HeartSaVioR commented on code in PR #47933:
URL: https://github.com/apache/spark/pull/47933#discussion_r1746512565
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python/pyspark/sql/streaming/stateful_processor.py:
##########
@@ -77,6 +78,58 @@ def clear(self) -> None:
self._value_state_client.clear(self._state_name)
+class ListState:
+ """
+ Class used for arbitrary stateful operations with transformWithState to
capture single value
+ state.
+
+ .. versionadded:: 4.0.0
+ """
+
+ def __init__(
+ self, list_state_client: ListStateClient, state_name: str, schema:
Union[StructType, str]
+ ) -> None:
+ self._list_state_client = list_state_client
+ self._state_name = state_name
+ self.schema = schema
+
+ def exists(self) -> bool:
+ """
+ Whether list state exists or not.
+ """
+ return self._list_state_client.exists(self._state_name)
+
+ def get(self) -> Iterator["PandasDataFrameLike"]:
Review Comment:
We consider Spark SQL's Row as the base type of the state. ValueState
followed the underlying implementation, and I expect all types to follow the
same.
I'm OK to accept PandasDataFrameLike, but it should be secondary type of
support (should be `Union[Row, PandasDataFrameLike]`) and we need to take care
of conversion to finally store them to "Row". (We need to error out when given
pandas DataFrame does not fit with the state schema.)
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