/
CachedTableSuite.scala
1792 lines (1564 loc) · 67.5 KB
/
CachedTableSuite.scala
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql
import java.io.{File, FilenameFilter}
import java.nio.file.{Files, Paths}
import java.time.{Duration, LocalDateTime, Period}
import scala.collection.mutable.HashSet
import scala.concurrent.duration._
import org.apache.commons.io.FileUtils
import org.apache.spark.CleanerListener
import org.apache.spark.executor.DataReadMethod._
import org.apache.spark.executor.DataReadMethod.DataReadMethod
import org.apache.spark.scheduler.{SparkListener, SparkListenerEvent, SparkListenerJobStart}
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.analysis.TempTableAlreadyExistsException
import org.apache.spark.sql.catalyst.expressions.SubqueryExpression
import org.apache.spark.sql.catalyst.plans.logical.{BROADCAST, Join, JoinStrategyHint, SHUFFLE_HASH}
import org.apache.spark.sql.catalyst.util.DateTimeConstants
import org.apache.spark.sql.execution.{ColumnarToRowExec, ExecSubqueryExpression, RDDScanExec, SparkPlan, SparkPlanInfo}
import org.apache.spark.sql.execution.adaptive.{AdaptiveSparkPlanHelper, AQEPropagateEmptyRelation}
import org.apache.spark.sql.execution.columnar._
import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec
import org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate
import org.apache.spark.sql.functions._
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.test.{SharedSparkSession, SQLTestUtils}
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.storage.{RDDBlockId, StorageLevel}
import org.apache.spark.storage.StorageLevel.{MEMORY_AND_DISK_2, MEMORY_ONLY}
import org.apache.spark.tags.SlowSQLTest
import org.apache.spark.unsafe.types.CalendarInterval
import org.apache.spark.util.{AccumulatorContext, Utils}
private case class BigData(s: String)
@SlowSQLTest
class CachedTableSuite extends QueryTest with SQLTestUtils
with SharedSparkSession
with AdaptiveSparkPlanHelper {
import testImplicits._
setupTestData()
override def afterEach(): Unit = {
try {
spark.catalog.clearCache()
} finally {
super.afterEach()
}
}
def rddIdOf(tableName: String): Int = {
val plan = spark.table(tableName).queryExecution.sparkPlan
plan.collect {
case InMemoryTableScanExec(_, _, relation) =>
relation.cacheBuilder.cachedColumnBuffers.id
case _ =>
fail(s"Table $tableName is not cached\n" + plan)
}.head
}
def isMaterialized(rddId: Int): Boolean = {
val maybeBlock = sparkContext.env.blockManager.get(RDDBlockId(rddId, 0))
maybeBlock.foreach(_ => sparkContext.env.blockManager.releaseLock(RDDBlockId(rddId, 0)))
maybeBlock.nonEmpty
}
def isExpectStorageLevel(rddId: Int, level: DataReadMethod): Boolean = {
val maybeBlock = sparkContext.env.blockManager.get(RDDBlockId(rddId, 0))
val isExpectLevel = maybeBlock.forall(_.readMethod === level)
maybeBlock.foreach(_ => sparkContext.env.blockManager.releaseLock(RDDBlockId(rddId, 0)))
maybeBlock.nonEmpty && isExpectLevel
}
private def getNumInMemoryRelations(ds: Dataset[_]): Int = {
val plan = ds.queryExecution.withCachedData
var sum = plan.collect { case _: InMemoryRelation => 1 }.sum
plan.transformAllExpressions {
case e: SubqueryExpression =>
sum += getNumInMemoryRelations(e.plan)
e
}
sum
}
private def getNumInMemoryTablesInSubquery(plan: SparkPlan): Int = {
plan.expressions.flatMap(_.collect {
case sub: ExecSubqueryExpression => getNumInMemoryTablesRecursively(sub.plan)
}).sum
}
private def getNumInMemoryTablesRecursively(plan: SparkPlan): Int = {
collect(plan) {
case inMemoryTable @ InMemoryTableScanExec(_, _, relation) =>
getNumInMemoryTablesRecursively(relation.cachedPlan) +
getNumInMemoryTablesInSubquery(inMemoryTable) + 1
case p =>
getNumInMemoryTablesInSubquery(p)
}.sum
}
test("cache temp table") {
withTempView("tempTable") {
testData.select("key").createOrReplaceTempView("tempTable")
assertCached(sql("SELECT COUNT(*) FROM tempTable"), 0)
spark.catalog.cacheTable("tempTable")
assertCached(sql("SELECT COUNT(*) FROM tempTable"))
uncacheTable("tempTable")
}
}
test("unpersist an uncached table will not raise exception") {
assert(None == spark.sharedState.cacheManager.lookupCachedData(testData))
testData.unpersist(blocking = true)
assert(None == spark.sharedState.cacheManager.lookupCachedData(testData))
testData.unpersist(blocking = false)
assert(None == spark.sharedState.cacheManager.lookupCachedData(testData))
testData.persist()
assert(None != spark.sharedState.cacheManager.lookupCachedData(testData))
testData.unpersist(blocking = true)
assert(None == spark.sharedState.cacheManager.lookupCachedData(testData))
testData.unpersist(blocking = false)
assert(None == spark.sharedState.cacheManager.lookupCachedData(testData))
}
test("cache table as select") {
withTempView("tempTable") {
sql("CACHE TABLE tempTable AS SELECT key FROM testData")
assertCached(sql("SELECT COUNT(*) FROM tempTable"))
uncacheTable("tempTable")
}
}
test("cache table as select - existing temp view") {
withTempView("tempView") {
sql("CREATE TEMPORARY VIEW tempView as SELECT 1")
val e = intercept[TempTableAlreadyExistsException] {
sql("CACHE TABLE tempView AS SELECT 1")
}
checkError(e,
errorClass = "TEMP_TABLE_OR_VIEW_ALREADY_EXISTS",
parameters = Map("relationName" -> "`tempView`"))
}
}
test("uncaching temp table") {
withTempView("tempTable1", "tempTable2") {
testData.select("key").createOrReplaceTempView("tempTable1")
testData.select("key").createOrReplaceTempView("tempTable2")
spark.catalog.cacheTable("tempTable1")
assertCached(sql("SELECT COUNT(*) FROM tempTable1"))
assertCached(sql("SELECT COUNT(*) FROM tempTable2"))
// Is this valid?
uncacheTable("tempTable2")
// Should this be cached?
assertCached(sql("SELECT COUNT(*) FROM tempTable1"), 0)
}
}
test("too big for memory") {
withTempView("bigData") {
val data = "*" * 1000
sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF()
.createOrReplaceTempView("bigData")
spark.table("bigData").persist(StorageLevel.MEMORY_AND_DISK)
assert(spark.table("bigData").count() === 200000L)
spark.table("bigData").unpersist(blocking = true)
}
}
test("calling .cache() should use in-memory columnar caching") {
spark.table("testData").cache()
assertCached(spark.table("testData"))
spark.table("testData").unpersist(blocking = true)
}
test("calling .unpersist() should drop in-memory columnar cache") {
spark.table("testData").cache()
spark.table("testData").count()
spark.table("testData").unpersist(blocking = true)
assertCached(spark.table("testData"), 0)
}
test("isCached") {
spark.catalog.cacheTable("testData")
assertCached(spark.table("testData"))
assert(spark.table("testData").queryExecution.withCachedData match {
case _: InMemoryRelation => true
case _ => false
})
uncacheTable("testData")
assert(!spark.catalog.isCached("testData"))
assert(spark.table("testData").queryExecution.withCachedData match {
case _: InMemoryRelation => false
case _ => true
})
}
test("SPARK-1669: cacheTable should be idempotent") {
assert(!spark.table("testData").logicalPlan.isInstanceOf[InMemoryRelation])
spark.catalog.cacheTable("testData")
assertCached(spark.table("testData"))
assertResult(1, "InMemoryRelation not found, testData should have been cached") {
getNumInMemoryRelations(spark.table("testData"))
}
spark.catalog.cacheTable("testData")
assertResult(0, "Double InMemoryRelations found, cacheTable() is not idempotent") {
spark.table("testData").queryExecution.withCachedData.collect {
case r: InMemoryRelation if r.cachedPlan.isInstanceOf[InMemoryTableScanExec] => r
}.size
}
uncacheTable("testData")
}
test("read from cached table and uncache") {
spark.catalog.cacheTable("testData")
checkAnswer(spark.table("testData"), testData.collect().toSeq)
assertCached(spark.table("testData"))
uncacheTable("testData")
checkAnswer(spark.table("testData"), testData.collect().toSeq)
assertCached(spark.table("testData"), 0)
