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Posted to issues@spark.apache.org by "Wenchen Fan (JIRA)" <ji...@apache.org> on 2016/08/05 08:01:20 UTC
[jira] [Resolved] (SPARK-15726) Make DatasetBenchmark fairer among
Dataset, DataFrame and RDD
[ https://issues.apache.org/jira/browse/SPARK-15726?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wenchen Fan resolved SPARK-15726.
---------------------------------
Resolution: Fixed
Fix Version/s: 2.1.0
Issue resolved by pull request 13459
[https://github.com/apache/spark/pull/13459]
> Make DatasetBenchmark fairer among Dataset, DataFrame and RDD
> -------------------------------------------------------------
>
> Key: SPARK-15726
> URL: https://issues.apache.org/jira/browse/SPARK-15726
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Hiroshi Inoue
> Priority: Minor
> Fix For: 2.1.0
>
>
> DatasetBenchmark compares the performances of RDD, DataFrame and Dataset while running the same operations.
> In backToBackMap test case, however, only DataFrame implementation executes less work compared to RDD or Dataset implementations. This test case processes Long+String pairs, but the output from the DataFrame implementation does not include String part while RDD or Dataset generates Long+String pairs as output. This difference significantly changes the performance characteristics due to the String manipulation and creation overheads. After the fix RDD outperforms DataFrame, while DataFrame was more than 2x faster than RDD without the fix. Also, the performance gap between DataFrame and Dataset becomes much narrower.
> Of course, this issue does not affect Spark users, but it may confuse Spark developers.
> {quote}
> *// DataFrame*
> val df = spark.range(1, numRows).select($"id".as("l"), $"id".cast(StringType).as("s"))
> var res = df
> {color:blue}res = res.select($"l" + 1 as "l"){color}
> // this should be {color:red}res = res.select($"l" + 1 as "l", $"s"){color} for fair comparison
> *// Dataset*
> case class Data(l: Long, s: String)
> val func = (d: Data) => Data(d.l + 1, d.s)
> var res = df.as\[Data\]
> res = res.map(func)
> {quote}
> Additionally, I added a new test case named "back-to-back map for primitive". This is almost equivalent with the old behavior of the DataFrame implementation of back-to-back map.
> {quote}
> without fix
> OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 3.10.0-229.el7.x86_64
> Intel Xeon E3-12xx v2 (Ivy Bridge)
> back-to-back map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
> ------------------------------------------------------------------------------------------------
> RDD 2051 / 2077 48.7 20.5 1.0X
> DataFrame 755 / 940 132.5 7.5 2.7X
> Dataset 6155 / 6680 16.2 61.6 0.3X
> with fix
> back-to-back map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
> ------------------------------------------------------------------------------------------------
> RDD 2077 / 2259 48.1 20.8 1.0X
> DataFrame 3030 / 3310 33.0 30.3 0.7X
> Dataset 6504 / 7006 15.4 65.0 0.3X
> back-to-back map for primitive: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
> ------------------------------------------------------------------------------------------------
> RDD 1073 / 1509 93.2 10.7 1.0X
> DataFrame 763 / 913 131.0 7.6 1.4X
> Dataset 4189 / 4312 23.9 41.9 0.3X
> {quote}
> Note that DatasetBenchmark causes JVM crash in an aggregate test case. This is not related to this issue.
> I already created a jira entry and submited a pull request for the aggregate issue.
> https://issues.apache.org/jira/browse/SPARK-15704
> https://github.com/apache/spark/pull/13446
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