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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-15526) Shade JPMML
Date Sun, 02 Jul 2017 08:35:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-15526?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16071552#comment-16071552
] 

Sean Owen commented on SPARK-15526:
-----------------------------------

I do think we should shade in 2.3.0 here. Yes, shading is the right answer, but I also think
this causes enough headaches that it's worth making sure Spark provides no version of JPMML
to the user classpath directly.

> Shade JPMML
> -----------
>
>                 Key: SPARK-15526
>                 URL: https://issues.apache.org/jira/browse/SPARK-15526
>             Project: Spark
>          Issue Type: Dependency upgrade
>          Components: ML, MLlib
>    Affects Versions: 2.0.0
>            Reporter: Villu Ruusmann
>            Priority: Minor
>   Original Estimate: 2h
>  Remaining Estimate: 2h
>
> The Spark-MLlib module depends on the JPMML-Model library (org.jpmml:pmml-model:1.2.7)
for its PMML export capabilities. The JPMML-Model library is included in the Apache Spark
assembly, which makes it very difficult to build and deploy competing PMML exporters that
may wish to depend on different versions (typically much newer) of the same library.
> JPMML-Model library classes are not part of Apache Spark public APIs, so it shouldn't
be a problem if they are relocated by prepending a prefix "org.spark_project" to their package
names using Maven Shade Plugin. The requested treatment is identical to how Google Guava and
Jetty dependencies are shaded in the final assembly.
> This issue is raised in relation to the JPMML-SparkML project (https://github.com/jpmml/jpmml-sparkml),
which provides PMML export capabilities for Spark ML Pipelines. Currently, application developers
who wish to use it must tweak their application classpath, which assumes familiarity with
build internals.



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