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https://issues.apache.org/jira/browse/PHOENIX-1071?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14388868#comment-14388868
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ASF GitHub Bot commented on PHOENIX-1071:
-----------------------------------------
Github user jmahonin commented on the pull request:
https://github.com/apache/phoenix/pull/59#issuecomment-88176830
Thanks for the feedback @mravi , point comments below:
1: Right, I'll try get that sorted out. The original phoenix-spark library
would not work with 1.7 for some reason, but that may no longer be the case.
2: Good catch. I think IntelliJ did something a little funny here on me,
that file was supposed to be in the main hierarchy.
3 / 4: It's my first kick at extending Spark (and Phoenix for that matter),
but the naming scheme and file separation was modelled off of DataStax'
Spark-Cassandra connector, which I figured is as good a model as any:
https://github.com/datastax/spark-cassandra-connector/tree/master/spark-cassandra-connector/src/main/scala/com/datastax/spark/connector
In theory, doing it that way means a user can have just one import to get
all the nice implicit definitions:
`import org.apache.spark.phoenix._`
5: I've never had much luck with getting the Scala integration working well
on any IDE, I just run 'mvn test' from the CLI.
Re: Good to haves
1. I totally agree, but I don't think I can afford the cycles at the
moment. My hope was that by modelling after the spark-cassandra-connector, it
would be relatively painless to add for either a third party, or myself in the
hopefully not-too-distant-future.
2. Great idea, I hadn't actually seen that usage with Spark SQL yet. We're
still using the RDD API internally. On a quick glance it looks fairly
straight-forward to implement.
> 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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