Getting Started with grafanalib

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:

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=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=single_y_axis(format=SECONDS_FORMAT),
      ),
    ]),
  ],
).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 for inspiration.

You can read the entire grafanlib documentation on readthedocs.io.

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, 3.5, 3.6 and 3.7.

Developing

If you're working on the project, and need to build from source, it's done as follows:

$ virtualenv .env
$ . ./.env/bin/activate
$ pip install -e .

Configuring Grafana Datasources

This repo used to contain a program gfdatasource for configuring Grafana data sources, but it has been retired since Grafana now has a built-in way to do it. See https://grafana.com/docs/administration/provisioning/#datasources

Community

We'd like you to join the grafanalib community! Talk to us on Slack (see the links), or join us for one of our next meetings):

• Next meeting: 2020-03-20 15:00 UTC
• https://zoom.us/j/405935052
• Meeting minutes and agenda

Getting Help

If you have any questions about, feedback for or problems with grafanalib:

• Invite yourself to the Weave Users Slack.
• Ask a question on the #grafanalib slack channel.
• File an issue.

Your feedback is always welcome!