Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend.
Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop.
Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does.
This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated!