marin-ma commented on code in PR #53040:
URL: https://github.com/apache/spark/pull/53040#discussion_r2555858014
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FilePartition.scala:
##########
@@ -87,28 +87,116 @@ object FilePartition extends SessionStateHelper with
Logging {
partitions.toSeq
}
+ private def getFilePartitionsByFileNum(
+ partitionedFiles: Seq[PartitionedFile],
+ outputPartitions: Int,
+ smallFileThreshold: Double): Seq[FilePartition] = {
+ // Flatten and sort descending by file size.
+ val filesSorted: Seq[(PartitionedFile, Long)] =
+ partitionedFiles
+ .map(f => (f, f.length))
+ .sortBy(_._2)(Ordering.Long.reverse)
+
+ val partitions =
Seq.fill(outputPartitions)(mutable.ArrayBuffer.empty[PartitionedFile])
+
+ def addToBucket(
+ heap: mutable.PriorityQueue[(Long, Int, Int)],
+ file: PartitionedFile,
+ sz: Long): Unit = {
+ val (load, numFiles, idx) = heap.dequeue()
+ partitions(idx) += file
+ heap.enqueue((load + sz, numFiles + 1, idx))
+ }
+
+ // First by load, then by numFiles.
+ val heapByFileSize =
+ mutable.PriorityQueue.empty[(Long, Int, Int)](
+ Ordering
+ .by[(Long, Int, Int), (Long, Int)] {
+ case (load, numFiles, _) =>
+ (load, numFiles)
+ }
+ .reverse
+ )
+
+ if (smallFileThreshold > 0) {
+ val smallFileTotalSize = filesSorted.map(_._2).sum * smallFileThreshold
+ // First by numFiles, then by load.
+ val heapByFileNum =
+ mutable.PriorityQueue.empty[(Long, Int, Int)](
+ Ordering
+ .by[(Long, Int, Int), (Int, Long)] {
+ case (load, numFiles, _) =>
+ (numFiles, load)
+ }
+ .reverse
+ )
+
+ (0 until outputPartitions).foreach(i => heapByFileNum.enqueue((0L, 0,
i)))
+
+ var numSmallFiles = 0
+ var smallFileSize = 0L
+ // Enqueue small files to the least number of files and the least load.
+ filesSorted.reverse.takeWhile(f => f._2 + smallFileSize <=
smallFileTotalSize).foreach {
+ case (file, sz) =>
+ addToBucket(heapByFileNum, file, sz)
+ numSmallFiles += 1
+ smallFileSize += sz
+ }
+
+ // Move buckets from heapByFileNum to heapByFileSize.
+ while (heapByFileNum.nonEmpty) {
+ heapByFileSize.enqueue(heapByFileNum.dequeue())
+ }
+
+ // Finally, enqueue remaining files.
+ filesSorted.take(filesSorted.size - numSmallFiles).foreach {
+ case (file, sz) =>
+ addToBucket(heapByFileSize, file, sz)
+ }
+ } else {
+ (0 until outputPartitions).foreach(i => heapByFileSize.enqueue((0L, 0,
i)))
+
+ filesSorted.foreach {
+ case (file, sz) =>
+ addToBucket(heapByFileSize, file, sz)
+ }
+ }
+
+ partitions.zipWithIndex.map {
+ case (p, idx) => FilePartition(idx, p.toArray)
+ }
+ }
+
def getFilePartitions(
sparkSession: SparkSession,
partitionedFiles: Seq[PartitionedFile],
maxSplitBytes: Long): Seq[FilePartition] = {
val conf = getSqlConf(sparkSession)
val openCostBytes = conf.filesOpenCostInBytes
val maxPartNum = conf.filesMaxPartitionNum
- val partitions = getFilePartitions(partitionedFiles, maxSplitBytes,
openCostBytes)
- if (maxPartNum.exists(partitions.size > _)) {
- val totalSizeInBytes =
- partitionedFiles.map(_.length +
openCostBytes).map(BigDecimal(_)).sum[BigDecimal]
- val desiredSplitBytes =
- (totalSizeInBytes / BigDecimal(maxPartNum.get)).setScale(0,
RoundingMode.UP).longValue
- val desiredPartitions = getFilePartitions(partitionedFiles,
desiredSplitBytes, openCostBytes)
- logWarning(log"The number of partitions is ${MDC(NUM_PARTITIONS,
partitions.size)}, " +
- log"which exceeds the maximum number configured: " +
- log"${MDC(MAX_NUM_PARTITIONS, maxPartNum.get)}. Spark rescales it to "
+
- log"${MDC(DESIRED_NUM_PARTITIONS, desiredPartitions.size)} by ignoring
the " +
- log"configuration of ${MDC(CONFIG,
SQLConf.FILES_MAX_PARTITION_BYTES.key)}.")
- desiredPartitions
- } else {
- partitions
+ val partitions = getFilePartitionsBySize(partitionedFiles, maxSplitBytes,
openCostBytes)
Review Comment:
In very extreme cases it might happen. If it does, the user should not
enable this algorithm. This algorithm is particularly beneficial in the common
scenario we observed in the user's production environment, where the input
contains many small files that are much smaller than most of the input splits.
In that case, there won’t be a wide mismatch.
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