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Posted to issues@spark.apache.org by "Imran Rashid (JIRA)" <ji...@apache.org> on 2015/04/29 21:02:06 UTC
[jira] [Commented] (SPARK-7075) Project Tungsten: Improving
Physical Execution and Memory Management
[ https://issues.apache.org/jira/browse/SPARK-7075?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14519956#comment-14519956 ]
Imran Rashid commented on SPARK-7075:
-------------------------------------
everything sounds awesome, but can we see design docs & longer review timelines? There are a lot of massive changes proposed here.
> Project Tungsten: Improving Physical Execution and Memory Management
> --------------------------------------------------------------------
>
> Key: SPARK-7075
> URL: https://issues.apache.org/jira/browse/SPARK-7075
> Project: Spark
> Issue Type: Epic
> Components: Block Manager, Shuffle, Spark Core, SQL
> Reporter: Reynold Xin
> Assignee: Reynold Xin
>
> Based on our observation, majority of Spark workloads are not bottlenecked by I/O or network, but rather CPU and memory. This project focuses on 3 areas to improve the efficiency of memory and CPU for Spark applications, to push performance closer to the limits of the underlying hardware.
> 1. Memory Management and Binary Processing: leveraging application semantics to manage memory explicitly and eliminate the overhead of JVM object model and garbage collection
> 2. Cache-aware computation: algorithms and data structures to exploit memory hierarchy
> 3. Code generation: using code generation to exploit modern compilers and CPUs
> Several parts of project Tungsten leverage the DataFrame model, which gives us more semantics about the application. We will also retrofit the improvements onto Spark’s RDD API whenever possible.
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