}
test("SELECT star from cached table") {
withTempView("selectStar") {
sql("SELECT * FROM testData").createOrReplaceTempView("selectStar")
spark.catalog.cacheTable("selectStar")
checkAnswer(
sql("SELECT * FROM selectStar WHERE key = 1"),
Seq(Row(1, "1")))
uncacheTable("selectStar")
}
}
test("Self-join cached") {
val unCachedAnswer =
sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key").collect()
spark.catalog.cacheTable("testData")
checkAnswer(
sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key"),
unCachedAnswer.toSeq)
uncacheTable("testData")
}
test("'CACHE TABLE' and 'UNCACHE TABLE' SQL statement") {
sql("CACHE TABLE testData")
assertCached(spark.table("testData"))
val rddId = rddIdOf("testData")
assert(
isMaterialized(rddId),
"Eagerly cached in-memory table should have already been materialized")
sql("UNCACHE TABLE testData")
assert(!spark.catalog.isCached("testData"), "Table 'testData' should not be cached")
eventually(timeout(10.seconds)) {
assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
}
}
test("CACHE TABLE tableName AS SELECT * FROM anotherTable") {
withTempView("testCacheTable") {
sql("CACHE TABLE testCacheTable AS SELECT * FROM testData")
assertCached(spark.table("testCacheTable"))
val rddId = rddIdOf("testCacheTable")
assert(
isMaterialized(rddId),
"Eagerly cached in-memory table should have already been materialized")
uncacheTable("testCacheTable")
eventually(timeout(10.seconds)) {
assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
}
}
}
test("CACHE TABLE tableName AS SELECT ...") {
withTempView("testCacheTable") {
sql("CACHE TABLE testCacheTable AS SELECT key FROM testData LIMIT 10")
assertCached(spark.table("testCacheTable"))
val rddId = rddIdOf("testCacheTable")
assert(
isMaterialized(rddId),
"Eagerly cached in-memory table should have already been materialized")
uncacheTable("testCacheTable")
eventually(timeout(10.seconds)) {
assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
}
}
}
test("CACHE LAZY TABLE tableName") {
sql("CACHE LAZY TABLE testData")
assertCached(spark.table("testData"))
val rddId = rddIdOf("testData")
assert(
!isMaterialized(rddId),
"Lazily cached in-memory table shouldn't be materialized eagerly")
sql("SELECT COUNT(*) FROM testData").collect()
assert(
isMaterialized(rddId),
"Lazily cached in-memory table should have been materialized")
uncacheTable("testData")
eventually(timeout(10.seconds)) {
assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
}
}
private def assertStorageLevel(cacheOptions: String, level: DataReadMethod): Unit = {
sql(s"CACHE TABLE testData OPTIONS$cacheOptions")
assertCached(spark.table("testData"))
val rddId = rddIdOf("testData")
assert(isExpectStorageLevel(rddId, level))
}
test("SQL interface support storageLevel(DISK_ONLY)") {
assertStorageLevel("('storageLevel' 'DISK_ONLY')", Disk)
}
test("SQL interface support storageLevel(DISK_ONLY) with invalid options") {
assertStorageLevel("('storageLevel' 'DISK_ONLY', 'a' '1', 'b' '2')", Disk)
}
test("SQL interface support storageLevel(MEMORY_ONLY)") {
assertStorageLevel("('storageLevel' 'MEMORY_ONLY')", Memory)
}
test("SQL interface cache SELECT ... support storageLevel(DISK_ONLY)") {
withTempView("testCacheSelect") {
sql("CACHE TABLE testCacheSelect OPTIONS('storageLevel' 'DISK_ONLY') SELECT * FROM testData")
assertCached(spark.table("testCacheSelect"))
val rddId = rddIdOf("testCacheSelect")
assert(isExpectStorageLevel(rddId, Disk))
}
}
test("SQL interface support storageLevel(Invalid StorageLevel)") {
val message = intercept[IllegalArgumentException] {
sql("CACHE TABLE testData OPTIONS('storageLevel' 'invalid_storage_level')")
}.getMessage
assert(message.contains("Invalid StorageLevel: INVALID_STORAGE_LEVEL"))
}
test("SQL interface support storageLevel(with LAZY)") {
sql("CACHE LAZY TABLE testData OPTIONS('storageLevel' 'disk_only')")
assertCached(spark.table("testData"))
