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https://issues.apache.org/jira/browse/SPARK-7025?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Patrick Wendell updated SPARK-7025:
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Target Version/s: 1.5.0 (was: 1.4.0)
> Create a Java-friendly input source API
> ---------------------------------------
>
> Key: SPARK-7025
> URL: https://issues.apache.org/jira/browse/SPARK-7025
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Reporter: Reynold Xin
> Assignee: Reynold Xin
>
> The goal of this ticket is to create a simple input source API that we can
> maintain and support long term.
> Spark currently has two de facto input source API:
> 1. RDD
> 2. Hadoop MapReduce InputFormat
> Neither of the above is ideal:
> 1. RDD: It is hard for Java developers to implement RDD, given the implicit
> class tags. In addition, the RDD API depends on Scala's runtime library,
> which does not preserve binary compatibility across Scala versions. If a
> developer chooses Java to implement an input source, it would be great if
> that input source can be binary compatible in years to come.
> 2. Hadoop InputFormat: The Hadoop InputFormat API is overly restrictive. For
> example, it forces key-value semantics, and does not support running
> arbitrary code on the driver side (an example of why this is useful is
> broadcast). In addition, it is somewhat awkward to tell developers that in
> order to implement an input source for Spark, they should learn the Hadoop
> MapReduce API first.
> So here's the proposal: an InputSource is described by:
> * an array of InputPartition that specifies the data partitioning
> * a RecordReader that specifies how data on each partition can be read
> This interface would be similar to Hadoop's InputFormat, except that there is
> no explicit key/value separation.
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