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applipy-metrics-1.2.3
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Performance metrics, based on Coda Hale's Yammer metrics
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | applipy-metrics-1.2.3 |
| نام | applipy-metrics |
| نسخه کتابخانه | 1.2.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Alessio Linares |
| ایمیل نویسنده | mail@alessio.cc |
| آدرس صفحه اصلی | https://gitlab.com/applipy/applipy_metrics |
| آدرس اینترنتی | https://pypi.org/project/applipy-metrics/ |
| مجوز | Apache 2.0 |
[](https://gitlab.com/applipy/applipy_metrics/-/pipelines?scope=branches&ref=master)
[](https://gitlab.com/applipy/applipy_metrics/-/graphs/master/charts)
[](https://pypi.org/project/applipy-metrics/)
[](https://pypi.org/project/applipy-metrics/)
[](https://pypi.org/project/applipy-metrics/)
[](https://pypi.org/project/applipy-metrics/)
[](https://pypi.org/project/applipy-metrics/)
# Applipy Metrics
> Note: This is a hard fork of
> [Lightricks/PyFormance](https://github.com/Lightricks/pyformance) at commit
> [`d59501e`](https://github.com/Lightricks/pyformance/commit/d59501ec06299b6af3b758f0ba9ce3f57bf6c73d)
A Python port of the core portion of a
[Java Metrics library by Coda Hale](http://metrics.dropwizard.io/), with
inspiration by [YUNOMI - Y U NO MEASURE IT?](https://github.com/richzeng/yunomi)
Applipy Metrics is a toolset for performance measurement and statistics, with a
signaling mechanism that allows to issue events in cases of unexpected behavior
## Core Features
### Gauge
A gauge metric is an instantaneous reading of a particular value.
### Counter
Simple interface to increment and decrement a value. For example, this can be
used to measure the total number of jobs sent to the queue, as well as the
pending (not yet complete) number of jobs in the queue. Simply increment the
counter when an operation starts and decrement it when it completes.
### Summary
Measures the statistical distribution of values in a data stream. Keeps track
of minimum, maximum, mean, standard deviation, etc. It also measures median,
75th, 90th, 95th, 98th, 99th, and 99.9th percentiles. An example use case would
be for looking at the number of daily logins for 99 percent of your days,
ignoring outliers.
### Regex Grouping
Useful when working with APIs. A RegexRegistry allows to group API calls and
measure from a single location instead of having to define different timers in
different places.
>>> from applipy_metrics.registry import RegexRegistry
>>> reg = RegexRegistry(pattern='^/api/(?P<model>)/\d+/(?P<verb>)?$')
>>> def rest_api_request(path):
... with reg.timer(path).time():
... # do stuff
>>> print(reg.dump_metrics())
### applipy
This library exposes an applipy module, if applipy is installed.
The module can be imported at `applipy_metrics.MetricsModule`.
It binds a `MetricsRegistry` instance, so that other modules can declare a
dependency and use it to create metrics.
#### Configurations available
* `metrics.clock`: fully qualified name of an object with a member function
named `time()` that returns unix timestamps. Will be used as the registry
clock, used for summaries.
* `metrics.summary.sample_provider`: fully qualified name of a function that
accepts a _clock_ instance and returns an instance of either
`applipy_metrics.stats.samples.ExpDecayingSample`,
`applipy_metrics.stats.samples.SlidingTimeWindowSample` or `None`.
#### Metrics Reporters
If you want to report your metrics using a
`applipy_metrics.reporters.reporter.Reporter` instance, simply bind your
reporter provider/instance to the base reporter type. The MetricsModule will
take care of its lifecycle.
## Examples
### Decorators
The simplest and easiest way to use the Applipy Metrics library.
##### Counter
You can use the `count_calls` decorator to count the number of times a function
is called.
>>> from applipy_metrics import counter, count_calls
>>> @count_calls
... def test():
... pass
...
>>> for i in range(10):
... test()
...
>>> print(counter("test_calls").get_count())
10
##### Timer
You can use the `time_calls` decorator to time the execution of a function and
get distribution data from it.
>>> import time
>>> from applipy_metrics import summary, time_calls
>>> @time_calls
... def test():
... time.sleep(0.1)
...
>>> for i in range(10):
... test()
...
>>> print(summary("test_calls").get_snapshot().get_mean())
0.100820207596
### With statement
You can also use a timer using the with statement
##### Chronometer
>>> import time
>>> from applipy_metrics import summary, Chronometer
>>> with Chronometer(on_stop=lambda x: summary("test").add(x)):
... time.sleep(0.1)
>>> print(summary("test").get_snapshot().get_mean())
0.10114598274230957
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl applipy-metrics-1.2.3:
pip install applipy-metrics-1.2.3.whl
نصب پکیج tar.gz applipy-metrics-1.2.3:
pip install applipy-metrics-1.2.3.tar.gz