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Prometheus Python Client

The official Python 2 and 3 client for Prometheus.

Three Step Demo

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!

Installation

pip install prometheus_client

This package can be found on PyPI.

Instrumenting

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.

Counter

Counters go up, and reset when the process restarts.

from prometheus_client import Counter
c = Counter('my_failures_total', 'Description of counter')
c.inc()     # Increment by 1
c.inc(1.6)  # Increment by given value

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):
  pass

Gauge

Gauges 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 value

There 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():
  pass

A Gauge can also take it's value from a callback:

d = Gauge('data_objects', 'Number of objects')
my_dict = {}
d.set_function(lambda: len(my_dict))

Summary

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():
  pass

The Python client doesn't store or expose quantile information at this time.

Histogram

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():
  pass

Labels

All metrics can have labels, allowing grouping of related time series.

See the best practices on naming and labels.

Taking a counter as an example:

from prometheus_client import Counter
c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
c.labels('get', '/').inc()
c.labels('post', '/submit').inc()

Labels can also be provided as a dict:

from prometheus_client import Counter
c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
c.labels({'method': 'get', 'endpoint': '/'}).inc()
c.labels({'method': 'post', 'endpoint': '/submit'}).inc()

Process Collector

The Python client automatically exports metrics about process CPU usage, RAM, file descriptors and start time. These all have the prefix process, and are only currently available on Linux.

The namespace and pid constructor arguments allows for exporting metrics about other processes, for example:

ProcessCollector(namespace='mydaemon', pid=lambda: open('/var/run/daemon.pid').read())

Exporting

There are several options for exporting metrics.

HTTP

Metrics are usually exposed over HTTP, to be read by the Prometheus server.

The easiest way to do this is via start_http_server, which will start a HTTP server in a daemon thread on the given port:

from prometheus_client import start_http_server
start_http_server(8000)

Visit http://localhost:8000/ to view the metrics.

To add Prometheus exposition to an existing HTTP server, see the MetricsServlet class which provides a BaseHTTPRequestHandler. It also serves as a simple example of how to write a custom endpoint.

Node exporter textfile collector

The textfile collector allows machine-level statistics to be exported out via the Node exporter.

This is useful for monitoring cronjobs, or for writing cronjobs to expose metrics about a machine system that the Node exporter does not support or would not make sense to perform at every scrape (for example, anything involving subprocesses).

from prometheus_client import CollectorRegistry,Gauge,write_to_textfile
registry = CollectorRegistry()
g = Gauge('raid_status', '1 if raid array is okay', registry=registry)
g.set(1)
write_to_textfile('/configured/textfile/path/raid.prom', registry)

A separate registry is used, as the default registry may contain other metrics such as those from the Process Collector.

Exporting to a Pushgateway

The Pushgateway allows ephemeral and batch jobs to expose their metrics to Prometheus.

from prometheus_client import CollectorRegistry,Gauge,push_to_gateway
registry = CollectorRegistry()
g = Gauge('job_last_success_unixtime', 'Last time a batch job successfully finished', registry=registry)
g.set_to_current_time()
push_to_gateway('localhost:9091', job='batchA', registry=registry)

A separate registry is used, as the default registry may contain other metrics such as those from the Process Collector.

Pushgateway functions take a grouping key. push_to_gateway replaces metrics with the same grouping key, pushadd_to_gateway only replaces metrics with the same name and grouping key and delete_from_gateway deletes metrics with the given job and grouping key. See the Pushgateway documentation for more information.

instance_ip_grouping_key returns a grouping key with the instance label set to the host's IP address.

Bridges

It is also possible to expose metrics to systems other than Prometheus. This allows you to take advantage of Prometheus instrumentation even if you are not quite ready to fully transition to Prometheus yet.

Graphite

Metrics are pushed over TCP in the Graphite plaintext format.

from prometheus_client.bridge.graphite import GraphiteBridge
gb = GraphiteBridge(('graphite.your.org', 2003))
# Push once.
gb.push()
# Push every 10 seconds in a daemon thread.
gb.start(10.0)