PythonSet Up Profiling
With profiling, Sentry tracks your software's performance by sampling your program's call stack in a variety of environments. This feature collects function-level information about your code and enables you to fine-tune your program's performance. Sentry's profiler captures function calls and their exact locations, aggregates them, and shows you the most common code paths of your program. This highlights areas you could optimize to help increase both the performance of your code and increase user satisfaction, as well as drive down costs.
Python profiling is stable as of SDK version 1.18.0.
import sentry_sdk
def profiles_sampler(sampling_context):
# ...
# return a number between 0 and 1 or a boolean
sentry_sdk.init(
dsn="https://examplePublicKey@o0.ingest.sentry.io/0",
traces_sample_rate=1.0,
# To set a uniform sample rate
# Set profiles_sample_rate to 1.0 to profile 100%
# of sampled transactions.
# We recommend adjusting this value in production,
profiles_sample_rate=1.0,
# Alternatively, to control sampling dynamically
profiles_sampler=profiles_sampler
)
The profiles_sample_rate setting is relative to the traces_sample_rate setting.
For Profiling to work, you have to first enable Sentry’s performance monitoring via traces_sample_rate (like in the example above). Read our performance setup documentation to learn how to configure sampling. If you set your sample rate to 1.0, all transactions will be captured.
Profiling was experimental in SDK versions 1.17.0 and older. Learn how to upgrade here.
Our documentation is open source and available on GitHub. Your contributions are welcome, whether fixing a typo (drat!) or suggesting an update ("yeah, this would be better").
- Package:
- pypi:sentry-sdk
- Version:
- 2.0.0
- Repository:
- https://github.com/getsentry/sentry-python
- API Documentation:
- https://getsentry.github.io/sentry-python/