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Use foldLeft instead of map+sum #1091

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Merged
richox merged 1 commit into from
Aug 4, 2025
Merged

Use foldLeft instead of map+sum #1091

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7 changes: 3 additions & 4 deletions spark-extension/src/main/scala/org/apache/spark/sql/blaze/SparkUDAFWrapperContext.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -444,13 +444,12 @@ case class TypedImperativeAggRowsColumn[B](
val estimRowSize =
if (rows.length >= N) {
val totalSize = (0 until N)
.map(i => {
serializedRow(i) match {
.foldLeft(0)((total, i) => {
total + (serializedRow(i) match {
case row if row != null => row.length
case null => 0
}
})
})
.sum
val estimRowSize = totalSize / N * 4 + 16
evaluator.estimatedRowSize = Some(estimRowSize)
estimRowSize
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2 changes: 1 addition & 1 deletion ...n/src/main/scala/org/apache/spark/sql/execution/blaze/plan/NativeFileSourceScanBase.scala
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Expand Up @@ -69,7 +69,7 @@ abstract class NativeFileSourceScanBase(basedFileScan: FileSourceScanExec)
private val fileSizes = inputFileScanRDD.filePartitions
.flatMap(_.files)
.groupBy(_.filePath)
.mapValues(_.map(_.length).sum)
.mapValues(_.foldLeft(0L)(_ + _.length))
.map(identity) // make this map serializable

protected def nativePruningPredicateFilters: Seq[pb.PhysicalExprNode] =
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2 changes: 1 addition & 1 deletion ...c/main/scala/org/apache/spark/sql/hive/execution/blaze/plan/NativeHiveTableScanBase.scala
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Expand Up @@ -63,7 +63,7 @@ abstract class NativeHiveTableScanBase(basedHiveScan: HiveTableScanExec)
private lazy val fileSizes = partitions
.flatMap(_.files)
.groupBy(_.filePath)
.mapValues(_.map(_.length).sum)
.mapValues(_.foldLeft(0L)(_ + _.length))
.map(identity) // make this map serializable

// should not include partition columns
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2 changes: 1 addition & 1 deletion tpcds/datagen/src/main/scala/org/apache/spark/sql/execution/benchmark/TPCDSDatagen.scala
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Expand Up @@ -179,7 +179,7 @@ class Tables(sqlContext: SQLContext, scaleFactor: Int) extends Serializable {
val cached = data.cache()
val plainDataSize = cached
.mapPartitions { iter =>
Iterator.single(iter.map(_.mkString("\t").length).sum.toLong)
Iterator.single(iter.foldLeft(0L)(_ + _.mkString("\t").length.toLong))
}
.collect()
.sum
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