hvanhovell commented on a change in pull request #25264:
[SPARK-28213][SQL][followup] code cleanup and bug fix for columnar execution
framework
URL: https://github.com/apache/spark/pull/25264#discussion_r309861632
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala
##########
@@ -334,37 +334,63 @@ case class FileSourceScanExec(
inputRDD :: Nil
}
- override lazy val metrics =
- Map("numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of
output rows"),
- "numFiles" -> SQLMetrics.createMetric(sparkContext, "number of files
read"),
- "metadataTime" -> SQLMetrics.createTimingMetric(sparkContext, "metadata
time"),
- "scanTime" -> SQLMetrics.createTimingMetric(sparkContext, "scan time"))
+ override lazy val metrics = Map(
+ "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output
rows"),
+ "numFiles" -> SQLMetrics.createMetric(sparkContext, "number of files
read"),
+ "metadataTime" -> SQLMetrics.createTimingMetric(sparkContext, "metadata
time"),
+ "scanTime" -> SQLMetrics.createTimingMetric(sparkContext, "scan time"))
+
+ private abstract class ScanTimeTrackingIterator[T](
+ fileScanIterator: Iterator[T],
+ scanTimeMetrics: SQLMetric) extends Iterator[T] {
+
+ override def hasNext: Boolean = {
+ // The `FileScanRDD` returns an iterator which scans the file during the
`hasNext` call.
Review comment:
Calling `System.nanoTime()` per tuple isn't exactly cheap. This probably
regresses cases where the scan produces rows instead of batches. I am actually
not sure if there is an (easy) way around this.
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