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:

  1. server_instrumented.py - instrumented manually

  2. server_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.