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Bazel rules for building Grafana dashboards
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README.md Bazel 0.24.1 compatibility (#4) Apr 22, 2019
WORKSPACE Bazel 0.24.1 compatibility (#4) Apr 22, 2019

README.md

rules_grafana for Bazel

Dashboards as code, the Bazel way. Write Grafana dashboards with Python and build them in into a reusable Docker image.

Try it out! bazel run //example:grafana to build and load a Docker image, then run it with docker run --rm -p 3000:3000 bazel/example:grafana. Then load Grafana in your browser at http://localhost:3000!

Installing

Load io_bazel_rules_grafana by adding the following to your WORKSPACE:

load("@bazel_tools//tools/build_defs/repo:git.bzl", "git_repository")
git_repository(
    name = "io_bazel_rules_grafana",
    commit = "{HEAD}", # replace with a real commit hash
    remote = "https://github.com/etsy/rules_grafana.git",
)

load("@io_bazel_rules_grafana//grafana:workspace.bzl", grafana_repositories="repositories")
grafana_repositories()
load("@io_bazel_rules_grafana_deps//:requirements.bzl", "pip_install")
pip_install()

rules_grafana also depends on rules_python and rules_docker. If you don't already have these libraries in your WORKSPACE, add them above the previous block:

Bazel compatibility

The current version has only been tested to work with Bazel 0.24.1, but may work with other versions.

Usage

rules_grafana makes it easy to build dashboards and incorporate them into your Bazel build, and to build a complete, runnable Docker image.

Dashboards can be either hard-coded JSON files or Python scripts that generate dashboards.

JSON dashboards

Use json_dashboards to add JSON files containing dashboard to your build. The JSON must be a complete, valid Grafana 5.0 dashboard; see the Grafana docs for details on the JSON format.

load("@io_bazel_rules_grafana//grafana:grafana.bzl", "json_dashboards")

json_dashboards(
    name = "dashboards",
    srcs = ["awesome_graphs.json"],
)

Unlike using the JSON files directly, json_dashboards will check the syntax of your files and ensure that each dashboard has a uid set, to ensure it has a consistent URL in Grafana.

Python dashboards

Dashboards can also be generated with Python, using the grafanalib library. grafanalib is automatically imported, and you can also add other deps to help build your dashboard.

Each Python dashboard file should print the complete JSON of a Grafana dashboard. An easy way to do that is to follow a template like this:

from grafanalib.core import *
from grafanalib._gen import print_dashboard

dashboard = Dashboard(
    # Fill in your dashboard!
)

print_dashboard(dashboard.auto_panel_ids()) # `auto_panel_ids()` call is required!

Use py_dashboards to add Python files that generate dashboards to your build.

load("@io_bazel_rules_grafana//grafana:grafana.bzl", "py_dashboards")

py_dashboards(
    name = "dashboards",
    srcs = ["amazing_graphs.py", "even_better_graphs.py"],
)

You can run the Python and see the generated JSON with the FOO_builder target created by py_dashboards, where FOO is the Python filename without .py. For example, run bazel run //example:sample_builder in this repository to see the output of sample.py. The JSON is generated at build time, not a run time, so Python isn't a runtime dependency.

Docker image

Use grafana_image to build your dashboards into a Docker image containing Grafana. When you run the image, it starts Grafana on port 3000 and serves all of the dashboards you've built, directly from the container.

The dashboards and datasources are added via Grafana provisioning, where the configuration and sources are declared and built into the image, alongside all the dashboards. You must provide a datasources.yaml file declaring your datasources; see the Grafana datasources docs for details of the format.

Grafana plugins can be installed into the image too. Use the grafana_plugin WORKSPACE rule to download the plugin ZIP, providing the URL from the "download the .zip file" on the Grafana plugin page's Installation tab. Then pass the plugin to the image rule's plugins list as @grafana_plugin_repository_name//:plugin.

API reference

json_dashboards

Processes a set of .json Grafana dashboards for inclusion in the image.

Arguments:

  • name: Unique name for this target. Required.
  • srcs: List of labels of .json files to build into dashboards. Required.

py_dashboards

Processes a set of .py Grafana dashboards for inclusion in the image.

Arguments:

  • name: Unique name for this target. Required.
  • srcs: List of labels of .py files to build into dashboards. Required.
  • deps: List of labels of additional py_library targets to use while executing the Python dashboards. Optional, default [].

grafana_image

Builds a Docker image containing Grafana and the provided dashboards and datasources.

Arguments:

  • name: Unique name for this target. Required.
  • dashboards: List of labels of json_dashboards and/or py_dashboards targets to include in the image. Required.
  • datasources: List of labels of datasources.yaml files to include in the image (Grafana datasources docs). Required.
  • plugins: List of labels of grafana_plugin targets, like @your_repository_name//:plugin. Optional.
  • env: Dictionary of environment variant names to values, set in the Docker image when Grafana is run. Optional. Useful for setting runtime configs with GF_ variables.

grafana_plugin

Repository rule to download a Grafana plugin for inclusion in a grafana_image.

Arguments:

  • name: Unique name for this target. Required.
  • urls: List of strings of mirror URLs referencing the plugin archive. Required.
  • sha256: String of the expected SHA-256 hash of the download. Required.
  • type: The archive type of the downloaded file as a string; takes the same values as the type attribute of Bazel's http_archive rule. Optional, as the archive type can be determined from the plugin's file extension.
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