richardc-db commented on code in PR #47253:
URL: https://github.com/apache/spark/pull/47253#discussion_r1673072352
##########
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:
If we want to do something like `fromDDL("v variant")`, `fromDDL` actually
calls a `udf` just to get the output schema. However, this PR disables variant
output UDFs during planning, so we have to get the data type another way
I've discussed a bit with @HyukjinKwon offline last night, who suggested
that we instead block variant ser/de, however I took a second look today and,
unless I'm missing something, I believe this route is preferable:
- This will fail earlier during planning rather than execution
- Blocking during ser/de may require more code changes, because pandas udfs
don't seem to have the same codepath as non-pandas udfs.
- the `_parse_datatype_string` also was made to work with spark connect
(which was @HyukjinKwon's initial concern) in
[this](https://github.com/apache/spark/commit/fa8aa571ad18441622bb7e3ac66032ab9e7cbc0a)
pr
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