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Posted to issues@spark.apache.org by "zunwen you (JIRA)" <ji...@apache.org> on 2016/12/20 13:12:58 UTC

[jira] [Created] (SPARK-18946) treeAggregate will be low effficiency when aggregate high dimension vector in ML algorithm

zunwen you created SPARK-18946:
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             Summary: treeAggregate will be low effficiency when aggregate high dimension vector in ML algorithm
                 Key: SPARK-18946
                 URL: https://issues.apache.org/jira/browse/SPARK-18946
             Project: Spark
          Issue Type: Improvement
          Components: ML, MLlib
            Reporter: zunwen you


In many machine learning algorithms, we have to treeAggregate large vectors/arrays due to the large number of features. Unfortunately, the treeAggregate operation of RDD will be low efficiency when the dimension of vectors/arrays is bigger than million. Because high dimension of vector/array always occupy more than 100MB Memory, transferring a 100MB element among executors is pretty low efficiency in Spark.



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