koenvo commented on code in PR #1995:
URL: https://github.com/apache/iceberg-python/pull/1995#discussion_r2123971388
##########
pyiceberg/io/pyarrow.py:
##########
@@ -1644,7 +1607,28 @@ def to_record_batches(self, tasks:
Iterable[FileScanTask]) -> Iterator[pa.Record
ValueError: When a field type in the file cannot be projected to
the schema type
"""
deletes_per_file = _read_all_delete_files(self._io, tasks)
- return self._record_batches_from_scan_tasks_and_deletes(tasks,
deletes_per_file)
+
+ total_row_count = 0
+ executor = ExecutorFactory.get_or_create()
+
+ limit_reached = False
+ for batches in executor.map(
+ lambda task:
list(self._record_batches_from_scan_tasks_and_deletes([task],
deletes_per_file)), tasks
+ ):
+ for batch in batches:
+ current_batch_size = len(batch)
+ if self._limit is not None and total_row_count +
current_batch_size >= self._limit:
+ yield batch.slice(0, self._limit - total_row_count)
+
+ # This break will also cancel all running tasks
+ limit_reached = True
+ break
+
+ yield batch
+ total_row_count += current_batch_size
+
+ if limit_reached:
+ break
Review Comment:
Yes. `Executor.map` maintains ordering by default. It first submits all
jobs, and then waits for the result in original order.
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