plancton 0.6.0

Main features

• Upgrade pilot jobs to pilot containers. Plancton is meant to run “pilot” containers: your container starts and tries to fetch something to do. When the container exits, Plancton will replace it with a brand new one. An example of application easy to containerize is [WorkQueue from cctools](https://github.com/cooperative-computing-lab/cctools)).

• Meant for clusters. Pilot applications are containerized and deployed on a cluster of nodes, each one of them running a Plancton instance. Plancton instances are totally independent, therefore it naturally scales.

• Monitoring. Sends monitoring data to [InfluxDB](https://www.influxdata.com/), easy to plot via [Grafana](http://grafana.org/).

• Containers for the masses. Plancton brings the features of Docker containers (environment consistency, isolation, sandboxing) to disposable cluster applications. Plancton is not a replacement to [Apache Mesos](http://mesos.apache.org/) or [Kubernetes](http://kubernetes.io/) but it is a very simple and lightweight alternative when you don’t need all the extra features they offer.

Instant gratification

[Docker](https://www.docker.com) is required, and a recent Linux operating system.

Install the latest version with pip:

pip install plancton

If you want to install from the master branch (use at your own risk):

pip install git+https://github.com/mconcas/plancton

Plancton can be run as root or as any user with Docker privileges:

planctonctl start

Configure

The configuration file is located under /etc/plancton/config.yaml and it can be modified while Plancton is running. By default it starts with an empty configuration running dummy busybox containers.

You can get configurations with:

plancton-bootstrap <gh-user/gh-repo:branch>

and they’ll be downloaded to the correct place. An example dry run configuration can be obtained with:

plancton-bootstrap <mconcas/plancton-conf:dryrun>

Credits

Credits for the name go to G.