[SPARK-2674] [SQL] [PySpark] support datetime type for SchemaRDD

Datetime and time in Python will be converted into java.util.Calendar after serialization, it will be converted into java.sql.Timestamp during inferSchema().

In javaToPython(), Timestamp will be converted into Calendar, then be converted into datetime in Python after pickling.

Author: Davies Liu <[email protected]>

Closes #1601 from davies/date and squashes the following commits:

f0599b0 [Davies Liu] remove tests for sets and tuple in sql, fix list of list
c9d607a [Davies Liu] convert datetype for runtime
709d40d [Davies Liu] remove brackets
96db384 [Davies Liu] support datetime type for SchemaRDD

Author: davies

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

@@ -550,11 +550,11 @@ private[spark] object PythonRDD extends Logging {
   def pythonToJavaMap(pyRDD: JavaRDD[Array[Byte]]): JavaRDD[Map[String, _]] = {
     pyRDD.rdd.mapPartitions { iter =>
       val unpickle = new Unpickler
-      // TODO: Figure out why flatMap is necessay for pyspark
       iter.flatMap { row =>
         unpickle.loads(row) match {
+          // in case of objects are pickled in batch mode
           case objs: java.util.ArrayList[JMap[String, _] @unchecked] => objs.map(_.toMap)
-          // Incase the partition doesn't have a collection
+          // not in batch mode
           case obj: JMap[String @unchecked, _] => Seq(obj.toMap)
         }
       }

python/pyspark/sql.py

@@ -47,12 +47,14 @@ def __init__(self, sparkContext, sqlContext=None):
             ...
         ValueError:...
 
-        >>> allTypes = sc.parallelize([{"int" : 1, "string" : "string", "double" : 1.0, "long": 1L,
-        ... "boolean" : True}])
+        >>> from datetime import datetime
+        >>> allTypes = sc.parallelize([{"int": 1, "string": "string", "double": 1.0, "long": 1L,
+        ... "boolean": True, "time": datetime(2010, 1, 1, 1, 1, 1), "dict": {"a": 1},
+        ... "list": [1, 2, 3]}])
         >>> srdd = sqlCtx.inferSchema(allTypes).map(lambda x: (x.int, x.string, x.double, x.long,
-        ... x.boolean))
+        ... x.boolean, x.time, x.dict["a"], x.list))
         >>> srdd.collect()[0]
-        (1, u'string', 1.0, 1, True)
+        (1, u'string', 1.0, 1, True, datetime.datetime(2010, 1, 1, 1, 1, 1), 1, [1, 2, 3])
         """
         self._sc = sparkContext
         self._jsc = self._sc._jsc
@@ -88,13 +90,13 @@ def inferSchema(self, rdd):
 
         >>> from array import array
         >>> srdd = sqlCtx.inferSchema(nestedRdd1)
-        >>> srdd.collect() == [{"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
-        ...                    {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}]
+        >>> srdd.collect() == [{"f1" : [1, 2], "f2" : {"row1" : 1.0}},
+        ...                    {"f1" : [2, 3], "f2" : {"row2" : 2.0}}]
         True
 
         >>> srdd = sqlCtx.inferSchema(nestedRdd2)
-        >>> srdd.collect() == [{"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
-        ...                    {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}]
+        >>> srdd.collect() == [{"f1" : [[1, 2], [2, 3]], "f2" : [1, 2]},
+        ...                    {"f1" : [[2, 3], [3, 4]], "f2" : [2, 3]}]
         True
         """
         if (rdd.__class__ is SchemaRDD):
@@ -509,8 +511,8 @@ def _test():
         {"f1": array('i', [1, 2]), "f2": {"row1": 1.0}},
         {"f1": array('i', [2, 3]), "f2": {"row2": 2.0}}])
     globs['nestedRdd2'] = sc.parallelize([
-        {"f1": [[1, 2], [2, 3]], "f2": set([1, 2]), "f3": (1, 2)},
-        {"f1": [[2, 3], [3, 4]], "f2": set([2, 3]), "f3": (2, 3)}])
+        {"f1": [[1, 2], [2, 3]], "f2": [1, 2]},
+        {"f1": [[2, 3], [3, 4]], "f2": [2, 3]}])
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:

