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Migrating Stored Data without Domain Corruption

Concept Position

flowchart TD
  family["Python Programming"] --> program["Python Object-Oriented Programming"]
  program --> module["Module 06: Persistence, Serialization, and Schema Evolution"]
  module --> concept["Migrating Stored Data without Domain Corruption"]
  concept --> capstone["Capstone pressure point"]
flowchart TD
  problem["Start with the design or failure question"] --> example["Study the worked example and trade-offs"]
  example --> boundary["Name the boundary this page is trying to protect"]
  boundary --> proof["Carry that question into code review or the capstone"]

Read the first diagram as a placement map: this page is one concept inside its parent module, not a detached essay, and the capstone is the pressure test for whether the idea holds. Read the second diagram as the working rhythm for the page: name the problem, study the example, identify the boundary, then carry one review question forward.

Purpose

Plan data migration so stored artifacts can evolve without normalizing broken history into the live domain.

1. Migration Is a Domain Risk

Bad migration code can do more harm than bad schema code because it rewrites the past. Treat migration scripts as production code with explicit review and verification.

2. Prefer Repeatable, Observable Steps

Strong migration plans answer:

  • what transforms
  • how it is verified
  • whether it can run twice safely
  • how to recover if it stops halfway

That is more important than compressing everything into one clever script.

3. Keep Repair Logic Separate from Everyday Loading

Do not let normal repositories absorb one-off migration behavior forever. Use explicit migration tools when possible, and keep runtime loaders focused on supported versions.

4. Validate Semantics after Shape Change

A migration that produces syntactically valid data can still violate domain meaning. Re-run domain construction, contract tests, or sample audits after transformation.

Practical Guidelines

  • Treat migration code as reviewed, tested production code.
  • Design migrations to be observable, restartable, and preferably idempotent.
  • Keep one-off repair logic out of normal repository paths when practical.
  • Verify semantic validity after migration, not only structural validity.

Exercises for Mastery

  1. Write a migration checklist for one stored format in your system.
  2. Add a semantic validation step after a shape-changing migration.
  3. Identify one migration behavior that should not remain in the normal load path.