[SPARK-18269][SQL] CSV datasource should read null properly when sche…

…ma is lager than parsed tokens

## What changes were proposed in this pull request?

Currently, there are the three cases when reading CSV by datasource when it is `PERMISSIVE` parse mode.

- schema == parsed tokens (from each line)
  No problem to cast the value in the tokens to the field in the schema as they are equal.

- schema < parsed tokens (from each line)
  It slices the tokens into the number of fields in schema.

- schema > parsed tokens (from each line)
  It appends `null` into parsed tokens so that safely values can be casted with the schema.

However, when `null` is appended in the third case, we should take `null` into account when casting the values.

In case of `StringType`, it is fine as `UTF8String.fromString(datum)` produces `null` when the input is `null`. Therefore, this case will happen only when schema is explicitly given and schema includes data types that are not `StringType`.

The codes below:

```scala
val path = "/tmp/a"
Seq("1").toDF().write.text(path.getAbsolutePath)
val schema = StructType(
  StructField("a", IntegerType, true) ::
  StructField("b", IntegerType, true) :: Nil)
spark.read.schema(schema).option("header", "false").csv(path).show()
```

prints

**Before**

```
java.lang.NumberFormatException: null
at java.lang.Integer.parseInt(Integer.java:542)
at java.lang.Integer.parseInt(Integer.java:615)
at scala.collection.immutable.StringLike$class.toInt(StringLike.scala:272)
at scala.collection.immutable.StringOps.toInt(StringOps.scala:29)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$.castTo(CSVInferSchema.scala:24)
```

**After**

```
+---+----+
|  a|   b|
+---+----+
|  1|null|
+---+----+
```

## How was this patch tested?

Unit test in `CSVSuite.scala` and `CSVTypeCastSuite.scala`

Author: hyukjinkwon <[email protected]>

Closes #15767 from HyukjinKwon/SPARK-18269.

(cherry picked from commit 556a3b7)
Signed-off-by: Reynold Xin <[email protected]>

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala

@@ -221,18 +221,27 @@ private[csv] object CSVTypeCast {
    * Currently we do not support complex types (ArrayType, MapType, StructType).
    *
    * For string types, this is simply the datum. For other types.
-   * For other nullable types, this is null if the string datum is empty.
+   * For other nullable types, returns null if it is null or equals to the value specified
+   * in `nullValue` option.
    *
    * @param datum string value
-   * @param castType SparkSQL type
+   * @param name field name in schema.
+   * @param castType data type to cast `datum` into.
+   * @param nullable nullability for the field.
+   * @param options CSV options.
    */
   def castTo(
       datum: String,
+      name: String,
       castType: DataType,
       nullable: Boolean = true,
       options: CSVOptions = CSVOptions()): Any = {
 
-    if (nullable && datum == options.nullValue) {
+    // datum can be null if the number of fields found is less than the length of the schema
+    if (datum == options.nullValue || datum == null) {
+      if (!nullable) {
+        throw new RuntimeException(s"null value found but field $name is not nullable.")
+      }
       null
     } else {
       castType match {
@@ -281,7 +290,7 @@ private[csv] object CSVTypeCast {
               DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(datum).getTime)
             }
         case _: StringType => UTF8String.fromString(datum)
-        case udt: UserDefinedType[_] => castTo(datum, udt.sqlType, nullable, options)
+        case udt: UserDefinedType[_] => castTo(datum, name, udt.sqlType, nullable, options)
         case _ => throw new RuntimeException(s"Unsupported type: ${castType.typeName}")
       }
     }

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVRelation.scala

@@ -124,6 +124,7 @@ object CSVRelation extends Logging {
             // value is not stored in the row.
             val value = CSVTypeCast.castTo(
               indexSafeTokens(index),
+              field.name,
               field.dataType,
               field.nullable,
               params)

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala

@@ -890,4 +890,19 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils {
       }
     }
   }
+
+  test("load null when the schema is larger than parsed tokens ") {
+    withTempPath { path =>
+      Seq("1").toDF().write.text(path.getAbsolutePath)
+      val schema = StructType(
+        StructField("a", IntegerType, true) ::
+        StructField("b", IntegerType, true) :: Nil)
+      val df = spark.read
+        .schema(schema)
+        .option("header", "false")
+        .csv(path.getAbsolutePath)
+
+      checkAnswer(df, Row(1, null))
+    }
+  }
 }

