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Posted to issues@spark.apache.org by "Reynold Xin (JIRA)" <ji...@apache.org> on 2015/07/22 20:07:05 UTC
[jira] [Updated] (SPARK-7075) Project Tungsten: Improving Physical
Execution
[ https://issues.apache.org/jira/browse/SPARK-7075?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Reynold Xin updated SPARK-7075:
-------------------------------
Target Version/s: 1.6.0 (was: )
> 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: 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.
> *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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