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Posted to issues@spark.apache.org by "Michael Armbrust (JIRA)" <ji...@apache.org> on 2015/08/03 21:09:05 UTC
[jira] [Updated] (SPARK-7148) Configure Parquet block size (row
group size) for ML model import/export
[ https://issues.apache.org/jira/browse/SPARK-7148?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Michael Armbrust updated SPARK-7148:
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Target Version/s: 1.6.0 (was: 1.5.0)
> 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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