Enabling the Python Profiler

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The profiler is shipped within Datadog tracing libraries. If you are already using APM to collect traces for your application, you can skip installing the library and go directly to enabling the profiler.

Requirements

To use profiling, ensure the following requirements are met:

Enable profiling through the Datadog tracing library. Using the latest stable release is recommended.
Verify your Python and tracing library versions are compatible by reviewing the Python Compatibility Requirements. Note: Some features depend on newer Python versions than the minimum required version for the tracing library. For more details, read Profile Types.

For a summary of the minimum and recommended runtime and tracer versions across all languages, read Supported Language and Tracer Versions.

The installation requires pip version 18 or above.

Continuous Profiler support is in Preview for some serverless platforms, such as AWS Lambda.

Installation

Ensure Datadog Agent v6+ is installed and running. Datadog recommends using Datadog Agent v7+.

Install ddtrace, which provides both tracing and profiling functionalities:

pip install ddtrace

If you are using a platform where ddtrace binary distribution is not available, first install a development environment.

For example, on Alpine Linux, this can be done with:

apk install gcc musl-dev linux-headers

Usage

To automatically profile your code, set the DD_PROFILING_ENABLED environment variable to true when you use ddtrace-run:

DD_PROFILING_ENABLED=true \
DD_ENV=prod \
DD_SERVICE=my-web-app \
DD_VERSION=1.0.3 \
ddtrace-run python app.py

See Configuration for more advanced usage.

Optionally, set up Source Code Integration to connect your profiling data with your Git repositories.

A couple of minutes after you start your application, your profiles appear on the Datadog APM > Profiler page. If they do not, refer to the Troubleshooting guide.

If you want to manually control the lifecycle of the profiler, use the ddtrace.profiling.Profiler object:

from ddtrace.profiling import Profiler

prof = Profiler(
    env="prod",  # if not specified, falls back to environment variable DD_ENV
    service="my-web-app",  # if not specified, falls back to environment variable DD_SERVICE
    version="1.0.3",   # if not specified, falls back to environment variable DD_VERSION
)
prof.start()  # Should be as early as possible, eg before other imports, to ensure everything is profiled

Caveats

When your process forks using os.fork, the profiler is automatically restarted in the child process on supported Python versions. No manual restart is required.

Configuration

You can configure the profiler using the environment variables.

Code provenance

The Python profiler supports code provenance reporting, which provides insight into the library that is running the code. While this is enabled by default, you can turn it off by setting DD_PROFILING_ENABLE_CODE_PROVENANCE=0.

Not sure what to do next?

The Getting Started with Profiler guide takes a sample service with a performance problem and shows you how to use Continuous Profiler to understand and fix the problem.