In the previous post we saw instrumenting a python application with OTel emitting metrics and traces along the way. While this is good, it can still be intimidating to an instrumentation-newbie.
To get over this barrier and to allow a quick-start, some languages have auto-instrumentation support. One only needs to install some libraries to get going.
Our sample web-app
import datetime
import flask
######################
## initialization
######################
app = flask.Flask(__name__)
start = datetime.datetime.now()
######################
## routes
######################
@app.route('/', methods=['GET'])
def root():
return flask.jsonify({'message': 'flask app root/'})
@app.route('/healthz', methods=['GET'])
def healthz():
now = datetime.datetime.now()
return flask.jsonify({'message': f'up and running since {(now - start)}'})
######################
if __name__ == '__main__':
######################
app.run(debug=True, host='0.0.0.0', port=5000)
Install necessary libraries
$ pip install flask
$ pip install opentelemetry-distro opentelemetry-instrumentation-flask
Note: some corner case made my install opentelemetry-api as well, but is not needed as per official documentation.
Initialize (to install extra libraries as needed)
$ opentelemetry-bootstrap -a install
Run your code
$ opentelemetry-instrument --traces_exporter console --metrics_exporter console flask run
* Serving Flask app 'app'
* Debug mode: on
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
* Running on all addresses (0.0.0.0)
* Running on http://127.0.0.1:5000
* Running on http://192.168.1.7:5000
Press CTRL+C to quit
* Restarting with stat
* Debugger is active!
* Debugger PIN: 121-673-590
127.0.0.1 - - [17/Oct/2022 07:21:05] "GET /healthz HTTP/1.1" 200 -
{
"name": "/healthz",
"context": {
"trace_id": "0xd0850752865577d2d8cd11aaef169574",
"span_id": "0x29c8ad5fd974de41",
"trace_state": "[]"
},
"kind": "SpanKind.SERVER",
"parent_id": null,
"start_time": "2022-10-17T01:52:45.522806Z",
"end_time": "2022-10-17T01:52:45.523615Z",
"status": {
"status_code": "UNSET"
},
"attributes": {
"http.method": "GET",
"http.server_name": "127.0.0.1",
"http.scheme": "http",
"net.host.port": 5000,
"http.host": "localhost:5000",
"http.target": "/healthz",
"net.peer.ip": "127.0.0.1",
"http.user_agent": "curl/7.79.1",
"net.peer.port": 55838,
"http.flavor": "1.1",
"http.route": "/healthz",
"http.status_code": 200
},
"events": [],
"links": [],
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "opentelemetry",
"telemetry.sdk.version": "1.13.0",
"telemetry.auto.version": "0.34b0",
"service.name": "unknown_service"
},
"schema_url": ""
}
}
{"resource_metrics": [{"resource": {"attributes": {"telemetry.sdk.language": "python", "telemetry.sdk.name": "opentelemetry", "telemetry.sdk.version": "1.13.0", "telemetry.auto.version": "0.34b0", "service.name": "unknown_service"}, "schema_url": ""}, "scope_metrics": [], "schema_url": ""}]}
As of this post, most popular frameworks like Django, FastAPI, Flask have instrumentation libraries for HTTP context propagation
Code size implications
Auto-instrumentation does add some extra libraries. This was the result in my case
$ du -sh manual/venv/ auto/venv/
29M manual/venv/
30M auto/venv/
Always refer to the official documentation which is up-to-date
Official documentation
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