Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[SPARK-24613][SQL] Cache with UDF could not be matched with subsequent dependent caches #21602

Closed
wants to merge 2 commits into from
Closed

[SPARK-24613][SQL] Cache with UDF could not be matched with subsequent dependent caches #21602

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
4a5c388
[SPARK-24613] Cache with UDF could not be matched with subsequent dep…
maryannxue Jun 20, 2018
377f213
Refine test
maryannxue Jun 21, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.internal.Logging
import org.apache.spark.sql.{Dataset, SparkSession}
import org.apache.spark.sql.catalyst.expressions.SubqueryExpression
import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, ResolvedHint}
import org.apache.spark.sql.catalyst.plans.logical.{AnalysisBarrier, LogicalPlan, ResolvedHint}
import org.apache.spark.sql.execution.columnar.InMemoryRelation
import org.apache.spark.sql.execution.datasources.{HadoopFsRelation, LogicalRelation}
import org.apache.spark.storage.StorageLevel
Expand Down Expand Up @@ -97,7 +97,7 @@ class CacheManager extends Logging {
val inMemoryRelation = InMemoryRelation(
sparkSession.sessionState.conf.useCompression,
sparkSession.sessionState.conf.columnBatchSize, storageLevel,
sparkSession.sessionState.executePlan(planToCache).executedPlan,
sparkSession.sessionState.executePlan(AnalysisBarrier(planToCache)).executedPlan,
tableName,
planToCache)
cachedData.add(CachedData(planToCache, inMemoryRelation))
Expand Down Expand Up @@ -142,7 +142,7 @@ class CacheManager extends Logging {
// Remove the cache entry before we create a new one, so that we can have a different
// physical plan.
it.remove()
val plan = spark.sessionState.executePlan(cd.plan).executedPlan
val plan = spark.sessionState.executePlan(AnalysisBarrier(cd.plan)).executedPlan
val newCache = InMemoryRelation(
cacheBuilder = cd.cachedRepresentation
.cacheBuilder.copy(cachedPlan = plan)(_cachedColumnBuffers = null),
Expand Down
15 changes: 15 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DatasetCacheSuite.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -132,4 +132,19 @@ class DatasetCacheSuite extends QueryTest with SharedSQLContext with TimeLimits
df.unpersist()
assert(df.storageLevel == StorageLevel.NONE)
}

test("SPARK-24613 Cache with UDF could not be matched with subsequent dependent caches") {
val expensiveUDF = udf({x: Int => Thread.sleep(10000); x})
Copy link
Contributor

@cloud-fan cloud-fan Jun 21, 2018 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

can we use accumulator and make sure this UDF only run 10 times? sleeping 10 seconds is not good in a unit test

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Accumulators probably wouldn't work. I'll do verify plan though.

val df = spark.range(0, 10).toDF("a").withColumn("b", expensiveUDF($"a"))
val df2 = df.agg(sum(df("b")))

df.cache()
df.count()
df2.cache()

// udf has been evaluated during caching, and thus should not be re-evaluated here
failAfter(5 seconds) {
df2.collect()
}
}
}