[SPARK-33075][SQL] Enable auto bucketed scan by default (disable only for cached query)

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -971,7 +971,7 @@ object SQLConf {
         "false, this configuration does not take any effect.")
       .version("3.1.0")
       .booleanConf
-      .createWithDefault(false)
+      .createWithDefault(true)
 
   val CROSS_JOINS_ENABLED = buildConf("spark.sql.crossJoin.enabled")
     .internal()

sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala

@@ -29,7 +29,7 @@ import org.apache.spark.{SPARK_VERSION, SparkConf, SparkContext, TaskContext}
 import org.apache.spark.annotation.{DeveloperApi, Experimental, Stable, Unstable}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.internal.Logging
-import org.apache.spark.internal.config.EXECUTOR_ALLOW_SPARK_CONTEXT
+import org.apache.spark.internal.config.{ConfigEntry, EXECUTOR_ALLOW_SPARK_CONTEXT}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationEnd}
 import org.apache.spark.sql.catalog.Catalog
@@ -1077,6 +1077,27 @@ object SparkSession extends Logging {
       throw new IllegalStateException("No active or default Spark session found")))
   }
 
+  /**
+   * Returns a cloned SparkSession with all specified configurations disabled, or
+   * the original SparkSession if all configurations are already disabled.
+   *
+   * @since 3.1.0
+   */
+  private[sql] def getOrCloneSessionWithConfigsOff(
+      session: SparkSession,
+      configurations: Seq[ConfigEntry[Boolean]]): SparkSession = {
+    val configsEnabled = configurations.filter(session.sessionState.conf.getConf(_))
+    if (configsEnabled.isEmpty) {
+      session
+    } else {
+      val newSession = session.cloneSession()
+      configsEnabled.foreach(conf => {
+        newSession.sessionState.conf.setConf(conf, false)
+      })
+      newSession
+    }
+  }
+
   ////////////////////////////////////////////////////////////////////////////////////////
   // Private methods from now on
   ////////////////////////////////////////////////////////////////////////////////////////

sql/core/src/main/scala/org/apache/spark/sql/execution/CacheManager.scala

@@ -22,6 +22,7 @@ import scala.collection.immutable.IndexedSeq
 import org.apache.hadoop.fs.{FileSystem, Path}
 
 import org.apache.spark.internal.Logging
+import org.apache.spark.internal.config.ConfigEntry
 import org.apache.spark.sql.{Dataset, SparkSession}
 import org.apache.spark.sql.catalyst.expressions.{Attribute, SubqueryExpression}
 import org.apache.spark.sql.catalyst.optimizer.EliminateResolvedHint
@@ -31,6 +32,7 @@ import org.apache.spark.sql.execution.columnar.{DefaultCachedBatchSerializer, In
 import org.apache.spark.sql.execution.command.CommandUtils
 import org.apache.spark.sql.execution.datasources.{FileIndex, HadoopFsRelation, LogicalRelation}
 import org.apache.spark.sql.execution.datasources.v2.{DataSourceV2Relation, FileTable}
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.storage.StorageLevel.MEMORY_AND_DISK
 
@@ -55,6 +57,17 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
   @transient @volatile
   private var cachedData = IndexedSeq[CachedData]()
 
+  /**
+   * Configurations needs to be turned off, to avoid regression for cached query, so that the
+   * outputPartitioning of the underlying cached query plan can be leveraged later.
+   * Configurations include:
+   * 1. AQE
+   * 2. Automatic bucketed table scan
+   */
+  private val forceDisableConfigs: Seq[ConfigEntry[Boolean]] = Seq(
+    SQLConf.ADAPTIVE_EXECUTION_ENABLED,
+    SQLConf.AUTO_BUCKETED_SCAN_ENABLED)
+
   /** Clears all cached tables. */
   def clearCache(): Unit = this.synchronized {
     cachedData.foreach(_.cachedRepresentation.cacheBuilder.clearCache())
@@ -79,10 +92,11 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
     if (lookupCachedData(planToCache).nonEmpty) {
       logWarning("Asked to cache already cached data.")
     } else {
-      // Turn off AQE so that the outputPartitioning of the underlying plan can be leveraged.
-      val sessionWithAqeOff = getOrCloneSessionWithAqeOff(query.sparkSession)
-      val inMemoryRelation = sessionWithAqeOff.withActive {
-        val qe = sessionWithAqeOff.sessionState.executePlan(planToCache)
+      // Turn off configs so that the outputPartitioning of the underlying plan can be leveraged.
+      val sessionWithConfigsOff = SparkSession.getOrCloneSessionWithConfigsOff(
+        query.sparkSession, forceDisableConfigs)
+      val inMemoryRelation = sessionWithConfigsOff.withActive {
+        val qe = sessionWithConfigsOff.sessionState.executePlan(planToCache)
         InMemoryRelation(
           storageLevel,
           qe,
@@ -188,10 +202,11 @@ class CacheManager extends Logging with AdaptiveSparkPlanHelper {
     }
     needToRecache.map { cd =>
       cd.cachedRepresentation.cacheBuilder.clearCache()
-      // Turn off AQE so that the outputPartitioning of the underlying plan can be leveraged.
-      val sessionWithAqeOff = getOrCloneSessionWithAqeOff(spark)
-      val newCache = sessionWithAqeOff.withActive {
-        val qe = sessionWithAqeOff.sessionState.executePlan(cd.plan)
+      // Turn off configs so that the outputPartitioning of the underlying plan can be leveraged.
+      val sessionWithConfigsOff = SparkSession.getOrCloneSessionWithConfigsOff(
+        spark, forceDisableConfigs)
+      val newCache = sessionWithConfigsOff.withActive {
+        val qe = sessionWithConfigsOff.sessionState.executePlan(cd.plan)
         InMemoryRelation(cd.cachedRepresentation.cacheBuilder, qe)
       }
       val recomputedPlan = cd.copy(cachedRepresentation = newCache)

sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveSparkPlanHelper.scala

@@ -17,9 +17,7 @@
 
 package org.apache.spark.sql.execution.adaptive
 
-import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.execution.SparkPlan
-import org.apache.spark.sql.internal.SQLConf
 
 /**
  * This class provides utility methods related to tree traversal of an [[AdaptiveSparkPlanExec]]
@@ -137,18 +135,4 @@ trait AdaptiveSparkPlanHelper {
     case a: AdaptiveSparkPlanExec => a.executedPlan
     case other => other
   }
-
-  /**
-   * Returns a cloned [[SparkSession]] with adaptive execution disabled, or the original
-   * [[SparkSession]] if its adaptive execution is already disabled.
-   */
-  def getOrCloneSessionWithAqeOff[T](session: SparkSession): SparkSession = {
-    if (!session.sessionState.conf.adaptiveExecutionEnabled) {
-      session
-    } else {
-      val newSession = session.cloneSession()
-      newSession.sessionState.conf.setConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED, false)
-      newSession
-    }
-  }
 }

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/FileSourceStrategySuite.scala

@@ -262,20 +262,22 @@ class FileSourceStrategySuite extends QueryTest with SharedSparkSession with Pre
           "p1=2/file7_0000" -> 1),
         buckets = 3)
 
-    // No partition pruning
-    checkScan(table) { partitions =>
-      assert(partitions.size == 3)
-      assert(partitions(0).files.size == 5)
-      assert(partitions(1).files.size == 0)
-      assert(partitions(2).files.size == 2)
-    }
+    withSQLConf(SQLConf.AUTO_BUCKETED_SCAN_ENABLED.key -> "false") {
+      // No partition pruning
+      checkScan(table) { partitions =>
+        assert(partitions.size == 3)
+        assert(partitions(0).files.size == 5)
+        assert(partitions(1).files.size == 0)
+        assert(partitions(2).files.size == 2)
+      }
 
-    // With partition pruning
-    checkScan(table.where("p1=2")) { partitions =>
-      assert(partitions.size == 3)
-      assert(partitions(0).files.size == 3)
-      assert(partitions(1).files.size == 0)
-      assert(partitions(2).files.size == 1)
+      // With partition pruning
+      checkScan(table.where("p1=2")) { partitions =>
+        assert(partitions.size == 3)
+        assert(partitions(0).files.size == 3)
+        assert(partitions(1).files.size == 0)
+        assert(partitions(2).files.size == 1)
+      }
     }
   }
 

sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala

@@ -432,22 +432,24 @@ abstract class BroadcastJoinSuiteBase extends QueryTest with SQLTestUtils
         // join1 is a broadcast join where df2 is broadcasted. Note that output partitioning on the
         // streamed side (t1) is HashPartitioning (bucketed files).
         val join1 = t1.join(df2, t1("i1") === df2("i2") && t1("j1") === df2("j2"))
-        val plan1 = join1.queryExecution.executedPlan
-        assert(collect(plan1) { case e: ShuffleExchangeExec => e }.isEmpty)
-        val broadcastJoins = collect(plan1) { case b: BroadcastHashJoinExec => b }
-        assert(broadcastJoins.size == 1)
-        assert(broadcastJoins(0).outputPartitioning.isInstanceOf[PartitioningCollection])
-        val p = broadcastJoins(0).outputPartitioning.asInstanceOf[PartitioningCollection]
-        assert(p.partitionings.size == 4)
-        // Verify all the combinations of output partitioning.
-        Seq(Seq(t1("i1"), t1("j1")),
-          Seq(t1("i1"), df2("j2")),
-          Seq(df2("i2"), t1("j1")),
-          Seq(df2("i2"), df2("j2"))).foreach { expected =>
-          val expectedExpressions = expected.map(_.expr)
-          assert(p.partitionings.exists {
-            case h: HashPartitioning => expressionsEqual(h.expressions, expectedExpressions)
-          })
+        withSQLConf(SQLConf.AUTO_BUCKETED_SCAN_ENABLED.key -> "false") {
+          val plan1 = join1.queryExecution.executedPlan
+          assert(collect(plan1) { case e: ShuffleExchangeExec => e }.isEmpty)
+          val broadcastJoins = collect(plan1) { case b: BroadcastHashJoinExec => b }
+          assert(broadcastJoins.size == 1)
+          assert(broadcastJoins(0).outputPartitioning.isInstanceOf[PartitioningCollection])
+          val p = broadcastJoins(0).outputPartitioning.asInstanceOf[PartitioningCollection]
+          assert(p.partitionings.size == 4)
+          // Verify all the combinations of output partitioning.
+          Seq(Seq(t1("i1"), t1("j1")),
+            Seq(t1("i1"), df2("j2")),
+            Seq(df2("i2"), t1("j1")),
+            Seq(df2("i2"), df2("j2"))).foreach { expected =>
+            val expectedExpressions = expected.map(_.expr)
+            assert(p.partitionings.exists {
+              case h: HashPartitioning => expressionsEqual(h.expressions, expectedExpressions)
+            })
+          }
         }
 
