The unofficial Python 2 and 3 client for Prometheus.

• Four types of metric are supported: Counter, Gauge, Summary(without quantiles) and Histogram.
• InMemoryStorage (do not use it for multiprocessing apps)
• UWSGI storage - share metrics between processes
• UWAGI flush storage - sync metrics with uwsgi sharedarea by flush call
• time decorator
• time context manager

To use pyprometheus use pip or easy_install:

pip install pyprometheus

or

easy_install pyprometheus

A gauge is a metric that represents a single numerical value that can arbitrarily go up and down.:

from pyprometheus import Gauge from pyprometheus import BaseRegistry, LocalMemoryStorage storage = LocalMemoryStorage() registry = CollectorRegistry(storage=storage) gauge = Gauge("job_in_progress", "Description", registry=registry) gauge.inc(10) gauge.dec(5) gauge.set(21.1) 

utilities:

gauge.set_to_current_time() # Set to current unixtime # Increment when entered, decrement when exited. @gauge.track_in_progress() def f(): pass with gauge.track_in_progress(): pass with gauge.time(): time.sleep(10) 

A counter is a cumulative metric that represents a single numerical value that only ever goes up.:

from pyprometheus import Counter from pyprometheus import BaseRegistry, LocalMemoryStorage storage = LocalMemoryStorage() registry = CollectorRegistry(storage=storage) counter = Counter("requests_total", "Description", registry=registry) counter.inc(10) 

Similar to a histogram, a summary samples observations (usually things like request durations and response sizes).:

from pyprometheus import Summary from pyprometheus import BaseRegistry, LocalMemoryStorage storage = LocalMemoryStorage() registry = CollectorRegistry(storage=storage) s = Summary("requests_duration_seconds", "Description", registry=registry) s.observe(0.100) 

utilities for timing code:

@gauge.time() def func(): time.sleep(10) with gauge.time(): time.sleep(10) 

A histogram samples observations (usually things like request durations or response sizes) and counts them in configurable buckets. It also provides a sum of all observed values.:

from pyprometheus import Summary from pyprometheus import BaseRegistry, LocalMemoryStorage storage = LocalMemoryStorage() registry = CollectorRegistry(storage=storage) histogram = Histogram("requests_duration_seconds", "Description", registry=registry) histogram.observe(1.1) 

utilities for timing code:

@histogram.time() def func(): time.sleep(10) with histogram.time(): time.sleep(10) 

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

Example:

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

or labels as keyword arguments:

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

Currently library support 2 storages: LocalMemoryStorage and UWSGIStorage

Every registry MUST have link to storage:

from pyprometheus import BaseRegistry, LocalMemoryStorage storage = LocalMemoryStorage() registry = CollectorRegistry(storage=storage) 

Simple storage that store samples to application memory. It can be used with threads.:

from pyprometheus import BaseRegistry, LocalMemoryStorage storage = LocalMemoryStorag() 

UWSGIStorage allow to use uwsgi sharedarea to sync metrics between processes.:

from pyprometheus.contrib.uwsgi_features import UWSGICollector, UWSGIStorage SHAREDAREA_ID = 0 storage = UWSGIStorage(SHAREDAREA_ID) 

also need to configure UWSGI sharedaread pages.

Library have some helpers to export metrics

You can convert registry to text format:

from pyprometheus import BaseRegistry, LocalMemoryStorage from pyprometheus.utils.exposition import registry_to_text from pyprometheus import Gauge storage = LocalMemoryStorage() registry = CollectorRegistry(storage=storage) g = Gauge('raid_status', '1 if raid array is okay', registry=registry) g.set(1) print(registry_to_text(registry)) 

This is useful for monitoring cronjobs, or for writing cronjobs to expose metrics about a machine system.:

from pyprometheus import BaseRegistry, LocalMemoryStorage from pyprometheus.utils.exposition import registry_to_text, write_to_textfile from pyprometheus import Gauge storage = LocalMemoryStorage() registry = CollectorRegistry(storage=storage) g = Gauge('raid_status', '1 if raid array is okay', registry=registry) g.set(1) write_to_textfile(registry, "/path/to/file/metrics.prom") 

You can configure text file collector to use generated file.

Some features that we plan to do:

• [ ] Add mmap storage
• [ ] Add features for async frameworks
• [ ] Optimize UWSGI storage byte pad
• [ ] Add quantiles

We create example project to show hot to use pyprometheus in real project.

Fork https://github.com/Lispython/pyprometheus/ , create commit and pull request to develop.