Python Custom Instrumentation using Datadog API
If you have not read the setup instructions for automatic instrumentation, start with the Python Setup Instructions
If you aren’t using supported library instrumentation (see library compatibility), you may want to manually instrument your code.
You may also want to extend the functionality of the ddtrace library or gain finer control over instrumenting your application. Several techniques are provided by the library to accomplish this.
Creating spans
The ddtrace library creates spans automatically with ddtrace-run for many libraries and frameworks. However, you may want to gain visibility into your own code and this is achieved by using spans.
Within your web request (for example, make_sandwich_request), you may perform several operations, like get_ingredients() and assemble_sandwich(), which are useful to measure.
def make_sandwich_request(request):
ingredients = get_ingredients()
sandwich = assemble_sandwich(ingredients)
ddtrace provides a decorator tracer.wrap() that can be used to decorate the functions of interest. This is useful if you would like to trace the function regardless of where it is being called from.
from ddtrace import tracer
@tracer.wrap(service="my-sandwich-making-svc", resource="resource_name")
def get_ingredients():
# go to the pantry
# go to the fridge
# maybe go to the store
return
# You can provide more information to customize the span
@tracer.wrap("assemble_sandwich", service="my-sandwich-making-svc", resource="resource_name")
def assemble_sandwich(ingredients):
return
To learn more, read API details for the decorator for ddtrace.Tracer.wrap().
To trace an arbitrary block of code, use the ddtrace.Span context manager as below, or view the advanced usage documentation.
from ddtrace import tracer
def make_sandwich_request(request):
# Capture both operations in a span
with tracer.trace("sandwich.make"):
ingredients = get_ingredients()
sandwich = assemble_sandwich(ingredients)
def make_sandwich_request(request):
# Capture both operations in a span
with tracer.trace("sandwich.create", resource="resource_name") as outer_span:
with tracer.trace("get_ingredients", resource="resource_name") as span:
ingredients = get_ingredients()
with tracer.trace("assemble_sandwich", resource="resource_name") as span:
sandwich = assemble_sandwich(ingredients)
To learn more, read the full API details for ddtrace.Tracer()
If the decorator and context manager methods are still not enough to satisfy your tracing needs, a manual API is provided which allows you to start and finish spans however you may require:
def make_sandwich_request(request):
span = tracer.trace("sandwich.create", resource="resource_name")
ingredients = get_ingredients()
sandwich = assemble_sandwich(ingredients)
span.finish() # remember to finish the span
For more API details of the decorator, read the ddtrace.Tracer.trace documentation or the ddtrace.Span.finish documentation.
Accessing active spans
The built-in instrumentation and your own custom instrumentation create spans around meaningful operations. You can access the active span in order to include meaningful data.
from ddtrace import tracer
def make_sandwich_request(request):
# Capture both operations in a span
with tracer.trace("sandwich.make") as my_span:
ingredients = get_ingredients()
sandwich = assemble_sandwich(ingredients)
def get_ingredients():
# Get the active span
span = tracer.current_span()
# this span is my_span from make_sandwich_request above
def assemble_sandwich(ingredients):
with tracer.trace("another.operation") as another_span:
# Get the active root span
span = tracer.current_root_span()
# this span is my_span from make_sandwich_request above
Tags can be added to a span using the set_tag method on a span:
from ddtrace import tracer
def make_sandwich_request(request):
with tracer.trace("sandwich.make") as span:
ingredients = get_ingredients()
span.set_tag("num_ingredients", len(ingredients))
Tags can be globally set on the tracer. These tags are be applied to every span that is created.
from ddtrace import tracer
from myapp import __version__
# This will be applied to every span
tracer.set_tags({"version": __version__, "<TAG_KEY_2>": "<TAG_VALUE_2>"})
Exception information is captured and attached to a span if there is one active when the exception is raised.
from ddtrace import tracer
with tracer.trace("throws.an.error") as span:
raise Exception("Oops!")
# `span` will be flagged as erroneous and have
# the stack trace and exception message attached as tags
Flagging a span as erroneous can also be done manually:
from ddtrace import tracer
span = tracer.trace("operation")
span.error = 1
span.finish()
In the event you want to flag the local root span with the error raised:
import os
from ddtrace import tracer
try:
raise TypeError
except TypeError as e:
root_span = tracer.current_root_span()
(exc_type, exc_val, exc_tb) = sys.exc_info()
# this sets the error type, marks the span as an error, and adds the traceback
root_span.set_exc_info(exc_type, exc_val, exc_tb)
Propagating context with headers extraction and injection
You can configure the propagation of context for distributed traces by injecting and extracting headers. Read Trace Context Propagation for information.
Resource filtering
Traces can be excluded based on their resource name, to remove synthetic traffic such as health checks from reporting traces to Datadog. This and other security and fine-tuning configurations can be found on the Security page or in Ignoring Unwanted Resources.
Further Reading
Additional helpful documentation, links, and articles:
