[jira] [Commented] (SPARK-7148) Configure Parquet block size (row group size) for ML model import/export

["Yanbo Liang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-7148?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14593265#comment-14593265 ] 

Yanbo Liang commented on SPARK-7148:
------------------------------------

parquet.block.size if one of the configuration of hadoop, users can set it at their own code by:

val sc : SparkContext   // An existing SparkContext.
sc.hadoopConfiguration.setInt("parquet.block.size", 1024 * 1024 * 1024)

before call write DataFrame to file.

> 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.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org