Github user falaki commented on a diff in the pull request:
https://github.com/apache/spark/pull/1025#discussion_r14622781
--- Diff:
core/src/main/scala/org/apache/spark/util/random/StratifiedSampler.scala ---
@@ -0,0 +1,335 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.random
+
+import scala.collection.Map
+import scala.collection.mutable.{ArrayBuffer, HashMap, Map => MMap}
+import scala.reflect.ClassTag
+
+import org.apache.commons.math3.random.RandomDataGenerator
+import org.apache.spark.{Logging, SparkContext, TaskContext}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.Utils
+
+/**
+ * Auxiliary functions and data structures for the sampleByKey method in
PairRDDFunctions.
+ *
+ * For more theoretical background on the sampling technqiues used here,
please refer to
+ * http://jmlr.org/proceedings/papers/v28/meng13a.html
+ */
+private[spark] object StratifiedSampler extends Logging {
+
+ /**
+ * A version of {@link #aggregate()} that passes the TaskContext to the
function that does
+ * aggregation for each partition. This function avoids creating an
extra depth in the RDD
+ * lineage, as opposed to using mapPartitionsWithIndex, which results in
slightly improved
+ * run time.
+ */
+ def aggregateWithContext[U: ClassTag, T: ClassTag](zeroValue: U)
+ (rdd: RDD[T],
+ seqOp: ((TaskContext, U), T) => U,
+ combOp: (U, U) => U): U = {
+ val sc: SparkContext = rdd.sparkContext
+ // Clone the zero value since we will also be serializing it as part
of tasks
+ var jobResult = Utils.clone(zeroValue,
sc.env.closureSerializer.newInstance())
+ // pad seqOp and combOp with taskContext to conform to aggregate's
signature in TraversableOnce
+ val paddedSeqOp = (arg1: (TaskContext, U), item: T) => (arg1._1,
seqOp(arg1, item))
+ val paddedCombOp = (arg1: (TaskContext, U), arg2: (TaskContext, U)) =>
+ (arg1._1, combOp(arg1._2, arg1._2))
+ val cleanSeqOp = sc.clean(paddedSeqOp)
+ val cleanCombOp = sc.clean(paddedCombOp)
+ val aggregatePartition = (tc: TaskContext, it: Iterator[T]) =>
+ (it.aggregate(tc, zeroValue)(cleanSeqOp, cleanCombOp))._2
+ val mergeResult = (index: Int, taskResult: U) => jobResult =
combOp(jobResult, taskResult)
+ sc.runJob(rdd, aggregatePartition, mergeResult)
+ jobResult
+ }
+
+ /**
+ * Returns the function used by aggregate to collect sampling statistics
for each partition.
+ */
+ def getSeqOp[K, V](withReplacement: Boolean,
+ fractions: Map[K, Double],
+ counts: Option[Map[K, Long]]): ((TaskContext, Result[K]), (K, V)) =>
Result[K] = {
+ val delta = 5e-5
+ (output: (TaskContext, Result[K]), item: (K, V)) => {
+ val result = output._2
+ val tc = output._1
+ val rng = result.getRand(tc.partitionId)
+ val fraction = fractions(item._1)
+ val stratum = result.getEntry(item._1)
+ if (withReplacement) {
+ // compute acceptBound and waitListBound 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.areBoundsEmpty) {
+ val n = counts.get(item._1)
+ val sampleSize = math.ceil(n * fraction).toLong
+ val lmbd1 = PoissonBounds.getLowerBound(sampleSize)
+ val minCount = PoissonBounds.getMinCount(lmbd1)
+ val lmbd2 = if (lmbd1 == 0) {
+ PoissonBounds.getUpperBound(sampleSize)
+ } else {
+ PoissonBounds.getUpperBound(sampleSize - minCount)
+ }
+ stratum.acceptBound = lmbd1 / n
+ stratum.waitListBound = lmbd2 / n
+ }
+ val x1 = if (stratum.acceptBound == 0.0) 0L else
rng.nextPoisson(stratum.acceptBound)
+ if (x1 > 0) {
+ stratum.incrNumAccepted(x1)
+ }
+ val x2 = rng.nextPoisson(stratum.waitListBound).toInt
+ if (x2 > 0) {
+ stratum.addToWaitList(ArrayBuffer.fill(x2)(rng.nextUniform(0.0,
1.0)))
+ }
+ } else {
+ // We use the streaming version of the algorithm for sampling
without replacement to avoid
+ // using an extra pass over the RDD for computing the count.
+ // Hence, acceptBound and waitListBound change on every iteration.
+ val g1 = - math.log(delta) / stratum.numItems // gamma1
+ val g2 = (2.0 / 3.0) * g1 // gamma 2
+ stratum.acceptBound = math.max(0, fraction + g2 - math.sqrt((g2 *
g2 + 3 * g2 * fraction)))
+ stratum.waitListBound = math.min(1, fraction + g1 + math.sqrt(g1 *
g1 + 2 * g1 * fraction))
+
+ val x = rng.nextUniform(0.0, 1.0)
+ if (x < stratum.acceptBound) {
+ stratum.incrNumAccepted()
+ } else if (x < stratum.waitListBound) {
+ stratum.addToWaitList(x)
+ }
+ }
+ stratum.incrNumItems()
+ result
+ }
+ }
+
+ /**
+ * Returns the function used by aggregate to combine results from
different partitions, as
+ * returned by seqOp.
+ */
+ def getCombOp[K](): (Result[K], Result[K]) => Result[K] = {
+ (r1: Result[K], r2: Result[K]) => {
+ // take union of both key sets in case one partition doesn't contain
all keys
+ val keyUnion = r1.resultMap.keySet.union(r2.resultMap.keySet)
+
+ // Use r2 to keep the combined result since r1 is usual empty
+ for (key <- keyUnion) {
+ val entry1 = r1.resultMap.get(key)
+ if (r2.resultMap.contains(key)) {
+ r2.resultMap(key).merge(entry1)
+ } else {
+ r2.addEntry(key, entry1)
+ }
+ }
+ r2
+ }
+ }
+
+ /**
+ * Given the result returned by the aggregate function, determine the
threshold for accepting
+ * items to generate exact sample size.
+ *
+ * To do so, we compute sampleSize = math.ceil(size * samplingRate) for
each stratum and compare
+ * it to the number of items that were accepted instantly and the number
of items in the waitlist
+ * for that stratum. Most of the time, numAccepted <= sampleSize <=
(numAccepted + numWaitlisted),
+ * which means we need to sort the elements in the waitlist by their
associated values in order
+ * to find the value T s.t. |{elements in the stratum whose associated
values <= T}| = sampleSize.
+ * Note that all elements in the waitlist have values >= bound for
instant accept, so a T value
+ * in the waitlist range would allow all elements that were instantly
accepted on the first pass
+ * to be included in the sample.
+ */
+ def computeThresholdByKey[K](finalResult: Map[K, Stratum],
+ fractions: Map[K, Double]):
--- End diff --
Style nit: Return type of the function is in a new line. We can join these
two lines.
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