This guide walks you through setting up Application Performance Monitoring (APM) on a Python application. These instructions can also be found on the Installation page in your Middleware Account. View example code here.

​

Prerequisites

python3 --version

pip --version

​

Install

​

Step 1: Install Python APM Package

Run the following command in your terminal:

pip install middleware-apm

Check if the middleware-apm has been installed with the following command:

pip list

​

Step 2: Import Middleware Tracker

Add the following lines to the beginning of your application:

import logging 
 
from middleware import MwTracker
tracker=MwTracker()

​

Step 3: Container Variables [Optional]

​

Docker

Applications running in a container require an additional environment variable. If your application is not running in a container, move to Step 4.

Add the following environment variable to your application:

MW_AGENT_SERVICE=<DOCKER_BRIDGE_GATEWAY_ADDRESS>

Add the following command to your Dockerfile after the pip install command:

RUN middleware-bootstrap -a install

​

Kubernetes

Identify the namespace where the Infra Agent is running:

kubectl get service --all-namespaces | grep mw-service

Then add the following environment variable to your application deployment YAML file:

MW_AGENT_SERVICE=mw-service.mw-agent-ns.svc.cluster.local

​

Step 4: Capture Application Data

​

Step 4a: Setup middleware.ini File

Create a middleware.ini file based on the features you want to observe and place it at the root of your app directory. Specify the location of the middleware.ini file with the MIDDLEWARE_CONFIG_FILE environment variable.

[middleware.common]

# The name of your application as service-name, as it will appear in the UI to filter out your data.
service_name = {APM-SERVICE-NAME}

# This Token binds the Python Agent's data and profiling data to your account.
access_token = {YOUR-ACCESS-TOKEN}

# The service name, where Infra Agent is running, in case of K8s.
mw_agent_service = mw-service.mw-agent-ns.svc.cluster.local

# Distributed traces for your application (false = disabled).
collect_traces = true

# Collection of metrics for your application (false = disabled).
collect_metrics = true

# Collection of logs for your application (false = disabled).
collect_logs = true

# Collection of profiling data for your application (false = disabled).
collect_profiling = true

​

Step 4b: Enable Custom Logs

To ingest custom logs, utilize the following functions based on desired log severity levels:

logging.info("info sample")
logging.warning("Sample Warning Log") 
logging.error("Sample Error Log.", extra={'tester': 'Alex'})

​

Step 4c: Stack Errors

Use tracker.record_error() method to record a stack trace when an error occurs:

try:
    not_possible = 12/0
except ZeroDivisionError as e:
    tracker.record_error(e)

​

Step 5: Deploy Your Django App [Optional]

​

Step 5a: Instrument Your App

Add the following to your main() function in manage.py:

tracker.django_instrument()

​

Step 5b: Start Your Django Project

Initialize your Django project with the following command:

DJANGO_SETTINGS_MODULE='mysite.settings' middleware-apm run python manage.py runserver

​

Step 5c: Start Your Django Project

After initializing your application, run the following command to start your project:

middleware-apm run python app.py

​

Step 6: Start Your Project

After deploying your application, run the following command to start your project:

middleware-apm run python app.py

​

Continuous Profiling

Continuous profiling captures real-time performance insights from your application to enable rapid identification of resource allocation, bottlenecks, and more. Navigate to the Continuous Profiling section to learn more about using Continuous Profiling with the Python APM.