Joining a large and a medium size Dataset
If the smaller DataFrame does not fit fully into memory, but its keyset does, it is possible to exploit this. As a join will discard all elements of the larger DataFrame that do not have a matching partner in the medium size DataFrame, we can use the medium key set to do this before the shuffle. If there is a significant amount of entries that get discarded this way, the resulting shuffle will need to transfer a lot fewer data.
import org.apache.spark.sql.functions._
val mediumDf = Seq((0, "zero"), (4, "one")).toDF("id", "value")
val largeDf = Seq((0, "zero"), (2, "two"), (3, "three"), (4, "four"), (5, "five")).toDF("id", "value")
mediumDf.show()
largeDf.show()
/*
+---+-----+
| id|value|
+---+-----+
| 0| zero|
| 4| one|
+---+-----+
+---+-----+
| id|value|
+---+-----+
| 0| zero|
| 2| two|
| 3|three|
| 4| four|
| 5| five|
+---+-----+
*/
val keys = mediumDf.select("id").as[Int].collect().toSeq
print(keys)
/*
keys: Seq[Int] = WrappedArray(0, 4)
*/
val reducedDataFrame = largeDf.filter(col("id").isin(keys:_*))
reducedDataFrame.show()
/*
+---+-----+
| id|value|
+---+-----+
| 0| zero|
| 4| four|
+---+-----+
*/
val result = reducedDataFrame.join(mediumDf, Seq("id"))
result.explain()
result.show()
/*
== Physical Plan ==
*(1) Project [id#246, value#247, value#238]
+- *(1) BroadcastHashJoin [id#246], [id#237], Inner, BuildRight
:- LocalTableScan [id#246, value#247]
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))), [id=#234]
+- LocalTableScan [id#237, value#238]
+---+-----+-----+
| id|value|value|
+---+-----+-----+
| 0| zero| zero|
| 4| four| one|
+---+-----+-----+
*/
It is important to note that the efficiency gain here depends on the filter operation, actually reducing the size of the larger DataFrame. If there are not a lot of entries lost here (e.g., because the medium size DataFrame is some king of large dimension table), there is nothing to be gained with this strategy.
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