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https://issues.apache.org/jira/browse/SPARK-27799?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-27799:
----------------------------------
    Affects Version/s:     (was: 3.0.0)
                       3.1.0

> Allow SerializerManager.canUseKryo whitelist to be extended via a 
> configuration
> -------------------------------------------------------------------------------
>
>                 Key: SPARK-27799
>                 URL: https://issues.apache.org/jira/browse/SPARK-27799
>             Project: Spark
>          Issue Type: New Feature
>          Components: Spark Core
>    Affects Versions: 3.1.0
>            Reporter: Josh Rosen
>            Priority: Major
>
> Kryo serialization can offer a substantial performance boost compared to Java 
> serialization and I generally recommend that users configure Spark to use it.
> That said, in general it may not be safe to _blindly_ flip the default to 
> Kryo: certain jobs might depend on Java serialization, so switching them to 
> Kryo might cause crashes or incorrect behavior.
> However, we may know that certain data types are safe to serialize with Kryo, 
> in which case we can whitelist _just those types_ for use with Kryo 
> serialization but keep everything else using the default Java serializer.
> Back in SPARK-13926 (Spark 2.0) I added a {{SerializerManager}} to implement 
> this idea for strings, primitives, primitive arrays, and a few other data 
> types: those types will automatically use Kryo serialization when used as 
> top-level types in RDDs. However, there's no ability for users to customize / 
> extend this whitelist.
> I propose to add a new user-facing configuration, name TBD, which accepts a 
> comma-separated list of class / interface names and uses them to expand the 
> {{SerializerMananger.canUseKryo}} whitelist.
> This will allow advanced users to incrementally default to Kryo for certain 
> types (e.g. Scrooge ThriftStructs).
> This feature is useful for "data platform" teams who provide 
> Spark-as-a-service to internal customers: with this proposed configuration, 
> platform teams can configure global defaults for serialization in a way which 
> is more incremental / narrow-in-scope than simply defaulting to Kryo 
> everywhere.



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