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