ddtrace installed, the application can be instrumented.
Python applications can easily be instrumented with
ddtrace by using the
ddtrace-run command. Simply prefix your Python execution command
ddtrace-run in order to auto-instrument the libraries in your
For example, if the command to run your application is:
$ python app.py
then to auto-instrument using Datadog, the corresponding command is:
$ ddtrace-run python app.py
For more advanced usage of
ddtrace-run refer to the documentation here.
To manually invoke the automatic instrumentation use
from ddtrace import patch_all 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
patch_all before importing libraries that are to be instrumented.
More information about
patch_all is available in our patch_all API
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:
@tracer.wrap() def business_logic(): """A method that would be of interest to trace.""" # ... # ...
API details of the decorator can be found here
To trace an arbitrary block of code, you can use
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
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 span.finish()
API details of the decorator can be found here:
To automatically profile your code, you can import the ddtrace.profiling.auto 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() prof.start() # At shutdown prof.stop()
The profiler has been designed to be always-on. The
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 myscript.py
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 ddtrace.profiling.auto.
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 ddtrace.profiling.auto can be started by calling ddtrace.profiling.auto.start_profiler.