feat: add dictionary_columns to to_arrow() / to_arrow_batch_reader() for memory-efficient reads

pyiceberg/io/pyarrow.py

@@ -1625,8 +1625,12 @@ def _task_to_record_batches(
     partition_spec: PartitionSpec | None = None,
     format_version: TableVersion = TableProperties.DEFAULT_FORMAT_VERSION,
     downcast_ns_timestamp_to_us: bool | None = None,
+    dictionary_columns: frozenset[str] = frozenset(),
 ) -> Iterator[pa.RecordBatch]:
-    arrow_format = _get_file_format(task.file.file_format, pre_buffer=True, buffer_size=(ONE_MEGABYTE * 8))
+    format_kwargs: dict[str, Any] = {"pre_buffer": True, "buffer_size": ONE_MEGABYTE * 8}
+    if dictionary_columns and task.file.file_format == FileFormat.PARQUET:
+        format_kwargs["dictionary_columns"] = tuple(dictionary_columns)
+    arrow_format = _get_file_format(task.file.file_format, **format_kwargs)
     with io.new_input(task.file.file_path).open() as fin:
         fragment = arrow_format.make_fragment(fin)
         physical_schema = fragment.physical_schema
@@ -1729,6 +1733,7 @@ class ArrowScan:
     _case_sensitive: bool
     _limit: int | None
     _downcast_ns_timestamp_to_us: bool | None
+    _dictionary_columns: frozenset[str]
     """Scan the Iceberg Table and create an Arrow construct.
 
     Attributes:
@@ -1738,6 +1743,7 @@ class ArrowScan:
         _bound_row_filter: Schema bound row expression to filter the data with
         _case_sensitive: Case sensitivity when looking up column names
         _limit: Limit the number of records.
+        _dictionary_columns: Column names to read as dictionary-encoded arrays.
     """
 
     def __init__(
@@ -1748,6 +1754,8 @@ def __init__(
         row_filter: BooleanExpression,
         case_sensitive: bool = True,
         limit: int | None = None,
+        *,
+        dictionary_columns: tuple[str, ...] = (),
     ) -> None:
         self._table_metadata = table_metadata
         self._io = io
@@ -1756,6 +1764,7 @@ def __init__(
         self._case_sensitive = case_sensitive
         self._limit = limit
         self._downcast_ns_timestamp_to_us = Config().get_bool(DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE)
+        self._dictionary_columns = frozenset(dictionary_columns)
 
     @property
     def _projected_field_ids(self) -> set[int]:
@@ -1866,6 +1875,7 @@ def _record_batches_from_scan_tasks_and_deletes(
                 self._table_metadata.specs().get(task.file.spec_id),
                 self._table_metadata.format_version,
                 self._downcast_ns_timestamp_to_us,
+                self._dictionary_columns,
             )
             for batch in batches:
                 if self._limit is not None:

pyiceberg/table/__init__.py

@@ -2072,13 +2072,11 @@ def _build_residual_evaluator(self, spec_id: int) -> Callable[[DataFile], Residu
         # The lambda created here is run in multiple threads.
         # So we avoid creating _EvaluatorExpression methods bound to a single
         # shared instance across multiple threads.
-        return lambda datafile: (
-            residual_evaluator_of(
-                spec=spec,
-                expr=self.row_filter,
-                case_sensitive=self.case_sensitive,
-                schema=self.table_metadata.schema(),
-            )
+        return lambda datafile: residual_evaluator_of(
+            spec=spec,
+            expr=self.row_filter,
+            case_sensitive=self.case_sensitive,
+            schema=self.table_metadata.schema(),
         )
 
     @staticmethod
@@ -2213,27 +2211,49 @@ def plan_files(self) -> Iterable[FileScanTask]:
             return self._plan_files_server_side()
         return self._plan_files_local()
 
-    def to_arrow(self) -> pa.Table:
+    def to_arrow(self, dictionary_columns: tuple[str, ...] = ()) -> pa.Table:
         """Read an Arrow table eagerly from this DataScan.
 
         All rows will be loaded into memory at once.
 
