You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/01/04 14:33:00 UTC
[jira] [Resolved] (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 ]
Hyukjin Kwon resolved SPARK-7148.
---------------------------------
Resolution: Not A Problem
Then, I'm leaving resolved. Please reopen this if I am mistaken.
> 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
> Assignee: Yanbo Liang
> 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
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org