Revert "[AE2.3-02][SPARK-23128] Add QueryStage and the framework for adaptive execution (auto setting the number of reducer)"

core/src/main/scala/org/apache/spark/MapOutputStatistics.scala

@@ -25,4 +25,3 @@ package org.apache.spark
  *   (may be inexact due to use of compressed map statuses)
  */
 private[spark] class MapOutputStatistics(val shuffleId: Int, val bytesByPartitionId: Array[Long])
-  extends Serializable

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

@@ -282,19 +282,14 @@ object SQLConf {
 
   val SHUFFLE_MIN_NUM_POSTSHUFFLE_PARTITIONS =
     buildConf("spark.sql.adaptive.minNumPostShufflePartitions")
-      .doc("The advisory minimum number of post-shuffle partitions used in adaptive execution.")
-      .intConf
-      .checkValue(numPartitions => numPartitions > 0, "The minimum shuffle partition number " +
-        "must be a positive integer.")
-      .createWithDefault(1)
-
-  val SHUFFLE_MAX_NUM_POSTSHUFFLE_PARTITIONS =
-    buildConf("spark.sql.adaptive.maxNumPostShufflePartitions")
-      .doc("The advisory maximum number of post-shuffle partitions used in adaptive execution.")
+      .internal()
+      .doc("The advisory minimal number of post-shuffle partitions provided to " +
+        "ExchangeCoordinator. This setting is used in our test to make sure we " +
+        "have enough parallelism to expose issues that will not be exposed with a " +
+        "single partition. When the value is a non-positive value, this setting will " +
+        "not be provided to ExchangeCoordinator.")
       .intConf
-      .checkValue(numPartitions => numPartitions > 0, "The maximum shuffle partition number " +
-        "must be a positive integer.")
-      .createWithDefault(500)
+      .createWithDefault(-1)
 
   val SUBEXPRESSION_ELIMINATION_ENABLED =
     buildConf("spark.sql.subexpressionElimination.enabled")
@@ -1783,9 +1778,8 @@ class SQLConf extends Serializable with Logging {
 
   def adaptiveExecutionEnabled: Boolean = getConf(ADAPTIVE_EXECUTION_ENABLED)
 
-  def minNumPostShufflePartitions: Int = getConf(SHUFFLE_MIN_NUM_POSTSHUFFLE_PARTITIONS)
-
-  def maxNumPostShufflePartitions: Int = getConf(SHUFFLE_MAX_NUM_POSTSHUFFLE_PARTITIONS)
+  def minNumPostShufflePartitions: Int =
+    getConf(SHUFFLE_MIN_NUM_POSTSHUFFLE_PARTITIONS)
 
   def minBatchesToRetain: Int = getConf(MIN_BATCHES_TO_RETAIN)
 

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

@@ -28,9 +28,9 @@ import org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, ReturnAnswer}
 import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.catalyst.util.StringUtils.StringConcat
 import org.apache.spark.sql.catalyst.util.truncatedString
-import org.apache.spark.sql.execution.adaptive.PlanQueryStage
 import org.apache.spark.sql.execution.command.{DescribeTableCommand, ExecutedCommandExec, ShowTablesCommand}
 import org.apache.spark.sql.execution.exchange.{EnsureRequirements, ReuseExchange}
 import org.apache.spark.sql.internal.SQLConf
@@ -95,11 +95,7 @@ class QueryExecution(
    * row format conversions as needed.
    */
   protected def prepareForExecution(plan: SparkPlan): SparkPlan = {
-    if (sparkSession.sessionState.conf.adaptiveExecutionEnabled) {
-      adaptivePreparations.foldLeft(plan) { case (sp, rule) => rule.apply(sp)}
-    } else {
-      preparations.foldLeft(plan) { case (sp, rule) => rule.apply(sp)}
-    }
+    preparations.foldLeft(plan) { case (sp, rule) => rule.apply(sp) }
   }
 
   /** A sequence of rules that will be applied in order to the physical plan before execution. */
@@ -110,15 +106,6 @@ class QueryExecution(
     ReuseExchange(sparkSession.sessionState.conf),
     ReuseSubquery(sparkSession.sessionState.conf))
 
-  protected def adaptivePreparations: Seq[Rule[SparkPlan]] = Seq(
-    PlanSubqueries(sparkSession),
-    EnsureRequirements(sparkSession.sessionState.conf),
-    ReuseSubquery(sparkSession.sessionState.conf),
-    // PlanQueryStage needs to be the last rule because it divides the plan into multiple sub-trees
-    // by inserting leaf node QueryStageInput. Transforming the plan after applying this rule will
-    // only transform node in a sub-tree.
-    PlanQueryStage(sparkSession.sessionState.conf))
-
   protected def stringOrError[A](f: => A): String =
     try f.toString catch { case e: AnalysisException => e.toString }
 

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

@@ -18,7 +18,6 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.execution.adaptive.QueryStageInput
 import org.apache.spark.sql.execution.exchange.ReusedExchangeExec
 import org.apache.spark.sql.execution.metric.SQLMetricInfo
 import org.apache.spark.sql.internal.SQLConf
@@ -53,7 +52,6 @@ private[execution] object SparkPlanInfo {
   def fromSparkPlan(plan: SparkPlan): SparkPlanInfo = {
     val children = plan match {
       case ReusedExchangeExec(_, child) => child :: Nil
-      case i: QueryStageInput => i.childStage :: Nil
       case _ => plan.children ++ plan.subqueries
     }
     val metrics = plan.metrics.toSeq.map { case (key, metric) =>