Converts types of values based on defined schema.

core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala

@@ -544,7 +544,8 @@ private[spark] object PythonRDD extends Logging {
   }
 
   /**
-   * Convert an RDD of serialized Python dictionaries to Scala Maps
+   * Convert an RDD of serialized Python dictionaries to Scala Maps (no recursive conversions).
+   * It is only used by pyspark.sql.
    * TODO: Support more Python types.
    */
   def pythonToJavaMap(pyRDD: JavaRDD[Array[Byte]]): JavaRDD[Map[String, _]] = {

python/pyspark/sql.py

@@ -111,12 +111,7 @@ def __repr__(self):
 class FloatType(object):
     """Spark SQL FloatType
 
-    For now, please use L{DoubleType} instead of using L{FloatType}.
-    Because query evaluation is done in Scala, java.lang.Double will be be used
-    for Python float numbers. Because the underlying JVM type of FloatType is
-    java.lang.Float (in Java) and Float (in scala), and we are trying to cast the type,
-    there will be a java.lang.ClassCastException
-    if FloatType (Python) is used.
+    The data type representing single precision floating-point values.
 
     """
     __metaclass__ = PrimitiveTypeSingleton
@@ -128,12 +123,7 @@ def __repr__(self):
 class ByteType(object):
     """Spark SQL ByteType
 
-    For now, please use L{IntegerType} instead of using L{ByteType}.
-    Because query evaluation is done in Scala, java.lang.Integer will be be used
-    for Python int numbers. Because the underlying JVM type of ByteType is
-    java.lang.Byte (in Java) and Byte (in scala), and we are trying to cast the type,
-    there will be a java.lang.ClassCastException
-    if ByteType (Python) is used.
+    The data type representing int values with 1 singed byte.
 
     """
     __metaclass__ = PrimitiveTypeSingleton
@@ -170,12 +160,7 @@ def __repr__(self):
 class ShortType(object):
     """Spark SQL ShortType
 
-    For now, please use L{IntegerType} instead of using L{ShortType}.
-    Because query evaluation is done in Scala, java.lang.Integer will be be used
-    for Python int numbers. Because the underlying JVM type of ShortType is
-    java.lang.Short (in Java) and Short (in scala), and we are trying to cast the type,
-    there will be a java.lang.ClassCastException
-    if ShortType (Python) is used.
+    The data type representing int values with 2 signed bytes.
 
     """
     __metaclass__ = PrimitiveTypeSingleton
@@ -198,7 +183,6 @@ def __init__(self, elementType, containsNull=False):
 
         :param elementType: the data type of elements.
         :param containsNull: indicates whether the list contains None values.
-        :return:
 
         >>> ArrayType(StringType) == ArrayType(StringType, False)
         True
@@ -238,7 +222,6 @@ def __init__(self, keyType, valueType, valueContainsNull=True):
         :param keyType: the data type of keys.
         :param valueType: the data type of values.
         :param valueContainsNull: indicates whether values contains null values.
-        :return:
 
         >>> MapType(StringType, IntegerType) == MapType(StringType, IntegerType, True)
         True
@@ -279,7 +262,6 @@ def __init__(self, name, dataType, nullable):
         :param name: the name of this field.
         :param dataType: the data type of this field.
         :param nullable: indicates whether values of this field can be null.
-        :return:
 
         >>> StructField("f1", StringType, True) == StructField("f1", StringType, True)
         True
@@ -314,8 +296,6 @@ class StructType(object):
     """
     def __init__(self, fields):
         """Creates a StructType
-        :param fields:
-        :return:
 
         >>> struct1 = StructType([StructField("f1", StringType, True)])
         >>> struct2 = StructType([StructField("f1", StringType, True)])
@@ -342,11 +322,7 @@ def __ne__(self, other):
 
 
 def _parse_datatype_list(datatype_list_string):
-    """Parses a list of comma separated data types.
-
-    :param datatype_list_string:
-    :return:
-    """
+    """Parses a list of comma separated data types."""
     index = 0
     datatype_list = []
     start = 0
@@ -372,9 +348,6 @@ def _parse_datatype_list(datatype_list_string):
 def _parse_datatype_string(datatype_string):
     """Parses the given data type string.
 
