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Apache Spark commented on SPARK-7075: ------------------------------------- User 'davies' has created a pull request for this issue: https://github.com/apache/spark/pull/7592 > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org