allisonwang-db commented on code in PR #47253:
URL: https://github.com/apache/spark/pull/47253#discussion_r1676188548
##########
python/pyspark/sql/types.py:
##########
@@ -194,16 +194,7 @@ def fromDDL(cls, ddl: str) -> "DataType":
>>> DataType.fromDDL("b: string, a: int")
StructType([StructField('b', StringType(), True), StructField('a',
IntegerType(), True)])
"""
- from pyspark.sql import SparkSession
- from pyspark.sql.functions import udf
-
- # Intentionally uses SparkSession so one implementation can be shared
with/without
- # Spark Connect.
- schema = (
- SparkSession.active().range(0).select(udf(lambda x: x,
returnType=ddl)("id")).schema
- )
- assert len(schema) == 1
- return schema[0].dataType
+ return _parse_datatype_string(ddl)
Review Comment:
Can we make sure the behaivor of `_parse_datatype_string` is the same as the
original `fromDDL`? My concern is that this might introduce unintentional
behavior change for a public API.
What's the error message if we do `fromDDL(a variant)` without this change?
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