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https://issues.apache.org/jira/browse/SPARK-16321?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15382007#comment-15382007
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Sean Owen commented on SPARK-16321:
-----------------------------------
I think you might want to set spark.memory.fraction as shown in this patch,
which is only in master --
https://github.com/apache/spark/commit/457126e420e66228cc68def4bc3d87e7a282069a
Or: set -XX:NewRatio=4 or so or higher. At least this would get rid of the
issue I have in mind to see if it is the difference.
I suppose I assume you are seeing GC time is the issue but that's also worth
verifying.
> Pyspark 2.0 performance drop vs pyspark 1.6
> -------------------------------------------
>
> 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
>
> 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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