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From cloud-fan <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-12649][SQL] support reading bucketed ta...
Date Fri, 15 Jan 2016 06:38:34 GMT
Github user cloud-fan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/10604#discussion_r49824982
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala ---
    @@ -683,7 +714,20 @@ abstract class HadoopFsRelation private[sql](
           }
         }
     
    -    buildInternalScan(requiredColumns, filters, inputStatuses, broadcastedConf)
    +    groupBucketFiles(inputStatuses).map { groupedBucketFiles =>
    +      // For each bucket id, firstly we get all files belong to this bucket, by detecting
bucket
    +      // id from file name. Then read these files into a RDD(use one-partition empty
RDD for empty
    +      // bucket), and coalesce it to one partition. Finally union all bucket RDDs to
one result.
    +      val perBucketRows = (0 until maybeBucketSpec.get.numBuckets).map { bucketId =>
    +        groupedBucketFiles.get(bucketId).map { inputStatuses =>
    +          buildInternalScan(requiredColumns, filters, inputStatuses, broadcastedConf).coalesce(1)
    --- End diff --
    
    This is a good point, but it requires more changes to the data source API. Here we pass
the bucket files to the underlying relation, and let it to scan the files to a RDD. So we
can't simply create a new hadooprdd to handle it, but somehow enable the underlying relation
to scan files to a RDD partition. How about we do it in follow-up PR after think more about
it?


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