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From "Vincent (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-22096) use aggregateByKeyLocally to save one stage in calculating ItemFrequency in NaiveBayes
Date Fri, 22 Sep 2017 04:35:01 GMT

     [ https://issues.apache.org/jira/browse/SPARK-22096?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Vincent updated SPARK-22096:
----------------------------
    Description: 
NaiveBayes currently takes aggreateByKey followed by a collect to calculate frequency for
each feature/label. We can implement a new function 'aggregateByKeyLocally' in RDD that merges
locally on each mapper before sending results to a reducer to save one stage.
We tested on NaiveBayes and see ~16% performance gain with these changes.
[^performance data for NB.png]

  was:
NaiveBayes currently takes aggreateByKey followed by a collect to calculate frequency for
each feature/label. We can implement a new function 'aggregateByKeyLocally' in RDD that merges
locally on each mapper before sending results to a reducer to save one stage.
We tested on NaiveBayes and see ~16% performance gain with these changes.


> use aggregateByKeyLocally to save one stage in calculating ItemFrequency in NaiveBayes
> --------------------------------------------------------------------------------------
>
>                 Key: SPARK-22096
>                 URL: https://issues.apache.org/jira/browse/SPARK-22096
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 2.2.0
>            Reporter: Vincent
>            Priority: Minor
>         Attachments: performance data for NB.png
>
>
> NaiveBayes currently takes aggreateByKey followed by a collect to calculate frequency
for each feature/label. We can implement a new function 'aggregateByKeyLocally' in RDD that
merges locally on each mapper before sending results to a reducer to save one stage.
> We tested on NaiveBayes and see ~16% performance gain with these changes.
> [^performance data for NB.png]



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