[SPARK-13671] [SPARK-13311] [SQL] Use different physical plans for RDD and data sources

## What changes were proposed in this pull request?

This PR split the PhysicalRDD into two classes, PhysicalRDD and PhysicalScan. PhysicalRDD is used for DataFrames that is created from existing RDD. PhysicalScan is used for DataFrame that is created from data sources. This enable use to apply different optimization on both of them.

Also fix the problem for sameResult() on two DataSourceScan.

Also fix the equality check to toString for `In`. It's better to use Seq there, but we can't break this public API (sad).

## How was this patch tested?

Existing tests. Manually tested with TPCDS query Q59 and Q64, all those duplicated exchanges can be re-used now, also saw there are 40+% performance improvement (saving half of the scan).

Author: Davies Liu <[email protected]>

Closes apache#11514 from davies/existing_rdd.

Author: Davies Liu

python/pyspark/sql/dataframe.py

@@ -173,8 +173,7 @@ def explain(self, extended=False):
 
         >>> df.explain()
         == Physical Plan ==
-        WholeStageCodegen
-        :  +- Scan ExistingRDD[age#0,name#1]
+        Scan ExistingRDD[age#0,name#1]
 
         >>> df.explain(True)
         == Parsed Logical Plan ==

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala

@@ -280,12 +280,12 @@ abstract class QueryPlan[PlanType <: QueryPlan[PlanType]] extends TreeNode[PlanT
    * can do better should override this function.
    */
   def sameResult(plan: PlanType): Boolean = {
-    val canonicalizedLeft = this.canonicalized
-    val canonicalizedRight = plan.canonicalized
-    canonicalizedLeft.getClass == canonicalizedRight.getClass &&
-      canonicalizedLeft.children.size == canonicalizedRight.children.size &&
-      canonicalizedLeft.cleanArgs == canonicalizedRight.cleanArgs &&
-      (canonicalizedLeft.children, canonicalizedRight.children).zipped.forall(_ sameResult _)
+    val left = this.canonicalized
+    val right = plan.canonicalized
+    left.getClass == right.getClass &&
+      left.children.size == right.children.size &&
+      left.cleanArgs == right.cleanArgs &&
+      (left.children, right.children).zipped.forall(_ sameResult _)
   }
 
   /**

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

@@ -101,17 +101,76 @@ private[sql] case class LogicalRDD(
 private[sql] case class PhysicalRDD(
     output: Seq[Attribute],
     rdd: RDD[InternalRow],
-    override val nodeName: String,
-    override val metadata: Map[String, String] = Map.empty,
-    isUnsafeRow: Boolean = false,
-    override val outputPartitioning: Partitioning = UnknownPartitioning(0))
+    override val nodeName: String) extends LeafNode {
+
+  private[sql] override lazy val metrics = Map(
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
+  protected override def doExecute(): RDD[InternalRow] = {
+    val numOutputRows = longMetric("numOutputRows")
+    rdd.mapPartitionsInternal { iter =>
+      val proj = UnsafeProjection.create(schema)
+      iter.map { r =>
+        numOutputRows += 1
+        proj(r)
+      }
+    }
+  }
+
+  override def simpleString: String = {
+    s"Scan $nodeName${output.mkString("[", ",", "]")}"
+  }
+}
+
+/** Physical plan node for scanning data from a relation. */
+private[sql] case class DataSourceScan(
+    output: Seq[Attribute],
+    rdd: RDD[InternalRow],
+    @transient relation: BaseRelation,
+    override val metadata: Map[String, String] = Map.empty)
   extends LeafNode with CodegenSupport {
 
+  override val nodeName: String = relation.toString
+
+  // Ignore rdd when checking results
+  override def sameResult(plan: SparkPlan ): Boolean = plan match {
+    case other: DataSourceScan => relation == other.relation && metadata == other.metadata
+    case _ => false
+  }
+
   private[sql] override lazy val metrics = Map(
     "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
 
