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Add docstrings for Counter, Histogram and Summary classes #44

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brian-brazil merged 1 commit into from
Sep 5, 2015
Merged

Add docstrings for Counter, Histogram and Summary classes #44

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66 changes: 66 additions & 0 deletions prometheus_client/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -183,6 +183,23 @@ def collect():

@_MetricWrapper
class Counter(object):
'''A Counter tracks counts of events or running totals.

Example use cases for Counters:
- Number of requests processed
- Number of items that were inserted into a queue
- Total amount of data that a system has processed

Counters can only go up (and be reset when the process restarts). If your use case can go down,
you should use a Gauge instead.

An example for a Counter:

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
'''
_type = 'counter'
_reserved_labelnames = []

Expand Down Expand Up @@ -346,6 +363,28 @@ def _samples(self):

@_MetricWrapper
class Summary(object):
'''A Summary tracks the size and number of events.

Example use cases for Summaries:
- Response latency
- Request size

Example for a Summary:

from prometheus_client import Summary
s = Summary('request_size_bytes', 'Request size (bytes)')
s.observe(512) # Observe 512 (bytes)

Example for a Summary using time:
from prometheus_client import Summary
REQUEST_TIME = Summary('response_latency_seconds', 'Response latency (seconds)')

@REQUEST_TIME.time()
def create_response(request):
"""A dummy function"""
time.sleep(1)

'''
_type = 'summary'
_reserved_labelnames = ['quantile']

Expand Down Expand Up @@ -404,6 +443,33 @@ def _floatToGoString(d):

@_MetricWrapper
class Histogram(object):
'''A Histogram tracks the size and number of events in buckets.

You can use Histograms for aggregatable calculation of quantiles.

Example use cases:
- Response latency
- Request size

Example for a Histogram:

from prometheus_client import Histogram
h = Histogram('request_size_bytes', 'Request size (bytes)')
h.observe(512) # Observe 512 (bytes)


Example for a Histogram using time:
from prometheus_client import Histogram
REQUEST_TIME = Histogram('response_latency_seconds', 'Response latency (seconds)')

@REQUEST_TIME.time()
def create_response(request):
"""A dummy function"""
time.sleep(1)

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`.
'''
_type = 'histogram'
_reserved_labelnames = ['histogram']

Expand Down