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https://issues.apache.org/jira/browse/HADOOP-3601?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12615533#action_12615533
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Owen O'Malley commented on HADOOP-3601:
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The straight to subproject path is only available if the code base is from a
single organization. Non-Apache projects that want to become Apache projects
need to go through the incubator. Getting out of incubator takes a fair amount
of effort.
Another serious advantage for the hbase approach was that the hbase
contributors got trained in the way that the Hadoop process and community
works. That didn't happen for pig and the training took longer. Hbase had its
first release after 2 months and pig hasn't released yet. Also the process and
infrastructure overhead was much much lower for creating hbase than pig or
zookeeper. It would take an hour to create Hive as a contrib module and a month
to create it as a subproject. I agree with the disadvantages though that if the
project gets busy, it can start to swamp the hadoop jiras and mailing lists.
Certainly, we would have pushed HBase to a subproject much sooner if Hadoop
hadn't been a subproject of Lucene at the time.
If we are going to take Hive in contrib, I think we probably should disengage
our process a bit from the current model. In particular, I don't think we
should run the contrib unit tests for our patches. The only downside to that is
that we should probably promote streaming and data_join into map/reduce, which
will take some cleanup.
> Hive as a contrib project
> -------------------------
>
> Key: HADOOP-3601
> URL: https://issues.apache.org/jira/browse/HADOOP-3601
> Project: Hadoop Core
> Issue Type: New Feature
> Affects Versions: 0.17.0
> Reporter: Joydeep Sen Sarma
> Priority: Minor
> Attachments: HiveTutorial.pdf
>
> Original Estimate: 1080h
> Remaining Estimate: 1080h
>
> Hive is a data warehouse built on top of flat files (stored primarily in
> HDFS). It includes:
> - Data Organization into Tables with logical and hash partitioning
> - A Metastore to store metadata about Tables/Partitions etc
> - A SQL like query language over object data stored in Tables
> - DDL commands to define and load external data into tables
> Hive's query language is executed using Hadoop map-reduce as the execution
> engine. Queries can use either single stage or multi-stage map-reduce. Hive
> has a native format for tables - but can handle any data set (for example
> json/thrift/xml) using an IO library framework.
> Hive uses Antlr for query parsing, Apache JEXL for expression evaluation and
> may use Apache Derby as an embedded database for MetaStore. Antlr has a BSD
> license and should be compatible with Apache license.
> We are currently thinking of contributing to the 0.17 branch as a contrib
> project (since that is the version under which it will get tested internally)
> - but looking for advice on the best release path.
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