==========
grafanalib
==========

.. image:: https://circleci.com/gh/weaveworks/grafanalib.svg?style=shield
    :target: https://circleci.com/gh/weaveworks/grafanalib

Do you like `Grafana <http://grafana.org/>`_ but wish you could version your
dashboard configuration? Do you find yourself repeating common patterns? If
so, grafanalib is for you.

grafanalib lets you generate Grafana dashboards from simple Python scripts.

Writing dashboards
==================

The following will configure a dashboard with a single row, with one QPS graph
broken down by status code and another latency graph showing median and 99th
percentile latency:

.. warning:: This example depends on unreleased changes to grafanalib. It will not work with grafanalib 0.1.2. See https://github.com/weaveworks/grafanalib/issues/44 for details.

.. code-block:: python

  import itertools

  from grafanalib.core import *


  dashboard = Dashboard(
    title="Frontend Stats",
    rows=[
      Row(panels=[
        Graph(
          title="Frontend QPS",
          dataSource='My Prometheus',
          targets=[
            Target(
              expr='sum(irate(nginx_http_requests_total{job="default/frontend",status=~"1.."}[1m]))',
              legendFormat="1xx",
              refId='A',
            ),
            Target(
              expr='sum(irate(nginx_http_requests_total{job="default/frontend",status=~"2.."}[1m]))',
              legendFormat="2xx",
              refId='B',
            ),
            Target(
              expr='sum(irate(nginx_http_requests_total{job="default/frontend",status=~"3.."}[1m]))',
              legendFormat="3xx",
              refId='C',
            ),
            Target(
              expr='sum(irate(nginx_http_requests_total{job="default/frontend",status=~"4.."}[1m]))',
              legendFormat="4xx",
              refId='D',
            ),
            Target(
              expr='sum(irate(nginx_http_requests_total{job="default/frontend",status=~"5.."}[1m]))',
              legendFormat="5xx",
              refId='E',
            ),
          ],
          yAxes=[
            YAxis(format=OPS_FORMAT),
            YAxis(format=SHORT_FORMAT),
          ],
          alert=Alert(
            name="Too many 500s on Nginx",
            message="More than 5 QPS of 500s on Nginx for 5 minutes",
            alertConditions=[
              AlertCondition(
                Target(
                  expr='sum(irate(nginx_http_requests_total{job="default/frontend",status=~"5.."}[1m]))',
                  legendFormat="5xx",
                  refId='A',
                ),
                timeRange=TimeRange("5m", "now"),
                evaluator=GreaterThan(5),
                operator=OP_AND,
                reducerType=RTYPE_SUM,
              ),
            ],
          )
        ),
        Graph(
          title="Frontend latency",
          dataSource='My Prometheus',
          targets=[
            Target(
              expr='histogram_quantile(0.5, sum(irate(nginx_http_request_duration_seconds_bucket{job="default/frontend"}[1m])) by (le))',
              legendFormat="0.5 quantile",
              refId='A',
            ),
            Target(
              expr='histogram_quantile(0.99, sum(irate(nginx_http_request_duration_seconds_bucket{job="default/frontend"}[1m])) by (le))',
              legendFormat="0.99 quantile",
              refId='B',
            ),
          ],
          yAxes=[
            YAxis(
              format=SECONDS_FORMAT,
            ),
            YAxis(
              format=SHORT_FORMAT,
              show=False,
            )
          ],
        ),
      ]),
    ],
  ).auto_panel_ids()

There is a fair bit of repetition here, but once you figure out what works for
your needs, you can factor that out.
See `our Weave-specific customizations <grafanalib/weave.py>`_ for inspiration.

Generating dashboards
=====================

If you save the above as ``frontend.dashboard.py`` (the suffix must be
``.dashboard.py``), you can then generate the JSON dashboard with:

.. code-block:: console

  $ generate-dashboard -o frontend.json frontend.dashboard.py

Installation
============

grafanalib is just a Python package, so:

.. code-block:: console

  $ pip install grafanalib

Support
=======

This library is in its very early stages. We'll probably make changes that
break backwards compatibility, although we'll try hard not to.

grafanalib works with Python 3.4 and 3.5.

`gfdatasource`
==============

This module also provides a script and docker image which can configure grafana
with new sources, or enable app plugins.

The script answers the `--help` with full usage information, but basic
invocation looks like the this:

```
<gfdatasource> --grafana-url http://grafana. datasource --data-source-url http://datasource

<gfdatasource> --grafana-url http://grafana. app --id my-plugin
```