statsdecor 0.4.2

A set of decorators and helper methods for adding statsd metrics to applications.

Statsdecor

A set of decorators and helper methods for adding statsd metrics to applications.

Installation

You can use pip to install statsdecor:

pip install statsdecor

Configuration

You must use statsdecor.configure to configure the internal statsd client before calling other methods:

import statsdecor

statsdecor.configure(host='localhost', prefix='superapp.')

Configuration is generally setup during your application's bootstrap. Once set configuration values are re-used in all clients that statsdecor creates.

By default Statsdecor uses the statsd client library, however it can be configured to use the datadog client:

import statsdecor

statsdecor.configure(host='localhost', prefix='superapp.', vendor='datadog')

The datadog client supports tagging metrics (see Usage).

Usage

You can track metrics with either the module functions, or decorators. Incrementing and decrementing counters looks like:

Metric functions

import statsdecor

statsdecor.incr('save.succeeded')
statsdecor.decr('attempts.remaining')
statsdecor.gauge('sessions.active', 9001)

When using the datadog client, Statsdecor supports tagging metrics:

statsdecor.incr('save.succeeded', tags=['DogStatsd_does_tags'])

Counters and timers can also be set through decorators:

import statsdecor.decorators as stats

@stats.increment('save.succeeded')
def save(self):
    pass

@stats.decrement('attempts.remaining')
def attempt():
    pass

@stats.timed('api_request.duration')
def perform_request(self, req)
    pass

When using decorators, metrics are only tracked if the decorated function does not raise an error.

Context

When using a statsd client that supports tagging metrics, Statsdecor includes a context manager that can help measure latency and volume while using metric tags to classify their success & failure. For example, suppose you are making a call to a remote service and wish to write a wrapper that collects latency, volume and failure metrics.

With our knowledge about how the client library indicates errors we can make a context manager based on StatsContext:

from statsdecor.context import StatsContext

class FoobarClientMetrics(StatsContext):
    def __init__(self, tags=None):
        tags = list(tags or [])
        tags += ['caller:example_1']
        super(ThingyStatsContext, self).__self__('thingy_client', tags=tags)

    def exit_hook(self, exc_type, exc_val, exc_tb):
        if exc_val is not None:
            self.add_tags('result:failure')
        else:
            self.add_tags('result:success')

        # Bonus: since we have the exception, classify the error type
        if isinstance(exc_val, PermissionDenied):
            self.add_tags('error:permissiondenied')
        elif isinstance(exc_val, TimeOut):
            self.add_tags('error:timeout')
        elif exc_val is not None:
            self.add_tags('error:exception')

Now writing wrapper functions with metrics is simple:

def foobar_get_clients(**args):
    with FoobarClientMetrics(tags=['method:get_clients']) as stats:
        result = call_foobar_get_client(**args)

        # We know all foo methods return result['status_code'] so let's
        # add a status_code tag!
        stats.add_tags('status_code:{}'.format(result["status_code"]'))
        return result

def foobar_add_client(**args):
    with FoobarClientMetrics(tags=['method:add_client']) as stats:
        result = call_foobar_add_client(**args)
        stats.add_tags('status_code:{}'.format(result["status_code"]'))
        return result

Now we can graph:

• volume of calls grouped by the method tag
• average response time, excluding errors (timeouts will no longer skew the average)
• volume of errors grouped by method, and/or type

Development

Testing

make lint
make test

Releasing

statsdecor uses semver for version numbers. Before tagging, check for all changes since the last tag for breaking changes, new features, and/or bugfixes.

To tag the new version:

make tag VERSION_PART=major|minor|patch

Proceed to github.com/amcintosh/statsdecor/releases and create a new release with the tag. Github actions should publish to pypi automatically.