Prometheus Flask Exporter

0.23.2 · active · verified Fri Apr 10

This library provides HTTP request metrics for Flask applications, allowing easy integration with Prometheus. It automatically collects default metrics like request duration and total counts, and enables defining custom metrics using decorators. The current version is 0.23.2, with new releases occurring periodically, often in response to updates in Flask or the underlying Prometheus client library.

Warnings

deprecated The `group_by_endpoint` argument in `PrometheusMetrics` is deprecated.
Fix: Use the `group_by` argument instead, which offers more flexible grouping options. For example, `PrometheusMetrics(app, group_by='endpoint')`.
deprecated The `static_labels` argument in `PrometheusMetrics` is deprecated.
Fix: Use the `default_labels` argument instead, which supports both static and dynamic label values.
gotcha Running Flask with `debug=True` (live-reloading) might not reflect metrics for the latest code unless the `DEBUG_METRICS` environment variable is set. Additionally, `PrometheusMetrics.start_http_server()` is not expected to work reliably in this debug scenario.
Fix: For accurate metrics during development with live-reloading, set the `DEBUG_METRICS` environment variable. For production, disable debug mode and use a WSGI server.
gotcha Carefully manage metric label cardinality to avoid 'cardinality explosion', which can lead to excessive memory usage and Prometheus server crashes.
Fix: Avoid using high-cardinality values (e.g., user IDs, full dynamic URL paths) as labels. Aggregate or generalize label values (e.g., `/user/{id}` instead of `/user/123`).
breaking Past versions experienced issues due to breaking changes in the underlying `prometheus_client` library's `exposition` module (`choose_encoder`).
Fix: Always pin the `prometheus_client` dependency to a known compatible version in your `requirements.txt` or `pyproject.toml` to prevent unexpected breakage during upgrades. Ensure `prometheus-flask-exporter` is also kept updated to versions that explicitly support newer `prometheus_client` releases.
gotcha Reports of memory leaks have been observed in specific pull-based Prometheus configurations when custom decorators for latency measurement are used.
Fix: Monitor memory usage closely in production. If a leak is suspected, review custom metric decorators, ensure proper resource cleanup, and consult `prometheus_flask_exporter` or `prometheus_client` issue trackers.

Install

pip install prometheus-flask-exporter Install latest version

Imports

PrometheusMetrics

from prometheus_flask_exporter import PrometheusMetrics

Flask
```
from flask import Flask
```
request
```
from flask import request
```
Required for custom label functions that inspect the request object.

Quickstart

Initializes a Flask application with Prometheus metrics. Default HTTP request metrics (duration, total, exceptions) are exposed on the '/metrics' endpoint. Custom metrics like 'in_progress' gauge can be added to specific routes using decorators. To view metrics, run the app and navigate to '/metrics' after making some requests.

from flask import Flask, request
from prometheus_flask_exporter import PrometheusMetrics
import os

app = Flask(__name__)
metrics = PrometheusMetrics(app)

# Optional: Add static info about the app
metrics.info('app_info', 'Application info', version='1.0.0')

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

@app.route('/long-running')
@metrics.gauge('in_progress', 'Long running requests in progress')
def long_running():
    import time
    time.sleep(2)
    return 'Done!'

# Run the app directly or using a WSGI server like Gunicorn
if __name__ == '__main__':
    # Example: Access http://localhost:5000/ and http://localhost:5000/metrics
    app.run(host='0.0.0.0', port=5000)

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