richardc-db commented on code in PR #47253:
URL: https://github.com/apache/spark/pull/47253#discussion_r1676341542
##########
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:
yeah makes sense. The function comment mentions that this was added for
Spark 4.0.0 (which won't be GA until 2025 IIUC?) so I thought the risk of a
breaking change for a public API would be a bit lower.
The error message without changing `fromDDL` for something like "a int v
variant" would be
```
org.apache.spark.sql.AnalysisException:
[DATATYPE_MISMATCH.UNSUPPORTED_UDF_OUTPUT_TYPE] Cannot resolve "<lambda>(id)"
due to data type mismatch: UDFs do not support 'STRUCT<a: INT, v: VARIANT>' as
an output data type. SQLSTATE: 42K09
```
which is pretty confusing for `fromDDL` in my opinion (i.e. why does
`fromDDL` need to call a UDF in the first place?)
It seems that the UDF `returnType` implementation directly calls
`_parse_datatype_string`
[here](https://github.com/apache/spark/blob/master/python/pyspark/sql/udf.py#L370).
I added an additional test to test that the old udf behavior matches the new
behavior
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