HyukjinKwon commented on code in PR #46139:
URL: https://github.com/apache/spark/pull/46139#discussion_r1573470003
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python/docs/source/user_guide/sql/python_data_source.rst:
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@@ -84,6 +87,46 @@ Define the reader logic to generate synthetic data. Use the
`faker` library to p
row.append(value)
yield tuple(row)
+** Implement the Stream Reader**
+
+.. code-block:: python
+
+ class RangePartition(InputPartition):
+ def __init__(self, start, end):
+ self.start = start
+ self.end = end
+
+ class FakeStreamReader(DataSourceStreamReader):
+ current = 0
+
+ def initialOffset(self):
+ return {"offset": 0}
+
+ def latestOffset(self):
+ self.current += 2
+ return {"offset": self.current}
+
+ def partitions(self, start, end):
+ return [RangePartition(start["offset"], end["offset"])]
+
+ def commit(self, end):
+ pass
+
+ def read(self, partition):
+ start, end = partition.start, partition.end
+ for i in range(start, end):
+ yield (i, str(i))
+
+This is a dummy streaming data reader that generate 2 rows in every
microbatch. The streamReader instance has a integer offset that increase by 2
in every microbatch.
+
+initialOffset() should return the initial start offset of the reader.
+
+latestOffset() return the current latest offset that the next microbatch will
read to.
+
+partitions() plans the partitioning of the current microbatch defined by start
and end offset, it needs to return a sequence of Partition object.
Review Comment:
I would reference `Partition` as a class too as shown above.
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