+  val outputUnsafeRows = relation match {
+    case r: HadoopFsRelation if r.fileFormat.isInstanceOf[ParquetSource] =>
+      !SQLContext.getActive().get.conf.getConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED)
+    case _: HadoopFsRelation => true
+    case _ => false
+  }
+
+  override val outputPartitioning = {
+    val bucketSpec = relation match {
+      // TODO: this should be closer to bucket planning.
+      case r: HadoopFsRelation if r.sqlContext.conf.bucketingEnabled() => r.bucketSpec
+      case _ => None
+    }
+
+    def toAttribute(colName: String): Attribute = output.find(_.name == colName).getOrElse {
+      throw new AnalysisException(s"bucket column $colName not found in existing columns " +
+        s"(${output.map(_.name).mkString(", ")})")
+    }
+
+    bucketSpec.map { spec =>
+      val numBuckets = spec.numBuckets
+      val bucketColumns = spec.bucketColumnNames.map(toAttribute)
+      HashPartitioning(bucketColumns, numBuckets)
+    }.getOrElse {
+      UnknownPartitioning(0)
+    }
+  }
+
   protected override def doExecute(): RDD[InternalRow] = {
-    val unsafeRow = if (isUnsafeRow) {
+    val unsafeRow = if (outputUnsafeRows) {
       rdd
     } else {
       rdd.mapPartitionsInternal { iter =>
@@ -187,7 +246,7 @@ private[sql] case class PhysicalRDD(
     ctx.INPUT_ROW = row
     ctx.currentVars = null
     val columns2 = exprs.map(_.gen(ctx))
-    val inputRow = if (isUnsafeRow) row else null
+    val inputRow = if (outputUnsafeRows) row else null
     val scanRows = ctx.freshName("processRows")
     ctx.addNewFunction(scanRows,
       s"""
@@ -221,42 +280,8 @@ private[sql] case class PhysicalRDD(
   }
 }
 
-private[sql] object PhysicalRDD {
+private[sql] object DataSourceScan {
   // Metadata keys
   val INPUT_PATHS = "InputPaths"
   val PUSHED_FILTERS = "PushedFilters"
-
-  def createFromDataSource(
-      output: Seq[Attribute],
-      rdd: RDD[InternalRow],
-      relation: BaseRelation,
-      metadata: Map[String, String] = Map.empty): PhysicalRDD = {
-
-    val outputUnsafeRows = relation match {
-      case r: HadoopFsRelation if r.fileFormat.isInstanceOf[ParquetSource] =>
-        !SQLContext.getActive().get.conf.getConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED)
-      case _: HadoopFsRelation => true
-      case _ => false
-    }
-
-    val bucketSpec = relation match {
-      // TODO: this should be closer to bucket planning.
-      case r: HadoopFsRelation if r.sqlContext.conf.bucketingEnabled() => r.bucketSpec
-      case _ => None
-    }
-
-    def toAttribute(colName: String): Attribute = output.find(_.name == colName).getOrElse {
-      throw new AnalysisException(s"bucket column $colName not found in existing columns " +
-        s"(${output.map(_.name).mkString(", ")})")
-    }
-
-    bucketSpec.map { spec =>
-      val numBuckets = spec.numBuckets
-      val bucketColumns = spec.bucketColumnNames.map(toAttribute)
-      val partitioning = HashPartitioning(bucketColumns, numBuckets)
-      PhysicalRDD(output, rdd, relation.toString, metadata, outputUnsafeRows, partitioning)
-    }.getOrElse {
-      PhysicalRDD(output, rdd, relation.toString, metadata, outputUnsafeRows)
-    }
-  }
 }

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

@@ -41,6 +41,7 @@ trait CodegenSupport extends SparkPlan {
     case _: BroadcastHashJoin => "bhj"
     case _: SortMergeJoin => "smj"
     case _: PhysicalRDD => "rdd"
+    case _: DataSourceScan => "scan"
     case _ => nodeName.toLowerCase
   }
 

