Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/7461#discussion_r38497342
--- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala ---
@@ -1080,6 +1080,21 @@ abstract class RDD[T: ClassTag](
var partiallyAggregated = mapPartitions(it =>
Iterator(aggregatePartition(it)))
var numPartitions = partiallyAggregated.partitions.length
val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 /
depth)).toInt, 2)
+
+ // Do one level of aggregation based on executorId before starting
the tree
+ // NOTE: exclude the driver from list of executors
+ val numExecutors = math.max(context.getExecutorStorageStatus.length
- 1, 1)
+ partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex {
case (idx, iter) =>
+ def isAllDigits(x: String) = x forall Character.isDigit
+ val execId = SparkEnv.get.executorId
+ if (isAllDigits(execId)) {
+ iter.map((execId.toInt, _))
+ } else {
+ iter.map((execId.hashCode, _))
--- End diff --
Ah I can actually reproduce it -- so the problem is that the keyspace get
un-balanced when we use hashCode on Strings. For example if you start with 100
exec ids and want to get 50 partitions out, some partitions will get 3 ids
while some will get 1
```
(0 until 100).map(_.toString).map(_.hashCode %
50).groupBy(identity).map(_._2.size)
```
while with integers you get a uniform distribution
```
(0 until 100).map(_.hashCode % 50).groupBy(identity).map(_._2.size)
```
So you could end up with unbalanced trees which is not desirable.
@andrewor14 Can we guarantee that execId will be an integer ? Is there any
other integer we can get a handle on otherwise ?
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