-    :param datatype_string:
-    :return:
-
     >>> def check_datatype(datatype):
     ...     scala_datatype = sqlCtx._ssql_ctx.parseDataType(datatype.__repr__())
     ...     python_datatype = _parse_datatype_string(scala_datatype.toString())
@@ -582,9 +555,6 @@ def inferSchema(self, rdd):
 
     def applySchema(self, rdd, schema):
         """Applies the given schema to the given RDD of L{dict}s.
-        :param rdd:
-        :param schema:
-        :return:
 
         >>> schema = StructType([StructField("field1", IntegerType(), False),
         ...     StructField("field2", StringType(), False)])
@@ -594,9 +564,27 @@ def applySchema(self, rdd, schema):
         >>> srdd2.collect() == [{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"},
         ...                    {"field1" : 3, "field2": "row3"}]
         True
+        >>> from datetime import datetime
+        >>> rdd = sc.parallelize([{"byte": 127, "short": -32768, "float": 1.0,
+        ... "time": datetime(2010, 1, 1, 1, 1, 1), "map": {"a": 1}, "struct": {"b": 2},
+        ... "list": [1, 2, 3]}])
+        >>> schema = StructType([
+        ...     StructField("byte", ByteType(), False),
+        ...     StructField("short", ShortType(), False),
+        ...     StructField("float", FloatType(), False),
+        ...     StructField("time", TimestampType(), False),
+        ...     StructField("map", MapType(StringType(), IntegerType(), False), False),
+        ...     StructField("struct", StructType([StructField("b", ShortType(), False)]), False),
+        ...     StructField("list", ArrayType(ByteType(), False), False),
+        ...     StructField("null", DoubleType(), True)])
+        >>> srdd = sqlCtx.applySchema(rdd, schema).map(
+        ...     lambda x: (
+        ...         x.byte, x.short, x.float, x.time, x.map["a"], x.struct["b"], x.list, x.null))
+        >>> srdd.collect()[0]
+        (127, -32768, 1.0, datetime.datetime(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None)
         """
         jrdd = self._pythonToJavaMap(rdd._jrdd)
-        srdd = self._ssql_ctx.applySchema(jrdd.rdd(), schema.__repr__())
+        srdd = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.__repr__())
         return SchemaRDD(srdd, self)
 
     def registerRDDAsTable(self, rdd, tableName):

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala

@@ -313,13 +313,13 @@ case class StructType(fields: Seq[StructField]) extends DataType {
    */
   lazy val fieldNames: Seq[String] = fields.map(_.name)
   private lazy val fieldNamesSet: Set[String] = fieldNames.toSet
-
+  private lazy val nameToField: Map[String, StructField] = fields.map(f => f.name -> f).toMap
   /**
    * Extracts a [[StructField]] of the given name. If the [[StructType]] object does not
    * have a name matching the given name, `null` will be returned.
    */
   def apply(name: String): StructField = {
-    fields.find(f => f.name == name).getOrElse(
+    nameToField.get(name).getOrElse(
       throw new IllegalArgumentException(s"Field ${name} does not exist."))
   }
 
@@ -333,6 +333,7 @@ case class StructType(fields: Seq[StructField]) extends DataType {
       throw new IllegalArgumentException(
         s"Field ${nonExistFields.mkString(",")} does not exist.")
     }
+    // Preserve the original order of fields.
     StructType(fields.filter(f => names.contains(f.name)))
   }
 

sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala

@@ -125,29 +125,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
     new SchemaRDD(this, logicalPlan)
   }
 
