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

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What changes were proposed in this pull request?

The broadcast hint of the cached plan is lost if we cache the plan. This PR is to correct it.

  val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
  val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
  broadcast(df2).cache()
  df2.collect()
  val df3 = df1.join(df2, Seq("key"), "inner")

How was this patch tested?

Added a test.

val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
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.

@gatorsmile
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@@ -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.

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()
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.

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LGTM

@sameeragarwal
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LGTM

@@ -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 .

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SparkQA commented Jan 24, 2018

Test build #86545 has finished for PR 20368 at commit 21e5321.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

@gatorsmile
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Thanks! Merged to master/2.3

asfgit pushed a commit that referenced this pull request Jan 24, 2018
## What changes were proposed in this pull request?
The broadcast hint of the cached plan is lost if we cache the plan. This PR is to correct it.

```Scala
  val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
  val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
  broadcast(df2).cache()
  df2.collect()
  val df3 = df1.join(df2, Seq("key"), "inner")
```

## How was this patch tested?
Added a test.

Author: gatorsmile <[email protected]>

Closes #20368 from gatorsmile/cachedBroadcastHint.

(cherry picked from commit 44cc4da)
Signed-off-by: gatorsmile <[email protected]>
@asfgit asfgit closed this in 44cc4da Jan 24, 2018
@dongjoon-hyun
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As a result, this seems to block SparkPullRequestBuilder, too.

@gatorsmile
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I am reverting this PR.

@dongjoon-hyun
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Oh, thank you so much for fast recovery, @gatorsmile .

@gatorsmile
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Thanks! revert it from master/2.3

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8 participants