[SPARK-10441] [SQL] Save data correctly to json.

https://issues.apache.org/jira/browse/SPARK-10441

Author: Yin Huai <[email protected]>

Closes apache#8597 from yhuai/timestampJson.

sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala

@@ -23,6 +23,8 @@ import java.math.MathContext
 
 import scala.util.Random
 
+import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.CalendarInterval
 
@@ -84,6 +86,7 @@ object RandomDataGenerator {
    * random data generator is defined for that data type. The generated values will use an external
    * representation of the data type; for example, the random generator for [[DateType]] will return
    * instances of [[java.sql.Date]] and the generator for [[StructType]] will return a [[Row]].
+   * For a [[UserDefinedType]] for a class X, an instance of class X is returned.
    *
    * @param dataType the type to generate values for
    * @param nullable whether null values should be generated
@@ -106,7 +109,22 @@ object RandomDataGenerator {
       })
       case BooleanType => Some(() => rand.nextBoolean())
       case DateType => Some(() => new java.sql.Date(rand.nextInt()))
-      case TimestampType => Some(() => new java.sql.Timestamp(rand.nextLong()))
+      case TimestampType =>
+        val generator =
+          () => {
+            var milliseconds = rand.nextLong() % 253402329599999L
+            // -62135740800000L is the number of milliseconds before January 1, 1970, 00:00:00 GMT
+            // for "0001-01-01 00:00:00.000000". We need to find a
+            // number that is greater or equals to this number as a valid timestamp value.
+            while (milliseconds < -62135740800000L) {
+              // 253402329599999L is the the number of milliseconds since
+              // January 1, 1970, 00:00:00 GMT for "9999-12-31 23:59:59.999999".
+              milliseconds = rand.nextLong() % 253402329599999L
+            }
+            // DateTimeUtils.toJavaTimestamp takes microsecond.
+            DateTimeUtils.toJavaTimestamp(milliseconds * 1000)
+          }
+        Some(generator)
       case CalendarIntervalType => Some(() => {
         val months = rand.nextInt(1000)
         val ns = rand.nextLong()
@@ -159,6 +177,27 @@ object RandomDataGenerator {
           None
         }
       }
+      case udt: UserDefinedType[_] => {
+        val maybeSqlTypeGenerator = forType(udt.sqlType, nullable, seed)
+        // Because random data generator at here returns scala value, we need to
+        // convert it to catalyst value to call udt's deserialize.
+        val toCatalystType = CatalystTypeConverters.createToCatalystConverter(udt.sqlType)
+
+        if (maybeSqlTypeGenerator.isDefined) {
+          val sqlTypeGenerator = maybeSqlTypeGenerator.get
+          val generator = () => {
+            val generatedScalaValue = sqlTypeGenerator.apply()
+            if (generatedScalaValue == null) {
+              null
+            } else {
+              udt.deserialize(toCatalystType(generatedScalaValue))
+            }
+          }
+          Some(generator)
+        } else {
+          None
+        }
+      }
       case unsupportedType => None
     }
     // Handle nullability by wrapping the non-null value generator:

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala

@@ -18,6 +18,7 @@
 package org.apache.spark.sql.execution.datasources.json
 
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 
 import scala.collection.Map
 
@@ -89,7 +90,7 @@ private[sql] object JacksonGenerator {
     def valWriter: (DataType, Any) => Unit = {
       case (_, null) | (NullType, _) => gen.writeNull()
       case (StringType, v) => gen.writeString(v.toString)
-      case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString)
+      case (TimestampType, v: Long) => gen.writeString(DateTimeUtils.toJavaTimestamp(v).toString)
       case (IntegerType, v: Int) => gen.writeNumber(v)
       case (ShortType, v: Short) => gen.writeNumber(v)
       case (FloatType, v: Float) => gen.writeNumber(v)
@@ -99,8 +100,12 @@ private[sql] object JacksonGenerator {
       case (ByteType, v: Byte) => gen.writeNumber(v.toInt)
       case (BinaryType, v: Array[Byte]) => gen.writeBinary(v)
       case (BooleanType, v: Boolean) => gen.writeBoolean(v)
-      case (DateType, v) => gen.writeString(v.toString)
-      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v))
+      case (DateType, v: Int) => gen.writeString(DateTimeUtils.toJavaDate(v).toString)
+      // For UDT values, they should be in the SQL type's corresponding value type.
+      // We should not see values in the user-defined class at here.
+      // For example, VectorUDT's SQL type is an array of double. So, we should expect that v is
+      // an ArrayData at here, instead of a Vector.
+      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, v)
 
       case (ArrayType(ty, _), v: ArrayData) =>
         gen.writeStartArray()

