chaoqin-li1123 commented on code in PR #45023:
URL: https://github.com/apache/spark/pull/45023#discussion_r1491463836
##########
python/pyspark/sql/datasource.py:
##########
@@ -298,6 +320,117 @@ def read(self, partition: InputPartition) ->
Iterator[Union[Tuple, Row]]:
...
+class DataSourceStreamReader(ABC):
+ """
+ A base class for streaming data source readers. Data source stream readers
are responsible
+ for outputting data from a streaming data source.
+
+ .. versionadded: 4.0.0
+ """
+
+ def initialOffset(self) -> dict:
+ """
+ Return the initial offset of the streaming data source.
+ A new streaming query starts reading data from the initial offset.
+ If Spark is restarting an existing query, it will restart from the
check-pointed offset
+ rather than the initial one.
+
+ Returns
+ -------
+ dict
+ A dict whose key and values are str type.
+ """
+ ...
+ raise PySparkNotImplementedError(
+ error_class="NOT_IMPLEMENTED",
+ message_parameters={"feature": "initialOffset"},
+ )
+
+ def latestOffset(self) -> dict:
+ """
+ Returns the most recent offset available.
+
+ Returns
+ -------
+ dict
+ A dict whose key and values are str type.
+ """
+ ...
+ raise PySparkNotImplementedError(
+ error_class="NOT_IMPLEMENTED",
+ message_parameters={"feature": "latestOffset"},
+ )
+
+ def partitions(self, start: dict, end: dict) -> Sequence[InputPartition]:
+ """
+ Returns a list of InputPartition given the start and end offsets.
Each InputPartition
+ represents a data split that can be processed by one Spark task.
+
+ Parameters
+ ----------
+ start : dict
+ The start offset of the microbatch to plan partitioning.
+ end : dict
+ The end offset of the microbatch to plan partitioning.
+
+ Returns
+ -------
+ Sequence[InputPartition]
+ A sequence of partitions for this data source. Each partition value
+ must be an instance of `InputPartition` or a subclass of it.
+ """
+ ...
+ raise PySparkNotImplementedError(
+ error_class="NOT_IMPLEMENTED",
+ message_parameters={"feature": "partitions"},
+ )
+
+ @abstractmethod
+ def read(self, partition) -> Iterator[Union[Tuple, Row]]:
+ """
+ Generates data for a given partition and returns an iterator of tuples
or rows.
+
+ This method is invoked once per partition to read the data.
Implementing
+ this method is required for stream reader. You can initialize any
+ non-serializable resources required for reading data from the data
source
+ within this method.
+ This method is static and stateless. You shouldn't access mutable
class member
+ or keep in memory state between different invocations of read().
+
+ Parameters
+ ----------
+ partition : object
+ The partition to read. It must be one of the partition values
returned by
+ ``partitions()``.
+
+ Returns
+ -------
+ Iterator[Tuple] or Iterator[Row]
+ An iterator of tuples or rows. Each tuple or row will be converted
to a row
+ in the final DataFrame.
+ """
+ ...
+
+ def commit(self, end: dict):
Review Comment:
This function is not supposed to return anything, how do we express that in
python?
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