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

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.

0. Install Docker;
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.

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 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 scripts/.gh-token if you don't want to be rate limited. The Makefile rule will ask if you want to generate a dummy one but this will be subject to rate limiting.

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 which will drop you into a container with all of the services running:

make shell
...
[root@7a738bbd4269 /]# ps ax
  PID TTY      STAT   TIME COMMAND
    1 ?        Ss+    0:00 /usr/bin/python /usr/bin/supervisord -c /etc/supervisord.d/supervisord.conf
   30 ?        Sl     0:00 /opt/influxdb/influxd -pidfile=/var/run/influxdb/influxd.pid -config=/etc/opt/influxdb/influxdb.conf
   31 ?        Sl     0:00 /usr/sbin/grafana-server --homepath=/usr/share/grafana --config=/etc/grafana/grafana.ini cfg:default.paths.data=/var/lib/grafana cfg:default.paths.logs
   32 ?        S      0:00 nginx: master process /usr/sbin/nginx
   33 ?        S      0:00 /usr/sbin/crond -n -p -m off
   46 ?        S      0:00 nginx: worker process

The cron jobs log both stdout and stderr of their last run to files in the root directory:

[root@7a738bbd4269 /]# cat /cron-stamp
Tue Jun 21 14:00:01 BST 2016

If you want to expose the InfluxDB ports then you can do so as follows:

PORTS="-p 8083:8083 -p 8086:8086" make <run|shell>

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).

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.

If you want to configure a machine to automatically run and display this dashboard then there are some instructions in INSTALL.md.