srowen commented on a change in pull request #23534: [SPARK-26610][PYTHON] Fix 
inconsistency between toJSON Method in Python and Scala.
URL: https://github.com/apache/spark/pull/23534#discussion_r249247482
 
 

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 File path: docs/sql-migration-guide-upgrade.md
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 @@ -45,6 +45,8 @@ displayTitle: Spark SQL Upgrading Guide
 
   - In Spark version 2.4 and earlier, if 
`org.apache.spark.sql.functions.udf(Any, DataType)` gets a Scala closure with 
primitive-type argument, the returned UDF will return null if the input values 
is null. Since Spark 3.0, the UDF will return the default value of the Java 
type if the input value is null. For example, `val f = udf((x: Int) => x, 
IntegerType)`, `f($"x")` will return null in Spark 2.4 and earlier if column 
`x` is null, and return 0 in Spark 3.0. This behavior change is introduced 
because Spark 3.0 is built with Scala 2.12 by default.
 
+  - Since Spark 3.0, `DataFrame.toJSON()` in PySpark returns `DataFrame` of 
JSON string instead of `RDD`. The method in Scala/Java was changed to return 
`DataFrame` before, but the one in PySpark was not changed at that time. If you 
still want to return `RDD`, you can restore the previous behavior by setting 
`spark.sql.legacy.pyspark.toJsonShouldReturnDataFrame` to `false`.
 
 Review comment:
   Backing up ... `Dataset` in Scala doesn't return a `Dataset[Row]` (i.e. 
`DataFrame`). It returns `Dataset[String]`. It already seems consistent. This 
isn't making it consistent then, but just an API change. That's possible of 
course, but is it really important? especially if it introduces a new flag, 
that toggles Pyspark behavior between what's consistent and inconsistent with 
Scala. (We can't have the flag change the return type in Scala dynamically)

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