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[SPARK-11504][SQL] API audit for distributeBy and localSort #9470

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[SPARK-11504][SQL] API audit for distributeBy and localSort #9470

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[SPARK-11504][SQL] API audit for distributeBy and localSort
rxin Nov 4, 2015
0c7e1cd
Added Yin's test case
rxin Nov 4, 2015
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Added Yin's test case
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@rxin
rxin committed Nov 4, 2015
commit 0c7e1cd60ede0306a54549847c7c3e30493a17a1
32 changes: 22 additions & 10 deletions sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
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Expand Up @@ -609,6 +609,9 @@ class DataFrame private[sql](

/**
* Returns a new [[DataFrame]] with each partition sorted by the given expressions.
*
* This is the same operation as "SORT BY" in SQL (Hive QL).
*
* @group dfops
* @since 1.6.0
*/
Expand All @@ -619,6 +622,9 @@ class DataFrame private[sql](

/**
* Returns a new [[DataFrame]] with each partition sorted by the given expressions.
*
* This is the same operation as "SORT BY" in SQL (Hive QL).
*
* @group dfops
* @since 1.6.0
*/
Expand Down Expand Up @@ -1504,22 +1510,28 @@ class DataFrame private[sql](
}

/**
* Returns a new [[DataFrame]] partitioned by the given partitioning expressions into
* `numPartitions`. The resulting DataFrame is hash partitioned.
* @group dfops
* @since 1.6.0
*/
* Returns a new [[DataFrame]] partitioned by the given partitioning expressions into
* `numPartitions`. The resulting DataFrame is hash partitioned.
*
* This is the same operation as "DISTRIBUTE BY" in SQL (Hive QL).
*
* @group dfops
* @since 1.6.0
*/
@scala.annotation.varargs
def repartition(numPartitions: Int, partitionExprs: Column*): DataFrame = {
RepartitionByExpression(partitionExprs.map(_.expr), logicalPlan, Some(numPartitions))
}

/**
* Returns a new [[DataFrame]] partitioned by the given partitioning expressions preserving
* the existing number of partitions. The resulting DataFrame is hash partitioned.
* @group dfops
* @since 1.6.0
*/
* Returns a new [[DataFrame]] partitioned by the given partitioning expressions preserving
* the existing number of partitions. The resulting DataFrame is hash partitioned.
*
* This is the same operation as "DISTRIBUTE BY" in SQL (Hive QL).
*
* @group dfops
* @since 1.6.0
*/
@scala.annotation.varargs
def repartition(partitionExprs: Column*): DataFrame = {
RepartitionByExpression(partitionExprs.map(_.expr), logicalPlan, numPartitions = None)
Expand Down
12 changes: 12 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
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Expand Up @@ -411,5 +411,17 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
sqlContext.uncacheTable("t2")
sqlContext.dropTempTable("t1")
sqlContext.dropTempTable("t2")

// One side of join is not partitioned in the desired way. Need to shuffle.
testData.repartition(6, $"value").registerTempTable("t1")
testData2.repartition(6, $"a").registerTempTable("t2")
sqlContext.cacheTable("t1")
sqlContext.cacheTable("t2")

verifyNumExchanges(sql("SELECT * FROM t1 t1 JOIN t2 t2 ON t1.key = t2.a"), 2)
sqlContext.uncacheTable("t1")
sqlContext.uncacheTable("t2")
sqlContext.dropTempTable("t1")
sqlContext.dropTempTable("t2")
}
}