This client is under active development.
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_total', 'Description of counter')
c.inc() # Increment by 1
c.inc(1.6) # Increment by given valueThere 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
93E3
.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():
passSummaries 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():
passHistograms 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():
passAll 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()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())
There are several options for exporting metrics.
Metrics are usually exposed over HTTP, to be read by the Prometheus server. For example:
Python 2:
from prometheus_client import MetricsHandler
from BaseHTTPServer import HTTPServer
server_address = ('', 8000)
httpd = HTTPServer(server_address, MetricsHandler)
httpd.serve_forever()Python 3:
from prometheus_client import MetricsHandler
from http.server import HTTPServer
server_address = ('', 8000)
httpd = HTTPServer(server_address, MetricsHandler)
httpd.serve_forever()Visit http://localhost:8000/ to view the metrics.
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.