Enabling the Python Profiler

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.


The Datadog Profiler requires Python 2.7+ and Agent version 7.20.2+ or 6.20.2+.

The following profiling features are available depending on your Python version:

FeatureSupported Python versions
Wall time profilingPython >= 2.7
CPU time profilingPython >= 2.7 on POSIX platforms
Exception profilingPython >= 3.7 on POSIX platforms
Lock profilingPython >= 2.7
Memory profilingPython >= 3.5

Continuous Profiler is not supported on serverless platforms, such as AWS Lambda.


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, install a development environment.

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

apk install gcc musl-dev linux-headers


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

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

It is strongly recommended that you add tags like service or version, as they provide the ability to filter and group your profiles across these dimensions. See Configuration below.

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.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


When your process forks using os.fork, the profiler is actually stopped in the child process and needs to be restarted. For Python 3.7+, a new profiler is automatically started.

If you use 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

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

prof = Profiler()


You can configure the profiler using the environment variables.

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