val rddId = rddIdOf("testData")
assert(
!isMaterialized(rddId),
"Lazily cached in-memory table shouldn't be materialized eagerly")
sql("SELECT COUNT(*) FROM testData").collect()
assert(
isMaterialized(rddId),
"Lazily cached in-memory table should have been materialized")
assert(isExpectStorageLevel(rddId, Disk))
}
test("InMemoryRelation statistics") {
sql("CACHE TABLE testData")
spark.table("testData").queryExecution.withCachedData.collect {
case cached: InMemoryRelation =>
val actualSizeInBytes = (1 to 100).map(i => 4 + i.toString.length + 4).sum
assert(cached.stats.sizeInBytes === actualSizeInBytes)
}
}
test("Drops temporary table") {
withTempView("t1") {
testData.select("key").createOrReplaceTempView("t1")
spark.table("t1")
spark.catalog.dropTempView("t1")
intercept[AnalysisException](spark.table("t1"))
}
}
test("Drops cached temporary table") {
withTempView("t1", "t2") {
testData.select("key").createOrReplaceTempView("t1")
testData.select("key").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
assert(spark.catalog.isCached("t1"))
assert(spark.catalog.isCached("t2"))
spark.catalog.dropTempView("t1")
intercept[AnalysisException](spark.table("t1"))
assert(!spark.catalog.isCached("t2"))
}
}
test("Clear all cache") {
withTempView("t1", "t2") {
sql("SELECT key FROM testData LIMIT 10").createOrReplaceTempView("t1")
sql("SELECT key FROM testData LIMIT 5").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
spark.catalog.clearCache()
assert(spark.sharedState.cacheManager.isEmpty)
sql("SELECT key FROM testData LIMIT 10").createOrReplaceTempView("t1")
sql("SELECT key FROM testData LIMIT 5").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
sql("Clear CACHE")
assert(spark.sharedState.cacheManager.isEmpty)
}
}
test("Ensure accumulators to be cleared after GC when uncacheTable") {
withTempView("t1", "t2") {
sql("SELECT key FROM testData LIMIT 10").createOrReplaceTempView("t1")
sql("SELECT key FROM testData LIMIT 5").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
sql("SELECT * FROM t1").count()
sql("SELECT * FROM t2").count()
sql("SELECT * FROM t1").count()
sql("SELECT * FROM t2").count()
val toBeCleanedAccIds = new HashSet[Long]
val accId1 = spark.table("t1").queryExecution.withCachedData.collect {
case i: InMemoryRelation => i.cacheBuilder.sizeInBytesStats.id
}.head
toBeCleanedAccIds += accId1
val accId2 = spark.table("t1").queryExecution.withCachedData.collect {
case i: InMemoryRelation => i.cacheBuilder.sizeInBytesStats.id
}.head
toBeCleanedAccIds += accId2
val cleanerListener = new CleanerListener {
def rddCleaned(rddId: Int): Unit = {}
def shuffleCleaned(shuffleId: Int): Unit = {}
def broadcastCleaned(broadcastId: Long): Unit = {}
def accumCleaned(accId: Long): Unit = {
toBeCleanedAccIds.synchronized { toBeCleanedAccIds -= accId }
}
def checkpointCleaned(rddId: Long): Unit = {}
}
spark.sparkContext.cleaner.get.attachListener(cleanerListener)
uncacheTable("t1")
uncacheTable("t2")
System.gc()
eventually(timeout(10.seconds)) {
assert(toBeCleanedAccIds.synchronized { toBeCleanedAccIds.isEmpty },
"batchStats accumulators should be cleared after GC when uncacheTable")
}
assert(AccumulatorContext.get(accId1).isEmpty)
assert(AccumulatorContext.get(accId2).isEmpty)
}
}
test("SPARK-10327 Cache Table is not working while subquery has alias in its project list") {
withTempView("abc") {
sparkContext.parallelize((1, 1) :: (2, 2) :: Nil)
.toDF("key", "value").selectExpr("key", "value", "key+1").createOrReplaceTempView("abc")
spark.catalog.cacheTable("abc")
val sparkPlan = sql(
"""select a.key, b.key, c.key from
|abc a join abc b on a.key=b.key
|join abc c on a.key=c.key""".stripMargin).queryExecution.sparkPlan
assert(sparkPlan.collect { case e: InMemoryTableScanExec => e }.size === 3)
assert(sparkPlan.collect { case e: RDDScanExec => e }.size === 0)
}
}
/**
* Verifies that the plan for `df` contains `expected` number of Exchange operators.
