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https://issues.apache.org/jira/browse/SPARK-7148?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14594261#comment-14594261
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Joseph K. Bradley commented on SPARK-7148:
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Hm, if it's that simple, then I wonder if we can adjust parquet.block.size
before saving/loading the ML models and reset the block size to its original
value afterwards. I'll have to try that!
> Configure Parquet block size (row group size) for ML model import/export
> ------------------------------------------------------------------------
>
> Key: SPARK-7148
> URL: https://issues.apache.org/jira/browse/SPARK-7148
> Project: Spark
> Issue Type: Improvement
> Components: MLlib, SQL
> Affects Versions: 1.3.0, 1.3.1, 1.4.0
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> It would be nice if we could configure the Parquet buffer size when using
> Parquet format for ML model import/export. Currently, for some models (trees
> and ensembles), the schema has 13+ columns. With a default buffer size of
> 128MB (I think), that puts the allocated buffer way over the default memory
> made available by run-example. Because of this problem, users have to use
> spark-submit and explicitly use a larger amount of memory in order to run
> some ML examples.
> Is there a simple way to specify {{parquet.block.size}}? I'm not familiar
> with this part of SparkSQL.
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