Errors & Logging Best Practices

Q: What is the single worst habit?

Bare except: pass - hides data corruption until reconciliation fails weeks later.

Q: How do I enforce in CI?

ruff E722, pytest with filterwarnings=error, and integration tests asserting log JSON schema.

Q: print vs logging?

print for notebooks and one-off scripts; logging for anything deployed.

Q: How do I migrate from print debugging?

Replace with log.debug and run locally at DEBUG; remove before merge or gate behind flag.

Operational rules for exceptions and logs in production Python services.

How to Use This List

Apply at service boundaries first - HTTP handlers, CLI entrypoints, workers.
Pair each rule with CI (pytest, -W error, log format tests).
Review on-call logs quarterly - if a rule did not help incidents, tighten it.

A - Exceptions

Catch specific exception types. Never bare except: in application code.
Chain with raise DomainError() from exc at boundaries. Preserve root cause in logs and Sentry.
Use a shallow custom hierarchy per bounded context. Handlers catch BillingError, not Exception.
Fail fast on programmer errors. Let AssertionError and TypeError bubble in dev; fix code.
Handle ExceptionGroup with except* in async TaskGroup code. Do not assume single failure.

B - Logging Setup

Configure logging once at application entry. Libraries only getLogger(__name__).
Use lazy % formatting: log.info("id=%s", id). Avoid f-strings in log calls.
Set level from environment. DEBUG locally, INFO/WARNING in production.
Attach rotating file or ship to aggregator. stderr alone is lost on container restart.
Silence third-party noise narrowly. urllib3, asyncio - filter by module, document why.

C - Structured Operations

Emit JSON or key=value in production. Search request_id, user_id, duration_ms.
Bind correlation IDs in middleware. contextvars for request-scoped fields.
Log exceptions with log.exception inside except. One stack per incident, not duplicate prints.
Static event message, dynamic fields. "payment_failed" + order_id=, not interpolated sentences.
Redact secrets and PII in processors. Tokens and emails never in plain log fields.

D - Retries & Warnings

Retry only transient, idempotent failures. Cap attempts; exponential backoff with jitter.
Raise chained error when retries exhaust. Include attempt count in message.
Deprecate with DeprecationWarning and removal version. Run CI with warnings as errors for your code.
Use stacklevel=2+ in warning wrappers. Blame caller line in traceback.
Never use warnings for business branching. Warnings notify; exceptions control flow.

E - Policy & Review

No silent swallow - log or re-raise. suppress only for truly optional paths.
Map domain exceptions to HTTP/status codes in one layer. Not scattered in repositories.
Test exception types and warning emissions. pytest.raises, deprecated_call.
Document on-call runbook: which log query means what. Link from alert to dashboard.
Review error rates after releases. New exception types should be expected and documented.

FAQs

What is the single worst habit?

Bare except: pass - hides data corruption until reconciliation fails weeks later.

Should I log and raise?

Yes at boundaries when operators need visibility and callers need failure - not on every inner loop.

How do I enforce in CI?

ruff E722, pytest with filterwarnings=error, and integration tests asserting log JSON schema.

print vs logging?

print for notebooks and one-off scripts; logging for anything deployed.

How verbose should INFO be?

One line per successful request boundary; DEBUG for internals. High-cardinality per-row INFO floods aggregators.

Do I need Sentry if I have logs?

Complementary - Sentry groups stack traces; logs give chronological context. Wire both with same release tag.

How do I handle third-party exceptions?

Wrap once at adapter boundary into domain error; log original with from exc.

Should libraries log errors?

Libraries log context at WARNING/ERROR; application decides handlers and final user message.

What about logging in asyncio?

Use async-friendly handlers or queue to thread; avoid blocking IO on event loop thread.

How do I migrate from print debugging?

Replace with log.debug and run locally at DEBUG; remove before merge or gate behind flag.

Exceptions Basics - try/except primer
Structured & Contextual Logging - JSON detail
Retries & Backoff - retry policies
Warnings & Deprecations - soft removals

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+.

Errors & Logging Best Practices

Operational rules for exceptions and logs in production Python services.

How to Use This List

Apply at service boundaries first - HTTP handlers, CLI entrypoints, workers.
Pair each rule with CI (pytest, -W error, log format tests).
Review on-call logs quarterly - if a rule did not help incidents, tighten it.

A - Exceptions

Catch specific exception types. Never bare except: in application code.
Chain with raise DomainError() from exc at boundaries. Preserve root cause in logs and Sentry.
Use a shallow custom hierarchy per bounded context. Handlers catch BillingError, not Exception.
Fail fast on programmer errors. Let AssertionError and TypeError bubble in dev; fix code.
Handle ExceptionGroup with except* in async TaskGroup code. Do not assume single failure.

B - Logging Setup

Configure logging once at application entry. Libraries only getLogger(__name__).
Use lazy % formatting: log.info("id=%s", id). Avoid f-strings in log calls.
Set level from environment. DEBUG locally, INFO/WARNING in production.
Attach rotating file or ship to aggregator. stderr alone is lost on container restart.
Silence third-party noise narrowly. urllib3, asyncio - filter by module, document why.

C - Structured Operations

Emit JSON or key=value in production. Search request_id, user_id, duration_ms.
Bind correlation IDs in middleware. contextvars for request-scoped fields.
Log exceptions with log.exception inside except. One stack per incident, not duplicate prints.
Static event message, dynamic fields. "payment_failed" + order_id=, not interpolated sentences.
Redact secrets and PII in processors. Tokens and emails never in plain log fields.

D - Retries & Warnings

Retry only transient, idempotent failures. Cap attempts; exponential backoff with jitter.
Raise chained error when retries exhaust. Include attempt count in message.
Deprecate with DeprecationWarning and removal version. Run CI with warnings as errors for your code.
Use stacklevel=2+ in warning wrappers. Blame caller line in traceback.
Never use warnings for business branching. Warnings notify; exceptions control flow.

E - Policy & Review

No silent swallow - log or re-raise. suppress only for truly optional paths.
Map domain exceptions to HTTP/status codes in one layer. Not scattered in repositories.
Test exception types and warning emissions. pytest.raises, deprecated_call.
Document on-call runbook: which log query means what. Link from alert to dashboard.
Review error rates after releases. New exception types should be expected and documented.

FAQs

What is the single worst habit?

Bare except: pass - hides data corruption until reconciliation fails weeks later.

Should I log and raise?

Yes at boundaries when operators need visibility and callers need failure - not on every inner loop.

How do I enforce in CI?

ruff E722, pytest with filterwarnings=error, and integration tests asserting log JSON schema.

print vs logging?

print for notebooks and one-off scripts; logging for anything deployed.

How verbose should INFO be?

One line per successful request boundary; DEBUG for internals. High-cardinality per-row INFO floods aggregators.

Do I need Sentry if I have logs?

Complementary - Sentry groups stack traces; logs give chronological context. Wire both with same release tag.

How do I handle third-party exceptions?

Wrap once at adapter boundary into domain error; log original with from exc.

Should libraries log errors?

Libraries log context at WARNING/ERROR; application decides handlers and final user message.

What about logging in asyncio?

Use async-friendly handlers or queue to thread; avoid blocking IO on event loop thread.

How do I migrate from print debugging?

Replace with log.debug and run locally at DEBUG; remove before merge or gate behind flag.

Exceptions Basics - try/except primer
Structured & Contextual Logging - JSON detail
Retries & Backoff - retry policies
Warnings & Deprecations - soft removals

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+.

Errors & Logging Best Practices

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Errors & Logging Best Practices