Skip to content
Open
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
32 changes: 24 additions & 8 deletions python/pyspark/sql/session.py
Original file line number Diff line number Diff line change
Expand Up @@ -1237,16 +1237,32 @@ def _createFromLocal(
Create an RDD for DataFrame from a list or pandas.DataFrame, returns
the RDD and schema.
"""
# make sure data could consumed multiple times
if not isinstance(data, list):
data = list(data)
import itertools

if any(isinstance(d, VariantVal) for d in data):
raise PySparkValueError("Rows cannot be of type VariantVal")
# Check the first element for VariantVal without exhausting the generator
data, peek_data = itertools.tee(data)
first_row = next(peek_data, None)
if first_row is not None and isinstance(first_row, VariantVal):
raise PySparkValueError(
errorClass="CANNOT_INFER_EMPTY_SCHEMA",
messageParameters={},
)

tupled_data: Iterable[Tuple]
if schema is None or isinstance(schema, (list, tuple)):
struct = self._inferSchemaFromList(data, names=schema)
if not isinstance(data, list):
data = list(data)

if len(data) == 0:
if schema is None:
raise PySparkValueError(
errorClass="CANNOT_INFER_EMPTY_SCHEMA",
messageParameters={},
)
struct = self._inferSchemaFromList([(None,) * len(schema)], names=schema)
else:
struct = self._inferSchemaFromList(data, names=schema)

converter = _create_converter(struct)
tupled_data = map(converter, data)
if isinstance(schema, (list, tuple)):
Expand All @@ -1268,8 +1284,8 @@ def _createFromLocal(
},
)

# convert python objects to sql data
internal_data = [struct.toInternal(row) for row in tupled_data]
# Use map to keep data lazy and avoid OutOfMemoryError
internal_data = map(struct.toInternal, tupled_data)
return self._sc.parallelize(internal_data), struct

@staticmethod
Expand Down