*/
private def verifyNumExchanges(df: DataFrame, expected: Int): Unit = {
withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") {
df.collect()
}
assert(
collect(df.queryExecution.executedPlan) { case e: ShuffleExchangeExec => e }.size == expected)
}
test("A cached table preserves the partitioning and ordering of its cached SparkPlan") {
// Set MAX_SINGLE_PARTITION_BYTES to Long.MaxValue to avoid inserting Exchange node
withSQLConf(SQLConf.MAX_SINGLE_PARTITION_BYTES.key -> Long.MaxValue.toString) {
val table3x = testData.union(testData).union(testData)
table3x.createOrReplaceTempView("testData3x")
sql("SELECT key, value FROM testData3x ORDER BY key").createOrReplaceTempView("orderedTable")
spark.catalog.cacheTable("orderedTable")
assertCached(spark.table("orderedTable"))
// Should not have an exchange as the query is already sorted on the group by key.
verifyNumExchanges(sql("SELECT key, count(*) FROM orderedTable GROUP BY key"), 0)
checkAnswer(
sql("SELECT key, count(*) FROM orderedTable GROUP BY key ORDER BY key"),
sql("SELECT key, count(*) FROM testData3x GROUP BY key ORDER BY key").collect())
uncacheTable("orderedTable")
spark.catalog.dropTempView("orderedTable")
// Set up two tables distributed in the same way. Try this with the data distributed into
// different number of partitions.
for (numPartitions <- 1 until 10 by 4) {
withTempView("t1", "t2") {
testData.repartition(numPartitions, $"key").createOrReplaceTempView("t1")
testData2.repartition(numPartitions, $"a").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
// Joining them should result in no exchanges.
verifyNumExchanges(sql("SELECT * FROM t1 t1 JOIN t2 t2 ON t1.key = t2.a"), 0)
checkAnswer(sql("SELECT * FROM t1 t1 JOIN t2 t2 ON t1.key = t2.a"),
sql("SELECT * FROM testData t1 JOIN testData2 t2 ON t1.key = t2.a"))
// Grouping on the partition key should result in no exchanges
verifyNumExchanges(sql("SELECT count(*) FROM t1 GROUP BY key"), 0)
checkAnswer(sql("SELECT count(*) FROM t1 GROUP BY key"),
sql("SELECT count(*) FROM testData GROUP BY key"))
uncacheTable("t1")
uncacheTable("t2")
}
}
// Distribute the tables into non-matching number of partitions. Need to shuffle one side.
withTempView("t1", "t2") {
testData.repartition(6, $"key").createOrReplaceTempView("t1")
testData2.repartition(3, $"a").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
val query = sql("SELECT key, value, a, b FROM t1 t1 JOIN t2 t2 ON t1.key = t2.a")
verifyNumExchanges(query, 1)
assert(
stripAQEPlan(query.queryExecution.executedPlan).outputPartitioning.numPartitions === 6)
checkAnswer(
query,
testData.join(testData2, $"key" === $"a").select($"key", $"value", $"a", $"b"))
uncacheTable("t1")
uncacheTable("t2")
}
// One side of join is not partitioned in the desired way. Need to shuffle one side.
withTempView("t1", "t2") {
testData.repartition(6, $"value").createOrReplaceTempView("t1")
testData2.repartition(6, $"a").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
val query = sql("SELECT key, value, a, b FROM t1 t1 JOIN t2 t2 ON t1.key = t2.a")
verifyNumExchanges(query, 1)
assert(
stripAQEPlan(query.queryExecution.executedPlan).outputPartitioning.numPartitions === 6)
checkAnswer(
query,
testData.join(testData2, $"key" === $"a").select($"key", $"value", $"a", $"b"))
uncacheTable("t1")
uncacheTable("t2")
}
withTempView("t1", "t2") {
testData.repartition(6, $"value").createOrReplaceTempView("t1")
testData2.repartition(12, $"a").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
val query = sql("SELECT key, value, a, b FROM t1 t1 JOIN t2 t2 ON t1.key = t2.a")
verifyNumExchanges(query, 1)
assert(
stripAQEPlan(query.queryExecution.executedPlan).outputPartitioning.numPartitions === 12)
checkAnswer(
query,
testData.join(testData2, $"key" === $"a").select($"key", $"value", $"a", $"b"))
uncacheTable("t1")
uncacheTable("t2")
}
// One side of join is not partitioned in the desired way. We'll only shuffle this side.
withTempView("t1", "t2") {
testData.repartition(6, $"value").createOrReplaceTempView("t1")
testData2.repartition(3, $"a").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
val query = sql("SELECT key, value, a, b FROM t1 t1 JOIN t2 t2 ON t1.key = t2.a")
verifyNumExchanges(query, 1)
checkAnswer(
query,
testData.join(testData2, $"key" === $"a").select($"key", $"value", $"a", $"b"))
uncacheTable("t1")
uncacheTable("t2")
}
// repartition's column ordering is different from group by column ordering.
