multimeter 0.1

Measure resource usage of your python code

Multimeter

Multimeter is a python library that allows easy capturing of in-process run-time metrics for specific code parts. It is meant for tracking the behavior of a python script in terms of memory usage, cpu load or disk and network consumption as long as a specific part of the code is being run.

What it does

Within a python script an object of type Multimeter can be used to start measurements that run a background thread and automatically capture values at a fixed schedule for different metrics. When the measurement ends, the captured values can be automatically stored in a file or send to time series databases.

How it works

The easiest way to measure the behavior of specific code is by wrapping it in a context manager:

import multimeter

...

mm = multimeter.Multimeter(multimeter.ResourceProbe())
with mm.measure(identifier='measurement-id') as measurement:
    # Here the code whose performance should be measured
    ...

result = measurement.result
print(f"Start {result.start}")
print(f"End {result.end}")
print(f"Duration {result.duration}")
print(f"CPU avg values: {result.values('process.cpu_rate_user')}")

After the with statement the results can be taken from the measurement. The Result type provides access to the captured metrics and the individual data points.

By providing a Storage object, the results can automatically be stored in the file system or in time series databases:

mm = multimeter.Multimeter(multimeter.ResourceProbe())
mm.set_storage(multimeter.JsonFileStorage(save_directory=os.path.dirname(__file__)))

For more information take a look at the latest user guide.

Develop

Multimeter uses tox to build and test the library. Tox runs all tests on different python versions, can generate the documentation and run linters and style checks to improve the code quality. In order to install all the necessary python modules, please run:

pip install tox

Afterwards the tests can be run by just calling

tox

from the project directory. For this to work, you need to have multiple python interpreters installed. If you don't want to run the tests on all supported platforms just edit the tox.ini file and set

envlist = py36,py37,py38

to contain only the python version you want to use. Another option is to run tox with the additional command line argument '--skip_missing_interpreters' which skips python versions that aren't installed.

Documentation

The latest version of the documentation can always be found under https://docs.kant.ai/multimeter/latest. The documentation is written in Markdown and is located in the docs directory of the project. It can be built into static HTML by using MkDocs. In order to manually generate the documentation we can use tox to build the HTML pages from our markdown.

tox -e docs

Release

Releasing a new package version

Releasing new versions of multimeter is done using flit.

pip install flit

In order to be able to publish a new release, you need an account with PyPI or their respective test environment.

Add those accounts into your ~.pypirc:

[distutils]
index-servers =
  pypi
  pypitest

[pypi]
username: <my-user>

[pypitest]
repository: https://test.pypi.org/legacy/
username: <my-test-user>

Publishing a new release to test

flit publish --repository pypitest

Releasing a new version of the documentation

The package uses mike to manage multiple versions of the documentation. The already generated documentation is kept in the docs-deployment branch and will be automatically deployed, if the branch is pushed to the repository.

In order to build a new version of the documentation, we need to use the corresponding tox environment:

VERSION_TAG='<my-version>' tox -e docs-release

The VERSION_TAG environment variable should be set to the new version in format '.'. This will build the documentation and add it as new commits to the docs-deployment branch.

By pushing the updated branch to the gitlab repository, the documentation will be automatically deployed to the official documentation website.