Python library for building Grafana dashboards
Python Makefile
Latest commit ebbde10 Feb 3, 2017 @jml jml committed on GitHub Merge pull request #16 from weaveworks/make-deps
Add make deps command

README.rst

grafanalib

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

Do you like Grafana 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:

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),
        ],
        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"),
              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',
          ),
        ],
        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 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:

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

Installation

grafanalib is just a Python package, so:

$ 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.