The official Python 2 and 3 client for Prometheus.
One: Install the client:
pip install prometheus-client
Two: Paste the following into a Python interpreter:
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/ 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!
pip install prometheus_client
This package can be found on PyPI.
Four types of metric are offered: Counter, Gauge, Summary and Histogram. See the documentation on metric types and instrumentation best practices on how to use them.
Counters go up, and reset when the process restarts.
from prometheus_client import Counter
c = Counter('my_failures', 'Description of counter')
c.inc() # Increment by 1
c.inc(1.6) # Increment by given valueIf there is a suffix of _total on the metric name, it will be removed. When
exposing the time series for counter, a _total suffix will be added. This is
for compatibility between OpenMetrics and the Prometheus text format, as OpenMetrics
requires the _total suffix.
There are utilities to count exceptions raised:
@c.count_exceptions()
def f():
pass
with c.count_exceptions():
pass
# Count only one type of exception
with c.count_exceptions(ValueError):
passGauges can go up and down.
from prometheus_client import Gauge
g = Gauge('my_inprogress_requests', 'Description of gauge')
g.inc() # Increment by 1
g.dec(10) # Decrement by given value
g.set(4.2) # Set to a given valueThere are utilities for common use cases:
g.set_to_current_time() # Set to current unixtime
# Increment when entered, decrement when exited.
@g.track_inprogress()
def f():
pass
with g.track_inprogress():
passA Gauge can also take its value from a callback:
d = Gauge('data_objects', 'Number of objects')
my_dict = {}
d.set_function(lambda: len(my_dict))Summaries track the size and number of events.
from prometheus_client import Summary
s = Summary('request_latency_seconds', 'Description of summary')
s.observe(4.7) # Observe 4.7 (seconds in this case)There are utilities for timing code:
@s.time()
def f():
pass
with s.time():
passThe Python client doesn't store or expose quantile information at this time.
Histograms track the size and number of events in buckets. This allows for aggregatable calculation of quantiles.
from prometheus_client import Histogram
h = Histogram('request_latency_seconds', 'Description of histogram')
h.observe(4.7) # Observe 4.7 (seconds in this case)The default buckets are intended to cover a typical web/rpc request from milliseconds to seconds.
They can be overridden by passing buckets keyword argument to Histogram.
There are utilities for timing code:
@h.time()
def f():
pass
with h.time():
passInfo tracks key-value information, usually about a whole target.
from prometheus_client import Info
i = Info('my_build_version', 'Description of info')
i.info({'version': '1.2.3', 'buildhost': 'foo@bar'})Enum tracks which of a set of states something is currently in.
from prometheus_client import Enum
e = Enum('my_task_state', 'Description of enum',
states=['starting', 'running', 'stopped'])
e.state('running')