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[SPARK-23195] [SQL] Keep the Hint of Cached Data #20368

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[SPARK-23195] [SQL] Keep the Hint of Cached Data #20368

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4 changes: 2 additions & 2 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryRelation.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@ case class InMemoryRelation(
tableName: Option[String])(
@transient var _cachedColumnBuffers: RDD[CachedBatch] = null,
val batchStats: LongAccumulator = child.sqlContext.sparkContext.longAccumulator,
statsOfPlanToCache: Statistics = null)
statsOfPlanToCache: Statistics)
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leave no default value is fine, we do not any default value actually

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Setting null by default is risky, because we might hit NullPointerException .

extends logical.LeafNode with MultiInstanceRelation {

override protected def innerChildren: Seq[SparkPlan] = Seq(child)
Expand All @@ -77,7 +77,7 @@ case class InMemoryRelation(
// Underlying columnar RDD hasn't been materialized, use the stats from the plan to cache
statsOfPlanToCache
} else {
Statistics(sizeInBytes = batchStats.value.longValue)
Statistics(sizeInBytes = batchStats.value.longValue, hints = statsOfPlanToCache.hints)
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Why don't you simply statsOfPlanToCache.copy(sizeInBytes = batchStats.value.longValue)?

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@gatorsmile gatorsmile Jan 23, 2018 •
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That is misleading. Conceptually, that is wrong. The values should be filled by the actual values from the materialized results.

}
}

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16 changes: 16 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
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Expand Up @@ -126,6 +126,22 @@ class BroadcastJoinSuite extends QueryTest with SQLTestUtils {
}
}

test("broadcast hint is retained in a cached plan") {
Seq(true, false).foreach { materialized =>
withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") {
val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
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Is spark.createDataFrame(...) wrapper really required? I thought Seq((1, "4"), (2, "2")).toDF("key", "value") would just work fine.

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That should not matter.

val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
broadcast(df2).cache()
if (materialized) df2.collect()
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This PR #19864 accidentally fixes the issue when the plan is not materialized. However, it does not resolve the issue when the cached plan is materialized.

val df3 = df1.join(df2, Seq("key"), "inner")
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val df3 = df1.join(df2, "key")? inner is implied, isn't it? (I'm proposing the change as this and other tests could be easily used as a learning tool to master Spark SQL's API)

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That should not matter.

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I tend to agree that tests are also examples for Spark users, we should pick the recommended usages.

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All the other cases are creating Dataframes like this. Anyway, I changed all of them in the new PR.

val numBroadCastHashJoin = df3.queryExecution.executedPlan.collect {
case b: BroadcastHashJoinExec => b
}.size
assert(numBroadCastHashJoin === 1)
}
}
}

private def assertBroadcastJoin(df : Dataset[Row]) : Unit = {
val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
val joined = df1.join(df, Seq("key"), "inner")
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