Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1562#discussion_r15439511
--- Diff: core/src/main/scala/org/apache/spark/Partitioner.scala ---
@@ -105,24 +108,91 @@ class RangePartitioner[K : Ordering : ClassTag, V](
private var ordering = implicitly[Ordering[K]]
+ @transient private[spark] var singlePass = true // for unit tests
+
// An array of upper bounds for the first (partitions - 1) partitions
private var rangeBounds: Array[K] = {
if (partitions == 1) {
- Array()
+ Array.empty
} else {
- val rddSize = rdd.count()
- val maxSampleSize = partitions * 20.0
- val frac = math.min(maxSampleSize / math.max(rddSize, 1), 1.0)
- val rddSample = rdd.sample(false, frac, 1).map(_._1).collect().sorted
- if (rddSample.length == 0) {
- Array()
+ // This is the sample size we need to have roughly balanced output
partitions.
+ val sampleSize = 20.0 * partitions
+ // Assume the input partitions are roughly balanced and over-sample
a little bit.
+ val sampleSizePerPartition = math.ceil(3.0 * sampleSize /
rdd.partitions.size).toInt
+ val shift = rdd.id
+ val classTagK = classTag[K]
+ val sketch = rdd.mapPartitionsWithIndex { (idx, iter) =>
+ val seed = byteswap32(idx + shift)
+ val (sample, n) = SamplingUtils.reservoirSampleAndCount(
+ iter.map(_._1), sampleSizePerPartition, seed)(classTagK)
+ Iterator((idx, n, sample))
+ }.collect()
+ var numItems = 0L
+ sketch.foreach { case (_, n, _) =>
+ numItems += n
+ }
+ if (numItems == 0L) {
+ Array.empty
} else {
- val bounds = new Array[K](partitions - 1)
- for (i <- 0 until partitions - 1) {
- val index = (rddSample.length - 1) * (i + 1) / partitions
- bounds(i) = rddSample(index)
+ // If a partition contains much more than the average number of
items, we re-sample from it
+ // to ensure that enough items are collected from that partition.
+ val fraction = math.min(sampleSize / math.max(numItems, 1L), 1.0)
+ val candidates = ArrayBuffer.empty[(K, Float)]
+ val imbalancedPartitions = ArrayBuffer.empty[Int]
+ sketch.foreach { case (idx, n, sample) =>
+ if (fraction * n > sampleSizePerPartition) {
+ imbalancedPartitions += idx
+ } else {
+ // The weight is 1 over the sampling probability.
+ val weight = (n.toDouble / sample.size).toFloat
+ sample.foreach { key =>
+ candidates += ((key, weight))
+ }
--- End diff --
I think `foreach` should be faster than `for` here.
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