Metadata-Version: 1.1
Name: grafanalib
Version: 0.1.2
Summary: Library for building Grafana dashboards
Home-page: https://github.com/weaveworks/grafanalib
Author: Weaveworks
Author-email: help@weave.works
License: Apache
Description: ==========
        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:
        
        .. code-block:: python
        
          import itertools
        
          from grafanalib.core import *
        
        
          GRAPH_ID = itertools.count(1)
        
        
          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',
                    ),
                  ],
                  id=next(GRAPH_ID),
                  yAxes=[
                    YAxis(format=OPS_FORMAT),
                    YAxis(format=SHORT_FORMAT),
                  ],
                ),
                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',
                    ),
                  ],
                  id=next(GRAPH_ID),
                  yAxes=[
                    YAxis(
                      format=SECONDS_FORMAT,
                    ),
                    YAxis(
                      format=SHORT_FORMAT,
                      show=False,
                    )
                  ],
                ),
              ]),
            ],
          )
        
        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.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: System :: Monitoring
