The Splunk distribution of OpenTelemetry Python provides multiple installable packages that automatically instruments your Python application to capture and report distributed traces to Splunk APM.

This Splunk distribution comes with the following defaults:

• B3 context propagation.
• Jaeger thrift exporter configured to send spans to a locally running SignalFx Smart Agent (http://localhost:9080/v1/trace).
• Unlimited default limits for configuration options to support full-fidelity traces.

If you're currently using the SignalFx Tracing Library for Python and want to migrate to the Splunk Distribution of OpenTelemetry Python, see Migrate from the SignalFx Tracing Library for Python.

🚧 This project is currently in BETA. It is officially supported by Splunk. However, breaking changes MAY be introduced.

This Splunk Distribution of OpenTelemetry requires Python 3.5 or later. If you're still using Python 2, continue using the SignalFx Tracing Library for Python.

The instrumentation works with Python version 3.6+. Supported libraries are listed here.

To get started, install the splunk-opentelemetry package, run the bootstrap script and wrap your run command with splk-py-trace.

For example, if the runtime parameters were:

python main.py --port=8000

Then the runtime parameters should be updated to:

$ pip install splunk-opentelemetry
$ splk-py-trace-bootstrap
$ SPLUNK_SERVICE_NAME=my-python-app \
    splk-py-trace python main.py --port=8000

Notes:

• Depending on the default python version on your system, you might want to use pip3 and python3 instead.

• To be able to run splk-py-trace and splk-py-trace-bootstrap, the directory pip installs scripts to will have to be on your system's PATH environment variable. Generally, this works out of the box when using virtual environments, installing packages system-wide or in container images. In some cases, pip may install packages into your user local environment. In that case you'll need to add your Python user base's bin directory to your system path. You can find out your Python user base as follows by running python -m site --user-base.

  For example, if python -m site --user-base reports that /Users/splunk/.local as the Python user base, then you can add the directory to your path on Unix like system as follows:

  export PATH="/Users/splunk/.local/bin:$PATH"
  

The service name is the only configuration option that typically needs to be specified. A couple other configuration options that may need to be changed or set are:

• Endpoint if not sending to a locally running Smart Agent with default configuration
• Environment attribute (example: OTEL_RESOURCE_ATTRIBUTES=deployment.environment=production) to specify what environment the span originated from.

Instrumentation works by patching supported libraries at runtime with an OpenTelemetry-compatible tracer to capture and export trace spans. The agent also registers an OpenTelemetry get_tracer so you can support existing custom instrumentation or add custom instrumentation to your application later.

To see the Python instrumentation in action with sample applications, see our examples.

The splk-py-trace-bootstrap command can optionally print out the list of packages it would install if you chose. In order to do so, pass -a=requirements CLI argument to it. For example,

splk-py-trace-bootstrap -a requirements

Will output something like the following:

opentelemetry-instrumentation-falcon>=0.15b0
opentelemetry-instrumentation-jinja2>=0.15b0
opentelemetry-instrumentation-requests>=0.15b0
opentelemetry-instrumentation-sqlite3>=0.15b0
opentelemetry-exporter-jaeger>=0.15b0

You can pipe the output of this command to append the new packages to your requirements.txt file or to something like poetry add.

If you cannot use splk-py-trace command, you can also add a couple of lines of code to your Python application to achieve the same result.

from splunk_otel.tracing import start_tracing

start_tracing()

# also accepts config options
# start_tracing(endpoint='http://localhost:9080/v1/trace, service_name='unnamed-python-service')

# rest of your python application's entrypoint script

This package exports spans in Jaeger Thrift format over HTTP and supports exporting to the SignalFx Smart Agent, OpenTelemetry collector and directly to SignalFx ingest API. You can use OTEL_EXPORTER_JAEGER_ENDPOINT environment variable to specify an export endpoint. The value must be a full URL including scheme and path.

This is the default option. You do not need to set any config options if you want to export to the Smart Agent and you are running the agent on the default port (9080). The exporter will default to http://localhost:9080/v1/trace when the environment variable is not specified.

In order to do this, you'll need to enable Jaeger Thrift HTTP receiver on OpenTelemetry Collector and set OTEL_EXPORTER_JAEGER_ENDPOINT to http://localhost:14268/api/traces assuming the collector is reachable via localhost.

In order to send traces directly to SignalFx ingest API, you need to:

1. Set OTEL_EXPORTER_JAEGER_ENDPOINT to https://ingest.<realm>.signalfx.com/v2/trace where realm is your SignalFx realm e.g, https://ingest.us0.signalfx.com/v2/trace.
2. Set SPLUNK_ACCESS_TOKEN to one of your SignalFx APM access tokens.

Tracing Celery workers works out of the box when you use the splk-py-trace command to start your Python application. However, if you are instrumenting your celery workers with code, you'll need to make sure you setup tracing for each worker by using Celery's celery.signalfx.worker_process_init signal.

For example:

from splunk_otel.tracing import start_tracing
from celery.signals import worker_process_init

@worker_process_init.connect(weak=False)
def on_worker_process_init(*args, **kwargs):
    start_tracing()

# rest of your python application's entrypoint script

Automatically instrumenting Django requires DJANGO_SETTINGS_MODULE environment variable to be set. The value should be the same as set in your manage.py or wsgi.py modules. For example, if your manage.py file sets this environment variable to mydjangoproject.settings and you start your Django project as ./manage.py runserver, then you can automatically instrument your Django project as follows:

export DJANGO_SETTINGS_MODULE=mydjangoproject.settings
splk-py-trace ./manage.py runserver

Like Celery, we'll also need to setup tracing per Gunicorn worker. This can be done by setting up tracing inside Gunicorn's post_fork() handler.

For example:

# gunicorn.config.py
from splunk_otel.tracing import start_tracing

def post_fork(server, worker):
    start_tracing()

Then add -c gunicorn.config.py CLI flag to your gunicorn command.

When using UWSGI, tracing must be setup as a response to the post_fork signal.

For example:

import uwsgidecorators
from splunk_otel.tracing import start_tracing

@uwsgidecorators.postfork
def setup_tracing():
    start_tracing()

uwsgi --http :9090 --wsgi-file <your_app.py> --callable <your_wsgi_callable> --master --enable-threads

The above snippet should be placed in the main python script that uwsgi imports and loads.

Using USWGI with Flask requires one additional little step. Calling start_tracing() does not auto-instrument pre-existing flask app instances but only flask instances created after. When running flask with uwsgi, we need to create a new flask app instance before the post_fork signal is emitted. This means your flask app will not be auto-instrumented. However, you can still auto-instrument an existing flask app explicitly by importing and calling the flask instrumentor.

For example:

# app.py
import uwsgidecorators
from splunk_otel.tracing import start_tracing
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from flask import Flask

app = Flask(__name__)

@uwsgidecorators.postfork
def setup_tracing():
    start_tracing()
    # instrument our flask app instance eplicitly
    FlaskInstrumentor().instrument_app(app)

@app.route('/')
def hello_world():
    return 'Hello, World!'

uwsgi --http :9090 --wsgi-file app.py --callable app --master --enable-threads

Documentation on how to manually instrument a Python application is available here.

Enable debug logging like you would for any Python application.

import logging

logging.basicConfig(level=logging.DEBUG)

⚠️ Debug logging is extremely verbose and resource intensive. Enable debug logging only when needed and disable when done.

The Splunk distribution of OpenTelemetry Python Instrumentation is a distribution of the OpenTelemetry Python project. It is released under the terms of the Apache Software License version 2.0. See the license file for more details.