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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-25038) Accelerate Spark Plan generation when Spark SQL read large amount of data
Date Tue, 07 Aug 2018 03:27:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-25038?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-25038:
------------------------------------

    Assignee:     (was: Apache Spark)

> Accelerate Spark Plan generation when Spark SQL read large amount of data
> -------------------------------------------------------------------------
>
>                 Key: SPARK-25038
>                 URL: https://issues.apache.org/jira/browse/SPARK-25038
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.3.1
>            Reporter: Jason Guo
>            Priority: Major
>         Attachments: issue sql optimized.png, issue sql original.png, job start optimized.png,
job start original.png
>
>
> When Spark SQL read large amount of data, it take a long time (more than 10 minutes)
to generate physical Plan and then ActiveJob
>  
> Example:
> There is a table which is partitioned by date and hour. There are more than 13 TB data
each hour and 185 TB per day. When we just issue a very simple SQL, it take a long time to
generate ActiveJob
>  
> The SQL statement is
> {code:java}
> select count(device_id) from test_tbl where date=20180731 and hour='21';
> {code}
>  
> Before optimization, it takes 2 minutes and 9 seconds to generate the Job
>  
> The SQL is issued at 2018-08-07 09:07:41
> !issue sql original.png!
> However, the job is submitted at 2018-08-07 09:09:53, which is 2minutes and 9 seconds
later than the SQL issue time
> !job start original.png!
>  
> After the optimization, it takes only 4 seconds to generate the Job
> The SQL is issued at 2018-08-07 09:20:15
> !issue sql optimized.png!
>  
> And the job is submitted at 2018-08-07 09:20:19, which is 4 seconds later than the SQL
issue time
> !job start optimized.png!
>  
>  



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