StatsD client for Python
Project description
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.
Install
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 statsd.py 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 git@github.com:gaelenh/python-statsd-client.git cd python-statsd-client python setup.py install
Install with pip:
pip install statsd-client
Or like I said above, just copy statsd.py into your code base.
Usage
Basic
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 keys:
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='127.0.0.1', port=9999, prefix='app', sample_rate=0.9)
Counters
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)
Timing
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')
timer.start()
# 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 'photos.pipeline.total'
Timers can be used as decorators too:
from statsd import StatsdTimer
@StatsdTimer.wrap('pipeline')
def process():
pass
process() # Sends timing data for bucket 'pipeline.total'
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
t.split('stage1')
# Do more stuff
t.split('stage2')
# 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):
@StatsdTimer('photos')
def proc(self):
# Do stuff
raise ValueError('Whoops')
f = Foo()
f.proc() # Raises exception, but sends timing data for bucket 'photos.total-except'
Misc
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.
Contributing
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.
