Python prometheus library github

Ada 116 Published: 11/17/2024

Python prometheus library github

I'd be happy to help you with that!

The Python Prometheus Library GitHub repository is maintained by the Prometheus community, and it's a widely used and well-maintained project for exposing metrics from Python applications.

Here are some key features of the library:

Prometheus Exporter: The primary feature of this library is the prometheus_exporter class, which allows you to expose your application's metrics using the Prometheus HTTP API.

Gauge and Counter Metrics: You can create gauge and counter metrics to track various aspects of your application's performance, such as request latency, error rates, or memory usage. Histogram Metrics: In addition to gauge and counter metrics, you can also create histogram metrics to track the distribution of values for a particular metric. Labels: You can add labels to your metrics to provide additional context and make it easier to filter and aggregate data.

Some key benefits of using this library include:

Easy integration with Prometheus: Since the library is designed specifically for Python, you can easily integrate it with your existing application and start exposing metrics in no time. High-performance: The library is built on top of the http.server module and uses asynchronous I/O operations to handle requests, making it suitable for high-traffic applications. Rich set of features: With this library, you get access to a wide range of metric types and labels, giving you the flexibility to track whatever metrics are important to your application.

The GitHub repository contains not only the source code but also extensive documentation, including tutorials, examples, and API references.

Here's an example snippet showing how to create a simple gauge metric:

from prometheus_client import Gauge
Create a gauge metric for request latency
request_latency = Gauge('request_latency_seconds', 'Request latency in seconds')
Update the metric value
def update_request_latency(latency_ms):
request_latency.set(latency_ms / 1000)
update_request_latency(500)  # Set the initial value to 0.5 seconds

This is just a taste of what you can do with this library. If you're interested in learning more, I encourage you to check out the official GitHub repository and documentation!

Prometheus Python tutorial

I'll make sure to respond in English and provide a comprehensive guide on Prometheus Python tutorial.

What is Prometheus?

Prometheus is an open-source monitoring system that collects metrics from various sources, processes them, and stores them in a time-series database (TSDB). This allows users to visualize and analyze the metrics using tools like Grafana. The name "Prometheus" comes from Greek mythology, as it's said to be inspired by the titan who stole fire from the gods.

Why use Prometheus?

Monitoring: Prometheus is designed to monitor diverse components of your infrastructure, including services, servers, and applications.

Metrics collection: It collects metrics at regular intervals, enabling you to track key performance indicators (KPIs) for your systems. Alerting: When a threshold or anomaly is detected, Prometheus triggers alerts, helping you identify potential issues before they become major problems.

What are the key components of Prometheus?

Prometheus server: The core component that collects and stores metrics. Time-series database (TSDB): Stores metrics, allowing for efficient querying and visualization. Push gateway: Handles push-based scraping for remote targets like Kubernetes pods or containerized applications.

How to set up Prometheus using Python?

To get started with Prometheus in Python, you'll need:

Python 3.x: Ensure you have a compatible version installed. prometheus-client: A Python library that provides APIs for exposing metrics and scraping targets.

Step-by-Step Instructions:

Install prometheus-client using pip:

pip install prometheus-client

Import the prometheus_client library in your Python script:

import prometheus_client as pc

Create a Prometheus exporter (a.k.a., a metrics collection point):

class MyMetricsExporter(pc.Snapshot):
def collect(self, request_info=None):
return [
Example metrics
pc.GaugeMetricFamily("my_gauge", "My gauge metric", {"label1": "value1"} ),
pc.CounterMetricFamily("my_counter", "My counter metric")
]

Start the exporter as a Python script or using a framework like Flask/Django:

if __name__ == "__main__":
pc.start_http_server(8000)  # listen on port 8000
print("Listening on port 8000...")

Next Steps:

Configure scrape targets: Define the endpoints, services, or applications you want to monitor using Prometheus' configuration file (e.g., prometheus.yml). Set up alerting rules: Configure custom alerting rules for specific metric thresholds, anomalies, or other conditions. Visualize metrics with Grafana: Use your chosen time-series database (e.g., InfluxDB) and Grafana to visualize and analyze collected metrics.

This guide provides a solid foundation for using Prometheus with Python. If you're interested in exploring more advanced topics or integrating Prometheus with specific technologies, I can help with that as well!