A highly-scalable, distributed metric data processing framework based on RabbitMQ


Keywords
library, metricq, python
Licenses
BSD-3-Clause/Zed
Install
pip install metricq==3.1.1

Documentation

BSD 3-clause Python package Code style: black PyPI PyPI - Wheel Docker pulls Documentation

metricq - python libraries

This is a python implementation of the MetricQ protocol. It allows you to write Sources and Sinks to easily send and receive data over the MetricQ infrastructure.

Installation

Install the package from PyPI:

$ pip install metricq

Examples

The examples directory contains some basic examples. To play around with them, check out a copy of this repository and (in your favourite venv) install their dependencies:

$ pip install -e '.[examples]'

A simple Source is implemented in metricq_source.py, as is a Sink in metricq_sink.py. We will use the former to produce data for a metric called test.py.dummy, which we will then receive and print with the latter.

Assuming a MetricQ instance is reachable at localhost, configure a client(consult the documentation of your favourite config provider on how to do that) named source-py-dummy to produce values with a frequence of 0.5Hz (i.e. every 2 seconds) :

{
    "rate": 0.5
}

To start the Source, run:

$ ./examples/metricq_source.py --server 'amqp://localhost/' --token 'source-py-dummy'

This should now send values for the metric test.py.dummy in 2-second intervals. To see (in detail) what's going on, add -v DEBUG to the arguments above.

On the other side, run

$ ./examples/metricq_sink.py --server 'amqp://localhost/' --metrics 'test.py.dummy'

and you should see new values for the metric test.py.dummy appear ever 2 seconds.

Tools and utility scripts

The repository metricq/metricq-tools contains a collection of tools and utility scripts to monitor and administrate a MetricQ network. Install them from PyPI:

$ pip install metricq-tools

Development setup

Clone the repository, and in a virtual environment run

$ pip install -e '.[dev]'

This will install all tools necessary for testing and linting. To test code manually, run pytest. Format code using black and isort, or lint with flake8. To make sure a source distribution (sdist) contains the correct files, run check-manifest. Tools are configured in setup.cfg respectively pyproject.toml.

To test code in a fresh environment, run the tox test harness:

$ tox

This runs the same step as our CI does. If tox passes locally there's high chances that CI steps will pass too.

We recommend to install our pre-commit hooks:

$ pre-commit install

This way commits that fail tests or do not comply with our code style are rejected right away.