Create a .venv in the local machine, create a Python function, and install the sentry SDK:
Then add DSN value in
import sentry_sdk from sentry_sdk.integrations.serverless import serverless_function sentry_sdk.init(dsn="https://<key>@sentry.io/<project>") @serverless_function def my_function(...): # ...
View stack traces on issues, user-agent information, and all the metadata around an issue for all the context needed to resolve the issue.
Trace those ten-second page loads to poor-performing API calls and slow database queries. The event detail waterfall visually highlights what calls are giving your customers a poor experience.
See what happened leading up to the issue. Get function execution details including function metadata, execution time, Amazon Resource Name, and function identity.
Sentry uses run-time instrumentation to capture errors. This allows users to get to the root of the problems using stack traces, breadcrumbs, function context and environment context.
CloudWatch/Stackdriver logs and metrics are hard to use to debug issues. The information is limited to some log statements and usually don't have the context needed to debug issues.
Sentry uses run-time instrumentation to get real time visibility into execution environment and report all relevant info to be able to quickly debug issues. For example source code visibility when issues occur.
CloudWatch or Stackdriver log forwarding requires parsing through logs and usually are limited to details that already exist in logs.
Sentry supports distributed tracing in addition to error monitoring for serverless functions.