Fix

Author: mswit-databricks

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala

@@ -30,6 +30,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.json._
 import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
 import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, ArrayData, BadRecordException, FailFastMode, GenericArrayData, MapData}
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
@@ -515,10 +516,15 @@ case class JsonToStructs(
     child: Expression,
     timeZoneId: Option[String] = None)
   extends UnaryExpression with TimeZoneAwareExpression with CodegenFallback with ExpectsInputTypes {
-  override def nullable: Boolean = true
 
-  def this(schema: DataType, options: Map[String, String], child: Expression) =
-    this(schema, options, child, None)
+  val forceNullableSchema = SQLConf.get.getConf(SQLConf.FROM_JSON_FORCE_NULLABLE_SCHEMA)
+
+  // The JSON input data might be missing certain fields. We force the nullability
+  // of the user-provided schema to avoid data corruptions. In particular, the parquet-mr encoder
+  // can generate incorrect files if values are missing in columns declared as non-nullable.
+  val nullableSchema = if (forceNullableSchema) schema.asNullable else schema
+
+  override def nullable: Boolean = true
 
   // Used in `FunctionRegistry`
   def this(child: Expression, schema: Expression) =
@@ -535,22 +541,22 @@ case class JsonToStructs(
       child = child,
       timeZoneId = None)
 
-  override def checkInputDataTypes(): TypeCheckResult = schema match {
+  override def checkInputDataTypes(): TypeCheckResult = nullableSchema match {
     case _: StructType | ArrayType(_: StructType, _) =>
       super.checkInputDataTypes()
     case _ => TypeCheckResult.TypeCheckFailure(
-      s"Input schema ${schema.simpleString} must be a struct or an array of structs.")
+      s"Input schema ${nullableSchema.simpleString} must be a struct or an array of structs.")
   }
 
   @transient
-  lazy val rowSchema = schema match {
+  lazy val rowSchema = nullableSchema match {
     case st: StructType => st
     case ArrayType(st: StructType, _) => st
   }
 
   // This converts parsed rows to the desired output by the given schema.
   @transient
-  lazy val converter = schema match {
+  lazy val converter = nullableSchema match {
     case _: StructType =>
       (rows: Seq[InternalRow]) => if (rows.length == 1) rows.head else null
     case ArrayType(_: StructType, _) =>
@@ -563,7 +569,7 @@ case class JsonToStructs(
       rowSchema,
       new JSONOptions(options + ("mode" -> FailFastMode.name), timeZoneId.get))
 
-  override def dataType: DataType = schema
+  override def dataType: DataType = nullableSchema
 
   override def withTimeZone(timeZoneId: String): TimeZoneAwareExpression =
     copy(timeZoneId = Option(timeZoneId))

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -493,6 +493,14 @@ object SQLConf {
     .stringConf
     .createWithDefault("_corrupt_record")
 
+  val FROM_JSON_FORCE_NULLABLE_SCHEMA = buildConf("spark.sql.fromJsonForceNullableSchema")
+    .internal()
+    .doc("When true, force the output schema of the from_json() function to be nullable " +
+      "(including all the fields). Otherwise, the schema might not be compatible with" +
+      "actual data, which leads to curruptions.")
+    .booleanConf
+    .createWithDefault(true)
+
   val BROADCAST_TIMEOUT = buildConf("spark.sql.broadcastTimeout")
     .doc("Timeout in seconds for the broadcast wait time in broadcast joins.")
     .timeConf(TimeUnit.SECONDS)

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala

@@ -23,6 +23,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, DateTimeTestUtils, DateTimeUtils, GenericArrayData, PermissiveMode}
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -680,4 +681,31 @@ class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       )
     }
   }
+
+  test("from_json missing fields") {
+    val conf = SQLConf.get
+    for (forceJsonNullableSchema <- Seq(false, true)) {
+      conf.setConf(SQLConf.FROM_JSON_FORCE_NULLABLE_SCHEMA, forceJsonNullableSchema)
+      val input =
+        """{
+          |  "a": 1,
+          |  "c": "foo"
+          |}
+          |"""
+          .stripMargin
+      val jsonSchema = new StructType()
+        .add("a", LongType, nullable = false)
+        .add("b", StringType, nullable = false)
+        .add("c", StringType, nullable = false)
+      val output = InternalRow(1L, null, UTF8String.fromString("foo"))
+      checkEvaluation(
+        JsonToStructs(jsonSchema, Map.empty, Literal.create(input, StringType), gmtId),
+        output
+      )
+      val schema = JsonToStructs(jsonSchema, Map.empty, Literal.create(input, StringType), gmtId)
+        .dataType
+      val schemaToCompare = if (forceJsonNullableSchema) jsonSchema.asNullable else jsonSchema
+      assert(schemaToCompare == schema);
+    }
+  }
 }

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala

@@ -43,6 +43,7 @@ import org.apache.spark.sql.catalyst.{InternalRow, ScalaReflection}
 import org.apache.spark.sql.catalyst.expressions.{GenericInternalRow, UnsafeRow}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.execution.datasources.SQLHadoopMapReduceCommitProtocol
+import org.apache.spark.sql.functions._
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
@@ -780,6 +781,25 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
       assert(option.compressionCodecClassName == "UNCOMPRESSED")
     }
   }
+
+  test("SPARK-23173 Writing a file with data converted from JSON with and incorrect user schema") {
+    withTempPath { file =>
+      val jsonData =
+        """{
+        |  "a": 1,
+        |  "c": "foo"
+        |}
+        |"""
+          .stripMargin
+      val jsonSchema = new StructType()
+        .add("a", LongType, nullable = false)
+        .add("b", StringType, nullable = false)
+        .add("c", StringType, nullable = false)
+      spark.range(1).select(from_json(lit(jsonData), jsonSchema) as "input")
+        .write.parquet(file.getAbsolutePath)
+      checkAnswer(spark.read.parquet(file.getAbsolutePath), Seq(Row(Row(1, null, "foo"))))
+    }
+  }
 }
 
 class JobCommitFailureParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)