Eu tive o mesmo problema e sampleSize corrige parcialmente esse problema, mas não o resolve se você tiver muitos dados.
Aqui está a solução de como você pode corrigir isso. Use essa abordagem junto com sampleSize maior (no meu caso é 100000):
def fix_schema(schema: StructType) -> StructType:
"""Fix spark schema due to inconsistent MongoDB schema collection.
It fixes such issues like:
Cannot cast STRING into a NullType
Cannot cast STRING into a StructType
:param schema: a source schema taken from a Spark DataFrame to be fixed
"""
if isinstance(schema, StructType):
return StructType([fix_schema(field) for field in schema.fields])
if isinstance(schema, ArrayType):
return ArrayType(fix_schema(schema.elementType))
if isinstance(schema, StructField) and is_struct_oid_obj(schema):
return StructField(name=schema.name, dataType=StringType(), nullable=schema.nullable)
elif isinstance(schema, StructField):
return StructField(schema.name, fix_schema(schema.dataType), schema.nullable)
if isinstance(schema, NullType):
return StringType()
return schema
def is_struct_oid_obj(struct_field: StructField) -> bool:
"""
Checks that our schema has StructType field with single oid name inside
:param struct_field: a StructField from Spark schema
:return bool
"""
return (isinstance(struct_field.dataType, StructType)
and len(struct_field.dataType.fields) == 1
and struct_field.dataType.fields[0].name == "oid")