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala

@@ -81,9 +81,37 @@ private[sql] object JacksonParser {
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, FloatType) =>
         parser.getFloatValue
 
+      case (VALUE_STRING, FloatType) =>
+        // Special case handling for NaN and Infinity.
+        val value = parser.getText
+        val lowerCaseValue = value.toLowerCase()
+        if (lowerCaseValue.equals("nan") ||
+          lowerCaseValue.equals("infinity") ||
+          lowerCaseValue.equals("-infinity") ||
+          lowerCaseValue.equals("inf") ||
+          lowerCaseValue.equals("-inf")) {
+          value.toFloat
+        } else {
+          sys.error(s"Cannot parse $value as FloatType.")
+        }
+
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DoubleType) =>
         parser.getDoubleValue
 
+      case (VALUE_STRING, DoubleType) =>
+        // Special case handling for NaN and Infinity.
+        val value = parser.getText
+        val lowerCaseValue = value.toLowerCase()
+        if (lowerCaseValue.equals("nan") ||
+          lowerCaseValue.equals("infinity") ||
+          lowerCaseValue.equals("-infinity") ||
+          lowerCaseValue.equals("inf") ||
+          lowerCaseValue.equals("-inf")) {
+          value.toDouble
+        } else {
+          sys.error(s"Cannot parse $value as DoubleType.")
+        }
+
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, dt: DecimalType) =>
         Decimal(parser.getDecimalValue, dt.precision, dt.scale)
 
@@ -126,6 +154,9 @@ private[sql] object JacksonParser {
 
       case (_, udt: UserDefinedType[_]) =>
         convertField(factory, parser, udt.sqlType)
+
+      case (token, dataType) =>
+        sys.error(s"Failed to parse a value for data type $dataType (current token: $token).")
     }
   }
 

sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala

@@ -28,6 +28,14 @@ class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
 
   override val dataSourceName: String = classOf[DefaultSource].getCanonicalName
 
+  // ORC does not play well with NullType and UDT.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: NullType => false
+    case _: CalendarIntervalType => false
+    case _: UserDefinedType[_] => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)

sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala

@@ -28,6 +28,14 @@ import org.apache.spark.sql.types._
 class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = "json"
 
+  // JSON does not write data of NullType and does not play well with BinaryType.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: NullType => false
+    case _: BinaryType => false
+    case _: CalendarIntervalType => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)

sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala

@@ -24,14 +24,21 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql.{execution, AnalysisException, SaveMode}
-import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+import org.apache.spark.sql.types._
 
 
 class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
   import testImplicits._
 
   override val dataSourceName: String = "parquet"
 
+  // Parquet does not play well with NullType.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: NullType => false
+    case _: CalendarIntervalType => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)

sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala

@@ -20,11 +20,28 @@ package org.apache.spark.sql.sources
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+import org.apache.spark.sql.types._
 
 class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName
 
+  // We have a very limited number of supported types at here since it is just for a
+  // test relation and we do very basic testing at here.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: BinaryType => false
+    // We are using random data generator and the generated strings are not really valid string.
+    case _: StringType => false
+    case _: BooleanType => false // see https://issues.apache.org/jira/browse/SPARK-10442
+    case _: CalendarIntervalType => false
+    case _: DateType => false
+    case _: TimestampType => false
+    case _: ArrayType => false
+    case _: MapType => false
+    case _: StructType => false
+    case _: UserDefinedType[_] => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)

sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala

@@ -68,7 +68,9 @@ class SimpleTextOutputWriter(path: String, context: TaskAttemptContext) extends
     new AppendingTextOutputFormat(new Path(path)).getRecordWriter(context)
 
   override def write(row: Row): Unit = {
-    val serialized = row.toSeq.map(_.toString).mkString(",")
+    val serialized = row.toSeq.map { v =>
+      if (v == null) "" else v.toString
+    }.mkString(",")
     recordWriter.write(null, new Text(serialized))
   }
 
