Set Up Performance
With performance monitoring, Sentry tracks your software performance, measuring metrics like throughput and latency, and displaying the impact of errors across multiple systems. Sentry captures distributed traces consisting of transactions and spans, which measure individual services and individual operations within those services. Learn more about our model in Distributed Tracing.
If you’re adopting Performance in a high-throughput environment, we recommend testing prior to deployment to ensure that your service’s performance characteristics maintain expectations.
- Setting a uniform sample rate for all transactions using the
traces_sample_rateoption in your SDK config to a number between
1. (For example, to send 20% of transactions, set
- Controlling the sample rate based on the transaction itself and the context in which it's captured, by providing a function to the
The two options are meant to be mutually exclusive. If you set both,
traces_sampler will take precedence.
Performance Monitoring is available for the Sentry Python SDK version ≥ 0.11.2.
import sentry_sdk def traces_sampler(sampling_context): # ... # return a number between 0 and 1 or a boolean sentry_sdk.init( dsn="https://examplePublicKey@o0.ingest.sentry.io/0", # To set a uniform sample rate # Set traces_sample_rate to 1.0 to capture 100% # of transactions for performance monitoring. # We recommend adjusting this value in production, traces_sample_rate=1.0, # Alternatively, to control sampling dynamically traces_sampler=traces_sampler )
Learn more about how the options work in Sampling Transactions.
While you're testing, set
1.0, as that ensures that every transaction will be sent to Sentry.
Once testing is complete, you may want to set a lower
traces_sample_rate value, or switch to using
traces_sampler to selectively sample and filter your transactions, based on contextual data.