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

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

The Datadog Profiler requires Python 2.7+.

The following profiling features are available depending on your Python version. For more details, read Profile Types:

The installation requires pip version 18 or above.

The following profiling features are available in the following minimum versions of the dd-trace-py library:

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

Note: Profiling requires the ddtrace library version 0.40+.

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.

After a couple of minutes, visualize your profiles on the Datadog APM > Profiler page.

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 needs to be started in the child process. In Python 3.7+, this is done automatically. In Python < 3.7, you need to manually start a new profiler in your child process:

# For ddtrace-run users, call this in your child process
ddtrace.profiling.auto.start_profiler()  # Should be as early as possible, eg before other imports, to ensure everything is profiled

# Alternatively, for manual instrumentation,
# create a new profiler in your child process:
from ddtrace.profiling import Profiler

prof = Profiler(...)
prof.start()  # Should be as early as possible, eg before other imports, to ensure everything is profiled

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 disabled by default, you can turn it on by setting DD_PROFILING_ENABLE_CODE_PROVENANCE=1.

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.

Further Reading

추가 유용한 문서, 링크 및 기사:

Getting Started with ProfilerDOCUMENTATION Learn more about available profile visualizationsDOCUMENTATION Fix problems you encounter while using the profilerDOCUMENTATION