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

@@ -352,8 +352,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
       case c: java.lang.Long => LongType
       case c: java.lang.Double => DoubleType
       case c: java.lang.Boolean => BooleanType
+      case c: java.math.BigDecimal => DecimalType
+      case c: java.sql.Timestamp => TimestampType
+      case c: java.util.Calendar => TimestampType
       case c: java.util.List[_] => ArrayType(typeFor(c.head))
-      case c: java.util.Set[_] => ArrayType(typeFor(c.head))
       case c: java.util.Map[_, _] =>
         val (key, value) = c.head
         MapType(typeFor(key), typeFor(value))
@@ -362,11 +364,43 @@ class SQLContext(@transient val sparkContext: SparkContext)
         ArrayType(typeFor(elem))
       case c => throw new Exception(s"Object of type $c cannot be used")
     }
-    val schema = rdd.first().map { case (fieldName, obj) =>
+    val firstRow = rdd.first()
+    val schema = firstRow.map { case (fieldName, obj) =>
       AttributeReference(fieldName, typeFor(obj), true)()
     }.toSeq
 
-    val rowRdd = rdd.mapPartitions { iter =>
+    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
+    }
+
+    // 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
+    }
+
+    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))}
+        }
+      }
+    } else rdd
+
+    val rowRdd = transformed.mapPartitions { iter =>
       iter.map { map =>
         new GenericRow(map.values.toArray.asInstanceOf[Array[Any]]): Row
       }

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

@@ -32,7 +32,7 @@ import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
-import org.apache.spark.sql.catalyst.types.{ArrayType, BooleanType, StructType}
+import org.apache.spark.sql.catalyst.types.{DataType, ArrayType, BooleanType, StructType, MapType}
 import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan}
 import org.apache.spark.api.java.JavaRDD
 
@@ -376,39 +376,27 @@ class SchemaRDD(
    * Converts a JavaRDD to a PythonRDD. It is used by pyspark.
    */
   private[sql] def javaToPython: JavaRDD[Array[Byte]] = {
+    def toJava(obj: Any, dataType: DataType): Any = dataType match {
+      case struct: StructType => rowToMap(obj.asInstanceOf[Row], struct)
+      case array: ArrayType => obj match {
+        case seq: Seq[Any] => seq.map(x => toJava(x, array.elementType)).asJava
+        case list: JList[_] => list.map(x => toJava(x, array.elementType)).asJava
+        case arr if arr != null && arr.getClass.isArray =>
+          arr.asInstanceOf[Array[Any]].map(x => toJava(x, array.elementType))
+        case other => other
+      }
+      case mt: MapType => obj.asInstanceOf[Map[_, _]].map {
+        case (k, v) => (k, toJava(v, mt.valueType)) // key should be primitive type
+      }.asJava
+      // Pyrolite can handle Timestamp
+      case other => obj
+    }
     def rowToMap(row: Row, structType: StructType): JMap[String, Any] = {
       val fields = structType.fields.map(field => (field.name, field.dataType))
       val map: JMap[String, Any] = new java.util.HashMap
       row.zip(fields).foreach {
-        case (obj, (attrName, dataType)) =>
-          dataType match {
-            case struct: StructType => map.put(attrName, rowToMap(obj.asInstanceOf[Row], struct))
-            case array @ ArrayType(struct: StructType) =>
-              val arrayValues = obj match {
-                case seq: Seq[Any] =>
-                  seq.map(element => rowToMap(element.asInstanceOf[Row], struct)).asJava
-                case list: JList[_] =>
-                  list.map(element => rowToMap(element.asInstanceOf[Row], struct))
-                case set: JSet[_] =>
-                  set.map(element => rowToMap(element.asInstanceOf[Row], struct))
-                case arr if arr != null && arr.getClass.isArray =>
-                  arr.asInstanceOf[Array[Any]].map {
-                    element => rowToMap(element.asInstanceOf[Row], struct)
-                  }
-                case other => other
-              }
-              map.put(attrName, arrayValues)
-            case array: ArrayType => {
-              val arrayValues = obj match {
-                case seq: Seq[Any] => seq.asJava
-                case other => other
-              }
-              map.put(attrName, arrayValues)
-            }
-            case other => map.put(attrName, obj)
-          }
+        case (obj, (attrName, dataType)) => map.put(attrName, toJava(obj, dataType))
       }
-
       map
     }