bogao007 commented on code in PR #47933:
URL: https://github.com/apache/spark/pull/47933#discussion_r1746370362
##########
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:
I circled back with `Iterator["PandasDataFrameLike"]` mainly because we
provide the same parameter in `handleInputRows` where users can directly use
`rows: Iterator["PandasDataFrameLike"]` to construct list state for `put()` and
`appendList()`.
```
def handleInputRows(
self, key: Any, rows: Iterator["PandasDataFrameLike"]
) -> Iterator["PandasDataFrameLike"]:
```
If we only support `Iterator[Row]` for `put()` and `appendList()`, users
might need extra transformation to achieve the same. @anishshri-db do you know
if that's a common use case?
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