[SPARK-14210] [SQL] Add a metric for time spent in scans.

## What changes were proposed in this pull request?

This adds a metric to parquet scans that measures the time in just the scan phase. This is
only possible when the scan returns ColumnarBatches, otherwise the overhead is too high.

This combined with the pipeline metric lets us easily see what percent of the time was
in the scan.

Author: Nong Li <nong@databricks.com>

Closes #12007 from nongli/spark-14210.

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

@@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Statistics}
-import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, Partitioning, UnknownPartitioning}
+import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, UnknownPartitioning}
 import org.apache.spark.sql.catalyst.util.toCommentSafeString
 import org.apache.spark.sql.execution.datasources.parquet.{DefaultSource => ParquetSource}
 import org.apache.spark.sql.execution.metric.SQLMetrics
@@ -139,8 +139,12 @@ private[sql] case class DataSourceScan(
     case _ => false
   }
 
-  private[sql] override lazy val metrics = Map(
-    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+  private[sql] override lazy val metrics = if (canProcessBatches()) {
+    Map("numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"),
+      "scanTime" -> SQLMetrics.createTimingMetric(sparkContext, "scan time"))
+  } else {
+    Map("numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+  }
 
   val outputUnsafeRows = relation match {
     case r: HadoopFsRelation if r.fileFormat.isInstanceOf[ParquetSource] =>
@@ -170,6 +174,17 @@ private[sql] case class DataSourceScan(
     }
   }
 
+  private def canProcessBatches(): Boolean = {
+    relation match {
+      case r: HadoopFsRelation if r.fileFormat.isInstanceOf[ParquetSource] &&
+        SQLContext.getActive().get.conf.getConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED) &&
+        SQLContext.getActive().get.conf.getConf(SQLConf.WHOLESTAGE_CODEGEN_ENABLED) =>
+        true
+      case _ =>
+        false
+    }
+  }
+
   protected override def doExecute(): RDD[InternalRow] = {
     val unsafeRow = if (outputUnsafeRows) {
       rdd
@@ -241,73 +256,89 @@ private[sql] case class DataSourceScan(
     // TODO: The abstractions between this class and SqlNewHadoopRDD makes it difficult to know
     // here which path to use. Fix this.
 
