The Splunk distribution of OpenTelemetry Python Instrumentation provides a Python agent that automatically instruments your Python application to capture and report distributed traces to SignalFx APM.
Project description
Splunk distribution of OpenTelemetry Python
The documentation below refers to the in development version of this package. Docs for the latest version (v0.13.0) can be found here.
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:
- W3C tracecontext and W3C baggage context propagation.
- OTLP gRPC exporter
configured to send spans to a locally running OpenTelemetry Collector
(
http://localhost:4317
). - 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.
:construction: This project is currently in BETA. It is officially supported by Splunk. However, breaking changes MAY be introduced.
Requirements
This Splunk Distribution of OpenTelemetry requires Python 3.6 or later. If you're still using Python 2, continue using the SignalFx Tracing Library for Python.
Getting Started
The instrumentation works with Python version 3.6+. Supported libraries are listed here.
To get started, install the splunk-opentelemetry[all]
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[all]
$ splk-py-trace-bootstrap
$ OTEL_SERVICE_NAME=my-python-app \
splk-py-trace python main.py --port=8000
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 OpenTelemetry Collector.
- 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.
To see the Python instrumentation in action with sample applications, see our examples.
All configuration options
Environment variable | Config Option | Default value | Notes |
---|---|---|---|
OTEL_SERVICE_NAME | service_name | unnamed-python-service |
The service name of this Python application. |
OTEL_TRACES_EXPORTER | exporter_factories | otlp |
The exporter(s) that should be used to export tracing data. |
OTEL_EXPORTER_OTLP_ENDPOINT | http://localhost:4317 |
The OTLP gRPC endpoint to connect to. Used when OTEL_TRACES_EXPORTER is set to otlp |
|
OTEL_EXPORTER_JAEGER_ENDPOINT | http://localhost:9080/v1/trace |
The Jaeger Thrift endpoint to connect to. Used when OTEL_TRACES_EXPORTER is set to jaeger-thrift-splunk |
|
SPLUNK_ACCESS_TOKEN | access_token | The optional organization access token for trace submission requests. | |
SPLUNK_MAX_ATTR_LENGTH | max_attr_length | 1200 |
Maximum length of string attribute value in characters. Longer values are truncated. |
SPLUNK_TRACE_RESPONSE_HEADER_ENABLED | trace_response_header_enabled | True | Enables adding server trace information to HTTP response headers. |
OTEL_RESOURCE_ATTRIBUTES | resource_attributes | unset | Comma-separated list of resource attributes added to every reported span. Exampleservice.name=my-python-service,service.version=3.1,deployment.environment=production |
OTEL_PROPAGATORS | tracecontext,baggage |
Comma-separated list of propagator names to be used. SeeConfiguring Propagators for more details. | |
OTEL_TRACE_ENABLED | true |
Globally enables tracer creation and auto-instrumentation. |
Exporting telemetry data
This package can export spans in the OTLP format over gRPRC or Jaeger Thrift format over HTTP. This allows you to export data to wide range of destinations such as OpenTelemetry Collector, SignalFx Smart Agent or even Splunk APM ingest.
To OpenTelemetry Collector
This is the default option. You do not need to set any config options if you want to exporter
to the OpenTelemetry collector, the collector has OTLP gRPC receiver enabled with default settings
and can be reached by localhost
as by default everything by be exported to http://localhost:4317
in OTLP over gRPC.
If your collector cannot be reached at http://localhost:4317
, you'll need to set the OTEL_EXPORTER_OTLP_ENDPOINT
to http://<otel-collector-address>:<port>
. Replace <otel-collector-address>
and <port>
with the address and
port of your OpenTelemetry Collector deployment.
Note: You'll make sure that the OTLP gRPC exporter is installed. This can be done by running pip install splunk-opentelemetry[all]
or splunk-opentelemetry[otlp]
.
To SignalFx Smart Agent
- Set
OTEL_TRACES_EXPORTER
environment variable tojaeger-thrift-splunk
. If you are running the SignalFx Smart Agent locally (reachable vialocalhost
) and it is listening on the default port (9080
), you do not need to perform any additional steps. Otherwise, follow the next step. - Set the
OTEL_EXPORTER_JAEGER_ENDPOINT
environment variable tohttp://<address>:<port>/v1/trace
. Replace<address>
and<port>
with the address and port of your Smart Agent deployment.
Note: You'll make sure that the Jaeger Thrift exporter is installed. This can be done by running pip install splunk-opentelemetry[all]
or splunk-opentelemetry[jaeger]
.
To Splunk Observability Cloud
In order to send traces directly to SignalFx ingest API, you need to:
- Set
OTEL_TRACES_EXPORTER
tojaeger-thrift-splunk
. - Set
OTEL_EXPORTER_JAEGER_ENDPOINT
tohttps://ingest.<realm>.signalfx.com/v2/trace
whererealm
is your SignalFx realm e.g,https://ingest.us0.signalfx.com/v2/trace
. - Set
SPLUNK_ACCESS_TOKEN
to one of your Splunk APM access tokens.
