allisonwang-db commented on code in PR #46139:
URL: https://github.com/apache/spark/pull/46139#discussion_r1602361195
##########
python/docs/source/user_guide/sql/python_data_source.rst:
##########
@@ -33,9 +33,15 @@ To create a custom Python data source, you'll need to
subclass the :class:`DataS
This example demonstrates creating a simple data source to generate synthetic
data using the `faker` library. Ensure the `faker` library is installed and
accessible in your Python environment.
-**Step 1: Define the Data Source**
+**Define the Data Source**
-Start by creating a new subclass of :class:`DataSource`. Define the source
name, schema, and reader logic as follows:
+Start by creating a new subclass of :class:`DataSource` with the source name,
schema.
+
+In order to read from the data source in a batch query, reader() method need
to be defined.
+
+In order to read from the data source in a streaming query, streamReader() or
simpleStreamReader() method need to be defined.
+
+In order to write to the data source in a streaming query, streamWriter()
method need to be defined.
Review Comment:
Do you think it's more clear to have a markdown table here? streaming/batch
x read/write.
##########
python/docs/source/user_guide/sql/python_data_source.rst:
##########
@@ -84,9 +101,158 @@ Define the reader logic to generate synthetic data. Use
the `faker` library to p
row.append(value)
yield tuple(row)
+Implementing Streaming Reader and Writer for Python Data Source
+---------------------------------------------------------------
+**Implement the Stream Reader**
+
+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.
+
+.. 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) -> dict:
+ """
+ Return the initial start offset of the reader.
+ """
+ return {"offset": 0}
+
+ def latestOffset(self) -> dict:
+ """
+ Return the current latest offset that the next microbatch will
read to.
+ """
+ self.current += 2
+ return {"offset": self.current}
+
+ def partitions(self, start: dict, end: dict):
+ """
+ Plans the partitioning of the current microbatch defined by start
and end offset,
+ it needs to return a sequence of :class:`InputPartition` object.
+ """
+ return [RangePartition(start["offset"], end["offset"])]
+
+ def commit(self, end: dict):
+ """
+ This is invoked when the query has finished processing data before
end offset, this can be used to clean up resource.
+ """
+ pass
+
+ def read(self, partition) -> Iterator[Tuple]:
+ """
+ Takes a partition as an input and read an iterator of tuples from
the data source.
+ """
+ start, end = partition.start, partition.end
+ for i in range(start, end):
+ yield (i, str(i))
+
+**Implement the Simple Stream Reader**
+
+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.
+
+.. code-block:: python
+
+ class SimpleStreamReader(SimpleDataSourceStreamReader):
+ def initialOffset(self):
+ """
+ Return the initial start offset of the reader.
+ """
+ return {"offset": 0}
+
+ def read(self, start: dict) -> (Iterator[Tuple], dict):
+ """
+ Takes start offset as an input, return an iterator of tuples and
the start offset of next read.
+ """
+ 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) ->
Iterator[Tuple]:
+ """
+ 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.
+ """
+ start_idx = start["offset"]
+ end_idx = end["offset"]
+ return iter([(i,) for i in range(start_idx, end_idx)])
+
+ def commit(self, end):
+ """
+ This is invoked when the query has finished processing data before
end offset, this can be used to clean up resource.
+ """
+ pass
+
+**Implement the Stream Writer**
+
+This is a streaming data writer that write the metadata information of each
microbatch to a local path.
+
+.. code-block:: python
+
+ class SimpleCommitMessage(WriterCommitMessage):
+ partition_id: int
+ count: int
+
+ class FakeStreamWriter(DataSourceStreamWriter):
+ def __init__(self, options):
+ self.options = options
+ self.path = self.options.get("path")
+ assert self.path is not None
+
+ def write(self, iterator):
+ """
+ Write the data and return the commit message of that partition
+ """
+ from pyspark import TaskContext
+ context = TaskContext.get()
+ partition_id = context.partitionId()
+ cnt = 0
+ for row in iterator:
+ cnt += 1
+ return SimpleCommitMessage(partition_id=partition_id, count=cnt)
+
Review Comment:
nit: extra line
##########
python/docs/source/user_guide/sql/python_data_source.rst:
##########
@@ -84,9 +101,158 @@ Define the reader logic to generate synthetic data. Use
the `faker` library to p
row.append(value)
yield tuple(row)
+Implementing Streaming Reader and Writer for Python Data Source
+---------------------------------------------------------------
+**Implement the Stream Reader**
+
+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.
