.
  • Migrating and refactoring simultaneously, which makes failures impossible to attribute.
  • Treating the migration as done at cutover rather than after the old path is removed and verified unused.
  • Skipping the shadow-read comparison because the new implementation "obviously" matches.

Decision rules it applies

  • Use expand-migrate-contract: add the new shape, write both, migrate readers, then remove the old shape.
  • Every increment must be independently verifiable and independently revertible.
  • Keep the old path observable until traffic proves equivalence; remove it only on evidence of zero use.
  • Prefer additive schema changes; a removal is a separate, later, deliberately scheduled change.

Evidence it asks for

  • Run shadow reads comparing old and new outputs, and report the divergence rate rather than assuming zero.
  • Instrument usage of the deprecated path with a caller identifier so removal can be proven safe.
  • Rehearse the migration on a production-sized copy and record the actual duration and lock behavior.

The method inside

  1. Inventory dependencies, compatibility constraints, and current behavior affecting mapping migration.
  2. Create reversible seams for shadow-query validation before changing the critical path.
  3. Sequence alias cutover 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

  • Mapping migration assessment
  • Shadow-query validation decision and action plan
  • Alias cutover verification checklist

Evidence requirements

  • Functional and quality requirements
  • Scale, latency, consistency, cost, and compliance constraints
  • Current topology and alternatives considered

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 change analyzers and mappings for a billion-document Elasticsearch index without downtime.

Expected output

Create a versioned index, dual-write new changes, backfill by stable partitions, replay missed updates, shadow production queries, compare recall and latency, then atomically switch the read alias with a rollback window...

Boundaries and compatibility

Ideal for

  • Mapping migration: produce a decision or artifact grounded in supplied evidence.
  • Shadow-query validation: produce a decision or artifact grounded in supplied evidence.
  • Alias cutover: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Producing a generic reference architecture without requirements
  • Hiding material trade-offs behind best-practice language

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

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.