kevinjqliu commented on code in PR #1621:
URL: https://github.com/apache/iceberg-python/pull/1621#discussion_r1948240540
##########
pyiceberg/io/pyarrow.py:
##########
@@ -1359,14 +1359,11 @@ def _task_to_record_batches(
# Apply the user filter
if pyarrow_filter is not None:
- # we need to switch back and forth between RecordBatch and
Table
- # as Expression filter isn't yet supported in RecordBatch
- # https://github.com/apache/arrow/issues/39220
- arrow_table = pa.Table.from_batches([batch])
- arrow_table = arrow_table.filter(pyarrow_filter)
- if len(arrow_table) == 0:
+ if batch.num_rows == 0:
continue
- output_batches = arrow_table.to_batches()
+ filtered_batch = batch.filter(pyarrow_filter)
+ if filtered_batch.num_rows > 0:
+ output_batches = [filtered_batch]
Review Comment:
> so if there are no positional deletes then it will hit directly the if
pyarrow_filter is not None line without checking if the batch is empty.
right, i assume this will not generate empty batches
```
next_index = next_index + len(batch)
current_index = next_index - len(batch)
# Start with original batch
current_batch = batch
```
we can also move
```
# Skip empty batches
if current_batch.num_rows == 0:
continue
```
outside the `if positional_deletes` branch, to catch both branches.
```
the use of output_batches here is kind of confusing.
WDYT of
batches = fragment_scanner.to_batches()
for batch in batches:
next_index = next_index + len(batch)
current_index = next_index - len(batch)
# Start with original batch
current_batch = batch
# Apply positional deletes if needed
if positional_deletes:
# Create the mask of indices that we're interested in
indices = _combine_positional_deletes(positional_deletes,
current_index, current_index + len(batch))
current_batch = current_batch.take(indices)
# Skip empty batches
if current_batch.num_rows == 0:
continue
# Apply user filter if needed
if pyarrow_filter is not None:
current_batch = current_batch.filter(pyarrow_filter)
# Skip empty batches
if current_batch.num_rows == 0:
continue
# Yield the final processed batch
yield _to_requested_schema(
projected_schema,
file_project_schema,
current_batch,
downcast_ns_timestamp_to_us=True,
use_large_types=use_large_types,
)
```
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