[SPARK-12031][Core][BUG]: Integer overflow when do sampling

core/src/main/scala/org/apache/spark/Partitioner.scala

@@ -253,7 +253,7 @@ private[spark] object RangePartitioner {
    */
   def sketch[K : ClassTag](
       rdd: RDD[K],
-      sampleSizePerPartition: Int): (Long, Array[(Int, Int, Array[K])]) = {
+      sampleSizePerPartition: Int): (Long, Array[(Int, Long, Array[K])]) = {
     val shift = rdd.id
     // val classTagK = classTag[K] // to avoid serializing the entire partitioner object
     val sketched = rdd.mapPartitionsWithIndex { (idx, iter) =>
@@ -262,7 +262,7 @@ private[spark] object RangePartitioner {
         iter, sampleSizePerPartition, seed)
       Iterator((idx, n, sample))
     }.collect()
-    val numItems = sketched.map(_._2.toLong).sum
+    val numItems = sketched.map(_._2).sum
     (numItems, sketched)
   }
 

core/src/main/scala/org/apache/spark/util/random/SamplingUtils.scala

@@ -34,7 +34,7 @@ private[spark] object SamplingUtils {
       input: Iterator[T],
       k: Int,
       seed: Long = Random.nextLong())
-    : (Array[T], Int) = {
+    : (Array[T], Long) = {
     val reservoir = new Array[T](k)
     // Put the first k elements in the reservoir.
     var i = 0
@@ -52,16 +52,17 @@ private[spark] object SamplingUtils {
       (trimReservoir, i)
     } else {
       // If input size > k, continue the sampling process.
+      var l = i.toLong
       val rand = new XORShiftRandom(seed)
       while (input.hasNext) {
         val item = input.next()
-        val replacementIndex = rand.nextInt(i)
+        val replacementIndex = (rand.nextDouble() * l).toLong
         if (replacementIndex < k) {
-          reservoir(replacementIndex) = item
+          reservoir(replacementIndex.toInt) = item
         }
-        i += 1
+        l += 1
       }
-      (reservoir, i)
+      (reservoir, l)
     }
   }