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https://issues.apache.org/jira/browse/SPARK-21926?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16154901#comment-16154901
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Nick Pentreath commented on SPARK-21926:
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For #2, (a) is definitely the correct solution.
> Some transformers in spark.ml.feature fail when trying to transform steaming
> dataframes
> ---------------------------------------------------------------------------------------
>
> Key: SPARK-21926
> URL: https://issues.apache.org/jira/browse/SPARK-21926
> Project: Spark
> Issue Type: Bug
> Components: ML, Structured Streaming
> Affects Versions: 2.2.0
> Reporter: Bago Amirbekian
>
> We've run into a few cases where ML components don't play nice with streaming
> dataframes (for prediction). This ticket is meant to help aggregate these
> known cases in one place and provide a place to discuss possible fixes.
> Failing cases:
> 1) VectorAssembler where one of the inputs is a VectorUDT column with no
> metadata.
> Possible fixes:
> a) Re-design vectorUDT metadata to support missing metadata for some
> elements. (This might be a good thing to do anyways SPARK-19141)
> b) drop metadata in streaming context.
> 2) OneHotEncoder where the input is a column with no metadata.
> Possible fixes:
> a) Make OneHotEncoder an estimator (SPARK-13030).
> b) Allow user to set the cardinality of OneHotEncoder.
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