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVTypeCastSuite.scala

@@ -36,7 +36,7 @@ class CSVTypeCastSuite extends SparkFunSuite {
 
     stringValues.zip(decimalValues).foreach { case (strVal, decimalVal) =>
       val decimalValue = new BigDecimal(decimalVal.toString)
-      assert(CSVTypeCast.castTo(strVal, decimalType) ===
+      assert(CSVTypeCast.castTo(strVal, "_1", decimalType) ===
         Decimal(decimalValue, decimalType.precision, decimalType.scale))
     }
   }
@@ -67,97 +67,108 @@ class CSVTypeCastSuite extends SparkFunSuite {
 
   test("Nullable types are handled") {
     assertNull(
-      CSVTypeCast.castTo("-", ByteType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", ByteType, nullable = true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", ShortType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", ShortType, nullable = true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", IntegerType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", IntegerType, nullable = true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", LongType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", LongType, nullable = true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", FloatType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", FloatType, nullable = true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", DoubleType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", DoubleType, nullable = true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", BooleanType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", BooleanType, nullable = true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", DecimalType.DoubleDecimal, true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", DecimalType.DoubleDecimal, true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", TimestampType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", TimestampType, nullable = true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", DateType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", DateType, nullable = true, CSVOptions("nullValue", "-")))
     assertNull(
-      CSVTypeCast.castTo("-", StringType, nullable = true, CSVOptions("nullValue", "-")))
+      CSVTypeCast.castTo("-", "_1", StringType, nullable = true, CSVOptions("nullValue", "-")))
+    assertNull(
+      CSVTypeCast.castTo(null, "_1", IntegerType, nullable = true, CSVOptions("nullValue", "-")))
+
+    // casting a null to not nullable field should throw an exception.
+    var message = intercept[RuntimeException] {
+      CSVTypeCast.castTo(null, "_1", IntegerType, nullable = false, CSVOptions("nullValue", "-"))
+    }.getMessage
+    assert(message.contains("null value found but field _1 is not nullable."))
+
+    message = intercept[RuntimeException] {
+      CSVTypeCast.castTo("-", "_1", StringType, nullable = false, CSVOptions("nullValue", "-"))
+    }.getMessage
+    assert(message.contains("null value found but field _1 is not nullable."))
   }
 
   test("String type should also respect `nullValue`") {
     assertNull(
-      CSVTypeCast.castTo("", StringType, nullable = true, CSVOptions()))
-    assert(
-      CSVTypeCast.castTo("", StringType, nullable = false, CSVOptions()) ==
-        UTF8String.fromString(""))
+      CSVTypeCast.castTo("", "_1", StringType, nullable = true, CSVOptions()))
 
     assert(
-      CSVTypeCast.castTo("", StringType, nullable = true, CSVOptions("nullValue", "null")) ==
+      CSVTypeCast.castTo("", "_1", StringType, nullable = true, CSVOptions("nullValue", "null")) ==
         UTF8String.fromString(""))
     assert(
-      CSVTypeCast.castTo("", StringType, nullable = false, CSVOptions("nullValue", "null")) ==
+      CSVTypeCast.castTo("", "_1", StringType, nullable = false, CSVOptions("nullValue", "null")) ==
         UTF8String.fromString(""))
 
     assertNull(
-      CSVTypeCast.castTo(null, StringType, nullable = true, CSVOptions("nullValue", "null")))
+      CSVTypeCast.castTo(null, "_1", StringType, nullable = true, CSVOptions("nullValue", "null")))
   }
 
   test("Throws exception for empty string with non null type") {
-    val exception = intercept[NumberFormatException]{
-      CSVTypeCast.castTo("", IntegerType, nullable = false, CSVOptions())
+    val exception = intercept[RuntimeException]{
+      CSVTypeCast.castTo("", "_1", IntegerType, nullable = false, CSVOptions())
     }
-    assert(exception.getMessage.contains("For input string: \"\""))
+    assert(exception.getMessage.contains("null value found but field _1 is not nullable."))
   }
 
   test("Types are cast correctly") {
-    assert(CSVTypeCast.castTo("10", ByteType) == 10)
-    assert(CSVTypeCast.castTo("10", ShortType) == 10)
-    assert(CSVTypeCast.castTo("10", IntegerType) == 10)
-    assert(CSVTypeCast.castTo("10", LongType) == 10)
-    assert(CSVTypeCast.castTo("1.00", FloatType) == 1.0)
-    assert(CSVTypeCast.castTo("1.00", DoubleType) == 1.0)
-    assert(CSVTypeCast.castTo("true", BooleanType) == true)
+    assert(CSVTypeCast.castTo("10", "_1", ByteType) == 10)
+    assert(CSVTypeCast.castTo("10", "_1", ShortType) == 10)
+    assert(CSVTypeCast.castTo("10", "_1", IntegerType) == 10)
+    assert(CSVTypeCast.castTo("10", "_1", LongType) == 10)
+    assert(CSVTypeCast.castTo("1.00", "_1", FloatType) == 1.0)
+    assert(CSVTypeCast.castTo("1.00", "_1", DoubleType) == 1.0)
+    assert(CSVTypeCast.castTo("true", "_1", BooleanType) == true)
 
