allisonwang-db commented on code in PR #45485:
URL: https://github.com/apache/spark/pull/45485#discussion_r1534755416
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python/pyspark/sql/worker/plan_data_source_read.py:
##########
@@ -264,11 +246,40 @@ def batched(iterator: Iterator, n: int) -> Iterator:
command = (data_source_read_func, return_type)
pickleSer._write_with_length(command, outfile)
- # Return the serialized partition values.
- write_int(len(partitions), outfile)
- for partition in partitions:
- pickleSer._write_with_length(partition, outfile)
-
+ if not is_streaming:
+ # The partitioning of python batch source read is determined
before query execution.
+ try:
+ partitions = reader.partitions() # type: ignore[attr-defined]
+ if not isinstance(partitions, list):
+ raise PySparkRuntimeError(
+ error_class="DATA_SOURCE_TYPE_MISMATCH",
+ message_parameters={
+ "expected": "'partitions' to return a list",
+ "actual": f"'{type(partitions).__name__}'",
+ },
+ )
+ if not all(isinstance(p, InputPartition) for p in partitions):
+ partition_types = ", ".join([f"'{type(p).__name__}'" for p
in partitions])
+ raise PySparkRuntimeError(
+ error_class="DATA_SOURCE_TYPE_MISMATCH",
+ message_parameters={
+ "expected": "elements in 'partitions' to be of
type 'InputPartition'",
+ "actual": partition_types,
+ },
+ )
+ if len(partitions) == 0:
+ partitions = [None]
+ except NotImplementedError:
+ partitions = [None]
+
+ # Return the serialized partition values.
+ write_int(len(partitions), outfile)
+ for partition in partitions:
+ pickleSer._write_with_length(partition, outfile)
+ else:
+ # Send an empty list of partition for stream reader because
partitions are planned
+ # in each microbatch during query execution.
+ write_int(0, outfile)
Review Comment:
Could you give me some code pointers on where the planning of partitions
takes place in streaming?
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