[jira] [Assigned] (SPARK-16360) Speed up SQL query performance by removing redundant analysis in `Dataset`

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-16360?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-16360:
------------------------------------

    Assignee: Apache Spark

> Speed up SQL query performance by removing redundant analysis in `Dataset`
> --------------------------------------------------------------------------
>
>                 Key: SPARK-16360
>                 URL: https://issues.apache.org/jira/browse/SPARK-16360
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>            Reporter: Dongjoon Hyun
>            Assignee: Apache Spark
>
> Currently, there are a few reports about Spark 2.0 query performance regression for large queries.
> This issue speeds up SQL query processing performance by removing redundant consecutive analysis in `Dataset.ofRows` function and `Dataset` instantiation. Specifically, this issue aims to reduce the overhead of SQL query analysis, not query execution.
> **Before**
> {code}
> scala> :pa
> // Entering paste mode (ctrl-D to finish)
> val n = 4000
> val values = (1 to n).map(_.toString).mkString(", ")
> val columns = (1 to n).map("column" + _).mkString(", ")
> val query =
>   s"""
>      |SELECT $columns
>      |FROM VALUES ($values) T($columns)
>      |WHERE 1=2 AND 1 IN ($columns)
>      |GROUP BY $columns
>      |ORDER BY $columns
>      |""".stripMargin
> def time[R](block: => R): R = {
>   val t0 = System.nanoTime()
>   val result = block
>   println("Elapsed time: " + ((System.nanoTime - t0) / 1e9) + "s")
>   result
> }
> time(sql(query))
> time(sql(query))
> // Exiting paste mode, now interpreting.
> Elapsed time: 30.138142577s
> Elapsed time: 25.787751452s
> {code}
> **After**
> {code}
> Elapsed time: 17.500279659s
> Elapsed time: 12.364812255s
> {code}



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