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From "Wenchen Fan (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-16907) Parquet table reading performance regression when vectorized record reader is not used
Date Fri, 05 Aug 2016 03:34:20 GMT

     [ https://issues.apache.org/jira/browse/SPARK-16907?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Wenchen Fan resolved SPARK-16907.
---------------------------------
       Resolution: Fixed
    Fix Version/s: 2.1.0
                   2.0.1

> Parquet table reading performance regression when vectorized record reader is not used
> --------------------------------------------------------------------------------------
>
>                 Key: SPARK-16907
>                 URL: https://issues.apache.org/jira/browse/SPARK-16907
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>            Reporter: Sean Zhong
>            Assignee: Sean Zhong
>             Fix For: 2.0.1, 2.1.0
>
>
> For this parquet reading benchmark, Spark 2.0 is 20%-30% slower than Spark 1.6.
> {code}
> // Test Env: Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz, Intel SSD SC2KW24
> // Generates parquet table with nested columns
> spark.range(100000000).select(struct($"id").as("nc")).write.parquet("/tmp/data4")
> def time[R](block: => R): Long = {
>     val t0 = System.nanoTime()
>     val result = block    // call-by-name
>     val t1 = System.nanoTime()
>     println("Elapsed time: " + (t1 - t0)/1000000 + "ms")
>     (t1 - t0)/1000000
> }
> val x = ((0 until 20).toList.map(x => time(spark.read.parquet("/tmp/data4").filter($"nc.id"
< 100).collect()))).sum/20
> {code}



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