Fokko commented on code in PR #1539:
URL: https://github.com/apache/iceberg-python/pull/1539#discussion_r1922765663
##########
pyiceberg/io/pyarrow.py:
##########
@@ -2594,42 +2566,46 @@ def _determine_partitions(spec: PartitionSpec, schema:
Schema, arrow_table: pa.T
We then retrieve the partition keys by offsets.
And slice the arrow table by offsets and lengths of each partition.
"""
- partition_columns: List[Tuple[PartitionField, NestedField]] = [
- (partition_field, schema.find_field(partition_field.source_id)) for
partition_field in spec.fields
- ]
- partition_values_table = pa.table(
- {
- str(partition.field_id):
partition.transform.pyarrow_transform(field.field_type)(arrow_table[field.name])
- for partition, field in partition_columns
- }
- )
+ # Assign unique names to columns where the partition transform has been
applied
+ # to avoid conflicts
+ partition_fields = [f"_partition_{field.name}" for field in spec.fields]
+
+ for partition, name in zip(spec.fields, partition_fields):
+ source_field = schema.find_field(partition.source_id)
+ arrow_table = arrow_table.append_column(
+ name,
partition.transform.pyarrow_transform(source_field.field_type)(arrow_table[source_field.name])
+ )
+
+ unique_partition_fields =
arrow_table.select(partition_fields).group_by(partition_fields).aggregate([])
Review Comment:
Thanks, good point. If the tests are stable, I don't think we need stable
ordering to allow for better performance using multithreading
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