[jira] [Commented] (SPARK-16321) Spark 2.0 performance drop vs Spark 1.6 when reading parquet file

["Xiao Li (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-16321?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15404652#comment-15404652 ] 

Xiao Li commented on SPARK-16321:
---------------------------------

Can you set  `spark.sql.parquet.enableVectorizedReader` to false and `spark.sql.parquet.filterPushdown` to true?

BTW, keep the other settings unchanged.

> Spark 2.0 performance drop vs Spark 1.6 when reading parquet file
> -----------------------------------------------------------------
>
>                 Key: SPARK-16321
>                 URL: https://issues.apache.org/jira/browse/SPARK-16321
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 2.0.0
>            Reporter: Maciej Bryński
>            Priority: Critical
>         Attachments: Spark16.nps, Spark2.nps, spark16._trace.png, spark16_query.nps, spark2_nofilterpushdown.nps, spark2_query.nps, spark2_trace.png, visualvm_spark16.png, visualvm_spark2.png, visualvm_spark2_G1GC.png
>
>
> *UPDATE*
> Please start with this comment 
> https://issues.apache.org/jira/browse/SPARK-16321?focusedCommentId=15383785&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15383785
> I assume that problem results from the performance problem with reading parquet files
> *Original Issue description*
> I did some test on parquet file with many nested columns (about 30G in
> 400 partitions) and Spark 2.0 is 2x slower.
> {code}
> df = sqlctx.read.parquet(path)
> df.where('id > some_id').rdd.flatMap(lambda r: [r.id] if not r.id %100000 else []).collect()
> {code}
> Spark 1.6 -> 2.3 min
> Spark 2.0 -> 4.6 min (2x slower)
> I used BasicProfiler for this task and cumulative time was:
> Spark 1.6 - 4300 sec
> Spark 2.0 - 5800 sec
> Should I expect such a drop in performance ?
> I don't know how to prepare sample data to show the problem.
> Any ideas ? Or public data with many nested columns ?



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