Timers are an incredibly powerful tool for tracking application performance. Statsd provides a number of ways to use them to instrument your code.
There are four ways to use timers.
Calling timing manually¶
The simplest way to use a timer is to record the time yourself and send it manually, using the timing method:
import time from statsd import StatsClient statsd = StatsClient() start = time.time() time.sleep(3) # You must convert to milliseconds: dt = int((time.time() - start) * 1000) statsd.timing('slept', dt)
Using a context manager¶
Each StatsClient instance contains a timer attribute that can be used as a context manager or a decorator. When used as a context manager, it will automatically report the time taken for the inner block:
from statsd import StatsClient statsd = StatsClient() with statsd.timer('foo'): # This block will be timed. for i in xrange(0, 100000): i ** 2 # The timing is sent immediately when the managed block exits.
Using a decorator¶
The timer attribute decorates your methods in a thread-safe manner. Every time the decorated function is called, the time it took to execute will be sent to the statsd server.
from statsd import StatsClient statsd = StatsClient() @statsd.timer('myfunc') def myfunc(a, b): """Calculate the most complicated thing a and b can do.""" # Timing information will be sent every time the function is called. myfunc(1, 2) myfunc(3, 7)
Using a Timer object directly¶
New in version 2.1.
statsd.client.Timer objects function as context managers and as decorators, but they can also be used directly. (Flat is, after all, better than nested.)
from statsd import StatsClient statsd = StatsClient() foo_timer = statsd.timer('foo') foo_timer.start() # Do something fun. foo_timer.stop()
When statsd.client.Timer.stop() is called, a :ref:`timing stat <timer-type>`_ will automatically be sent to StatsD. You can over ride this behavior with the send=False keyword argument to stop():
Use statsd.client.Timer.send() to send the stat when you’re ready.
This use of timers is compatible with :ref:`Pipelines <pipeline-chapter>`_ but be careful with the send() method. It must be called for the stat to be included when the Pipeline finally sends data, but send() will not immediately cause data to be sent in the context of a Pipeline. For example:
with statsd.pipeline() as pipe: foo_timer = pipe.timer('foo').start() # Do something... pipe.incr('bar') foo_timer.stop() # Will be sent when the managed block exits. with statsd.pipeline() as pipe: foo_timer = pipe.timer('foo').start() # Do something... pipe.incr('bar') foo_timer.stop(send=False) # Will not be sent. foo_timer.send() # Will be sent when the managed block exits. # Do something else...