+        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.Table: Materialized Arrow Table from the Iceberg table's DataScan
         """
         from pyiceberg.io.pyarrow import ArrowScan
 
         return ArrowScan(
-            self.table_metadata, self.io, self.projection(), self.row_filter, self.case_sensitive, self.limit
+            self.table_metadata,
+            self.io,
+            self.projection(),
+            self.row_filter,
+            self.case_sensitive,
+            self.limit,
+            dictionary_columns=dictionary_columns,
         ).to_table(self.plan_files())
 
-    def to_arrow_batch_reader(self) -> pa.RecordBatchReader:
+    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.
@@ -2244,7 +2264,13 @@ def to_arrow_batch_reader(self) -> pa.RecordBatchReader:
 
         target_schema = schema_to_pyarrow(self.projection())
         batches = ArrowScan(
-            self.table_metadata, self.io, self.projection(), self.row_filter, self.case_sensitive, self.limit
+            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(

tests/io/test_pyarrow.py

@@ -5103,3 +5103,73 @@ def test_partition_column_projection_with_schema_evolution(catalog: InMemoryCata
     result_sorted = result.sort_by("name")
     assert result_sorted["name"].to_pylist() == ["Alice", "Bob", "Charlie", "David"]
     assert result_sorted["new_column"].to_pylist() == [None, None, "new1", "new2"]
+
+
+def test_dictionary_columns_produces_dict_encoded_output(tmpdir: str) -> None:
+    """dictionary_columns passed to ArrowScan must yield dictionary-encoded arrays.
+
+    Verifies that:
+    1. The requested column is returned as a pa.DictionaryArray.
+    2. Values are identical to a plain (non-dict) scan.
+    3. A column NOT in dictionary_columns is still returned as a plain array.
+    """
+    from pyiceberg.expressions import AlwaysTrue
+    from pyiceberg.io.pyarrow import ArrowScan, PyArrowFileIO
+    from pyiceberg.partitioning import PartitionSpec
+    from pyiceberg.table import FileScanTask
+    from pyiceberg.table.metadata import TableMetadataV2
+
+    arrow_schema = pa.schema(
+        [
+            pa.field("id", pa.int32(), nullable=True, metadata={PYARROW_PARQUET_FIELD_ID_KEY: "1"}),
+            pa.field("label", pa.string(), nullable=True, metadata={PYARROW_PARQUET_FIELD_ID_KEY: "2"}),
+        ]
+    )
+    arrow_table = pa.table(
+        [pa.array([1, 2, 3, 4], type=pa.int32()), pa.array(["a", "b", "a", "b"], type=pa.string())],
+        schema=arrow_schema,
+    )
+    data_file = _write_table_to_data_file(f"{tmpdir}/test_dict_cols.parquet", arrow_schema, arrow_table)
+    data_file.spec_id = 0
+
+    iceberg_schema = Schema(
+        NestedField(1, "id", IntegerType(), required=False),
+        NestedField(2, "label", StringType(), required=False),
+    )
+    table_metadata = TableMetadataV2(
+        location=f"file://{tmpdir}",
+        last_column_id=2,
+        format_version=2,
+        schemas=[iceberg_schema],
+        partition_specs=[PartitionSpec()],
+    )
+    io = PyArrowFileIO()
+    task = FileScanTask(data_file)
+
+    scan_plain = ArrowScan(
+        table_metadata=table_metadata,
+        io=io,
+        projected_schema=iceberg_schema,
+        row_filter=AlwaysTrue(),
+    )
+    scan_dict = ArrowScan(
+        table_metadata=table_metadata,
+        io=io,
+        projected_schema=iceberg_schema,
+        row_filter=AlwaysTrue(),
+        dictionary_columns=("label",),
+    )
+
+    result_plain = scan_plain.to_table([task])
+    result_dict = scan_dict.to_table([task])
+
+    # id column is not in dictionary_columns — both scans should return int32
+    assert result_plain.schema.field("id").type == pa.int32()
+    assert result_dict.schema.field("id").type == pa.int32()
+
+    # label column: plain scan → string, dict scan → dictionary<values=string, indices=int32>
+    assert result_plain.schema.field("label").type == pa.string()
+    assert pa.types.is_dictionary(result_dict.schema.field("label").type)
+
+    # Values must be identical
+    assert result_plain.column("label").to_pylist() == result_dict.column("label").to_pylist()