Dongjoon Hyun created SPARK-26950:
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Summary: 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
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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