Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/6686#discussion_r32650869
--- Diff: python/pyspark/sql/types.py ---
@@ -368,8 +367,49 @@ def __init__(self, fields):
>>> struct1 == struct2
False
"""
- assert all(isinstance(f, DataType) for f in fields), "fields
should be a list of DataType"
- self.fields = fields
+ if not fields:
+ self.fields = []
+ else:
+ self.fields = fields
+ assert all(isinstance(f, StructField) for f in fields),\
+ "fields should be a list of StructField"
+
+ def add(self, name_or_struct_field, data_type=NullType(),
nullable=True, metadata=None):
--- End diff --
What's the use cases that we should have StructType without specifying the
dataType of each column?
In `createDataFrame`, if a schema of StructType is provided, it will not
try to infer the data types, so it does not work with StructType with NoneType
in it.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
