[SPARK-5722] [SQL] [PySpark] infer int as LongType in Python (for 1.2 branch)

python/pyspark/sql.py

@@ -577,7 +577,7 @@ def _parse_datatype_json_value(json_value):
 _type_mappings = {
     type(None): NullType,
     bool: BooleanType,
-    int: IntegerType,
+    int: LongType,
     long: LongType,
     float: DoubleType,
     str: StringType,
@@ -926,11 +926,11 @@ def _infer_schema_type(obj, dataType):
     >>> schema = _parse_schema_abstract("a b c d")
     >>> row = (1, 1.0, "str", datetime.date(2014, 10, 10))
     >>> _infer_schema_type(row, schema)
-    StructType...IntegerType...DoubleType...StringType...DateType...
+    StructType...LongType...DoubleType...StringType...DateType...
     >>> row = [[1], {"key": (1, 2.0)}]
     >>> schema = _parse_schema_abstract("a[] b{c d}")
     >>> _infer_schema_type(row, schema)
-    StructType...a,ArrayType...b,MapType(StringType,...c,IntegerType...
+    StructType...a,ArrayType...b,MapType(StringType,...c,LongType...
     """
     if dataType is None:
         return _infer_type(obj)
@@ -985,7 +985,7 @@ def _verify_type(obj, dataType):
 
     >>> _verify_type(None, StructType([]))
     >>> _verify_type("", StringType())
-    >>> _verify_type(0, IntegerType())
+    >>> _verify_type(0, LongType())
     >>> _verify_type(range(3), ArrayType(ShortType()))
     >>> _verify_type(set(), ArrayType(StringType())) # doctest: +IGNORE_EXCEPTION_DETAIL
     Traceback (most recent call last):

python/pyspark/tests.py

@@ -51,7 +51,7 @@
     CloudPickleSerializer, CompressedSerializer
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, ExternalSorter
 from pyspark.sql import SQLContext, IntegerType, Row, ArrayType, StructType, StructField, \
-    UserDefinedType, DoubleType
+    UserDefinedType, DoubleType, LongType, _infer_type
 from pyspark import shuffle
 
 _have_scipy = False
@@ -985,6 +985,27 @@ def test_parquet_with_udt(self):
         point = srdd1.first().point
         self.assertEquals(point, ExamplePoint(1.0, 2.0))
 
+    def test_infer_long_type(self):
+        longrow = [Row(f1='a', f2=100000000000000)]
+        rdd = self.sc.parallelize(longrow)
+        srdd = self.sqlCtx.inferSchema(rdd)
+        self.assertEqual(srdd.schema().fields[1].dataType, LongType())
+
+        # this saving as Parquet caused issues as well.
+        output_dir = os.path.join(self.tempdir.name, "infer_long_type")
+        srdd.saveAsParquetFile(output_dir)
+        df1 = self.sqlCtx.parquetFile(output_dir)
+        self.assertEquals('a', df1.first().f1)
+        self.assertEquals(100000000000000, df1.first().f2)
+
+        self.assertEqual(_infer_type(1), LongType())
+        self.assertEqual(_infer_type(2**10), LongType())
+        self.assertEqual(_infer_type(2**20), LongType())
+        self.assertEqual(_infer_type(2**31 - 1), LongType())
+        self.assertEqual(_infer_type(2**31), LongType())
+        self.assertEqual(_infer_type(2**61), LongType())
+        self.assertEqual(_infer_type(2**71), LongType())
+
 
 class InputFormatTests(ReusedPySparkTestCase):
 

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

@@ -479,6 +479,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       case ByteType => true
       case ShortType => true
       case FloatType => true
+      case LongType => true
       case DateType => true
       case TimestampType => true
       case ArrayType(_, _) => true

sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala

@@ -187,6 +187,7 @@ object EvaluatePython {
     case (c: Int, ShortType) => c.toShort
     case (c: Long, ShortType) => c.toShort
     case (c: Long, IntegerType) => c.toInt
+    case (c: Int,  LongType) => c.toLong
     case (c: Double, FloatType) => c.toFloat
     case (c, StringType) if !c.isInstanceOf[String] => c.toString