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https://issues.apache.org/jira/browse/SPARK-58584?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ruifeng Zheng updated SPARK-58584:
----------------------------------
    Description: 
Umbrella ticket for reducing memory use, serialization overhead, and redundant 
computation in Spark ML implementations running on long-lived Spark Connect 
servers.

Track opportunities to:
 - reuse immutable singleton objects;
 - avoid retaining or eagerly creating algorithm-specific auxiliary state;
 - avoid allocating or serializing large aggregation zero values;
 - reduce UDF closure sizes to lower driver memory use from retained query-plan 
object graphs and serialized payloads; and
 - use compact representations and avoid unnecessary driver-side collections or 
broadcasts.


  was:
Umbrella ticket for reducing memory use of Spark ML implementations in 
long-lived Spark Connect servers.

Track opportunities to:
 - reuse immutable singleton objects;
 - avoid retaining or eagerly creating algorithm-specific auxiliary state;
 - avoid allocating or serializing large aggregation zero values;
 - reduce UDF closure sizes to lower driver memory use from retained query-plan 
object graphs and serialized payloads; and
 - use compact representations and avoid unnecessary driver-side collections or 
broadcasts.



> Optimize ML implementations to reduce Spark Connect server pressure
> -------------------------------------------------------------------
>
>                 Key: SPARK-58584
>                 URL: https://issues.apache.org/jira/browse/SPARK-58584
>             Project: Spark
>          Issue Type: Improvement
>          Components: Connect, ML
>    Affects Versions: 5.0.0
>            Reporter: Ruifeng Zheng
>            Priority: Major
>
> Umbrella ticket for reducing memory use, serialization overhead, and 
> redundant computation in Spark ML implementations running on long-lived Spark 
> Connect servers.
> Track opportunities to:
>  - reuse immutable singleton objects;
>  - avoid retaining or eagerly creating algorithm-specific auxiliary state;
>  - avoid allocating or serializing large aggregation zero values;
>  - reduce UDF closure sizes to lower driver memory use from retained 
> query-plan object graphs and serialized payloads; and
>  - use compact representations and avoid unnecessary driver-side collections 
> or broadcasts.



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