+
+.. 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) -> dict:
+ """
+ Return the initial start offset of the reader.
+ """
+ return {"offset": 0}
+
+ def latestOffset(self) -> dict:
+ """
+ Return the current latest offset that the next microbatch will
read to.
+ """
+ self.current += 2
+ return {"offset": self.current}
+
+ def partitions(self, start: dict, end: dict):
+ """
+ Plans the partitioning of the current microbatch defined by start
and end offset,
+ it needs to return a sequence of :class:`InputPartition` object.
+ """
+ return [RangePartition(start["offset"], end["offset"])]
+
+ def commit(self, end: dict):
+ """
+ This is invoked when the query has finished processing data before
end offset, this can be used to clean up resource.
+ """
+ pass
+
+ def read(self, partition) -> Iterator[Tuple]:
+ """
+ Takes a partition as an input and read an iterator of tuples from
the data source.
+ """
+ start, end = partition.start, partition.end
+ for i in range(start, end):
+ yield (i, str(i))
+
+**Implement the Simple Stream Reader**
+
+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.
+
+.. code-block:: python
+
+ class SimpleStreamReader(SimpleDataSourceStreamReader):
+ def initialOffset(self):
+ """
+ Return the initial start offset of the reader.
+ """
+ return {"offset": 0}
+
+ def read(self, start: dict) -> (Iterator[Tuple], dict):
+ """
+ Takes start offset as an input, return an iterator of tuples and
the start offset of next read.
+ """
+ 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) ->
Iterator[Tuple]:
+ """
+ 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.
+ """
+ start_idx = start["offset"]
+ end_idx = end["offset"]
+ return iter([(i,) for i in range(start_idx, end_idx)])
+
+ def commit(self, end):
+ """
+ This is invoked when the query has finished processing data before
end offset, this can be used to clean up resource.
+ """
+ pass
+
+**Implement the Stream Writer**
+
+This is a streaming data writer that write the metadata information of each
microbatch to a local path.
+
+.. code-block:: python
+
+ class SimpleCommitMessage(WriterCommitMessage):
+ partition_id: int
+ count: int
+
+ class FakeStreamWriter(DataSourceStreamWriter):
+ def __init__(self, options):
+ self.options = options
+ self.path = self.options.get("path")
+ assert self.path is not None
+
+ def write(self, iterator):
+ """
+ Write the data and return the commit message of that partition
+ """
+ from pyspark import TaskContext
+ context = TaskContext.get()
+ partition_id = context.partitionId()
+ cnt = 0
+ for row in iterator:
+ cnt += 1
+ return SimpleCommitMessage(partition_id=partition_id, count=cnt)
+
+
+ def commit(self, messages, batchId) -> None:
+ """
+ Receives a sequence of :class:`WriterCommitMessage` when all write
tasks succeed and decides what to do with it.
+ In this FakeStreamWriter, we write the metadata of the
microbatch(number of rows and partitions) into a json file inside commit().
+ """
+ status = dict(num_partitions=len(messages), rows=sum(m.count for m
in messages))
+ with open(os.path.join(self.path, f"{batchId}.json"), "a") as file:
+ file.write(json.dumps(status) + "\n")
+
+ def abort(self, messages, batchId) -> None:
+ """
+ Receives a sequence of :class:`WriterCommitMessage` from successful
tasks when some tasks fail and decides what to do with it.
+ In this FakeStreamWriter, we write a failure message into a txt
file inside abort().
+ """
+ with open(os.path.join(self.path, f"{batchId}.txt"), "w") as file:
Review Comment:
can we use `.json` here to make it consistent with `commit`?
##########
python/docs/source/user_guide/sql/python_data_source.rst:
##########
@@ -59,8 +59,17 @@ Start by creating a new subclass of :class:`DataSource`.
Define the source name,
def reader(self, schema: StructType):
return FakeDataSourceReader(schema, self.options)
+ def streamReader(self, schema: StructType):
Review Comment:
+1 I wonder if we should split the streaming and batch implementation in the
user guide.
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