chaoqin-li1123 commented on code in PR #46139:
URL: https://github.com/apache/spark/pull/46139#discussion_r1590525766
##########
python/docs/source/user_guide/sql/python_data_source.rst:
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@@ -84,6 +93,131 @@ 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):
+ def __init__(self, schema, options):
+ self.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.
+
+:meth:`pyspark.sql.datasource.DataSourceStreamReader.initialOffset` should
return the initial start offset of the reader.
+
+:meth:`pyspark.sql.datasource.DataSourceStreamReader.latestOffset` return the
current latest offset that the next microbatch will read to.
+
+:meth:`pyspark.sql.datasource.DataSourceStreamReader.partitions` plans the
partitioning of the current microbatch defined by start and end offset, it
needs to return a sequence of :class:`InputPartition` object.
+
+:meth:`pyspark.sql.datasource.DataSourceStreamReader.read` takes a partition
as an input and read an iterator of tuples from the data source.
+
+:meth:`pyspark.sql.datasource.DataSourceStreamReader.commit` is invoked when
the query has finished processing data before end offset, this can be used to
clean up resource.
+
+**Implement the Simple Stream Reader**
+
+.. code-block:: python
+
+ class SimpleStreamReader(SimpleDataSourceStreamReader):
+ def initialOffset(self):
+ return {"offset": 0}
+
+ def read(self, start: dict):
+ start_idx = start["offset"]
+ it = iter([(i,) for i in range(start_idx, start_idx + 2)])
+ return (it, {"offset": start_idx + 2})
+
+ def readBetweenOffsets(self, start: dict, end: dict):
+ start_idx = start["offset"]
+ end_idx = end["offset"]
+ return iter([(i,) for i in range(start_idx, end_idx)])
+
+ def commit(self, end):
+ pass
+
+If the data source has low throughput and doesn't require partitioning, you
can implement SimpleDataSourceStreamReader instead of DataSourceStreamReader.
+
+One of simpleStreamReader() and streamReader() must be implemented for
readable streaming data source. And simpleStreamReader() will only be invoked
when streamReader() is not implemented.
+
+This is the same dummy streaming reader that generate 2 rows every batch
implemented with SimpleDataSourceStreamReader interface.
+
+:meth:`pyspark.sql.datasource.SimpleDataSourceStreamReader.initialOffset`
should return the initial start offset of the reader.
+
+:meth:`pyspark.sql.datasource.SimpleDataSourceStreamReader.read` takes start
offset as an input, return an iterator of tuples and the start offset of next
read.
+
+:meth:`pyspark.sql.datasource.SimpleDataSourceStreamReader.readBetweenOffsets`
takes start and end offset as input and read an iterator of data
deterministically. This is called whe query replay batches during restart or
after failure.
Review Comment:
Instead of duplicating all the information in method docstring here, can we
point user to refer to datasource.py as @HeartSaVioR suggested?
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