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https://issues.apache.org/jira/browse/PHOENIX-1071?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14391048#comment-14391048
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ASF GitHub Bot commented on PHOENIX-1071:
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Github user jmahonin commented on the pull request:
https://github.com/apache/phoenix/pull/59#issuecomment-88569122
I was able to spend a bit more time on the RelationProvider work. The DDL
for custom providers doesn't work through the 'sql()' method on
SparkSQLContext, perhaps by design or perhaps due to a bug, but the 'load()'
method does work to create DataFrames using arbitrary data sources.
I'm still not entirely familiar with their API, and a lot of it is still
very new and probably subject to churn, but I tried to base the it on existing
examples in the Spark repo, such as the JDBC and Parquet data sources.
I've got a new commit on a side branch here:
https://github.com/FileTrek/phoenix/commit/16f4540ef0889fc6534c91b8638c16001114ba1a
If you're all OK with those changes going in on this PR, I can push them up
here. Otherwise, I'll stash them aside for a new ticket.
> Provide integration for exposing Phoenix tables as Spark RDDs
> -------------------------------------------------------------
>
> Key: PHOENIX-1071
> URL: https://issues.apache.org/jira/browse/PHOENIX-1071
> Project: Phoenix
> Issue Type: New Feature
> Reporter: Andrew Purtell
>
> A core concept of Apache Spark is the resilient distributed dataset (RDD), a
> "fault-tolerant collection of elements that can be operated on in parallel".
> One can create a RDDs referencing a dataset in any external storage system
> offering a Hadoop InputFormat, like PhoenixInputFormat and
> PhoenixOutputFormat. There could be opportunities for additional interesting
> and deep integration.
> Add the ability to save RDDs back to Phoenix with a {{saveAsPhoenixTable}}
> action, implicitly creating necessary schema on demand.
> Add support for {{filter}} transformations that push predicates to the server.
> Add a new {{select}} transformation supporting a LINQ-like DSL, for example:
> {code}
> // Count the number of different coffee varieties offered by each
> // supplier from Guatemala
> phoenixTable("coffees")
> .select(c =>
> where(c.origin == "GT"))
> .countByKey()
> .foreach(r => println(r._1 + "=" + r._2))
> {code}
> Support conversions between Scala and Java types and Phoenix table data.
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