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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-7075) Project Tungsten: Improving Physical Execution
Date Wed, 22 Jul 2015 08:26:10 GMT

     [ https://issues.apache.org/jira/browse/SPARK-7075?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-7075:
-----------------------------------

    Assignee: Apache Spark  (was: Reynold Xin)

> Project Tungsten: Improving Physical Execution
> ----------------------------------------------
>
>                 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: Apache Spark
>
> 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.
> *Memory Management and Binary Processing*
> - Avoiding non-transient Java objects (store them in binary format), which reduces GC
overhead.
> - Minimizing memory usage through denser in-memory data format, which means we spill
less.
> - Better memory accounting (size of bytes) rather than relying on heuristics
> - For operators that understand data types (in the case of DataFrames and SQL), work
directly against binary format in memory, i.e. have no serialization/deserialization
> *Cache-aware Computation*
> - Faster sorting and hashing for aggregations, joins, and shuffle
> *Code Generation*
> - Faster expression evaluation and DataFrame/SQL operators
> - Faster serializer
> 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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