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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2019/12/11 07:20:24 UTC
[GitHub] [spark] zhengruifeng commented on issue #26803: [SPARK-30178][ML]
RobustScaler support large numFeatures
zhengruifeng commented on issue #26803: [SPARK-30178][ML] RobustScaler support large numFeatures
URL: https://github.com/apache/spark/pull/26803#issuecomment-564413213
@srowen I impl a simple `treeAggregateByKey` [here](https://github.com/apache/spark/compare/master...zhengruifeng:treeAggByKey?expand=1), and made several local tests like:
```scala
val rdd = sc.range(0, 10000, 1, 100)
val rdd2 = rdd.map{i => (i % 10, i)}
val rdd3 = rdd2.treeAggregateByKey(0.0, new HashPartitioner(3))(_+_, _+_, 2)
rdd3.collect
```
and ran successfully.
BTW, it is reasonable to call `compress` before reduce tasks, so maybe a `aggregateByKeyWithinPartitions` is needed? Then we can call `compress` after locally aggregation within each partition. I guess they maybe common functions and we add this method in RDD/PairRDD?
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