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Posted to issues@spark.apache.org by "Reynold Xin (JIRA)" <ji...@apache.org> on 2015/08/21 07:17:46 UTC
[jira] [Updated] (SPARK-9983) Local physical operators for query
execution
[ https://issues.apache.org/jira/browse/SPARK-9983?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Reynold Xin updated SPARK-9983:
-------------------------------
Description:
In distributed query execution, there are two kinds of operators:
(1) operators that exchange data between different executors or threads: examples include broadcast, shuffle.
(2) operators that process data in a single thread: examples include project, filter, group by, etc.
This ticket proposes clearly differentiating them and create local operators in Spark. This leads to a lot of benefits: easier to test, easier to optimize data exchange, better design (single responsibility), and potentially even having a hyper-optimized single-node version of DataFrame.
was:
In distributed query execution, there are two kinds of operators:
(1) operators that exchange data between different executors or threads: examples include broadcast, shuffle.
(2) operators that process data in a single thread: examples include project, filter, group by, etc.
This ticket proposes clearly differentiating them and create local operators in Spark. This leads to a lot of benefits: easier to test, easier to optimize data exchange, and better design (single responsibility).
> Local physical operators for query execution
> --------------------------------------------
>
> Key: SPARK-9983
> URL: https://issues.apache.org/jira/browse/SPARK-9983
> Project: Spark
> Issue Type: Story
> Components: SQL
> Reporter: Reynold Xin
> Assignee: Shixiong Zhu
>
> In distributed query execution, there are two kinds of operators:
> (1) operators that exchange data between different executors or threads: examples include broadcast, shuffle.
> (2) operators that process data in a single thread: examples include project, filter, group by, etc.
> This ticket proposes clearly differentiating them and create local operators in Spark. This leads to a lot of benefits: easier to test, easier to optimize data exchange, better design (single responsibility), and potentially even having a hyper-optimized single-node version of DataFrame.
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