-    ctx.currentVars = null
-    val columns1 = (output zip colVars).map { case (attr, colVar) =>
-      genCodeColumnVector(ctx, colVar, rowidx, attr.dataType, attr.nullable) }
-    val scanBatches = ctx.freshName("processBatches")
-    ctx.addNewFunction(scanBatches,
-      s"""
-      | private void $scanBatches() throws java.io.IOException {
-      |  while (true) {
-      |     int numRows = $batch.numRows();
-      |     if ($idx == 0) {
-      |       ${columnAssigns.mkString("", "\n", "\n")}
-      |       $numOutputRows.add(numRows);
-      |     }
-      |
-      |     // this loop is very perf sensitive and changes to it should be measured carefully
-      |     while ($idx < numRows) {
-      |       int $rowidx = $idx++;
-      |       ${consume(ctx, columns1).trim}
-      |       if (shouldStop()) return;
-      |     }
-      |
-      |     if (!$input.hasNext()) {
-      |       $batch = null;
-      |       break;
-      |     }
-      |     $batch = ($columnarBatchClz)$input.next();
-      |     $idx = 0;
-      |   }
-      | }""".stripMargin)
-
     val exprRows =
-      output.zipWithIndex.map(x => new BoundReference(x._2, x._1.dataType, x._1.nullable))
+        output.zipWithIndex.map(x => new BoundReference(x._2, x._1.dataType, x._1.nullable))
     ctx.INPUT_ROW = row
     ctx.currentVars = null
-    val columns2 = exprRows.map(_.gen(ctx))
+    val columnsRowInput = exprRows.map(_.gen(ctx))
     val inputRow = if (outputUnsafeRows) row else null
     val scanRows = ctx.freshName("processRows")
     ctx.addNewFunction(scanRows,
       s"""
-       | private void $scanRows(InternalRow $row) throws java.io.IOException {
-       |   boolean firstRow = true;
-       |   while (firstRow || $input.hasNext()) {
-       |     if (firstRow) {
-       |       firstRow = false;
-       |     } else {
-       |       $row = (InternalRow) $input.next();
-       |     }
-       |     $numOutputRows.add(1);
-       |     ${consume(ctx, columns2, inputRow).trim}
-       |     if (shouldStop()) return;
-       |   }
-       | }""".stripMargin)
-
-    val value = ctx.freshName("value")
-    s"""
-       | if ($batch != null) {
-       |   $scanBatches();
-       | } else if ($input.hasNext()) {
-       |   Object $value = $input.next();
-       |   if ($value instanceof $columnarBatchClz) {
-       |     $batch = ($columnarBatchClz)$value;
-       |     $scanBatches();
-       |   } else {
-       |     $scanRows((InternalRow) $value);
-       |   }
-       | }
-     """.stripMargin
+         | private void $scanRows(InternalRow $row) throws java.io.IOException {
+         |   boolean firstRow = true;
+         |   while (!shouldStop() && (firstRow || $input.hasNext())) {
+         |     if (firstRow) {
+         |       firstRow = false;
+         |     } else {
+         |       $row = (InternalRow) $input.next();
+         |     }
+         |     $numOutputRows.add(1);
+         |     ${consume(ctx, columnsRowInput, inputRow).trim}
+         |   }
+         | }""".stripMargin)
+
+    // Timers for how long we spent inside the scan. We can only maintain this when using batches,
+    // otherwise the overhead is too high.
+    if (canProcessBatches()) {
+      val scanTimeMetric = metricTerm(ctx, "scanTime")
+      val getBatchStart = ctx.freshName("scanStart")
+      val scanTimeTotalNs = ctx.freshName("scanTime")
+      ctx.currentVars = null
+      val columnsBatchInput = (output zip colVars).map { case (attr, colVar) =>
+        genCodeColumnVector(ctx, colVar, rowidx, attr.dataType, attr.nullable) }
+      val scanBatches = ctx.freshName("processBatches")
+      ctx.addMutableState("long", scanTimeTotalNs, s"$scanTimeTotalNs = 0;")
+
+      ctx.addNewFunction(scanBatches,
+        s"""
+        | private void $scanBatches() throws java.io.IOException {
+        |  while (true) {
+        |     int numRows = $batch.numRows();
+        |     if ($idx == 0) {
+        |       ${columnAssigns.mkString("", "\n", "\n")}
+        |       $numOutputRows.add(numRows);
+        |     }
+        |
+        |     while (!shouldStop() && $idx < numRows) {
+        |       int $rowidx = $idx++;
+        |       ${consume(ctx, columnsBatchInput).trim}
+        |     }
+        |     if (shouldStop()) return;
+        |
+        |     long $getBatchStart = System.nanoTime();
+        |     if (!$input.hasNext()) {
+        |       $batch = null;
+        |       $scanTimeMetric.add($scanTimeTotalNs / (1000 * 1000));
+        |       break;
+        |     }
+        |     $batch = ($columnarBatchClz)$input.next();
+        |     $scanTimeTotalNs += System.nanoTime() - $getBatchStart;
+        |     $idx = 0;
+        |   }
+        | }""".stripMargin)
+
+      val value = ctx.freshName("value")
+      s"""
+         | if ($batch != null) {
+         |   $scanBatches();
+         | } else if ($input.hasNext()) {
+         |   Object $value = $input.next();
+         |   if ($value instanceof $columnarBatchClz) {
+         |     $batch = ($columnarBatchClz)$value;
+         |     $scanBatches();
+         |   } else {
+         |     $scanRows((InternalRow) $value);
+         |   }
+         | }
+       """.stripMargin
+    } else {
+      s"""
+         |if ($input.hasNext()) {
+         |  $scanRows((InternalRow) $input.next());
+         |}
+       """.stripMargin
+    }
   }
 }