-  /**
-   * Parses the data type in our internal string representation. The data type string should
-   * have the same format as the one generated by `toString` in scala.
-   * It is only used by PySpark.
-   */
-  private[sql] def parseDataType(dataTypeString: String): DataType = {
-    val parser = org.apache.spark.sql.catalyst.types.DataType
-    parser(dataTypeString)
-  }
-
-  /**
-   * Apply a schema defined by the schemaString to an RDD. It is only used by PySpark.
-   */
-  private[sql] def applySchema(rdd: RDD[Map[String, _]], schemaString: String): SchemaRDD = {
-    val schema = parseDataType(schemaString).asInstanceOf[StructType]
-    val rowRdd = rdd.mapPartitions { iter =>
-      iter.map { map =>
-        new GenericRow(map.values.toArray.asInstanceOf[Array[Any]]): Row
-      }
-    }
-    new SchemaRDD(this, SparkLogicalPlan(ExistingRdd(schema.toAttributes, rowRdd)))
-  }
-
   /**
    * Loads a Parquet file, returning the result as a [[SchemaRDD]].
    *
@@ -438,6 +415,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    */
   private[sql] def inferSchema(rdd: RDD[Map[String, _]]): SchemaRDD = {
     import scala.collection.JavaConversions._
+
     def typeOfComplexValue: PartialFunction[Any, DataType] = {
       case c: java.util.Calendar => TimestampType
       case c: java.util.List[_] =>
@@ -453,48 +431,116 @@ class SQLContext(@transient val sparkContext: SparkContext)
     def typeOfObject = ScalaReflection.typeOfObject orElse typeOfComplexValue
 
     val firstRow = rdd.first()
-    val schema = StructType(
-      firstRow.map { case (fieldName, obj) =>
-        StructField(fieldName, typeOfObject(obj), true)
-      }.toSeq)
-
-    def needTransform(obj: Any): Boolean = obj match {
-      case c: java.util.List[_] => true
-      case c: java.util.Map[_, _] => true
-      case c if c.getClass.isArray => true
-      case c: java.util.Calendar => true
-      case c => false
+    val fields = firstRow.map {
+      case (fieldName, obj) => StructField(fieldName, typeOfObject(obj), true)
+    }.toSeq
+
+    applySchemaToPythonRDD(rdd, StructType(fields))
+  }
+
+  /**
+   * Parses the data type in our internal string representation. The data type string should
+   * have the same format as the one generated by `toString` in scala.
+   * It is only used by PySpark.
+   */
+  private[sql] def parseDataType(dataTypeString: String): DataType = {
+    val parser = org.apache.spark.sql.catalyst.types.DataType
+    parser(dataTypeString)
+  }
+
+  /**
+   * Apply a schema defined by the schemaString to an RDD. It is only used by PySpark.
+   */
+  private[sql] def applySchemaToPythonRDD(
+      rdd: RDD[Map[String, _]],
+      schemaString: String): SchemaRDD = {
+    val schema = parseDataType(schemaString).asInstanceOf[StructType]
+    applySchemaToPythonRDD(rdd, schema)
+  }
+
+  /**
+   * Apply a schema defined by the schema to an RDD. It is only used by PySpark.
+   */
+  private[sql] def applySchemaToPythonRDD(
+      rdd: RDD[Map[String, _]],
+      schema: StructType): SchemaRDD = {
+    import scala.collection.JavaConversions._
+    import scala.collection.convert.Wrappers.{JListWrapper, JMapWrapper}
+
+    def needsConversion(dataType: DataType): Boolean = dataType match {
+      case ByteType => true
+      case ShortType => true
+      case FloatType => true
+      case TimestampType => true
+      case ArrayType(_, _) => true
+      case MapType(_, _, _) => true
+      case StructType(_) => true
+      case other => false
     }
 
