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From "Wenchen Fan (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-26950) Make RandomDataGenerator use Float.NaN or Double.NaN for all NaN values
Date Fri, 22 Feb 2019 04:29:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-26950?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Wenchen Fan reassigned SPARK-26950:
-----------------------------------

    Assignee: Dongjoon Hyun

> Make RandomDataGenerator use Float.NaN or Double.NaN for all NaN values
> -----------------------------------------------------------------------
>
>                 Key: SPARK-26950
>                 URL: https://issues.apache.org/jira/browse/SPARK-26950
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL, Tests
>    Affects Versions: 2.3.4, 2.4.2, 3.0.0
>            Reporter: Dongjoon Hyun
>            Assignee: Dongjoon Hyun
>            Priority: Major
>
> Apache Spark uses the predefined `Float.NaN` and `Double.NaN` for NaN values, but there
exists more NaN values with different binary presentations.
> {code}
> scala> java.nio.ByteBuffer.allocate(4).putFloat(Float.NaN).array
> res1: Array[Byte] = Array(127, -64, 0, 0)
> scala> val x = java.lang.Float.intBitsToFloat(-6966608)
> x: Float = NaN
> scala> java.nio.ByteBuffer.allocate(4).putFloat(x).array
> res2: Array[Byte] = Array(-1, -107, -78, -80)
> {code}
> `RandomDataGenerator` generates these NaN values. It's good, but it causes `checkEvaluationWithUnsafeProjection`
failures due to the difference between `UnsafeRow` binary presentation. The following is the
UT failure instance. This issue aims to fix this flakiness.
> https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/102528/testReport/
> {code}
> Failed
> org.apache.spark.sql.avro.AvroCatalystDataConversionSuite.flat schema struct<col_0:decimal(16,11),col_1:float,col_2:decimal(38,0),col_3:decimal(38,0),col_4:string>
with seed -81044812370056695
> {code}



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