statman 1.4.5

Collection of metrics collection tools, including a simple stopwatch

statman

Overview

Statman is a collection of metric collectors to embed within your python application. It includes a registry to easily access your metrics.

Statman => registry
Metric => set of classes that can perform metric collection
Stopwatch => a metric class responsible for tracking time delta
Gauge => a metric class responsible for providing a single value
Calculation => a metric class responsible for performing calculations
Rate => a specialized calculation metric which calculates x/y rate

Install it!

Statman is availble from pypi.

It can be manually installed by:

pip install statman

or by adding the following to your requirements.txt:

statman=*

Use it

Statman (Registry)

Statman offers a registery to make it easily to globally access metrics. Perhaps you will create and register a stopwatch in the depths of your codebase to measure the time to write to a database, and then want to access that result in some other part of your application.

Register

• register(name, metric) => manually register a new metric

Get

• get(name) => get a metric by name

Count

• count() => returns a count of the registered metrics.

Reset

• reset() => clears all metrics from the registry.

Specialized register / get

• stopwatch(name) => returns a stopwatch instance. If there is a registered stopwatch with this name, return it. If there is no registered stopwatch with this name, create a new instance, register it, and return it.

Stopwatch

Stopwatch is for timing operations within your system. Suppose that you are trying to track down where the system is slow. Put a stopwatch around certain critical areas, time those operations, and compare.

Constructor

• Stopwatch(name=None, autostart=False, initial_delta=None) => create an instance of a stopwatch.
  • If autostart set to true, the stopwatch will automatically start
  • If initial_delta is set to a value, and read of the stopwatch is incremented by this amount. This can be helpful if you adding timings together.
  • name is used for to string / reporting for identification of this metric. Defaults to blank
  • If enable_history is set to true, when a timing is collected (stop invoked), an event is collected. This can be accessed by the history property to examing statistics on this stopwatch

Start

• start() => starts the stopwatch, let the timing begin!

Read

• read(units, precision) => reads the stopwatch to determine how much time has elapsed. Returns the time elapsed in seconds.
  • The elapsed time will be returned based upon the units ('m' minutes, 's' seconds, 'ms', milliseconds). Defaults to seconds.
  • If precision is provided, read() will round to the number of decimals places based on precision.
  • Note: read does NOT stop the stopwatch - if the stopwatch is runnning, it will continues to run.
• time(units, precision) => alias for read()

Stop

• stop(units, precision) => stops the stopwatch, and returns the time elapsed in seconds
  • See read for the role of units and precision

Reset

• reset() => restores the stopwatch back to init state and clears start and stop times

Restart

• restart() => resets the stopwatch, then starts it

History

• history => if enable_history set during stopwatch construction, the history property returns an instance of a history object, which can be used for examing statistics

Gauge

A gauge is an instantaneous measurement of a value. Suppose that you are interested in counting the number of messages that have been processed. A gauge can be used to count events and produce a value.

Constructor

• Gauge(name=None, value: float = 0) => create an instance of a gauge
  • If value is provided, this will be used as the initial value of the gauge

Value

• value() => get / set the current value of the gauge

Increment / Decrement

• increment(amount: int = 1) => adds to the current value
• decrement(amount: int = 1) => subtracts from the current value

Calculation

Constructor

• Calculation(name=None, function=None) => creates a new instance of a calculation metric

Function

• function => set the function used the calculation.
  • The function is to be a parameterless function that returns a numeric value.
  • The function can internally reference other items, such as other Statman metrics or access to other resources.
  • The function can be a named or lambda function.

Value / Read

• read(precision: int = None) => execute the function, and returns the value rounded based on specified precision
• value(self) => execute the function, and returns the value

Rate

Constructor

• Rate(name=None, numerator_metric_name=None, denominator_metric_name=None) => creates a new instance of a rate metric
  • The Rate metric extends the Calculation metric, where the function is numerator/denominator
  • The numerator_metric_name and denominator_metric_name refer to other metrics within the Statman registry

Examples

Maually Register Metric

from statman import Statman
Statman.register('expensive-operation-timing',Stopwatch())

stopwatch = Statman.get('expensive-operation-timing')

Stopwatch via Statman Registry

from statman import Statman

Statman.stopwatch('stopwatch-name').start()
# do some expensive operation that you want to measure
Statman.stopwatch('stopwatch-name').read()

print(f'event took {Statman.stopwatch('stopwatch-name').read(precision=1)}s to execute')  # event took 1.0s to execute

Stopwatch: Direct Usage (no registry)

from statman import Stopwatch
sw = Stopwatch()
sw.start()

# do some expensive operation that you want to measure

delta = sw.stop()
print(f'event took {sw.read(precision=1)}s to execute')  # event took 1.0s to execute

Stopwatch: History

from statman import Stopwatch
number_of_events = 1000000

sw = Stopwatch(enable_history=True)
for i in range(0, number_of_events):
    sw.start()
    # do some expensive operation that you want to measure
    sw.stop()

print('number of measurements:', sw.history.count())
print('min:', sw.history.min_value())
print('max:', sw.history.max_value())
print('ave:', sw.history.average_value())
print('mode:', sw.history.mode_value())

Gauge using increment via Statman Registry

from statman import Statman
Statman.gauge('number-of-messages-processed')

# in area where something interesting occurs, update gauge
# update can occur using .increment() or .value=
Statman.gauge('number-of-messages_processed').increment()

print('number-of-messages_processed:', Statman.gauge('number-of-messages_processed').value)

Calculation via Statman Registry

from statman import Statman

Statman.calculation('messages-per-second').function = lambda: (Statman.gauge('messages-processed').value / Statman.stopwatch('sw').value)
Statman.stopwatch('time-to-process-batch').start()

# code to process batch, incrementing each time message is handles
Statman.gauge('messages-processed').increment()

Statman.stopwatch('sw').stop()

print(Statman.calculation('messages-per-second').value)

Rate via Statman Registry

from statman import Statman

Statman.stopwatch('sw').start()
time.sleep(0.5)
Statman.stopwatch('sw').stop()

Statman.gauge('messages_processed').value = 100

Statman.rate(name='messages_per_second', numerator_metric_name='messages_processed', denominator_metric_name='sw')
print(Statman.rate('messages_per_second').value)