`dictionary_columns` is not properly propagated in `‎DataScan.to_arrow_batch_reader‎`

[kevinjqliu]

I found one propagation gap: dictionary_columns reaches ArrowScan, but to_arrow_batch_reader() wraps the batches with the plain projected schema and casts back to it:

pa.RecordBatchReader.from_batches(target_schema, batches).cast(target_schema)

That erases the dictionary type, so to_arrow(dictionary_columns=...) preserves dictionary encoding, but to_arrow_batch_reader(dictionary_columns=...).read_all() returns the normal string type.

iceberg-python/pyiceberg/table/__init__.py

Lines 2242 to 2279 in 6da06ad

	def to_arrow_batch_reader(self, dictionary_columns: tuple[str, ...] = ()) -> pa.RecordBatchReader:
	"""Return an Arrow RecordBatchReader from this DataScan.
	For large results, using a RecordBatchReader requires less memory than
	loading an Arrow Table for the same DataScan, because a RecordBatch
	is read one at a time.
	Args:
	dictionary_columns:
	A tuple of column names that PyArrow should read as
	dictionary-encoded (``pa.DictionaryArray``). Dictionary
	encoding can substantially reduce memory usage for columns
	with low-cardinality repeated string values.
	Only applies to Parquet files; silently ignored for ORC.
	Returns:
	pa.RecordBatchReader: Arrow RecordBatchReader from the Iceberg table's DataScan
	which can be used to read a stream of record batches one by one.
	"""
	import pyarrow as pa
	from pyiceberg.io.pyarrow import ArrowScan, schema_to_pyarrow
	target_schema = schema_to_pyarrow(self.projection())
	batches = ArrowScan(
	self.table_metadata,
	self.io,
	self.projection(),
	self.row_filter,
	self.case_sensitive,
	self.limit,
	dictionary_columns=dictionary_columns,
	).to_record_batches(self.plan_files())
	return pa.RecordBatchReader.from_batches(
	target_schema,
	batches,
	).cast(target_schema)

Example public-path regression test:

def test_to_arrow_batch_reader_preserves_dictionary_columns(catalog: Catalog) -> None:
 arrow_table = pa.table(
 {
 "id": pa.array([1, 2, 3, 4], type=pa.int32()),
 "label": pa.array(["a", "b", "a", "b"], type=pa.string()),
 }
 )
 catalog.create_namespace_if_not_exists("default")
 table = catalog.create_table("default.dict_test", schema=arrow_table.schema)
 table.append(arrow_table)

 result = table.scan().to_arrow_batch_reader(dictionary_columns=("label",)).read_all()

 assert pa.types.is_dictionary(result.schema.field("label").type)
 assert result.column("label").to_pylist() == ["a", "b", "a", "b"]

I didn’t see another user-facing path where the option is accepted and dropped. Minor adjacent cleanup: update the abstract TableScan.to_arrow signature so type checkers see the new keyword.

Originally posted by @kevinjqliu in #3461 (comment)

[tanmayrauth]

@kevinjqliu I would like to work on this issue, can you please aaign it to me?

[sungwy]

Assigned it to you @tanmayrauth