[ https://issues.apache.org/jira/browse/SPARK-26950?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wenchen Fan resolved SPARK-26950. --------------------------------- Resolution: Fixed Fix Version/s: 2.4.1 3.0.0 Issue resolved by pull request 23851 [https://github.com/apache/spark/pull/23851] > 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: 3.0.0, 2.4.1 > > > 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} -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org