Github user aarondav commented on a diff in the pull request:
https://github.com/apache/spark/pull/2839#discussion_r19046865
--- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala ---
@@ -911,32 +911,15 @@ abstract class RDD[T: ClassTag](
}
/**
- * Return the count of each unique value in this RDD as a map of (value,
count) pairs. The final
- * combine step happens locally on the master, equivalent to running a
single reduce task.
+ * Return the count of each unique value in this RDD as a local map of
(value, count) pairs.
+ *
+ * Note that this method should only be used if the resulting map is
expected to be small, as
+ * the whole thing is loaded into the driver's memory.
+ * To handle very large results, consider using rdd.map(x => (x,
1)).reduceByKey(_ + _), which
+ * returns an RDD[T, Long] instead of a map.
*/
def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = {
- if (elementClassTag.runtimeClass.isArray) {
- throw new SparkException("countByValue() does not support arrays")
--- End diff --
Note that we still don't support arrays, but this is caught by combineByKey:
`org.apache.spark.SparkException: Cannot use map-side combining with array
keys.`
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