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', 'Description of counter')
c.inc() # Increment by 1
c.inc(1.6) # Increment by given value
If 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):
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 its 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
Info
Info 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
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')
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 passed as keyword-arguments:
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()
Metrics with labels are not initialized when declared, because the client can't know what values the label can have. It is recommended to initialize the label values by calling the .labels() method alone:
from prometheus_client import Counter
c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
c.labels('get', '/')
c.labels('post', '/submit')
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())
Platform Collector
The client also automatically exports some metadata about Python. If using Jython, metadata about the JVM in use is also included. This information is available as labels on the python_info metric. The value of the metric is 1, since it is the labels that carry information.
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 MetricsHandler class which provides a BaseHTTPRequestHandler. It also serves as a simple example of how to write a custom endpoint.
Twisted
To use prometheus with twisted, there is MetricsResource which exposes metrics as a twisted resource.
from prometheus_client.twisted import MetricsResource
from twisted.web.server import Site
from twisted.web.resource import Resource
from twisted.internet import reactor
root = Resource()
root.putChild(b'metrics', MetricsResource())
factory = Site(root)
reactor.listenTCP(8000, factory)
reactor.run()
WSGI
To use Prometheus with WSGI, there is make_wsgi_app which creates a WSGI application.
from prometheus_client import make_wsgi_app
from wsgiref.simple_server import make_server
app = make_wsgi_app()
httpd = make_server('', 8000, app)
httpd.serve_forever()
Such an application can be useful when integrating Prometheus metrics with WSGI apps.
The method start_wsgi_server can be used to serve the metrics through the WSGI reference implementation in a new thread.
from prometheus_client import start_wsgi_server
start_wsgi_server(8000)
ASGI
To use Prometheus with ASGI, there is make_asgi_app which creates an ASGI application.
from prometheus_client import make_asgi_app
app = make_asgi_app()
Such an application can be useful when integrating Prometheus metrics with ASGI apps.
Flask
To use Prometheus with Flask we need to serve metrics through a Prometheus WSGI application. This can be achieved using Flask's application dispatching. Below is a working example.
Save the snippet below in a myapp.py file
from flask import Flask
from werkzeug.middleware.dispatcher import DispatcherMiddleware
from prometheus_client import make_wsgi_app
# Create my app
app = Flask(__name__)
# Add prometheus wsgi middleware to route /metrics requests
app.wsgi_app = DispatcherMiddleware(app.wsgi_app, {
'/metrics': make_wsgi_app()
})
Run the example web application like this
# Install uwsgi if you do not have it
pip install uwsgi
uwsgi --http 127.0.0.1:8000 --wsgi-file myapp.py --callable app
Visit http://localhost:8000/metrics to see the metrics
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.
Handlers for authentication
If the push gateway you are connecting to is protected with HTTP Basic Auth, you can use a special handler to set the Authorization header.
from prometheus_client import CollectorRegistry, Gauge, push_to_gateway
from prometheus_client.exposition import basic_auth_handler
def my_auth_handler(url, method, timeout, headers, data):
username = 'foobar'
password = 'secret123'
return basic_auth_handler(url, method, timeout, headers, data, username, password)
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, handler=my_auth_handler)
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)
Graphite tags are also supported.
from prometheus_client.bridge.graphite import GraphiteBridge
gb = GraphiteBridge(('graphite.your.org', 2003), tags=True)
c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
c.labels('get', '/').inc()
gb.push()
Custom Collectors
Sometimes it is not possible to directly instrument code, as it is not in your control. This requires you to proxy metrics from other systems.
To do so you need to create a custom collector, for example:
from prometheus_client.core import GaugeMetricFamily, CounterMetricFamily, REGISTRY
class CustomCollector(object):
def collect(self):
yield GaugeMetricFamily('my_gauge', 'Help text', value=7)
c = CounterMetricFamily('my_counter_total', 'Help text', labels=['foo'])
c.add_metric(['bar'], 1.7)
c.add_metric(['baz'], 3.8)
yield c
REGISTRY.register(CustomCollector())
SummaryMetricFamily, HistogramMetricFamily and InfoMetricFamily work similarly.
