[jira] [Resolved] (SPARK-22969) aggregateByKey with aggregator compression

["zhengruifeng (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-22969?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

zhengruifeng resolved SPARK-22969.
----------------------------------
    Resolution: Not A Problem

> aggregateByKey with aggregator compression
> ------------------------------------------
>
>                 Key: SPARK-22969
>                 URL: https://issues.apache.org/jira/browse/SPARK-22969
>             Project: Spark
>          Issue Type: Question
>          Components: Spark Core
>    Affects Versions: 2.4.0
>            Reporter: zhengruifeng
>            Priority: Minor
>
> I encounter a special case that the aggregator can be represented as two types:
> a) high memory-footprint, but fast {{update}}
> b) compact, but must be converted to type a before calling {{update}} and {{merge}}.
> I wonder whether it is possible to compress the fat aggregators in {{aggregateByKey}} before shuffle, how can I impl it?  [~cloud_fan]  
> One similar case maybe:
> Using {{aggregateByKey}}/{{reduceByKey}} to compute the nnz vector (number of non-zero value) for different keys on a large sparse dataset.
> We can use {{DenseVector}} as the aggregators to count the nnz, and then compress it by call {{Vector#compressed}} before send it to the network.
> Another similar case maybe calling {{QuantileSummaries#compress}} before communication.



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