         // Join on the column from the broadcasted side (i2, j2) and make sure output partitioning

sql/core/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala

@@ -81,22 +81,24 @@ abstract class BucketedReadSuite extends QueryTest with SQLTestUtils {
         .bucketBy(8, "j", "k")
         .saveAsTable("bucketed_table")
 
-      val bucketValue = Random.nextInt(maxI)
-      val table = spark.table("bucketed_table").filter($"i" === bucketValue)
-      val query = table.queryExecution
-      val output = query.analyzed.output
-      val rdd = query.toRdd
-
-      assert(rdd.partitions.length == 8)
-
-      val attrs = table.select("j", "k").queryExecution.analyzed.output
-      val checkBucketId = rdd.mapPartitionsWithIndex((index, rows) => {
-        val getBucketId = UnsafeProjection.create(
-          HashPartitioning(attrs, 8).partitionIdExpression :: Nil,
-          output)
-        rows.map(row => getBucketId(row).getInt(0) -> index)
-      })
-      checkBucketId.collect().foreach(r => assert(r._1 == r._2))
+      withSQLConf(SQLConf.AUTO_BUCKETED_SCAN_ENABLED.key -> "false") {
+        val bucketValue = Random.nextInt(maxI)
+        val table = spark.table("bucketed_table").filter($"i" === bucketValue)
+        val query = table.queryExecution
+        val output = query.analyzed.output
+        val rdd = query.toRdd
+
+        assert(rdd.partitions.length == 8)
+
+        val attrs = table.select("j", "k").queryExecution.analyzed.output
+        val checkBucketId = rdd.mapPartitionsWithIndex((index, rows) => {
+          val getBucketId = UnsafeProjection.create(
+            HashPartitioning(attrs, 8).partitionIdExpression :: Nil,
+            output)
+          rows.map(row => getBucketId(row).getInt(0) -> index)
+        })
+        checkBucketId.collect().foreach(r => assert(r._1 == r._2))
+      }
     }
   }
 

sql/core/src/test/scala/org/apache/spark/sql/sources/DisableUnnecessaryBucketedScanSuite.scala

@@ -18,7 +18,10 @@
 package org.apache.spark.sql.sources
 
 import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
+import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning
 import org.apache.spark.sql.execution.FileSourceScanExec
+import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.internal.StaticSQLConf.CATALOG_IMPLEMENTATION
 import org.apache.spark.sql.test.{SharedSparkSession, SQLTestUtils}
@@ -218,4 +221,24 @@ abstract class DisableUnnecessaryBucketedScanSuite extends QueryTest with SQLTes
       }
     }
   }
+
+  test("SPARK-33075: not disable bucketed table scan for cached query") {
+    withTable("t1") {
+      withSQLConf(SQLConf.AUTO_BUCKETED_SCAN_ENABLED.key -> "true") {
+        df1.write.format("parquet").bucketBy(8, "i").saveAsTable("t1")
+        spark.catalog.cacheTable("t1")
+        assertCached(spark.table("t1"))
+
+        // Verify cached bucketed table scan not disabled
+        val partitioning = spark.table("t1").queryExecution.executedPlan
+          .outputPartitioning
+        assert(partitioning match {
+          case HashPartitioning(Seq(column: AttributeReference), 8) if column.name == "i" => true
+          case _ => false
+        })
+        val aggregateQueryPlan = sql("SELECT SUM(i) FROM t1 GROUP BY i").queryExecution.executedPlan
+        assert(aggregateQueryPlan.find(_.isInstanceOf[ShuffleExchangeExec]).isEmpty)
+      }
+    }
+  }
 }