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[jvm-packages] Do not repartition when nWorker = 1 #7676

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Feb 19, 2022
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[jvm-packages] Do not repartition when nWorker = 1 #7676

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17 changes: 14 additions & 3 deletions ...boost4j-spark-gpu/src/main/scala/ml/dmlc/xgboost4j/scala/rapids/spark/GpuPreXGBoost.scala
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
/*
Copyright (c) 2021 by Contributors
Copyright (c) 2021-2022 by Contributors

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
Expand Down Expand Up @@ -397,11 +397,22 @@ object GpuPreXGBoost extends PreXGBoostProvider {
// No light cost way to get number of partitions from DataFrame, so always repartition
val newDF = colData.groupColName
.map(gn => repartitionForGroup(gn, colData.rawDF, nWorkers))
.getOrElse(colData.rawDF.repartition(nWorkers))
.getOrElse(repartitionInputData(colData.rawDF, nWorkers))
name -> ColumnDataBatch(newDF, colData.colIndices, colData.groupColName)
}
}

private def repartitionInputData(dataFrame: DataFrame, nWorkers: Int): DataFrame = {
// We can't check dataFrame.rdd.getNumPartitions == nWorkers here, since dataFrame.rdd is
// a lazy variable. If we call it here, we will not directly extract RDD[Table] again,
// instead, we will involve Columnar -> Row -> Columnar and decrease the performance
if (nWorkers == 1) {
dataFrame.coalesce(1)
} else {
dataFrame.repartition(nWorkers)
}
}

private def repartitionForGroup(
groupName: String,
dataFrame: DataFrame,
Expand All @@ -415,7 +426,7 @@ object GpuPreXGBoost extends PreXGBoostProvider {

implicit val encoder = RowEncoder(schema)
// Expand the grouped rows after repartition
groupedDF.repartition(nWorkers).mapPartitions(iter => {
repartitionInputData(groupedDF, nWorkers).mapPartitions(iter => {
new Iterator[Row] {
var iterInRow: Iterator[Any] = Iterator.empty

Expand Down