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From dongjoon-hyun <...@git.apache.org>
Subject [GitHub] spark pull request #15868: [SPARK-18413][SQL] Control the number of JDBC con...
Date Mon, 14 Nov 2016 18:07:15 GMT
Github user dongjoon-hyun commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15868#discussion_r87857574
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
---
    @@ -667,9 +667,15 @@ object JdbcUtils extends Logging {
         val getConnection: () => Connection = createConnectionFactory(options)
         val batchSize = options.batchSize
         val isolationLevel = options.isolationLevel
    -    df.foreachPartition(iterator => savePartition(
    -      getConnection, table, iterator, rddSchema, nullTypes, batchSize, dialect, isolationLevel)
    -    )
    +    if (options.numPartitions != null && options.numPartitions.toInt != df.rdd.getNumPartitions)
{
    +      df.repartition(options.numPartitions.toInt).foreachPartition(iterator => savePartition(
    --- End diff --
    
    Thank you for review, @srowen .
    
    First, the property `numPartitions` already exists in [JDBCOptions.scala: Optional parameters
only for reading](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCOptions.scala#L73-L83).
    This PR makes that option meaningful during write operation.
    
    Second, for dataframe usecases, we can call `repartition` before saving to manage this.
Actually, I asked @lichenglin that way. But, the main purpose of issue requested by @lichenglin
is about providing pure SQL way to control the number of partitions for writing. In SQL, this
looks reasonable to me.


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