1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
|
---
title: Three Step Demo
weight: 1
---
This tutorial shows the quickest way to get started with the Prometheus Python library.
# Three Step Demo
**One**: Install the client:
```
pip install prometheus-client
```
**Two**: Paste the following into a Python interpreter:
```python
from prometheus_client import start_http_server, Summary
import random
import time
# Create a metric to track time spent and requests made.
REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')
# Decorate function with metric.
@REQUEST_TIME.time()
def process_request(t):
"""A dummy function that takes some time."""
time.sleep(t)
if __name__ == '__main__':
# Start up the server to expose the metrics.
start_http_server(8000)
# Generate some requests.
while True:
process_request(random.random())
```
**Three**: Visit [http://localhost:8000/](http://localhost:8000/) to view the metrics.
From one easy to use decorator you get:
* `request_processing_seconds_count`: Number of times this function was called.
* `request_processing_seconds_sum`: Total amount of time spent in this function.
Prometheus's `rate` function allows calculation of both requests per second,
and latency over time from this data.
In addition if you're on Linux the `process` metrics expose CPU, memory and
other information about the process for free!
|