     val timestampsOptions = CSVOptions("timestampFormat", "dd/MM/yyyy hh:mm")
     val customTimestamp = "31/01/2015 00:00"
     val expectedTime = timestampsOptions.timestampFormat.parse(customTimestamp).getTime
     val castedTimestamp =
-      CSVTypeCast.castTo(customTimestamp, TimestampType, nullable = true, timestampsOptions)
+      CSVTypeCast.castTo(customTimestamp, "_1", TimestampType, nullable = true, timestampsOptions)
     assert(castedTimestamp == expectedTime * 1000L)
 
     val customDate = "31/01/2015"
     val dateOptions = CSVOptions("dateFormat", "dd/MM/yyyy")
     val expectedDate = dateOptions.dateFormat.parse(customDate).getTime
-    val castedDate = CSVTypeCast.castTo(customTimestamp, DateType, nullable = true, dateOptions)
+    val castedDate =
+      CSVTypeCast.castTo(customTimestamp, "_1", DateType, nullable = true, dateOptions)
     assert(castedDate == DateTimeUtils.millisToDays(expectedDate))
 
     val timestamp = "2015-01-01 00:00:00"
-    assert(CSVTypeCast.castTo(timestamp, TimestampType) ==
+    assert(CSVTypeCast.castTo(timestamp, "_1", TimestampType) ==
       DateTimeUtils.stringToTime(timestamp).getTime  * 1000L)
-    assert(CSVTypeCast.castTo("2015-01-01", DateType) ==
+    assert(CSVTypeCast.castTo("2015-01-01", "_1", DateType) ==
       DateTimeUtils.millisToDays(DateTimeUtils.stringToTime("2015-01-01").getTime))
   }
 
   test("Float and Double Types are cast without respect to platform default Locale") {
     val originalLocale = Locale.getDefault
     try {
       Locale.setDefault(new Locale("fr", "FR"))
-      assert(CSVTypeCast.castTo("1,00", FloatType) == 100.0) // Would parse as 1.0 in fr-FR
-      assert(CSVTypeCast.castTo("1,00", DoubleType) == 100.0)
+      assert(CSVTypeCast.castTo("1,00", "_1", FloatType) == 100.0) // Would parse as 1.0 in fr-FR
+      assert(CSVTypeCast.castTo("1,00", "_1", DoubleType) == 100.0)
     } finally {
       Locale.setDefault(originalLocale)
     }
   }
 
   test("Float NaN values are parsed correctly") {
     val floatVal: Float = CSVTypeCast.castTo(
-      "nn", FloatType, nullable = true, CSVOptions("nanValue", "nn")).asInstanceOf[Float]
+      "nn", "_1", FloatType, nullable = true, CSVOptions("nanValue", "nn")).asInstanceOf[Float]
 
     // Java implements the IEEE-754 floating point standard which guarantees that any comparison
     // against NaN will return false (except != which returns true)
@@ -166,32 +177,32 @@ class CSVTypeCastSuite extends SparkFunSuite {
 
   test("Double NaN values are parsed correctly") {
     val doubleVal: Double = CSVTypeCast.castTo(
-      "-", DoubleType, nullable = true, CSVOptions("nanValue", "-")).asInstanceOf[Double]
+      "-", "_1", DoubleType, nullable = true, CSVOptions("nanValue", "-")).asInstanceOf[Double]
 
     assert(doubleVal.isNaN)
   }
 
   test("Float infinite values can be parsed") {
     val floatVal1 = CSVTypeCast.castTo(
-      "max", FloatType, nullable = true, CSVOptions("negativeInf", "max")).asInstanceOf[Float]
+      "max", "_1", FloatType, nullable = true, CSVOptions("negativeInf", "max")).asInstanceOf[Float]
 
     assert(floatVal1 == Float.NegativeInfinity)
 
     val floatVal2 = CSVTypeCast.castTo(
-      "max", FloatType, nullable = true, CSVOptions("positiveInf", "max")).asInstanceOf[Float]
+      "max", "_1", FloatType, nullable = true, CSVOptions("positiveInf", "max")).asInstanceOf[Float]
 
     assert(floatVal2 == Float.PositiveInfinity)
   }
 
   test("Double infinite values can be parsed") {
     val doubleVal1 = CSVTypeCast.castTo(
-      "max", DoubleType, nullable = true, CSVOptions("negativeInf", "max")
+      "max", "_1", DoubleType, nullable = true, CSVOptions("negativeInf", "max")
     ).asInstanceOf[Double]
 
     assert(doubleVal1 == Double.NegativeInfinity)
 
     val doubleVal2 = CSVTypeCast.castTo(
-      "max", DoubleType, nullable = true, CSVOptions("positiveInf", "max")
+      "max", "_1", DoubleType, nullable = true, CSVOptions("positiveInf", "max")
     ).asInstanceOf[Double]
 
     assert(doubleVal2 == Double.PositiveInfinity)