A collector may implement a describe method which returns metrics in the same format as collect (though you don't have to include the samples). This is used to predetermine the names of time series a CollectorRegistry exposes and thus to detect collisions and duplicate registrations.
Usually custom collectors do not have to implement describe. If describe is not implemented and the CollectorRegistry was created with auto_describe=True (which is the case for the default registry) then collect will be called at registration time instead of describe. If this could cause problems, either implement a proper describe, or if that's not practical have describe return an empty list.
Multiprocess Mode (Gunicorn)
Prometheus client libraries presume a threaded model, where metrics are shared across workers. This doesn't work so well for languages such as Python where it's common to have processes rather than threads to handle large workloads.
To handle this the client library can be put in multiprocess mode. This comes with a number of limitations:
- Registries can not be used as normal, all instantiated metrics are exported
- Custom collectors do not work (e.g. cpu and memory metrics)
- Info and Enum metrics do not work
- The pushgateway cannot be used
- Gauges cannot use the
pidlabel
There's several steps to getting this working:
1. Gunicorn deployment:
The prometheus_multiproc_dir environment variable must be set to a directory that the client library can use for metrics. This directory must be wiped between Gunicorn runs (before startup is recommended).
This environment variable should be set from a start-up shell script, and not directly from Python (otherwise it may not propagate to child processes).
2. Metrics collector:
The application must initialize a new CollectorRegistry, and store the multi-process collector inside.
from prometheus_client import multiprocess
from prometheus_client import generate_latest, CollectorRegistry, CONTENT_TYPE_LATEST
# Expose metrics.
def app(environ, start_response):
registry = CollectorRegistry()
multiprocess.MultiProcessCollector(registry)
data = generate_latest(registry)
status = '200 OK'
response_headers = [
('Content-type', CONTENT_TYPE_LATEST),
('Content-Length', str(len(data)))
]
start_response(status, response_headers)
return iter([data])
3. Gunicorn configuration:
The gunicorn configuration file needs to include the following function:
from prometheus_client import multiprocess
def child_exit(server, worker):
multiprocess.mark_process_dead(worker.pid)
4. Metrics tuning (Gauge):
When Gauge metrics are used, additional tuning needs to be performed. Gauges have several modes they can run in, which can be selected with the multiprocess_mode parameter.
- 'all': Default. Return a timeseries per process alive or dead.
- 'liveall': Return a timeseries per process that is still alive.
- 'livesum': Return a single timeseries that is the sum of the values of alive processes.
- 'max': Return a single timeseries that is the maximum of the values of all processes, alive or dead.
- 'min': Return a single timeseries that is the minimum of the values of all processes, alive or dead.
from prometheus_client import Gauge
# Example gauge
IN_PROGRESS = Gauge("inprogress_requests", "help", multiprocess_mode='livesum')
Parser
The Python client supports parsing the Prometheus text format. This is intended for advanced use cases where you have servers exposing Prometheus metrics and need to get them into some other system.
from prometheus_client.parser import text_string_to_metric_families
for family in text_string_to_metric_families(u"my_gauge 1.0\n"):
for sample in family.samples:
print("Name: {0} Labels: {1} Value: {2}".format(*sample))
139 Dec 25, 2022
33 Dec 8, 2022
703 Dec 30, 2022
5.8k Dec 31, 2022
4.1k Jan 1, 2023
1.9k Dec 25, 2022
162 Dec 19, 2022
558 Dec 24, 2022
3 Nov 19, 2021
1.2k Jan 2, 2023
4 Apr 4, 2022
2 Jul 18, 2022
1.3k Jan 3, 2023
8 Dec 12, 2022
2.2k Dec 25, 2022