// But they use the same set of columns.
withTempView("t1") {
testData.repartition(6, $"value", $"key").createOrReplaceTempView("t1")
spark.catalog.cacheTable("t1")
val query = sql("SELECT value, key from t1 group by key, value")
verifyNumExchanges(query, 0)
checkAnswer(
query,
testData.distinct().select($"value", $"key"))
uncacheTable("t1")
}
// repartition's column ordering is different from join condition's column ordering.
// We will still shuffle because hashcodes of a row depend on the column ordering.
// If we do not shuffle, we may actually partition two tables in totally two different way.
// See PartitioningSuite for more details.
withTempView("t1", "t2") {
val df1 = testData
df1.repartition(6, $"value", $"key").createOrReplaceTempView("t1")
val df2 = testData2.select($"a", $"b".cast("string"))
df2.repartition(6, $"a", $"b").createOrReplaceTempView("t2")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
val query =
sql("SELECT key, value, a, b FROM t1 t1 JOIN t2 t2 ON t1.key = t2.a and t1.value = t2.b")
verifyNumExchanges(query, 1)
assert(
stripAQEPlan(query.queryExecution.executedPlan).outputPartitioning.numPartitions === 6)
checkAnswer(
query,
df1.join(df2, $"key" === $"a" && $"value" === $"b").select($"key", $"value", $"a", $"b"))
uncacheTable("t1")
uncacheTable("t2")
}
}
}
test("SPARK-15870 DataFrame can't execute after uncacheTable") {
withTempView("selectStar") {
val selectStar = sql("SELECT * FROM testData WHERE key = 1")
selectStar.createOrReplaceTempView("selectStar")
spark.catalog.cacheTable("selectStar")
checkAnswer(
selectStar,
Seq(Row(1, "1")))
uncacheTable("selectStar")
checkAnswer(
selectStar,
Seq(Row(1, "1")))
}
}
test("SPARK-15915 Logical plans should use canonicalized plan when override sameResult") {
withTempView("localRelation") {
val localRelation = Seq(1, 2, 3).toDF()
localRelation.createOrReplaceTempView("localRelation")
spark.catalog.cacheTable("localRelation")
assert(getNumInMemoryRelations(localRelation) == 1)
}
}
test("SPARK-19093 Caching in side subquery") {
withTempView("t1") {
Seq(1).toDF("c1").createOrReplaceTempView("t1")
spark.catalog.cacheTable("t1")
val ds =
sql(
"""
|SELECT * FROM t1
|WHERE
|NOT EXISTS (SELECT * FROM t1)
""".stripMargin)
assert(getNumInMemoryRelations(ds) == 2)
}
}
test("SPARK-19093 scalar and nested predicate query") {
withTempView("t1", "t2", "t3", "t4") {
Seq(1).toDF("c1").createOrReplaceTempView("t1")
Seq(2).toDF("c1").createOrReplaceTempView("t2")
Seq(1).toDF("c1").createOrReplaceTempView("t3")
Seq(1).toDF("c1").createOrReplaceTempView("t4")
spark.catalog.cacheTable("t1")
spark.catalog.cacheTable("t2")
spark.catalog.cacheTable("t3")
spark.catalog.cacheTable("t4")
// Nested predicate subquery
val ds =
sql(
"""
|SELECT * FROM t1
|WHERE
|c1 IN (SELECT c1 FROM t2 WHERE c1 IN (SELECT c1 FROM t3 WHERE c1 = 1))
""".stripMargin)
assert(getNumInMemoryRelations(ds) == 3)
// Scalar subquery and predicate subquery