Note: You'll make sure that the Jaeger Thrift exporter is installed. This can be done by running pip install splunk-opentelemetry[all]
or splunk-opentelemetry[jaeger]
.
Configuring Propagators
This package uses W3C trace context and W3C baggage propagators by default. You can override
this by setting the OTEL_PROPAGATORS
environment variable to a comma separated list of one
more propagators. The SDK will use Python's entry points mechanism to load the specified
propagator implementation(s) and use it.
For example, to only use W3C trace context without baggage, you can set the environment variable
OTEL_PROPAGATORS
environment variable to tracecontext
.
You can specify any propagator name as long as the propagator implementation can be found via entry points by that name.
Configuring B3 propagator
If you'd like to use b3
instead of or in addition to the default propagators, you can set OTEL_PROPAGATORS
to b3
for B3 single header or b3multi
for
B3 multi header implementation. For example, to configure
your service to use B3 multi header and W3C baggage, set the environment variable as
OTEL_PROPAGATORS=b3multi,baggage
You can specify any combination of supported propagators. Choices are tracecontext
, baggae
, b3
and b3multi
. Note that
b3
and b3multi
are only available when the opentelemetry-propagator-b3
package is installed. This is installed automatically
by installing splunk-opentelemetry[all]
or splunk-opentelemetry[b3]
.
Advanced Getting Started
Instrument and configure with code
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 optional config options:
# start_tracing(
# service_name='my-python-service',
# exporter_factories=[OTLPSpanExporter]
# access_token='',
# max_attr_length=1200,
# trace_response_header_enabled=True,
# resource_attributes={
# 'service.version': '3.1',
# 'deployment.environment': 'production',
# })
# rest of your python application's entrypoint script
Using a different exporter
The splunk-opentelemetry
Python package does not install any exporters by default. You can install it with the OTLP or Jaeger Thrift exporter by
using the otlp
or jaeger
extra options. For example, installing splunk-opentelemetry[otlp]
will also pull in the OTLP gRPC exporter. Similarly,
installing splunk-opentelemetry[jaeger]
will install the Jaeger Thrift exporter. You can also install both exporters by mentioning them
both like splunk-opentelemetry[jaeger,otlp]
The distributions uses OTLP by default so we recommend installing splunk-opentelemetry[otlp]
unless you want to use another exporter.
Once you install the exporter package you want to use, you can tell the distribution to use a different exporter by setting the OTEL_TRACES_EXPORTER
environment variables.
For example, to use the Jaeger exporter, set it as follows:
OTEL_TRACES_EXPORTER=jaeger-thrift-splunk
Using multiple exporters
The environment variable accepts multiple comma-separated values. If multiple exporters are specified, all of them will be used. This can be used to export to multiple destinations or to debug with the console exporter while still exporting to another destination. For example, the following configuration will export all spans using both the OTLP exporter and the Console exporter.
OTEL_TRACES_EXPORTER=otlp,console_span
Accepted values for OTEL_TRACES_EXPORTER
This package uses Python's entry points mechanism to look up the requested exporters. As a result, you can install any thrid party or custom exporter package and
as long as it specifies a opentelemetry_exporter
entry point to the exporter implementation, you can specify it as a value in OTEL_TRACES_EXPORTER
.
Known values and the Python packages they ship in are listed below
Exporter name | Python package | Additional comments |
---|---|---|
otlp | opentelemetry-exporter-otlp-proto-grpc | Can be installed with pip install splunk-opentelemetry[otlp] |
jaeger-thrift-splunk | opentelemetry-exporter-jaeger-thrift | Can be installed with pip install splunk-opentelemetry[jaeger] |
console_span | opentelemetry-sdk | Always installed with splunk-opentelemetry |
Bootstrap: List requirements instead of installing them
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
You can pipe the output of this command to append the new packages to your
requirements.txt file or to something like poetry add
.
Installing only a subset of dependencies
Installing splunk-opentelemetry[all]
automatically pulls in all of the optional dependencies. These include the OTLP gRPC exporter, the Jaeger Thrift exporter
and the B3 propagator. If you'd like to install only the packages you need, you can use any combination of oltp
, jaeger
and b3
. For example, in order
to install only otlp
exporter, you can run
pip install splunk-opentelemetry[otlp]
To install the Jaeger Thrift exporter and the B3 propagator, you can run
pip install splunk-opentelemetry[jaeger,b3]
Special Cases
Celery
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.signals.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
Django
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
Gunicorn
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.
UWSGI
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()
Running with uwsgi
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.
UWSGI and Flask
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!'
Running with uWSGI:
uwsgi --http :9090 --wsgi-file app.py --callable app --master --enable-threads
Manually instrument an application
Documentation on how to manually instrument a Python application is available here.
Troubleshooting
-
Depending on the default python version on your system, you might want to use
pip3
andpython3
instead. -
To be able to run
splk-py-trace
andsplk-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 runningpython -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"
-
Enable debug logging like you would for any Python application.
import logging logging.basicConfig(level=logging.DEBUG)
:warning: Debug logging is extremely verbose and resource intensive. Enable debug logging only when needed and disable when done.
License and versioning
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.
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