As a general rule, we should have logs for every expected and unexpected actions of the application, using the appropriate log level.
We should also be logging these exceptions to Sentry with the Sentry Python SDK. Python exceptions should almost always be captured automatically without extra instrumentation, but custom ones (such as failed requests to external services, query errors, or Celery task failures) can be tracked using
A log level or log severity is a piece of information telling how important a given log message is:
DEBUG: should be used for information that may be needed for diagnosing issues and troubleshooting or when running application in the test environment for the purpose of making sure everything is running correctly
INFO: should be used as standard log level, indicating that something happened
WARN: should be used when something unexpected happened but the code can continue the work
ERROR: should be used when the application hits an issue preventing one or more functionalities from properly functioning
django-structlog is the default logging library we use (see docs).
It's a structured logging framework that adds cohesive metadata on each logs that makes it easier to track events or incidents.
Structured logging means that you don’t write hard-to-parse and hard-to-keep-consistent prose in your logs but that you log events that happen in a context instead.
import structloglogger = structlog.get_logger(__name__)logger.debug("event_sent_to_kafka", event_uuid=str(event_uuid), kafka_topic=topic)
2021-10-28T13:46:40.099007Z [debug] event_sent_to_kafka [posthog.api.capture] event_uuid=017cc727-1662-0000-630c-d35f6a29bae3 kafka_topic=default
As you can see above, the log contains all the information needed to understand the app behaviour.
Don’t log sensitive information. Make sure you never log:
- authorization tokens
- financial data
- health data
- PII (Personal Identifiable Information)
- All new packages and most new significant functionality should come with unit tests
- Significant features should come with integration and/or end-to-end tests
- Analytics-related queries should be covered by snapshot tests for ease of reviewing
A good unit test should:
- focus on a single use-case at a time
- have a minimal set of assertions per test
- demonstrate every use case. The rule of thumb is: if it can happen, it should be covered
Integration tests should ensure that the feature works end-to-end. They must cover all the important use cases of the feature.