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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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