Basic Usage


With ddtrace installed, the application can be instrumented.

Auto Instrumentation


Python applications can easily be instrumented with ddtrace by using the included ddtrace-run command. Simply prefix your Python execution command with ddtrace-run in order to auto-instrument the libraries in your application.

For example, if the command to run your application is:

$ python

then to auto-instrument using Datadog, the corresponding command is:

$ ddtrace-run python

For more advanced usage of ddtrace-run refer to the documentation here.


To manually invoke the automatic instrumentation use patch_all:

from ddtrace import patch_all

To toggle instrumentation for a particular module:

from ddtrace import patch_all
patch_all(redis=False, cassandra=False)

By default all supported libraries will be patched when patch_all is invoked.

Note: To ensure that the supported libraries are instrumented properly in the application, they must be patched prior to being imported. So make sure to call patch_all before importing libraries that are to be instrumented.

More information about patch_all is available in our patch_all API documentation.

Manual Instrumentation

If you would like to extend the functionality of the ddtrace library or gain finer control over instrumenting your application, several techniques are provided by the library.


ddtrace provides a decorator that can be used to trace a particular method in your application:

def business_logic():
  """A method that would be of interest to trace."""
  # ...
  # ...

API details of the decorator can be found here ddtrace.Tracer.wrap().

Context Manager

To trace an arbitrary block of code, you can use ddtrace.Tracer.trace() that returns a ddtrace.Span which can be used as a context manager:

# trace some interesting operation
with tracer.trace('interesting.operations'):
  # do some interesting operation(s)
  # ...
  # ...

Further API details can be found here ddtrace.Tracer().

Using the API

If the above methods are still not enough to satisfy your tracing needs, a manual API is provided which will allow you to start and finish spans however you may require:

span = tracer.trace('operations.of.interest')

# do some operation(s) of interest in between

# NOTE: make sure to call span.finish() or the entire trace will not be sent
# to Datadog

API details of the decorator can be found here:


Via module

To automatically profile your code, you can import the module. As soon as it is imported, it will start catching CPU profiling information on your behalf:



If you want to control which part of your code should be profiled, you can use the ddtrace.profiling.Profiler object:

from ddtrace.profiling import Profiler

prof = Profiler()

# At shutdown


The profiler has been designed to be always-on. The start and stop methods are provided in case you need a fine-grained control over the profiler lifecycle. They are not provided for starting and stopping the profiler many times during your application lifecycle. Do not use them for e.g. building a context manager.

Via command line

You can run your program with profiling enabled by using the wrapper pyddprofile. This will automatically enable the profiling of your application:

$ pyddprofile

Handling os.fork

When your process forks using os.fork, the profiler is stopped in the child process.

For Python 3.7 and later on POSIX platforms, a new profiler will be started if you enabled the profiler via pyddprofile or

If you manually instrument the profiler, or if you rely on Python 3.6 or a non-POSIX platform and earlier version, you’ll have to manually restart the profiler in your child.

The global profiler instrumented by pyddprofile and can be started by calling