You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Bowen Zhang (JIRA)" <ji...@apache.org> on 2015/09/23 07:41:04 UTC
[jira] [Commented] (SPARK-10000) Consolidate cache memory
management and execution memory management
[ https://issues.apache.org/jira/browse/SPARK-10000?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14903995#comment-14903995 ]
Bowen Zhang commented on SPARK-10000:
-------------------------------------
[~rxin], I am very interested in this new story. I am trying to understand the story here. In addition to consolidate these two parts of memory into one memory, are there other tricky things that can pose a challenge to this story or other use case considerations that should be taken into account for this story?
> Consolidate cache memory management and execution memory management
> -------------------------------------------------------------------
>
> Key: SPARK-10000
> URL: https://issues.apache.org/jira/browse/SPARK-10000
> Project: Spark
> Issue Type: Story
> Components: Block Manager, Spark Core
> Reporter: Reynold Xin
>
> As a Spark user, I want Spark to manage the memory more intelligently so I do not need to worry about how to statically partition the execution (shuffle) memory fraction and cache memory fraction.
--
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