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Track incoming requests with OpenCensus Python

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  • 3 minutes to read

Incoming request data is collected using OpenCensus Python and its various integrations. Track incoming request data sent to your web applications built on top of the popular web frameworks django, flask and pyramid. The data is then sent to Application Insights under Azure Monitor as requests telemetry.

First, instrument your Python application with latest OpenCensus Python SDK.

Tracking Django applications

  1. Download and install opencensus-ext-django from PyPI and instrument your application with the django middleware. Incoming requests sent to your django application will be tracked.

  2. Include opencensus.ext.django.middleware.OpencensusMiddleware in your settings.py file under MIDDLEWARE.

    MIDDLEWARE = (
    ...
    'opencensus.ext.django.middleware.OpencensusMiddleware',
    ...
)

  3. Make sure AzureExporter is properly configured in your settings.py under OPENCENSUS. For requests from urls that you do not wish to track, add them to EXCLUDELIST_PATHS.

    OPENCENSUS = {
    'TRACE': {
        'SAMPLER': 'opencensus.trace.samplers.ProbabilitySampler(rate=1)',
        'EXPORTER': '''opencensus.ext.azure.trace_exporter.AzureExporter(
            connection_string="InstrumentationKey=<your-ikey-here>"
        )''',
        'EXCLUDELIST_PATHS': ['https://example.com'],  <--- These sites will not be traced if a request is sent to it.
    }
}

You can find a Django sample application in the sample Azure Monitor OpenCensus Python samples repository located here.

Tracking Flask applications

  1. Download and install opencensus-ext-flask from PyPI and instrument your application with the flask middleware. Incoming requests sent to your flask application will be tracked.

    
from flask import Flask
from opencensus.ext.azure.trace_exporter import AzureExporter
from opencensus.ext.flask.flask_middleware import FlaskMiddleware
from opencensus.trace.samplers import ProbabilitySampler

app = Flask(__name__)
middleware = FlaskMiddleware(
    app,
    exporter=AzureExporter(connection_string="InstrumentationKey=<your-ikey-here>"),
    sampler=ProbabilitySampler(rate=1.0),
)

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

if __name__ == '__main__':
    app.run(host='localhost', port=8080, threaded=True)

  2. You can also configure your flask application through app.config. For requests from urls that you do not wish to track, add them to EXCLUDELIST_PATHS.

    app.config['OPENCENSUS'] = {
    'TRACE': {
        'SAMPLER': 'opencensus.trace.samplers.ProbabilitySampler(rate=1.0)',
        'EXPORTER': '''opencensus.ext.azure.trace_exporter.AzureExporter(
            connection_string="InstrumentationKey=<your-ikey-here>",
        )''',
        'EXCLUDELIST_PATHS': ['https://example.com'],  <--- These sites will not be traced if a request is sent to it.
    }
}

    Note

    To run Flask under uWSGI in a Docker environment, you must first add lazy-apps = true to the uWSGI configuration file (uwsgi.ini). For more information, see the issue description.

You can find a Flask sample application that tracks requests in the Azure Monitor OpenCensus Python samples repository located here.

Tracking Pyramid applications

  1. Download and install opencensus-ext-django from PyPI and instrument your application with the pyramid tween. Incoming requests sent to your pyramid application will be tracked.

    def main(global_config, **settings):
    config = Configurator(settings=settings)

    config.add_tween('opencensus.ext.pyramid'
                     '.pyramid_middleware.OpenCensusTweenFactory')

  2. You can configure your pyramid tween directly in the code. For requests from urls that you do not wish to track, add them to EXCLUDELIST_PATHS.

    settings = {
    'OPENCENSUS': {
        'TRACE': {
            'SAMPLER': 'opencensus.trace.samplers.ProbabilitySampler(rate=1.0)',
            'EXPORTER': '''opencensus.ext.azure.trace_exporter.AzureExporter(
                connection_string="InstrumentationKey=<your-ikey-here>",
            )''',
            'EXCLUDELIST_PATHS': ['https://example.com'],  <--- These sites will not be traced if a request is sent to it.
        }
    }
}
config = Configurator(settings=settings)

Tracking FastAPI applications

OpenCensus doesn't have an extension for FastAPI. To write your own FastAPI middleware, complete the following steps:

  1. The following dependencies are required:

  2. Add FastAPI middleware. Make sure that you set the span kind server: span.span_kind = SpanKind.SERVER.

  3. Run your application. Calls made to your FastAPI application should be automatically tracked and telemetry should be logged directly to Azure Monitor.

    # Opencensus imports
from opencensus.ext.azure.trace_exporter import AzureExporter
from opencensus.trace.samplers import ProbabilitySampler
from opencensus.trace.tracer import Tracer
from opencensus.trace.span import SpanKind
from opencensus.trace.attributes_helper import COMMON_ATTRIBUTES
# FastAPI imports
from fastapi import FastAPI, Request
# uvicorn
import uvicorn

app = FastAPI()

HTTP_URL = COMMON_ATTRIBUTES['HTTP_URL']
HTTP_STATUS_CODE = COMMON_ATTRIBUTES['HTTP_STATUS_CODE']

APPINSIGHTS_CONNECTION_STRING='<your-appinsights_connection-string-here>'
exporter=AzureExporter(connection_string=f'{APPINSIGHTS_CONNECTION_STRING}')
sampler=ProbabilitySampler(1.0)

# fastapi middleware for opencensus
@app.middleware("http")
async def middlewareOpencensus(request: Request, call_next):  
    tracer = Tracer(exporter=exporter, sampler=sampler)       
    with tracer.span("main") as span:
        span.span_kind = SpanKind.SERVER

        response = await call_next(request)

        tracer.add_attribute_to_current_span(
            attribute_key=HTTP_STATUS_CODE,
            attribute_value=response.status_code)
        tracer.add_attribute_to_current_span(
            attribute_key=HTTP_URL,
            attribute_value=str(request.url))

    return response

@app.get("/")
async def root():
    return "Hello World!"

if __name__ == '__main__':
    uvicorn.run("example:app", host="127.0.0.1", port=5000, log_level="info")

Next steps