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala

@@ -33,7 +33,7 @@ import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning
 import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.execution.PhysicalRDD.{INPUT_PATHS, PUSHED_FILTERS}
+import org.apache.spark.sql.execution.DataSourceScan.{INPUT_PATHS, PUSHED_FILTERS}
 import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.execution.command.ExecutedCommand
 import org.apache.spark.sql.execution.vectorized.{ColumnarBatch, ColumnVectorUtils}
@@ -239,7 +239,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       }
 
     case l @ LogicalRelation(baseRelation: TableScan, _, _) =>
-      execution.PhysicalRDD.createFromDataSource(
+      execution.DataSourceScan(
         l.output, toCatalystRDD(l, baseRelation.buildScan()), baseRelation) :: Nil
 
     case i @ logical.InsertIntoTable(l @ LogicalRelation(t: InsertableRelation, _, _),
@@ -639,7 +639,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         // Don't request columns that are only referenced by pushed filters.
         .filterNot(handledSet.contains)
 
-      val scan = execution.PhysicalRDD.createFromDataSource(
+      val scan = execution.DataSourceScan(
         projects.map(_.toAttribute),
         scanBuilder(requestedColumns, candidatePredicates, pushedFilters),
         relation.relation, metadata)
@@ -649,7 +649,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       val requestedColumns =
         (projectSet ++ filterSet -- handledSet).map(relation.attributeMap).toSeq
 
-      val scan = execution.PhysicalRDD.createFromDataSource(
+      val scan = execution.DataSourceScan(
         requestedColumns,
         scanBuilder(requestedColumns, candidatePredicates, pushedFilters),
         relation.relation, metadata)

sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SparkPlanGraph.scala

@@ -93,6 +93,10 @@ private[sql] object SparkPlanGraph {
       case "Subquery" if subgraph != null =>
         // Subquery should not be included in WholeStageCodegen
         buildSparkPlanGraphNode(planInfo, nodeIdGenerator, nodes, edges, parent, null, exchanges)
+      case "ReusedExchange" =>
+        // Point to the re-used exchange
+        val node = exchanges(planInfo.children.head)
+        edges += SparkPlanGraphEdge(node.id, parent.id)
       case name =>
         val metrics = planInfo.metrics.map { metric =>
           SQLPlanMetric(metric.name, metric.accumulatorId,
@@ -106,7 +110,7 @@ private[sql] object SparkPlanGraph {
         } else {
           subgraph.nodes += node
         }
-        if (name == "ShuffleExchange" || name == "BroadcastExchange") {
+        if (name.contains("Exchange")) {
           exchanges += planInfo -> node
         }
 

sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala

@@ -82,7 +82,24 @@ case class LessThanOrEqual(attribute: String, value: Any) extends Filter
  *
  * @since 1.3.0
  */
-case class In(attribute: String, values: Array[Any]) extends Filter
+case class In(attribute: String, values: Array[Any]) extends Filter {
+  override def hashCode(): Int = {
+    var h = attribute.hashCode
+    values.foreach { v =>
+      h *= 41
+      h += v.hashCode()
+    }
+    h
+  }
+  override def equals(o: Any): Boolean = o match {
+    case In(a, vs) =>
+      a == attribute && vs.length == values.length && vs.zip(values).forall(x => x._1 == x._2)
+    case _ => false
+  }
+  override def toString: String = {
+    s"In($attribute, [${values.mkString(",")}]"
+  }
+}
 
 /**
  * A filter that evaluates to `true` iff the attribute evaluates to null.

sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala

@@ -26,7 +26,7 @@ import org.scalatest.{BeforeAndAfter, PrivateMethodTester}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.{DataFrame, Row}
-import org.apache.spark.sql.execution.PhysicalRDD
+import org.apache.spark.sql.execution.DataSourceScan
 import org.apache.spark.sql.execution.command.ExplainCommand
 import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD
@@ -210,8 +210,8 @@ class JDBCSuite extends SparkFunSuite
       // the plan only has PhysicalRDD to scan JDBCRelation.
       assert(parentPlan.isInstanceOf[org.apache.spark.sql.execution.WholeStageCodegen])
       val node = parentPlan.asInstanceOf[org.apache.spark.sql.execution.WholeStageCodegen]
-      assert(node.child.isInstanceOf[org.apache.spark.sql.execution.PhysicalRDD])
-      assert(node.child.asInstanceOf[PhysicalRDD].nodeName.contains("JDBCRelation"))
+      assert(node.child.isInstanceOf[org.apache.spark.sql.execution.DataSourceScan])
+      assert(node.child.asInstanceOf[DataSourceScan].nodeName.contains("JDBCRelation"))
       df
     }
     assert(checkPushdown(sql("SELECT * FROM foobar WHERE THEID < 1")).collect().size == 0)

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

@@ -312,7 +312,7 @@ class FilteredScanSuite extends DataSourceTest with SharedSQLContext with Predic
       try {
         val queryExecution = sql(sqlString).queryExecution
         val rawPlan = queryExecution.executedPlan.collect {
-          case p: execution.PhysicalRDD => p
+          case p: execution.DataSourceScan => p
         } match {
           case Seq(p) => p
           case _ => fail(s"More than one PhysicalRDD found\n$queryExecution")

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

@@ -124,7 +124,7 @@ class PrunedScanSuite extends DataSourceTest with SharedSQLContext {
       try {
         val queryExecution = sql(sqlString).queryExecution
         val rawPlan = queryExecution.executedPlan.collect {
-          case p: execution.PhysicalRDD => p
+          case p: execution.DataSourceScan => p
         } match {
           case Seq(p) => p
           case _ => fail(s"More than one PhysicalRDD found\n$queryExecution")

sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala

@@ -20,7 +20,7 @@ package org.apache.spark.sql.hive
 import java.io.File
 
 import org.apache.spark.sql._
-import org.apache.spark.sql.execution.PhysicalRDD
+import org.apache.spark.sql.execution.DataSourceScan
 import org.apache.spark.sql.execution.command.ExecutedCommand
 import org.apache.spark.sql.execution.datasources.{InsertIntoDataSource, InsertIntoHadoopFsRelation, LogicalRelation}
 import org.apache.spark.sql.hive.execution.HiveTableScan
@@ -196,7 +196,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
       }.isEmpty)
     assert(
       sql("SELECT * FROM normal_parquet").queryExecution.sparkPlan.collect {
-        case _: PhysicalRDD => true
+        case _: DataSourceScan => true
       }.nonEmpty)
   }
 

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

@@ -22,7 +22,7 @@ import java.io.File
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning
-import org.apache.spark.sql.execution.PhysicalRDD
+import org.apache.spark.sql.execution.DataSourceScan
 import org.apache.spark.sql.execution.datasources.{BucketSpec, DataSourceStrategy}
 import org.apache.spark.sql.execution.exchange.ShuffleExchange
 import org.apache.spark.sql.execution.joins.SortMergeJoin
@@ -93,7 +93,7 @@ class BucketedReadSuite extends QueryTest with SQLTestUtils with TestHiveSinglet
 
       // Filter could hide the bug in bucket pruning. Thus, skipping all the filters
       val plan = bucketedDataFrame.filter(filterCondition).queryExecution.executedPlan
-      val rdd = plan.find(_.isInstanceOf[PhysicalRDD])
+      val rdd = plan.find(_.isInstanceOf[DataSourceScan])
       assert(rdd.isDefined, plan)
 
       val checkedResult = rdd.get.execute().mapPartitionsWithIndex { case (index, iter) =>