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Python StatsD Client
`StatsD <>`_ is a stats server that plays
with `Graphite <>`_. Together, they collect,
aggregate, and show stats. If you don't know what either of those are,
well, why are you still reading this? If you write software or know
someone that does, I bet collecting stats will make your software
better, or at the very least give you something to look at and think
about. StatsD makes it really easy to send stats within your code. This
client maks it even easier to get stats out of your python code.
Annoyed with managing external packages? There are plenty of statsd
clients that come up under 'pip search statsd'. Who has time to keep
track of tiny dependencies for small projects? Just copy into
your project if you're into that kind of thing. No need to depend on
some multi-file package for what should be a simple client. Grab and Go!
If you're a stickler for dependencies, you probably don't need to know
how to install this, but here you go any how:
Clone and install:
git clone
cd python-statsd-client
python install
Install with pip:
pip install statsd-client
Or like I said above, just copy into your code base.
Setup is easy. By default, the client will connect to localhost on the
default statsd port (8125).
import statsd
statsd.incr('processed') # Increment processed bucket by 1
statsd.incr('processed', 5) # This time by 5
statsd.incr('processed', sample_rate=0.9) # Increment with a sample rate of .9
statsd.timing('pipeline', 2468.34) # Pipeline took 2468.34 ms to execute
Want to connect to a non-local statsd? Use
statsd.init\_statsd(settings). Settings is a dict with any of these
STATSD_HOST (Default 'localhost'): String host name.
STATSD_PORT (Default 8125): Integer port number.
STATSD_SAMPLE_RATE (Default None (same as 1.0)): Integer/Float between 0 and 1.
STATSD_BUCKET_PREFIX (Default None): String prefix added to all buckets. The code will handle dotting them together.
If you do not want to use init\_statsd, you can always pass in your
settings when you create the clients, timers or counters:
from statsd import StatsdClient
client = StatsdClient(host='', port=9999, prefix='app', sample_rate=0.9)
Want to count things? Use StatsdCounter:
import statsd
statsd.init_statsd({'STATSD_BUCKET_PREFIX': 'photos'})
counter = statsd.StatsdCounter('processed')
# calls on counter will send updates to bucket named 'photos.processed'
counter += 1 # equivalent to counter.incr() or counter.incr(1)
counter += 5 # equivalent to counter.incr(5)
counter -= 10 # equivalent to counter.decr(10)
Interested in timing? Check out all the ways you can time things:
import statsd
statsd.init_statsd({'STATSD_BUCKET_PREFIX': 'photos'})
timer = statsd.StatsdTimer('pipeline')
# Do stuff
timer.split('stage1') # Sends timing data for bucket 'photos.pipeline.stage1'
# Do more stuff
timer.split('stage2') # Sends timing data for bucket 'photos.pipeline.stage2'
# Do even more stuff
timer.stop() # Sends timing data for bucket ''
Timers can be used as decorators too:
from statsd import StatsdTimer
def process():
process() # Sends timing data for bucket ''
Fancy with statement usage!
from statsd import StatsdTimer
with StatsdTimer('photos'):
pass # Do stuff
Even fancier:
from statsd import StatsdTimer
with StatsdTimer('photos') as t:
# Do stuff
# Do more stuff
# Finish up
Using timers with decorators or the with statement will still sends
stats if an exception is raised in the code block:
from statsd import StatsdTimer
class Foo(object):
def proc(self):
# Do stuff
raise ValueError('Whoops')
f = Foo()
f.proc() # Raises exception, but sends timing data for bucket ''
The client integrates great with `Flask <>`_.
Just call statsd.init\_statsd when you're initializing all your other
framework components. Once that's done, you can use the timers and
counters anywhere in your code.
If you find a bug and want to fix it, fork, branch, and submit a pull
request. The master branch will always have the latest working code.
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