HyukjinKwon commented on code in PR #33436:
URL: https://github.com/apache/spark/pull/33436#discussion_r863271189
##########
docs/sql-migration-guide.md:
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@@ -22,6 +22,10 @@ license: |
* Table of contents
{:toc}
+## Upgrading from Spark SQL 3.2 to 3.3
+
+ - Since Spark 3.3, Spark turns a non-nullable schema into nullable for API
`DataFrameReader.schema(schema: StructType).json(jsonDataset: Dataset[String])`
and `DataFrameReader.schema(schema: StructType).csv(csvDataset:
Dataset[String])` when the schema is specified by the user and contains
non-nullable fields.
Review Comment:
I actually underestimated this problem. It can actually change the results:
```scala
import org.apache.spark.sql.types._
val ds = Seq("a,", "a,b").toDS
spark.read.schema(
StructType(
StructField("f1", StringType, nullable = false) ::
StructField("f2", StringType, nullable = false) :: Nil)
).option("mode", "FAILFAST").csv(ds).show()
```
Before:
```
+---+---+
| f1| f2|
+---+---+
| a| b|
+---+---+
```
After:
```
+---+----+
| f1| f2|
+---+----+
| a|null|
| a| b|
+---+----+
```
I think we should at least add a legacy configuration .. let me make a quick
followup.
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