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https://issues.apache.org/jira/browse/SPARK-7075?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Reynold Xin updated SPARK-7075:
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Target Version/s: 1.5.0 (was: 1.6.0)
> Project Tungsten (Spark 1.5 Phase 1)
> ------------------------------------
>
> 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.
> This epic tracks work items for Spark 1.5. More tickets can be found in:
> SPARK-7075: Tungsten-related work in Spark 1.5
> SPARK-9697: Tungsten-related work in Spark 1.6
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