-    // convert JList, JArray into Seq, convert JMap into Map
-    // convert Calendar into Timestamp
-    def transform(obj: Any): Any = obj match {
-      case c: java.util.List[_] => c.map(transform).toSeq
-      case c: java.util.Map[_, _] => c.map {
-        case (key, value) => (key, transform(value))
-      }.toMap
-      case c if c.getClass.isArray =>
-        c.asInstanceOf[Array[_]].map(transform).toSeq
-      case c: java.util.Calendar =>
-        new java.sql.Timestamp(c.getTime().getTime())
-      case c => c
+    // Converts value to the type specified by the data type.
+    // Because Python does not have data types for TimestampType, FloatType, ShortType, and
+    // ByteType, we need to explicitly convert values in columns of these data types to the desired
+    // JVM data types.
+    def convert(obj: Any, dataType: DataType): Any = (obj, dataType) match {
+      // TODO: We should check nullable
+      case (null, _) => null
+
+      case (c: java.util.List[_], ArrayType(elementType, _)) =>
+        val converted = c.map { e => convert(e, elementType)}
+        JListWrapper(converted)
+
+      case (c: java.util.Map[_, _], struct: StructType) =>
+        val row = new GenericMutableRow(struct.fields.length)
+        struct.fields.zipWithIndex.foreach {
+          case (field, i) =>
+            val value = convert(c.get(field.name), field.dataType)
+            row.update(i, value)
+        }
+        row
+
+      case (c: java.util.Map[_, _], MapType(keyType, valueType, _)) =>
+        val converted = c.map {
+          case (key, value) =>
+            (convert(key, keyType), convert(value, valueType))
+        }
+        JMapWrapper(converted)
+
+      case (c, ArrayType(elementType, _)) if c.getClass.isArray =>
+        val converted = c.asInstanceOf[Array[_]].map(e => convert(e, elementType))
+        converted: Seq[Any]
+
+      case (c: java.util.Calendar, TimestampType) => new java.sql.Timestamp(c.getTime().getTime())
+      case (c: Int, ByteType) => c.toByte
+      case (c: Int, ShortType) => c.toShort
+      case (c: Double, FloatType) => c.toFloat
+
+      case (c, _) => c
+    }
+
+    val convertedRdd = if (schema.fields.exists(f => needsConversion(f.dataType))) {
+      rdd.map(m => m.map { case (key, value) => (key, convert(value, schema(key).dataType)) })
+    } else {
+      rdd
     }
 
-    val need = firstRow.exists { case (key, value) => needTransform(value) }
-    val transformed = if (need) {
-      rdd.mapPartitions { iter =>
-        iter.map {
-          m => m.map {case (key, value) => (key, transform(value))}
+    val rowRdd = convertedRdd.mapPartitions { iter =>
+      val row = new GenericMutableRow(schema.fields.length)
+      val fieldsWithIndex = schema.fields.zipWithIndex
+      iter.map { m =>
+        // We cannot use m.values because the order of values returned by m.values may not
+        // match fields order.
+        fieldsWithIndex.foreach {
+          case (field, i) =>
+            val value =
+              m.get(field.name).flatMap(v => Option(v)).map(v => convert(v, field.dataType)).orNull
+            row.update(i, value)
         }
-      }
-    } else rdd
 
-    val rowRdd = transformed.mapPartitions { iter =>
-      iter.map { map =>
-        new GenericRow(map.values.toArray.asInstanceOf[Array[Any]]): Row
+        row: Row
       }
     }
+
     new SchemaRDD(this, SparkLogicalPlan(ExistingRdd(schema.toAttributes, rowRdd)))
   }
-
 }

sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql
 
-import java.util.{Map => JMap, List => JList, Set => JSet}
+import java.util.{Map => JMap, List => JList}
 
 import scala.collection.JavaConversions._
 import scala.collection.JavaConverters._
@@ -380,6 +380,8 @@ class SchemaRDD(
    * Converts a JavaRDD to a PythonRDD. It is used by pyspark.
    */
   private[sql] def javaToPython: JavaRDD[Array[Byte]] = {
+    import scala.collection.Map
+
     def toJava(obj: Any, dataType: DataType): Any = dataType match {
       case struct: StructType => rowToMap(obj.asInstanceOf[Row], struct)
       case array: ArrayType => obj match {