val ds2 =
sql(
"""
|SELECT * FROM (SELECT c1, max(c1) FROM t1 GROUP BY c1)
|WHERE
|c1 = (SELECT max(c1) FROM t2 GROUP BY c1)
|OR
|EXISTS (SELECT c1 FROM t3)
|OR
|c1 IN (SELECT c1 FROM t4)
""".stripMargin)
assert(getNumInMemoryRelations(ds2) == 4)
}
}
test("SPARK-19765: UNCACHE TABLE should un-cache all cached plans that refer to this table") {
withTable("t") {
withTempPath { path =>
Seq(1 -> "a").toDF("i", "j").write.parquet(path.getCanonicalPath)
sql(s"CREATE TABLE t USING parquet LOCATION '${path.toURI}'")
spark.catalog.cacheTable("t")
spark.table("t").select($"i").cache()
checkAnswer(spark.table("t").select($"i"), Row(1))
assertCached(spark.table("t").select($"i"))
Utils.deleteRecursively(path)
spark.sessionState.catalog.refreshTable(TableIdentifier("t"))
uncacheTable("t")
assert(spark.table("t").select($"i").count() == 0)
assert(getNumInMemoryRelations(spark.table("t").select($"i")) == 0)
}
}
}
test("refreshByPath should refresh all cached plans with the specified path") {
withTempDir { dir =>
val path = dir.getCanonicalPath()
spark.range(10).write.mode("overwrite").parquet(path)
spark.read.parquet(path).cache()
spark.read.parquet(path).filter($"id" > 4).cache()
assert(spark.read.parquet(path).filter($"id" > 4).count() == 5)
spark.range(20).write.mode("overwrite").parquet(path)
spark.catalog.refreshByPath(path)
assert(spark.read.parquet(path).count() == 20)
assert(spark.read.parquet(path).filter($"id" > 4).count() == 15)
}
}
test("SPARK-19993 simple subquery caching") {
withTempView("t1", "t2") {
Seq(1).toDF("c1").createOrReplaceTempView("t1")
Seq(2).toDF("c1").createOrReplaceTempView("t2")
val sql1 =
"""
|SELECT * FROM t1
|WHERE
|NOT EXISTS (SELECT * FROM t2)
""".stripMargin
sql(sql1).cache()
val cachedDs = sql(sql1)
assert(getNumInMemoryRelations(cachedDs) == 1)
// Additional predicate in the subquery plan should cause a cache miss
val cachedMissDs =
sql(
"""
|SELECT * FROM t1
|WHERE
|NOT EXISTS (SELECT * FROM t2 where c1 = 0)
""".stripMargin)
assert(getNumInMemoryRelations(cachedMissDs) == 0)
}
}
test("SPARK-19993 subquery caching with correlated predicates") {
withTempView("t1", "t2") {
Seq(1).toDF("c1").createOrReplaceTempView("t1")
Seq(1).toDF("c1").createOrReplaceTempView("t2")
// Simple correlated predicate in subquery
val sqlText =
"""
|SELECT * FROM t1
|WHERE
|t1.c1 in (SELECT t2.c1 FROM t2 where t1.c1 = t2.c1)
""".stripMargin
sql(sqlText).cache()
val cachedDs = sql(sqlText)
assert(getNumInMemoryRelations(cachedDs) == 1)
}
}
test("SPARK-19993 subquery with cached underlying relation") {
withTempView("t1") {
Seq(false, true).foreach { enabled =>
withSQLConf(
SQLConf.CAN_CHANGE_CACHED_PLAN_OUTPUT_PARTITIONING.key -> enabled.toString,
SQLConf.ADAPTIVE_OPTIMIZER_EXCLUDED_RULES.key ->
AQEPropagateEmptyRelation.ruleName) {
Seq(1).toDF("c1").createOrReplaceTempView("t1")
spark.catalog.cacheTable("t1")
// underlying table t1 is cached as well as the query that refers to it.
