Github user falaki commented on a diff in the pull request:
https://github.com/apache/spark/pull/1025#discussion_r13630894
--- Diff: core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
---
@@ -156,6 +161,182 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
}
/**
+ * Return a subset of this RDD sampled by key (via stratified sampling).
+ * We guarantee a sample size = math.ceil(fraction * S_i), where S_i is
the size of the ith
+ * stratum.
+ *
+ * @param withReplacement whether to sample with or without replacement
+ * @param fraction sampling rate
+ * @param seed seed for the random number generator
+ * @return RDD containing the sampled subset
+ */
+ def sampleByKey(withReplacement: Boolean,
+ fraction: Double,
+ seed: Long = Utils.random.nextLong): RDD[(K, V)]= {
+
+ class Stratum(var numItems: Long = 0L, var numAccepted: Long = 0L)
extends Serializable {
+ var waitList: ArrayBuffer[Double] = new ArrayBuffer[Double]
+ var q1: Option[Double] = None
+ var q2: Option[Double] = None
+
+ def incrNumItems(by: Long = 1L) = numItems += by
+
+ def incrNumAccepted(by: Long = 1L) = numAccepted += by
+
+ def addToWaitList(elem: Double) = waitList += elem
+
+ def addToWaitList(elems: ArrayBuffer[Double]) = waitList ++= elems
+
+ override def toString() = {
+ "numItems: " + numItems + " numAccepted: " + numAccepted + " q1: "
+ q1 + " q2: " + q2 +
+ " waitListSize:" + waitList.size
+ }
+ }
+
+ class Result(var resultMap: Map[K, Stratum], var cachedPartitionId:
Option[Int] = None)
+ extends Serializable {
+ var rand: RandomDataGenerator = new RandomDataGenerator
+
+ def getEntry(key: K, numItems: Long = 0L): Stratum = {
+ if (resultMap.get(key).isEmpty) {
+ resultMap += (key -> new Stratum(numItems))
+ }
+ resultMap.get(key).get
+ }
+
+ def getRand(partitionId: Int): RandomDataGenerator = {
+ if (cachedPartitionId.isEmpty || cachedPartitionId.get !=
partitionId) {
+ cachedPartitionId = Some(partitionId)
+ rand.reSeed(seed + partitionId)
+ }
+ rand
+ }
+ }
+
+ // TODO implement the streaming version of sampling w/ replacement
that doesn't require counts
+ // in order to save one pass over the RDD
+ val counts = if (withReplacement) Some(this.countByKey()) else None
+
+ val seqOp = (U: (TaskContext, Result), item: (K, V)) => {
+ val delta = 5e-5
+ val result = U._2
+ val tc = U._1
+ val rng = result.getRand(tc.partitionId)
+ val stratum = result.getEntry(item._1)
+ if (withReplacement) {
+ // compute q1 and q2 only if they haven't been computed already
+ // since they don't change from iteration to iteration.
+ // TODO change this to the streaming version
+ if (stratum.q1.isEmpty || stratum.q2.isEmpty) {
+ val n = counts.get(item._1)
+ val s = math.ceil(n * fraction).toLong
+ val lmbd1 = PB.getLambda1(s)
+ val minCount = PB.getMinCount(lmbd1)
+ val lmbd2 = if (lmbd1 == 0) PB.getLambda2(s) else
PB.getLambda2(s - minCount)
+ val q1 = lmbd1 / n
+ val q2 = lmbd2 / n
+ stratum.q1 = Some(q1)
+ stratum.q2 = Some(q2)
+ }
+ val x1 = if (stratum.q1.get == 0) 0L else
rng.nextPoisson(stratum.q1.get)
+ if (x1 > 0) {
+ stratum.incrNumAccepted(x1)
+ }
+ val x2 = rng.nextPoisson(stratum.q2.get).toInt
+ if (x2 > 0) {
+ stratum.addToWaitList(ArrayBuffer.fill(x2)(rng.nextUniform(0.0,
1.0)))
+ }
+ } else {
+ val g1 = - math.log(delta) / stratum.numItems
+ val g2 = (2.0 / 3.0) * g1
+ val q1 = math.max(0, fraction + g2 - math.sqrt((g2 * g2 + 3 * g2 *
fraction)))
+ val q2 = math.min(1, fraction + g1 + math.sqrt(g1 * g1 + 2 * g1 *
fraction))
+
+ val x = rng.nextUniform(0.0, 1.0)
+ if (x < q1) {
+ stratum.incrNumAccepted()
+ } else if ( x < q2) {
+ stratum.addToWaitList(x)
+ }
+ stratum.q1 = Some(q1)
+ stratum.q2 = Some(q2)
+ }
+ stratum.incrNumItems()
+ result
+ }
+
+ val combOp = (r1: Result, r2: Result) => {
+ // take union of both key sets in case one partion doesn't contain
all keys
+ val keyUnion = r1.resultMap.keys.toSet.union(r2.resultMap.keys.toSet)
+
+ // Use r2 to keep the combined result since r1 is usual empty
+ for (key <- keyUnion) {
+ val entry1 = r1.resultMap.get(key)
+ val entry2 = r2.resultMap.get(key)
+ if (entry2.isEmpty && entry1.isDefined) {
+ r2.resultMap += (key -> entry1.get)
+ } else if (entry1.isDefined && entry2.isDefined) {
+ entry2.get.addToWaitList(entry1.get.waitList)
+ entry2.get.incrNumAccepted(entry1.get.numAccepted)
+ entry2.get.incrNumItems(entry1.get.numItems)
+ }
+ }
+ r2
+ }
+
+ val zeroU = new Result(Map[K, Stratum]())
+
+ // determine threshold for each stratum and resample
+ val finalResult = self.aggregateWithContext(zeroU)(seqOp,
combOp).resultMap
+ val thresholdByKey = new mutable.HashMap[K, Double]()
+ for ((key, stratum) <- finalResult) {
+ val s = math.ceil(stratum.numItems * fraction).toLong
+ breakable {
+ if (stratum.numAccepted > s) {
+ logWarning("Pre-accepted too many")
+ thresholdByKey += (key -> stratum.q1.get)
+ break
+ }
+ val numWaitListAccepted = (s - stratum.numAccepted).toInt
+ if (numWaitListAccepted >= stratum.waitList.size) {
+ logWarning("WaitList too short")
+ thresholdByKey += (key -> stratum.q2.get)
+ } else {
+ thresholdByKey += (key ->
stratum.waitList.sorted.apply(numWaitListAccepted))
+ }
+ }
+ }
+
+ if (withReplacement) {
+ // Poisson sampler
+ self.mapPartitionsWithIndex((idx: Int, iter: Iterator[(K, V)]) => {
+ val random = new RandomDataGenerator()
+ random.reSeed(seed + idx)
+ iter.flatMap { t =>
+ val q1 = finalResult.get(t._1).get.q1.get
+ val q2 = finalResult.get(t._1).get.q2.get
+ val x1 = if (q1 == 0) 0L else random.nextPoisson(q1)
+ val x2 = random.nextPoisson(q2).toInt
+ val x = x1 + (0 until x2).filter(i => random.nextUniform(0.0,
1.0) <
+ thresholdByKey.get(t._1).get).size
--- End diff --
You can make it simpler by using count:
```scala
(0 until x2).count(i => random.nextUniform(0.0, 1.0) <
thresholdByKey.get(t._1).get)
```
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