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[SPARK-35912][SQL] Fix nullability of spark.read.json/spark.read.csv #33436

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[SPARK-35912][SQL] Fix nullability of spark.read.json/spark.read.csv #33436

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3974a43
fix nullability of spark.read.json/spark.read.csv
cfmcgrady Jul 20, 2021
741d0c9
update migration guide
cfmcgrady Jul 21, 2021
56ceec4
review
cfmcgrady Jul 21, 2021
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4 changes: 4 additions & 0 deletions docs/sql-migration-guide.md
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Expand Up @@ -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.
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@HyukjinKwon HyukjinKwon May 3, 2022

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I actually underestimated this problem. It can actually change the results:

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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@HyukjinKwon HyukjinKwon May 3, 2022

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#36435


## Upgrading from Spark SQL 3.1 to 3.2

- Since Spark 3.2, ADD FILE/JAR/ARCHIVE commands require each path to be enclosed by `"` or `'` if the path contains whitespaces.
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4 changes: 2 additions & 2 deletions sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
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Expand Up @@ -449,7 +449,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
sparkSession.sessionState.conf.sessionLocalTimeZone,
sparkSession.sessionState.conf.columnNameOfCorruptRecord)

val schema = userSpecifiedSchema.getOrElse {
val schema = userSpecifiedSchema.map(_.asNullable).getOrElse {
TextInputJsonDataSource.inferFromDataset(jsonDataset, parsedOptions)
}

Expand Down Expand Up @@ -521,7 +521,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
None
}

val schema = userSpecifiedSchema.getOrElse {
val schema = userSpecifiedSchema.map(_.asNullable).getOrElse {
TextInputCSVDataSource.inferFromDataset(
sparkSession,
csvDataset,
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22 changes: 21 additions & 1 deletion sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
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Expand Up @@ -34,7 +34,7 @@ import org.apache.hadoop.io.SequenceFile.CompressionType
import org.apache.hadoop.io.compress.GzipCodec

import org.apache.spark.{SparkConf, SparkException, TestUtils}
import org.apache.spark.sql.{AnalysisException, Column, DataFrame, QueryTest, Row}
import org.apache.spark.sql.{AnalysisException, Column, DataFrame, Encoders, QueryTest, Row}
import org.apache.spark.sql.catalyst.util.DateTimeUtils
import org.apache.spark.sql.execution.datasources.CommonFileDataSourceSuite
import org.apache.spark.sql.internal.SQLConf
Expand Down Expand Up @@ -2463,6 +2463,26 @@ abstract class CSVSuite
.option("ignoreTrailingWhiteSpace", "true").load(path.getAbsolutePath).count() == 1)
}
}

test("SPARK-35912: turn non-nullable schema into a nullable schema") {
val inputCSVString = """1,"""

val schema = StructType(Seq(
StructField("c1", IntegerType, nullable = false),
StructField("c2", IntegerType, nullable = false)))
val expected = schema.asNullable

Seq("DROPMALFORMED", "FAILFAST", "PERMISSIVE").foreach { mode =>
val csv = spark.createDataset(
spark.sparkContext.parallelize(inputCSVString:: Nil))(Encoders.STRING)
val df = spark.read
.option("mode", mode)
.schema(schema)
.csv(csv)
assert(df.schema == expected)
checkAnswer(df, Row(1, null) :: Nil)
}
}
}

class CSVv1Suite extends CSVSuite {
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25 changes: 25 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
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Expand Up @@ -2919,6 +2919,31 @@ abstract class JsonSuite
}
}
}

test("SPARK-35912: turn non-nullable schema into a nullable schema") {
// JSON field is missing.
val missingFieldInput = """{"c1": 1}"""
// JSON filed is null.
val nullValueInput = """{"c1": 1, "c2": null}"""

val schema = StructType(Seq(
StructField("c1", IntegerType, nullable = false),
StructField("c2", IntegerType, nullable = false)))
val expected = schema.asNullable

Seq(missingFieldInput, nullValueInput).foreach { jsonString =>
Seq("DROPMALFORMED", "FAILFAST", "PERMISSIVE").foreach { mode =>
val json = spark.createDataset(
spark.sparkContext.parallelize(jsonString:: Nil))(Encoders.STRING)
val df = spark.read
.option("mode", mode)
.schema(schema)
.json(json)
assert(df.schema == expected)
checkAnswer(df, Row(1, null) :: Nil)
}
}
}
}

class JsonV1Suite extends JsonSuite {
Expand Down