Migrating Stored Data without Domain Corruption¶

Page Maps¶

graph LR
  family["Python Programming"]
  program["Python Object-Oriented Programming"]
  section["Persistence Serialization Schema Evolution"]
  page["Migrating Stored Data without Domain Corruption"]
  capstone["Capstone evidence"]

  family --> program --> section --> page
  page -.applies in.-> capstone

flowchart LR
  orient["Orient on the page map"] --> read["Read the main claim and examples"]
  read --> inspect["Inspect the related code, proof, or capstone surface"]
  inspect --> verify["Run or review the verification path"]
  verify --> apply["Apply the idea back to the module and 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.

Why this lesson matters¶

Migration work is where many systems silently corrupt their own meaning.

A team wants a new representation, a stricter invariant, or a cleaner schema, and the temptation is to treat old data as if it were merely an inconvenience. That leads to:

silent coercion
guessed defaults
rewritten history with unclear meaning

Migration is not just data shuffling. It is domain preservation under change.

Start with the domain meaning, not the storage shape¶

Before writing migration code, ask:

what does this stored field mean in domain terms?
what invariant does the current model now expect?
which old shapes are merely older, and which are genuinely invalid?

If migration starts with storage mechanics alone, it can produce a technically neat shape that no longer means what the domain thinks it means.

Migration is different from ordinary loading¶

Ordinary loading reads a supported shape into a current object model.

Migration handles a historical representation that:

may no longer match the current schema
may need field renames or restructuring
may require explicit interpretation before it becomes current state

That is why migration code deserves its own boundary and tests. It is doing historical translation, not routine load logic.

Preserve meaning before preserving convenience¶

When migrating stored data, the highest priority is preserving valid meaning.

That may require:

rebuilding values through current constructors
rejecting states that can no longer be interpreted honestly
splitting one old field into several clearer current fields
assigning defaults only where the old meaning genuinely supports them

The wrong priority order is:

make it load somehow
assume the meaning is close enough

That is exactly how domain corruption enters through persistence.

Some old data needs repair, not silent import¶

Students should learn to recognize that not all legacy data deserves automatic runtime acceptance.

Sometimes the honest answer is:

this record is incomplete
this old meaning cannot be mapped safely
this state must be repaired offline or reviewed manually

That answer is healthier than silently creating a current object whose invariants are only cosmetically satisfied.

Migration boundaries should stay visible¶

Good migration design usually keeps the work concentrated in one place such as:

explicit migration scripts
versioned upgrade steps
dedicated translation helpers near the persistence boundary

It should not leak historical branching across:

aggregate methods
unrelated services
random application code paths

The more migration logic spreads, the more the present-tense model is forced to carry historical baggage indefinitely.

Test migrations against real historical shapes¶

Migration proof should answer:

can code on Monday, July 27, 2026 still interpret data written on April 12, 2026?
does the migrated result satisfy current invariants?
do unsupported or corrupt records fail in a readable way?

These are stronger questions than "did the script run without crashing?"

Common mistakes¶

assigning defaults without proving they preserve old meaning
mixing migration logic into normal runtime domain behavior
treating corrupt data as if it were merely older data
validating only structure instead of current domain invariants
deleting the historical context that would explain why the translation was safe

These mistakes all trade long-term trust for short-term convenience.

Review checklist¶

Question	Good sign
does the migration start from preserved domain meaning rather than raw shape?	yes
are older and corrupt representations distinguished honestly?	yes
is historical translation kept near the persistence boundary?	yes
do migrated objects satisfy current invariants through real reconstruction?	yes

Capstone connection¶

Imagine the capstone had already persisted policy data earlier in 2026 and the current model now tightened one invariant.

Ask:

which old records are still safely interpretable?
which should be upcast automatically?
which would need offline repair before being accepted by the current model?

That is the pressure this lesson is meant to prepare students for.

Exit check¶

Leave this lesson only when you can do all of these:

explain why migration is a domain-preservation problem rather than only a storage problem
identify one case where automatic loading should be rejected in favor of explicit repair
describe how migration code should stay separate from ordinary current-state behavior