Github user falaki commented on a diff in the pull request:
https://github.com/apache/spark/pull/1025#discussion_r14542021
--- Diff: core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
---
@@ -195,6 +193,40 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
}
/**
+ * Return a subset of this RDD sampled by key (via stratified sampling).
+ *
+ * If exact set to true, we guarantee, with high probability, a sample
size =
+ * math.ceil(fraction * S_i), where S_i is the size of the ith stratum
(collection of entries
+ * that share the same key). When sampling without replacement, we need
one additional pass over
+ * the RDD to guarantee sample size with a 99.99% confidence; when
sampling with replacement, we
+ * need two additional passes.
+ *
+ * @param withReplacement whether to sample with or without replacement
+ * @param fractions map of specific keys to sampling rates
+ * @param seed seed for the random number generator
+ * @param exact whether sample size needs to be exactly
math.ceil(fraction * size) per stratum
+ * @return RDD containing the sampled subset
+ */
+ def sampleByKey(withReplacement: Boolean,
+ fractions: Map[K, Double],
+ exact: Boolean = true,
+ seed: Long = Utils.random.nextLong): RDD[(K, V)]= {
+
+ require(fractions.forall({case(k, v) => v >= 0.0}), "Invalid sampling
rates.")
+
+ if (withReplacement) {
--- End diff --
This can be written more functional (and readable) like this:
```scala
val samplingFunc = if (withReplacement) {
val counts = if (exact) Some(this.countByKey()) else None
StratifiedSampler.getPoissonSamplingFunction(self, fractions,
exact, counts, seed)
} else {
StratifiedSampler.getBernoulliSamplingFunction(self, fractions,
exact, seed)
}
self.mapPartitionsWithIndex(samplingFunc, preservesPartitioning = true)
```
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