statsdmetrics 0.1.0

Metric classes for Statsd

Metric classes for Statsd and and functionality to create, parse and send Statsd requests (each metric in a single request, or send batch requests).

Metric Classes

Available metrics:

• Counter

• Timer

• Gauge

• Set

• GaugeDelta

from statsdmetrics import Counter, Timer

counter = Counter('event.login', 1, 0.2)
counter.to_request() # returns event.login:1|c|@0.2

timer = Timer('db.search.username', 27.4)
timer.to_request() # returns db.search.username:27.4|ms

Parse metrics from a Statsd request

from statsdmetrics import parse_metric_from_request

event_login = parse_metric_from_request('event.login:1|c|@.2')
# event_login is a Counter object with count = 1 and sample_rate = 0.2

mem_usage = parse_metric_from_request('resource.memory:2048|g')
# mem_usage is a Gauge object with value = 2028

Statsd Client

Send Statsd requests

from statsdmetrics.client import Client

client = Client("stats.example.org")
client.increment("login")
client.timing("db.search.username", 3500)
client.set("unique.ip_address", "10.10.10.1")
client.gauge("memory", 20480)
# settings can be updated later
client.host = "localhost"
client.port = 8126
client.gauge_delta("memory", -256)
client.decrement(name="connections", 2)

Sending multiple metrics in batch requests is supported through BatchClient class, either by using an available client as the context manager:

from statsdmetrics.client import Client

client = Client("stats.example.org")
with client.batch_client() as batch_client:
    batch_client.increment("login")
    batch_client.decrement(name="connections", 2)
    batch_client.timing("db.search.username", 3500)
# now all metrics are flushed automatically in batch requests

or by creating a BatchClient object explicitly:

from statsdmetrics.client import BatchClient

client = BatchClient("stats.example.org")
client.set("unique.ip_address", "10.10.10.1")
client.gauge("memory", 20480)
client.flush() # sends one UDP packet to remote server, carrying both metrics

Dependencies

There are no specific dependencies, it runs on Python 2.7+ (CPython 2.7, 3.2, 3.3 3.4 and 3.5, PyPy 2.6 and PyPy3 2.4, and Jython 2.7 are tested)

However on development (and test) environment mock is required, and distutilazy (or setuptools as a fallback) is used to run the tests.

# on dev/test env
pip install -r requirements-dev.txt

Tests

If you have make available

make test

You can always use the setup.py file

python setup.py test

License

Statsd metrics is released under the terms of the MIT license.