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https://issues.apache.org/jira/browse/SPARK-14051?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun updated SPARK-14051:
----------------------------------
Description:
Since SPARK-9079 and SPARK-9145, `NaN = NaN` returns true and works well. The
only exception case is direct comparison between `Row(Float.NaN)` and
`Row(Double.NaN)`. The following is the example: the last two expressions had
better be *true* and *List([NaN])* for consistency.
{code}
scala>
Seq((1d,1f),(Double.NaN,Float.NaN)).toDF("a","b").registerTempTable("tmp")
scala> sql("select a,b,a=b from tmp").collect()
res1: Array[org.apache.spark.sql.Row] = Array([1.0,1.0,true], [NaN,NaN,true])
scala> val row_a = sql("select a from tmp").collect()
row_a: Array[org.apache.spark.sql.Row] = Array([1.0], [NaN])
scala> val row_b = sql("select b from tmp").collect()
row_b: Array[org.apache.spark.sql.Row] = Array([1.0], [NaN])
scala> row_a(0) == row_b(0)
res2: Boolean = true
scala> List(row_a(0),row_b(0)).distinct
res3: List[org.apache.spark.sql.Row] = List([1.0])
scala> row_a(1) == row_b(1)
res4: Boolean = false
scala> List(row_a(1),row_b(1)).distinct
res5: List[org.apache.spark.sql.Row] = List([NaN], [NaN])
{code}
Please note that the following background truths as of today.
* Double.NaN != Double.NaN (Scala/Java/IEEE Standard)
* Float.NaN != Float.NaN (Scala/Java/IEEE Standard)
* Double.NaN != Float.NaN (Scala/Java/IEEE Standard)
* Row(Double.NaN) == Row(Double.NaN)
* Row(Float.NaN) == Row(Float.NaN)
* *Row(Double.NaN) != Row(Float.NaN)* <== The problem of this issue.
was:
Since SPARK-9079 and SPARK-9145, `NaN = NaN` returns true and works well. The
only exception case is direct comparison between `Row(Float.NaN)` and
`Row(Double.NaN)`. The following is the example: the last two expressions had
better be *true* and *List([NaN])* for consistency.
{code}
scala>
Seq((1d,1f),(Double.NaN,Float.NaN)).toDF("a","b").registerTempTable("tmp")
scala> sql("select a,b,a=b from tmp").collect()
res1: Array[org.apache.spark.sql.Row] = Array([1.0,1.0,true], [NaN,NaN,true])
scala> val row_a = sql("select a from tmp").collect()
row_a: Array[org.apache.spark.sql.Row] = Array([1.0], [NaN])
scala> val row_b = sql("select b from tmp").collect()
row_b: Array[org.apache.spark.sql.Row] = Array([1.0], [NaN])
scala> row_a(0) == row_b(0)
res2: Boolean = true
scala> List(row_a(0),row_b(0)).distinct
res3: List[org.apache.spark.sql.Row] = List([1.0])
scala> row_a(1) == row_b(1)
res4: Boolean = false
scala> List(row_a(1),row_b(1)).distinct
res5: List[org.apache.spark.sql.Row] = List([NaN], [NaN])
{code}
Please note that the following background truths as of today.
* Double.NaN != Double.NaN (Scala/Java/IEEE Standard)
* Float.NaN != Float.NaN (Scala/Java/IEEE Standard)
* Double.NaN != Float.NaN (Scala/Java/IEEE Standard)
* Row(Double.NaN) == Row(Double.NaN)
* Row(Float.NaN) == Row(Float.NaN)
* *Row(Double.NaN) != Row(Float.NaN)* <== This Issue.
> Implement `Double.NaN==Float.NaN` for consistency
> -------------------------------------------------
>
> Key: SPARK-14051
> URL: https://issues.apache.org/jira/browse/SPARK-14051
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Reporter: Dongjoon Hyun
> Priority: Minor
>
> Since SPARK-9079 and SPARK-9145, `NaN = NaN` returns true and works well. The
> only exception case is direct comparison between `Row(Float.NaN)` and
> `Row(Double.NaN)`. The following is the example: the last two expressions had
> better be *true* and *List([NaN])* for consistency.
> {code}
> scala>
> Seq((1d,1f),(Double.NaN,Float.NaN)).toDF("a","b").registerTempTable("tmp")
> scala> sql("select a,b,a=b from tmp").collect()
> res1: Array[org.apache.spark.sql.Row] = Array([1.0,1.0,true], [NaN,NaN,true])
> scala> val row_a = sql("select a from tmp").collect()
> row_a: Array[org.apache.spark.sql.Row] = Array([1.0], [NaN])
> scala> val row_b = sql("select b from tmp").collect()
> row_b: Array[org.apache.spark.sql.Row] = Array([1.0], [NaN])
> scala> row_a(0) == row_b(0)
> res2: Boolean = true
> scala> List(row_a(0),row_b(0)).distinct
> res3: List[org.apache.spark.sql.Row] = List([1.0])
> scala> row_a(1) == row_b(1)
> res4: Boolean = false
> scala> List(row_a(1),row_b(1)).distinct
> res5: List[org.apache.spark.sql.Row] = List([NaN], [NaN])
> {code}
> Please note that the following background truths as of today.
> * Double.NaN != Double.NaN (Scala/Java/IEEE Standard)
> * Float.NaN != Float.NaN (Scala/Java/IEEE Standard)
> * Double.NaN != Float.NaN (Scala/Java/IEEE Standard)
> * Row(Double.NaN) == Row(Double.NaN)
> * Row(Float.NaN) == Row(Float.NaN)
> * *Row(Double.NaN) != Row(Float.NaN)* <== The problem of this issue.
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