Configurable Python library for metrics and events reporting
Project description
Kaneda
======
.. image:: https://travis-ci.org/APSL/kaneda.svg?branch=master
:target: https://travis-ci.org/APSL/kaneda
.. image:: https://readthedocs.org/projects/kaneda/badge/?version=latest
:target: https://readthedocs.org/projects/kaneda/?badge=latest
Kaneda is a Python library that allows to report events and metrics of your applications.
It provides a several builtin :doc:`metrics` methods in order to store any amount of data that you want to then
analyze it or for performance studies.
Basic usage
~~~~~~~~~~~
First of all, you need to install `Kaneda` package::
pip install kaneda
Then you need a backend in order to keep data in a persistent storage.
The following example it shows how to send metrics with Elasticsearch as a backend:
.. code-block:: python
from kaneda.backend import ElasticsearchBackend
from kaneda import Metrics
backend = ElasticsearchBackend(index_name='myindex', app_name='myapp', host='localhost',
port=9200, user='kaneda', password='kaneda')
metrics = Metrics(backend=backend)
metrics.gauge('answer_of_life', 42)
Features
~~~~~~~~
* Builtin `metrics <http://kaneda.readthedocs.io/en/latest/metrics.html>`_ functions and custom metric reports.
* Configurable reporting `backends <http://kaneda.readthedocs.io/en/latest/backends.html>`_ classes and `asynchronous <http://kaneda.readthedocs.io/en/latest/queues.html>`_ queue classes.
* Builtin Elasticsearch, MongoDB, InfluxDB and RethinkDB backends.
* Builtin Celery, RQ and ZMQ asynchronous queue classes.
* Django support.
======
.. image:: https://travis-ci.org/APSL/kaneda.svg?branch=master
:target: https://travis-ci.org/APSL/kaneda
.. image:: https://readthedocs.org/projects/kaneda/badge/?version=latest
:target: https://readthedocs.org/projects/kaneda/?badge=latest
Kaneda is a Python library that allows to report events and metrics of your applications.
It provides a several builtin :doc:`metrics` methods in order to store any amount of data that you want to then
analyze it or for performance studies.
Basic usage
~~~~~~~~~~~
First of all, you need to install `Kaneda` package::
pip install kaneda
Then you need a backend in order to keep data in a persistent storage.
The following example it shows how to send metrics with Elasticsearch as a backend:
.. code-block:: python
from kaneda.backend import ElasticsearchBackend
from kaneda import Metrics
backend = ElasticsearchBackend(index_name='myindex', app_name='myapp', host='localhost',
port=9200, user='kaneda', password='kaneda')
metrics = Metrics(backend=backend)
metrics.gauge('answer_of_life', 42)
Features
~~~~~~~~
* Builtin `metrics <http://kaneda.readthedocs.io/en/latest/metrics.html>`_ functions and custom metric reports.
* Configurable reporting `backends <http://kaneda.readthedocs.io/en/latest/backends.html>`_ classes and `asynchronous <http://kaneda.readthedocs.io/en/latest/queues.html>`_ queue classes.
* Builtin Elasticsearch, MongoDB, InfluxDB and RethinkDB backends.
* Builtin Celery, RQ and ZMQ asynchronous queue classes.
* Django support.
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