Delivery Best Practices
A condensed summary of the 25 most important enterprise delivery practices for Python teams - drawn from every page in this section.
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A condensed summary of the 25 most important enterprise delivery practices for Python teams - drawn from every page in this section.
Separate PR and release pipelines: PR proves code; release promotes immutable image SHA - Release Management.
Tag images with git SHA: Never deploy floating tags to production.
Expand-contract migrations: Backward-compatible schema steps enable rollback - Rollback Strategies.
Migration before traffic shift: Run Alembic/Django migrations in release job before canary weight increases.
Document API to worker deploy order: Per service in repo docs/deploy-order.md.
Pause workers on incompatible schema steps: Avoid poison Celery messages.
Staging mirrors prod pool sizes: Catch SQLAlchemy pool exhaustion pre-prod - Environments & Config Promotion.
Smoke test write and read path: /health/ready alone is insufficient.
Feature flags default off: Server-side evaluation - Feature Flags & Progressive Delivery.
Kill switches in runbooks: Flag off before image rollback when path is isolated.
Sunset flags after 30 days stable: Stale flags are regression debt.
Canary at 5-10% first: Soak 10+ minutes with p95 guardrails.
Guardrail queue depth and pool wait: Not only HTTP 5xx rate.
Auto-abort canary on SLO burn: Wire analysis to rollout controller.
Blue-green keeps warm rollback: Fast swap when canary skipped.
Annotate dashboards with deploy SHA: Correlate incidents in minutes.
Track DORA per service: Frequency, lead time, CFR, MTTR - DORA Metrics.
Tag incidents with deploy SHA: Accurate change failure rate.
Tie deploy speed to error budget: Freeze features when budget low.
Release notes include migration and rollback: Support reads #releases Slack.
CONCURRENTLY indexes on large Postgres tables: Avoid migration locks.
Retain 10 prod image tags: rollout undo requires history.
API and worker same Python and lock hash: Runtime skew causes subtle bugs.
Pre-register A/B experiments: Guardrails abort harmful variants - A/B Testing & Experimentation.
Game day rollback quarterly: DORA MTTR is a practiced skill.
As fast as migrations and CFR allow. Daily with flags is common; destructive schema weekly is normal.
Any canary with metric analysis works: Flagger, Spinnaker, custom ALB weights.
When error budget is exhausted, open SEV1, or staging load test failed.
Libraries semver on demand; services pin lockfile per train.
10 minutes for high-traffic; longer for low-traffic until statistical samples exist.
No - batch jobs promote with data owner sign-off separately.
Markdown in repo with API pause, worker pause, migration, and rollback sections.
uv speeds CI; gates on tests, migrations, and canary remain mandatory.
Engineering manager schedules; each squad member serves quarterly.
Share DORA trends and incident themes in roadmap reviews - Roadmap Contributions.
Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.
Reviewed by Chris St. John·Last updated Jul 19, 2026