@@ -112,7 +114,8 @@ class SimpleTextRelation(
     val fields = dataSchema.map(_.dataType)
 
     sparkContext.textFile(inputStatuses.map(_.getPath).mkString(",")).map { record =>
-      Row(record.split(",").zip(fields).map { case (value, dataType) =>
+      Row(record.split(",", -1).zip(fields).map { case (v, dataType) =>
+        val value = if (v == "") null else v
         // `Cast`ed values are always of Catalyst types (i.e. UTF8String instead of String, etc.)
         val catalystValue = Cast(Literal(value), dataType).eval()
         // Here we're converting Catalyst values to Scala values to test `needsConversion`

sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala

@@ -38,6 +38,8 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
 
   val dataSourceName: String
 
+  protected def supportsDataType(dataType: DataType): Boolean = true
+
   val dataSchema =
     StructType(
       Seq(
@@ -98,6 +100,83 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
     }
   }
 
+  test("test all data types") {
+    withTempPath { file =>
+      // Create the schema.
+      val struct =
+        StructType(
+          StructField("f1", FloatType, true) ::
+            StructField("f2", ArrayType(BooleanType), true) :: Nil)
+      // TODO: add CalendarIntervalType to here once we can save it out.
+      val dataTypes =
+        Seq(
+          StringType, BinaryType, NullType, BooleanType,
+          ByteType, ShortType, IntegerType, LongType,
+          FloatType, DoubleType, DecimalType(25, 5), DecimalType(6, 5),
+          DateType, TimestampType,
+          ArrayType(IntegerType), MapType(StringType, LongType), struct,
+          new MyDenseVectorUDT())
+      val fields = dataTypes.zipWithIndex.map { case (dataType, index) =>
+        StructField(s"col$index", dataType, nullable = true)
+      }
+      val schema = StructType(fields)
+
+      // Generate data at the driver side. We need to materialize the data first and then
+      // create RDD.
+      val maybeDataGenerator =
+        RandomDataGenerator.forType(
+          dataType = schema,
+          nullable = true,
+          seed = Some(System.nanoTime()))
+      val dataGenerator =
+        maybeDataGenerator
+          .getOrElse(fail(s"Failed to create data generator for schema $schema"))
+      val data = (1 to 10).map { i =>
+        dataGenerator.apply() match {
+          case row: Row => row
+          case null => Row.fromSeq(Seq.fill(schema.length)(null))
+          case other =>
+            fail(s"Row or null is expected to be generated, " +
+              s"but a ${other.getClass.getCanonicalName} is generated.")
+        }
+      }
+
+      // Create a DF for the schema with random data.
+      val rdd = sqlContext.sparkContext.parallelize(data, 10)
+      val df = sqlContext.createDataFrame(rdd, schema)
+
+      // All columns that have supported data types of this source.
+      val supportedColumns = schema.fields.collect {
+        case StructField(name, dataType, _, _) if supportsDataType(dataType) => name
+      }
+      val selectedColumns = util.Random.shuffle(supportedColumns.toSeq)
+
+      val dfToBeSaved = df.selectExpr(selectedColumns: _*)
+
+      // Save the data out.
+      dfToBeSaved
+        .write
+        .format(dataSourceName)
+        .option("dataSchema", dfToBeSaved.schema.json) // This option is just used by tests.
+        .save(file.getCanonicalPath)
+
+      val loadedDF =
+        sqlContext
+          .read
+          .format(dataSourceName)
+          .schema(dfToBeSaved.schema)
+          .option("dataSchema", dfToBeSaved.schema.json) // This option is just used by tests.
+          .load(file.getCanonicalPath)
+          .selectExpr(selectedColumns: _*)
+
+      // Read the data back.
+      checkAnswer(
+        loadedDF,
+        dfToBeSaved
+      )
+    }
+  }
+
   test("save()/load() - non-partitioned table - Overwrite") {
     withTempPath { file =>
       testDF.write.mode(SaveMode.Overwrite).format(dataSourceName).save(file.getCanonicalPath)