docker-xs-dev-dash

A docker image for the Xenserver Ring3 team's information radiatory dashboard using InfluxDB1 and Grafana2.

There are scripts to query the REST APIs of JIRA and Github to obtain open bug counts and pull-requests respectively. These write to a database in a locally-hosted InfluxDB. It also records other useful information like the current build status.

This has all been packaged up into a Docker container for seamless deployment. No need to worry about the host environment or dependencies, so long as you have Docker.

Usage

0. Install Docker3;
1. Get a Github API key (see below);
2. make run;
3. Profit.

This has now deployed the whole application in a container. It has mapped your local port 80 to the container port 80 which is pointing to Grafana. So you should be able to open up a broswer and see the dashboard at localhost.

Persistent storage

The Dockerfile and Makefile have been constructed so that a "Data Volume Container" (see man docker-run) is created that will persist the data in InfluxDB across different containers/instances.

If you want to start from scratch, you can run make purge.

Github Authentication

Github imposes rate-limiting on its API. The limit is significantly higher if you autenticate your requests. The Github script supports this. The Dockerfile will ADD a file containing your key to the container so that it can be used by this script. This file must be present when you build the container in a file called .gh-token.

Development

The python scripts all supprt a --dry-run (or -n) option so that you can try them out. If you are developing outside the container you will want to install the python packages that the scripts use on your host (see the pip install command in the Dockerfile). However, it's recommended to do your development inside the container. To enter the container use:

make shell

This will drop you into the container with none of the services running. If you want them running you can execute supervisord as in the Dockerfile. It also mounts the repo inside the container.

Customisation for other teams

Most of the scripts to gather data have all of their parameters at the top. E.g. tickets.py speifies a dictionary at the top of the file of JIRA filter names to gather information for. To track different metrics, just edit these scripts and run make run (you may want to get rid of the old data using make purge).

Importing old data

If you have some InfluxDB data to import then then you can use make shell which mounts the current directory inside the container. This allows you to place your data in the directory alongside the Makefile and from within the container move whatever you need into /var/opt/influxdb which is the volume exposed by the data volume container. Note: this should be done with no other containers accessing the data volume container to avoid any corruption.