Auto-instrumentation¶
One of the best ways to instrument Python applications is to use OpenTelemetry automatic instrumentation (auto-instrumentation). This approach is simple, easy, and doesn’t require many code changes. You only need to install a few Python packages to successfully instrument your application’s code.
Overview¶
This example demonstrates how to use auto-instrumentation in OpenTelemetry. The example is based on a previous OpenTracing example that you can find here.
The source files for these examples are available here.
This example uses two different scripts. The main difference between them is whether or not they’re instrumented manually:
server_instrumented.py
- instrumented manuallyserver_uninstrumented.py
- not instrumented manually
Run the first script without the automatic instrumentation agent and the second with the agent. They should both produce the same results, demonstrating that the automatic instrumentation agent does exactly the same thing as manual instrumentation.
To better understand auto-instrumentation, see the relevant part of both scripts:
Manually instrumented server¶
server_instrumented.py
@app.route("/server_request")
def server_request():
with tracer.start_as_current_span(
"server_request",
context=extract(request.headers),
kind=trace.SpanKind.SERVER,
attributes=collect_request_attributes(request.environ),
):
print(request.args.get("param"))
return "served"
Server not instrumented manually¶
server_uninstrumented.py
@app.route("/server_request")
def server_request():
print(request.args.get("param"))
return "served"
Prepare¶
Execute the following example in a separate virtual environment. Run the following commands to prepare for auto-instrumentation:
$ mkdir auto_instrumentation
$ virtualenv auto_instrumentation
$ source auto_instrumentation/bin/activate
Install¶
Run the following commands to install the appropriate packages. The
opentelemetry-instrumentation
package provides several
commands that help automatically instruments a program.
$ pip install opentelemetry-sdk
$ pip install opentelemetry-instrumentation
$ pip install opentelemetry-instrumentation-flask
$ pip install requests
Execute¶
This section guides you through the manual process of instrumenting a server as well as the process of executing an automatically instrumented server.
Execute a manually instrumented server¶
Execute the server in two separate consoles, one to run each of the scripts that make up this example:
$ source auto_instrumentation/bin/activate
$ python server_instrumented.py
$ source auto_instrumentation/bin/activate
$ python client.py testing
When you execute server_instrumented.py
it returns a JSON response
similar to the following example:
{
"name": "server_request",
"context": {
"trace_id": "0xfa002aad260b5f7110db674a9ddfcd23",
"span_id": "0x8b8bbaf3ca9c5131",
"trace_state": "{}"
},
"kind": "SpanKind.SERVER",
"parent_id": null,
"start_time": "2020-04-30T17:28:57.886397Z",
"end_time": "2020-04-30T17:28:57.886490Z",
"status": {
"status_code": "OK"
},
"attributes": {
"http.method": "GET",
"http.server_name": "127.0.0.1",
"http.scheme": "http",
"host.port": 8082,
"http.host": "localhost:8082",
"http.target": "/server_request?param=testing",
"net.peer.ip": "127.0.0.1",
"net.peer.port": 52872,
"http.flavor": "1.1"
},
"events": [],
"links": [],
"resource": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "opentelemetry",
"telemetry.sdk.version": "0.16b1"
}
}
Execute an automatically instrumented server¶
Stop the execution of server_instrumented.py
with ctrl + c
and run the following command instead:
$ opentelemetry-instrument --trace-exporter console_span python server_uninstrumented.py
In the console where you previously executed client.py
, run the following
command again:
$ python client.py testing
When you execute server_uninstrumented.py
it returns a JSON response
similar to the following example:
{
"name": "server_request",
"context": {
"trace_id": "0x9f528e0b76189f539d9c21b1a7a2fc24",
"span_id": "0xd79760685cd4c269",
"trace_state": "{}"
},
"kind": "SpanKind.SERVER",
"parent_id": "0xb4fb7eee22ef78e4",
"start_time": "2020-04-30T17:10:02.400604Z",
"end_time": "2020-04-30T17:10:02.401858Z",
"status": {
"status_code": "OK"
},
"attributes": {
"http.method": "GET",
"http.server_name": "127.0.0.1",
"http.scheme": "http",
"host.port": 8082,
"http.host": "localhost:8082",
"http.target": "/server_request?param=testing",
"net.peer.ip": "127.0.0.1",
"net.peer.port": 48240,
"http.flavor": "1.1",
"http.route": "/server_request",
"http.status_text": "OK",
"http.status_code": 200
},
"events": [],
"links": [],
"resource": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "opentelemetry",
"telemetry.sdk.version": "0.16b1",
"service.name": ""
}
}
You can see that both outputs are the same because automatic instrumentation does exactly what manual instrumentation does.
Instrumentation while debugging¶
The debug mode can be enabled in the Flask app like this:
if __name__ == "__main__":
app.run(port=8082, debug=True)
The debug mode can break instrumentation from happening because it enables a
reloader. To run instrumentation while the debug mode is enabled, set the
use_reloader
option to False
:
if __name__ == "__main__":
app.run(port=8082, debug=True, use_reloader=False)
Additional resources¶
In order to send telemetry to an OpenTelemetry Collector without doing any additional configuration, read about the OpenTelemetry Distro package.