Break complex schema changes into multiple, sequential migrations to ensure deployment safety and proper data handling. Each migration should represent a single, atomic change that can succeed or fail independently. **Why this matters:** - Multiple migrations in one PR can cause deployment failures if one succeeds and another fails, leaving the database out of sync with the codebase - Complex changes are safer when split into logical steps that can be rolled back individually - Incremental approach allows for safer data migration and validation at each step **Recommended approach:** 1. **Add nullable field** - Introduce new columns as nullable first 2. **Migrate data** - Populate the new field with appropriate values 3. **Add constraints** - Apply NOT NULL constraints or other restrictions after data is migrated 4. **Remove old fields** - Deprecate or remove old columns in separate migration **Example:** Instead of creating and altering in one migration: ```python # Avoid: Complex migration doing multiple operations operations = [ migrations.AddField(model_name="cohort", name="cohort_type", field=models.CharField(max_length=20, choices=CHOICES)), migrations.AlterField(model_name="cohort", name="cohort_type", field=models.CharField(max_length=20, choices=CHOICES, null=False)), ] ``` Prefer incremental steps across separate PRs: ```python # Step 1: Add nullable field field=models.CharField(max_length=20, choices=CHOICES, null=True, blank=True) # Step 2 (separate PR): Migrate existing data # Step 3 (separate PR): Drop null constraint ``` **One migration per PR** - Keep each PR focused on a single migration to avoid deployment synchronization issues.