Python

Use the Middleware Python APM SDK to collect traces, metrics, logs and profiles from Python applications such as Django, Flask and FastAPI, with automatic instrumentation. The table shows which data types it collects.

1. Prerequisites#

Before you instrument anything, make sure the basics are in place:

• Python 3.9+ and pip 23.1.2+ – verify your versions:
  bash

  python3 --version
  pip --version

• (Host mode only) If your app runs in a container, point it at the Host Agent using MW_AGENT_SERVICE.
  • Docker default bridge gateway: 172.17.0.1
  • Kubernetes service name: mw-service.mw-agent-ns.svc.cluster.local (discover with kubectl get service --all-namespaces | grep mw-service).

2. Install#

Create an isolated environment, install the SDK, then add auto-instrumentation shims for your frameworks:

python -m venv .venv
source .venv/bin/activate

# SDK
pip install middleware-io

# (optional) enable continuous profiling features
pip install 'middleware-io[profiling]'

# Add OpenTelemetry auto-instrumentation libraries for installed packages (e.g., Flask)
middleware-bootstrap -a install

What middleware-bootstrap -a install does: it scans your active site-packages and installs the matching opentelemetry-instrumentation-* libraries for any frameworks you already have (e.g., Flask → opentelemetry-instrumentation-flask).

You can confirm with pip list | grep -i flask. For containers, add RUN middleware-bootstrap -a install to your Dockerfile.

3. Instrumentation#

Pick Host (with the Middleware Host Agent) or Serverless (send directly to Middleware). Within each, choose Auto (zero-code) or Manual (call the tracker in code).

export MW_SERVICE_NAME='MyPythonApp'
middleware-run python app.py

from middleware import mw_tracker, MWOptions

mw_tracker(MWOptions(
  service_name="MyPythonApp",
  console_exporter=True,   # echo telemetry to stdout (dev only)
  log_level="DEBUG",
))

export MW_TRACKER=True
middleware-run python app.py

export MW_API_KEY='<MW_API_KEY>'
export MW_TARGET='https://<MW_UID>.middleware.io:443'
export MW_SERVICE_NAME='MyPythonApp'
middleware-run python app.py

from middleware import mw_tracker, MWOptions, DETECT_AWS_EC2

mw_tracker(MWOptions(
  access_token="<MW_API_KEY>",
  target="https://<MW_UID>.middleware.io:443",
  service_name="MyPythonApp",
  detectors=[DETECT_AWS_EC2],     # example
  otel_propagators="b3,tracecontext",
  console_exporter=True,
  log_level="DEBUG",
))

export MW_TRACKER=True
middleware-run python app.py

4. Advanced configuration (optional)#

export MW_SERVICE_NAME='MyPythonApp'

pip install 'middleware-io[profiling]'
export MW_APM_COLLECT_PROFILING=True
# serverless requires MW_API_KEY + MW_TARGET as above

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

export MW_API_KEY='<MW_API_KEY>'
export MW_TARGET='https://<MW_UID>.middleware.io:443'
export DJANGO_SETTINGS_MODULE='demo.settings'
middleware-run gunicorn -c conf/gunicorn.conf.py --workers=4 --bind 0.0.0.0:8000 --timeout 120 demo.wsgi

export MW_API_KEY='<MW_API_KEY>'
export MW_TARGET='https://<MW_UID>.middleware.io:443'
middleware-run uvicorn main:app --host localhost --port 5002

What “Zero-code” actually does: automatic patching via monkey-patching of popular libraries at runtime (e.g., Flask), driven by the instrumentation packages you installed with middleware-bootstrap.

5. Sending custom data#

from opentelemetry.metrics import get_meter_provider

meter = get_meter_provider().get_meter("custom_meter")

request_counter = meter.create_counter(
  "request_counter", description="Counts the number of requests"
)

request_counter.add(1, {"endpoint": "/home"})

from opentelemetry.trace import get_tracer

tracer = get_tracer("custom_tracer")

with tracer.start_as_current_span("custom_span"):
  print("Doing some work within the span")

import logging

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

from opentelemetry.trace import get_tracer

tracer = get_tracer("custom_tracer")

with tracer.start_as_current_span("span_with_attributes") as span:
  span.set_attribute("user.email", "johndoe@gmail.com")
  span.set_attribute("user.id", 1234)

from middleware import record_exception

try:
  print("Divide by zero:", 1 / 0)
except Exception as e:
  record_exception(e)

6. Environment variables#

OTel env vars are supported and take the highest priority if present. Then Middleware env vars, then code options.

7. View your data#

After you start the app, give it 3–5 minutes, then open APM → Traces, Logs, and APM → Continuous Profiling in Middleware. See “Application Instrumentation” for where to find dashboards, trace viewer, Log Explorer, and Alerts.

8. Troubleshooting#

• No data (Host mode): Ensure you’re using middleware-run, the Host Agent is installed, and (in containers) MW_AGENT_SERVICE is set to the correct address/service.
• No data (Serverless): Make sure both MW_API_KEY and MW_TARGET are exported before starting.
• Instrumentation didn’t hook: Re-run middleware-bootstrap -a install after adding frameworks, and verify with pip list that opentelemetry-instrumentation-* packages exist.
• Need to confirm register/init ran? Temporarily set MW_CONSOLE_EXPORTER=true (and MW_DEBUG_LOG_FILE=true) to echo spans/logs to stdout/files while you test.
• Kubernetes reachability: find the service, then set MW_AGENT_SERVICE=mw-service.mw-agent-ns.svc.cluster.local.

OpenTelemetry Python#

Use this option if you want upstream OpenTelemetry auto-instrumentation (opentelemetry-distro, opentelemetry-instrument) and OTLP export directly to Middleware.

• Full guide: Python (OpenTelemetry)