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https://issues.apache.org/jira/browse/SPARK-26950?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-26950:
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Fix Version/s: 2.3.4
> 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
> Fix For: 2.3.4, 2.4.1, 3.0.0
>
>
> 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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