First install the library with pip
or easy_install
:
# Install in system python ... sudo pip install monasca-statsd # .. or into a virtual env pip install monasca-statsd
Then start instrumenting your code:
# Import the module. import monascastatsd as mstatsd # Create the connection conn = mstatsd.Connection(host='localhost', port=8125) # Create the client with optional dimensions client = mstatsd.Client(connection=conn, dimensions={'env': 'test'}) NOTE: You can also create a client without specifying the connection and it will create the client with the default connection information for the monasca-agent statsd processor daemon which uses host='localhost' and port=8125. client = mstatsd.Client(dimensions={'env': 'test'}) # Increment and decrement a counter. counter = client.get_counter(name='page.views') counter.increment() counter += 3 counter.decrement() counter -= 3 # Record a gauge 50% of the time. gauge = client.get_gauge('gauge', dimensions={'env': 'test'}) gauge.send('metric', 123.4, sample_rate=0.5) # Sample a histogram. histogram = client.get_histogram('histogram', dimensions={'test': 'True'}) histogram.send('metric', 123.4, dimensions={'color': 'red'}) # Time a function call. timer = client.get_timer() @timer.timed('page.render') def render_page(): # Render things ... pass # Time a block of code. timer = client.get_timer() with timer.time('t'): # Do stuff time.sleep(2) # Add dimensions to any metric. histogram = client.get_histogram('my_hist') histogram.send('query.time', 10, dimensions = {'version': '1.0', 'environment': 'dev'})
To suggest a feature, report a bug, or participate in the general discussion, head over to StoryBoard.
See LICENSE file. Code was originally forked from Datadog’s dogstatsd-python, hence the dual license.