jingz-db commented on code in PR #48005:
URL: https://github.com/apache/spark/pull/48005#discussion_r1799925611
##########
python/pyspark/sql/pandas/group_ops.py:
##########
@@ -505,22 +514,75 @@ def transformWithStateUDF(
return result
- if isinstance(outputStructType, str):
- outputStructType = cast(StructType,
_parse_datatype_string(outputStructType))
+ def transformWithStateWithInitStateUDF(
+ statefulProcessorApiClient: StatefulProcessorApiClient,
+ key: Any,
+ inputRows: Iterator["PandasDataFrameLike"],
+ # for non first batch, initialStates will be None
+ initialStates: Iterator["PandasDataFrameLike"] = None
+ ) -> Iterator["PandasDataFrameLike"]:
Review Comment:
Add the above in the comment.
##########
python/pyspark/sql/pandas/group_ops.py:
##########
@@ -505,22 +514,75 @@ def transformWithStateUDF(
return result
- if isinstance(outputStructType, str):
- outputStructType = cast(StructType,
_parse_datatype_string(outputStructType))
+ def transformWithStateWithInitStateUDF(
+ statefulProcessorApiClient: StatefulProcessorApiClient,
+ key: Any,
+ inputRows: Iterator["PandasDataFrameLike"],
+ # for non first batch, initialStates will be None
Review Comment:
Would be None. This is a bit hacky. We pass in the python eval type purely
based on whether the input `initialState` dataframe is None or not. For
non-empty input initial state and non first batch, we will still eval UDF as
`transformWithStateWithInitStateUDF`
[here](https://github.com/apache/spark/pull/48005/files#diff-5862151bb5e9fe7a6b2d1978301c235504dcc6c1bbbd1f9745a204a3ba93146eR576).
As JVM will start a eval type of `transformWithStateUDF` PythonRunner for non
first batch, we will get `initialStates` as None as it is the positional value:
`initialStates: Iterator["PandasDataFrameLike"] = None`
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