val sqlText =
"""
|SELECT * FROM t1
|WHERE
|NOT EXISTS (SELECT * FROM t1)
""".stripMargin
val ds = sql(sqlText)
assert(getNumInMemoryRelations(ds) == 2)
val cachedDs = sql(sqlText).cache()
cachedDs.collect()
assert(getNumInMemoryTablesRecursively(cachedDs.queryExecution.executedPlan) == 3)
cachedDs.unpersist()
spark.catalog.uncacheTable("t1")
}
}
}
}
test("SPARK-19993 nested subquery caching and scalar + predicate subqueries") {
withTempView("t1", "t2", "t3", "t4") {
Seq(1).toDF("c1").createOrReplaceTempView("t1")
Seq(2).toDF("c1").createOrReplaceTempView("t2")
Seq(1).toDF("c1").createOrReplaceTempView("t3")
Seq(1).toDF("c1").createOrReplaceTempView("t4")
// Nested predicate subquery
val sql1 =
"""
|SELECT * FROM t1
|WHERE
|c1 IN (SELECT c1 FROM t2 WHERE c1 IN (SELECT c1 FROM t3 WHERE c1 = 1))
""".stripMargin
sql(sql1).cache()
val cachedDs = sql(sql1)
assert(getNumInMemoryRelations(cachedDs) == 1)
// Scalar subquery and predicate subquery
val sql2 =
"""
|SELECT * FROM (SELECT c1, max(c1) FROM t1 GROUP BY c1)
|WHERE
|c1 = (SELECT max(c1) FROM t2 GROUP BY c1)
|OR
|EXISTS (SELECT c1 FROM t3)
|OR
|c1 IN (SELECT c1 FROM t4)
""".stripMargin
sql(sql2).cache()
val cachedDs2 = sql(sql2)
assert(getNumInMemoryRelations(cachedDs2) == 1)
}
}
test("SPARK-23312: vectorized cache reader can be disabled") {
Seq(true, false).foreach { vectorized =>
withSQLConf(SQLConf.CACHE_VECTORIZED_READER_ENABLED.key -> vectorized.toString) {
val df1 = spark.range(10).cache()
val df2 = spark.range(10).cache()
val union = df1.union(df2)
union.queryExecution.executedPlan.foreach {
case i: InMemoryTableScanExec =>
assert(i.supportsColumnar == vectorized)
case _ =>
}
}
}
}
test("SPARK-37369: Avoid redundant ColumnarToRow transition on InMemoryTableScan") {
Seq(true, false).foreach { vectorized =>
withSQLConf(SQLConf.CACHE_VECTORIZED_READER_ENABLED.key -> vectorized.toString) {
val cache = spark.range(10).cache()
val df = cache.filter($"id" > 0)
val columnarToRow = df.queryExecution.executedPlan.collect {
case c: ColumnarToRowExec => c
}
assert(columnarToRow.isEmpty)
}
}
}
private def checkIfNoJobTriggered[T](f: => T): T = {
var numJobTriggered = 0
val jobListener = new SparkListener {
override def onJobStart(jobStart: SparkListenerJobStart): Unit = {
numJobTriggered += 1
}
}
sparkContext.addSparkListener(jobListener)
try {
val result = f
sparkContext.listenerBus.waitUntilEmpty()
assert(numJobTriggered === 0)
result
} finally {
sparkContext.removeSparkListener(jobListener)
}
}
test("SPARK-23880 table cache should be lazy and don't trigger any jobs") {
val cachedData = checkIfNoJobTriggered {
spark.range(1002).filter($"id" > 1000).orderBy($"id".desc).cache()
}
assert(cachedData.collect() === Seq(1001))
}
test("SPARK-24596 Non-cascading Cache Invalidation - uncache temporary view") {
withTempView("t1", "t2") {
sql("CACHE TABLE t1 AS SELECT * FROM testData WHERE key > 1")
sql("CACHE TABLE t2 as SELECT * FROM t1 WHERE value > 1")
assert(spark.catalog.isCached("t1"))
assert(spark.catalog.isCached("t2"))
sql("UNCACHE TABLE t1")
assert(!spark.catalog.isCached("t1"))
assert(spark.catalog.isCached("t2"))
}
}
test("SPARK-24596, SPARK-34052: cascading cache invalidation - drop temporary view") {
Seq(true, false).foreach { storeAnalyzed =>
withSQLConf(SQLConf.STORE_ANALYZED_PLAN_FOR_VIEW.key -> storeAnalyzed.toString) {
withTempView("t1", "t2") {
sql("CACHE TABLE t1 AS SELECT * FROM testData WHERE key > 1")
sql("CACHE TABLE t2 as SELECT * FROM t1 WHERE value > 1")
assert(spark.catalog.isCached("t1"))
assert(spark.catalog.isCached("t2"))
val oldView = spark.table("t2")
sql("DROP VIEW t1")
// dropping a temp view trigger cache invalidation on dependents iff the config is
// turned off
assert(storeAnalyzed ==
spark.sharedState.cacheManager.lookupCachedData(oldView).isDefined)
if (!storeAnalyzed) {
// t2 should become invalid after t1 is dropped
val e = intercept[AnalysisException](spark.catalog.isCached("t2"))
checkErrorTableNotFound(e, "`t1`",
ExpectedContext("VIEW", "t2", 14, 15, "t1"))
}
}
}
}
}
test("SPARK-24596, SPARK-34052: cascading cache invalidation - drop persistent view") {
Seq(true, false).foreach { storeAnalyzed =>