[SPARK-12300] [SQL] [PYSPARK] fix schema inferance on local collections

Current schema inference for local python collections halts as soon as there are no NullTypes. This is different than when we specify a sampling ratio of 1.0 on a distributed collection. This could result in incomplete schema information.

Author: Holden Karau <[email protected]>

Closes apache#10275 from holdenk/SPARK-12300-fix-schmea-inferance-on-local-collections.

Author: holdenk

python/pyspark/sql/context.py

@@ -18,6 +18,7 @@
 import sys
 import warnings
 import json
+from functools import reduce
 
 if sys.version >= '3':
     basestring = unicode = str
@@ -236,14 +237,9 @@ def _inferSchemaFromList(self, data):
         if type(first) is dict:
             warnings.warn("inferring schema from dict is deprecated,"
                           "please use pyspark.sql.Row instead")
-        schema = _infer_schema(first)
+        schema = reduce(_merge_type, map(_infer_schema, data))
         if _has_nulltype(schema):
-            for r in data:
-                schema = _merge_type(schema, _infer_schema(r))
-                if not _has_nulltype(schema):
-                    break
-            else:
-                raise ValueError("Some of types cannot be determined after inferring")
+            raise ValueError("Some of types cannot be determined after inferring")
         return schema
 
     def _inferSchema(self, rdd, samplingRatio=None):

python/pyspark/sql/tests.py

@@ -353,6 +353,17 @@ def test_apply_schema_to_row(self):
         df3 = self.sqlCtx.createDataFrame(rdd, df.schema)
         self.assertEqual(10, df3.count())
 
+    def test_infer_schema_to_local(self):
+        input = [{"a": 1}, {"b": "coffee"}]
+        rdd = self.sc.parallelize(input)
+        df = self.sqlCtx.createDataFrame(input)
+        df2 = self.sqlCtx.createDataFrame(rdd, samplingRatio=1.0)
+        self.assertEqual(df.schema, df2.schema)
+
+        rdd = self.sc.parallelize(range(10)).map(lambda x: Row(a=x))
+        df3 = self.sqlCtx.createDataFrame(rdd, df.schema)
+        self.assertEqual(10, df3.count())
+
     def test_serialize_nested_array_and_map(self):
         d = [Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})]
         rdd = self.sc.parallelize(d)