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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-23963) Queries on text-based Hive tables grow disproportionately slower as the number of columns increase
Date Wed, 11 Apr 2018 17:30:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-23963?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16434265#comment-16434265
] 

Apache Spark commented on SPARK-23963:
--------------------------------------

User 'bersprockets' has created a pull request for this issue:
https://github.com/apache/spark/pull/21043

> Queries on text-based Hive tables grow disproportionately slower as the number of columns
increase
> --------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-23963
>                 URL: https://issues.apache.org/jira/browse/SPARK-23963
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.3.0
>            Reporter: Bruce Robbins
>            Priority: Minor
>
> TableReader gets disproportionately slower as the number of columns in the query increase.
> For example, reading a table with 6000 columns is 4 times more expensive per record
than reading a table with 3000 columns, rather than twice as expensive.
> The increase in processing time is due to several Lists (fieldRefs, fieldOrdinals,
and unwrappers), each of which the reader accesses by column number for each column in
a record. Because each List has O\(n\) time for lookup by column number, these lookups
grow increasingly expensive as the column count increases.
> When I patched the code to change those 3 Lists to Arrays, the query times became proportional.
>  
>  
>  
>  



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