Spark Plugin
============

The Spark plugin for sahara provides a way to provision Apache Spark clusters
on OpenStack in a single click and in an easily repeatable fashion.

Currently Spark is installed in standalone mode, with no YARN or Mesos
support.

Images
------

For cluster provisioning, prepared images should be used. The Spark plugin
has been developed and tested with the images generated by
sahara-image-elements:

* https://git.openstack.org/cgit/openstack/sahara-image-elements

The latest Ubuntu images generated by sahara-image-elements have Cloudera
CDH 5.4.0 HDFS and Apache Spark installed.

The Spark plugin requires an image to be tagged in the sahara image registry
with two tags: 'spark' and '<Spark version>' (e.g. '1.6.0').

Also you should specify the username of the default cloud-user used in the
image. For the images available at the URLs listed above and for all the ones
generated with the DIB it is `ubuntu`.

Note that the Spark cluster is deployed using the scripts available in the
Spark distribution, which allow the user to start all services (master and
slaves), stop all services and so on. As such (and as opposed to CDH HDFS
daemons), Spark is not deployed as a standard Ubuntu service and if the
virtual machines are rebooted, Spark will not be restarted.

Spark configuration
-------------------

Spark needs few parameters to work and has sensible defaults. If needed they
can be changed when creating the sahara cluster template. No node group
options are available.

Once the cluster is ready, connect with ssh to the master using the `ubuntu`
user and the appropriate ssh key. Spark is installed in `/opt/spark` and
should be completely configured and ready to start executing jobs. At the
bottom of the cluster information page from the OpenStack dashboard, a link to
the Spark web interface is provided.

Cluster Validation
------------------

When a user creates an Hadoop cluster using the Spark plugin, the cluster
topology requested by user is verified for consistency.

Currently there are the following limitations in cluster topology for the
Spark plugin:

+ Cluster must contain exactly one HDFS namenode
+ Cluster must contain exactly one Spark master
+ Cluster must contain at least one Spark slave
+ Cluster must contain at least one HDFS datanode

The tested configuration co-locates the NameNode with the master and a
DataNode with each slave to maximize data locality.
