Prometheus python client and instrumentation library
Project description
The unofficial Python 2 and 3 client for Prometheus.
Features
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
INSTALLATION
To use pyprometheus use pip or easy_install:
pip install pyprometheus
or
easy_install pyprometheus
HOW TO INSTRUMENTING CODE
Gauge
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)
Counter
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)
Summary
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)
Histogram
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)
Labels
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()
STORAGES
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)
Use LocalMemoryStorage
Simple storage that store samples to application memory. It can be used with threads.:
from pyprometheus import BaseRegistry, LocalMemoryStorage storage = LocalMemoryStorag()
Use UWSGIStorage
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.
EXPORTING
Library have some helpers to export metrics
To text format
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))
Text file export
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.
TODO
Some features that we plan to do:
[ ] Add mmap storage
[ ] Add features for async frameworks
[ ] Optimize UWSGI storage byte pad
[ ] Add quantiles
EXAMPLE PROJECT
We create example project to show hot to use pyprometheus in real project.
CONTRIBUTE
Fork https://github.com/Lispython/pyprometheus/ , create commit and pull request to develop.
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