feat: add dictionary_columns to scan API for memory-efficient string reads

pyiceberg/io/pyarrow.py

@@ -1614,8 +1614,13 @@ 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: tuple[str, ...] | None = None,
 ) -> Iterator[pa.RecordBatch]:
-    arrow_format = _get_file_format(task.file.file_format, pre_buffer=True, buffer_size=(ONE_MEGABYTE * 8))
+    # Only pass dictionary_columns for Parquet — ORC does not support this kwarg.
+    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"] = 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
@@ -1718,6 +1723,7 @@ class ArrowScan:
     _case_sensitive: bool
     _limit: int | None
     _downcast_ns_timestamp_to_us: bool | None
+    _dictionary_columns: tuple[str, ...] | None
     """Scan the Iceberg Table and create an Arrow construct.
 
     Attributes:
@@ -1737,6 +1743,8 @@ def __init__(
         row_filter: BooleanExpression,
         case_sensitive: bool = True,
         limit: int | None = None,
+        *,
+        dictionary_columns: tuple[str, ...] | None = None,
     ) -> None:
         self._table_metadata = table_metadata
         self._io = io
@@ -1745,6 +1753,7 @@ 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 = dictionary_columns
 
     @property
     def _projected_field_ids(self) -> set[int]:
@@ -1773,6 +1782,15 @@ def to_table(self, tasks: Iterable[FileScanTask]) -> pa.Table:
             ValueError: When a field type in the file cannot be projected to the schema type
         """
         arrow_schema = schema_to_pyarrow(self._projected_schema, include_field_ids=False)
+        if self._dictionary_columns:
+            dict_cols_set = set(self._dictionary_columns)
+            arrow_schema = pa.schema(
+                [
+                    field.with_type(pa.dictionary(pa.int32(), field.type)) if field.name in dict_cols_set else field
+                    for field in arrow_schema
+                ],
+                metadata=arrow_schema.metadata,
+            )
 
         batches = self.to_record_batches(tasks)
         try:
@@ -1855,6 +1873,7 @@ 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,
+                dictionary_columns=self._dictionary_columns,
             )
             for batch in batches:
                 if self._limit is not None:

pyiceberg/table/__init__.py

@@ -1121,6 +1121,7 @@ def scan(
         snapshot_id: int | None = None,
         options: Properties = EMPTY_DICT,
         limit: int | None = None,
+        dictionary_columns: tuple[str, ...] | None = None,
     ) -> DataScan:
         """Fetch a DataScan based on the table's current metadata.
 
@@ -1147,6 +1148,13 @@ def scan(
                 An integer representing the number of rows to
                 return in the scan result. If None, fetches all
                 matching rows.
+            dictionary_columns:
+                A tuple of column names that PyArrow should read as
+                dictionary-encoded (DictionaryArray). Reduces memory
+                usage for columns with large or repeated string values
+                (e.g. large JSON blobs). Only applies to Parquet files;
+                silently ignored for ORC. Columns absent from the file
+                are silently skipped. Default is None (no dictionary encoding).
 
         Returns:
             A DataScan based on the table's current metadata.
@@ -1162,6 +1170,7 @@ def scan(
             limit=limit,
             catalog=self.catalog,
             table_identifier=self._identifier,
+            dictionary_columns=dictionary_columns,
         )
 
     @property
@@ -1664,6 +1673,7 @@ def scan(
         snapshot_id: int | None = None,
         options: Properties = EMPTY_DICT,
         limit: int | None = None,
+        dictionary_columns: tuple[str, ...] | None = None,
     ) -> DataScan:
         raise ValueError("Cannot scan a staged table")
 
@@ -1916,6 +1926,36 @@ def _min_sequence_number(manifests: list[ManifestFile]) -> int:
 
 
 class DataScan(TableScan):
+    dictionary_columns: tuple[str, ...] | None
+
+    def __init__(
+        self,
+        table_metadata: TableMetadata,
+        io: FileIO,
+        row_filter: str | BooleanExpression = ALWAYS_TRUE,
+        selected_fields: tuple[str, ...] = ("*",),
+        case_sensitive: bool = True,
+        snapshot_id: int | None = None,
+        options: Properties = EMPTY_DICT,
+        limit: int | None = None,
+        catalog: Catalog | None = None,
+        table_identifier: Identifier | None = None,
+        dictionary_columns: tuple[str, ...] | None = None,
+    ) -> None:
+        super().__init__(
+            table_metadata=table_metadata,
+            io=io,
+            row_filter=row_filter,
+            selected_fields=selected_fields,
+            case_sensitive=case_sensitive,
+            snapshot_id=snapshot_id,
+            options=options,
+            limit=limit,
+            catalog=catalog,
+            table_identifier=table_identifier,
+        )
+        self.dictionary_columns = dictionary_columns
+
     def _build_partition_projection(self, spec_id: int) -> BooleanExpression:
         project = inclusive_projection(self.table_metadata.schema(), self.table_metadata.specs()[spec_id], self.case_sensitive)
         return project(self.row_filter)
@@ -2113,7 +2153,13 @@ def to_arrow(self) -> pa.Table:
         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=self.dictionary_columns,
         ).to_table(self.plan_files())
 
     def to_arrow_batch_reader(self) -> pa.RecordBatchReader:
@@ -2132,8 +2178,29 @@ def to_arrow_batch_reader(self) -> pa.RecordBatchReader:
         from pyiceberg.io.pyarrow import ArrowScan, schema_to_pyarrow
 
         target_schema = schema_to_pyarrow(self.projection())
+
+        # When dictionary_columns is set, PyArrow returns DictionaryArray for those columns.
+        # target_schema uses plain string types, so .cast(target_schema) would silently decode
+        # them back to plain strings. Rebuild target_schema with dictionary types for the listed
+        # columns so from_batches and cast both preserve the encoding.
+        if self.dictionary_columns:
+            dict_cols_set = set(self.dictionary_columns)
+            target_schema = pa.schema(
+                [
+                    field.with_type(pa.dictionary(pa.int32(), field.type)) if field.name in dict_cols_set else field
+                    for field in target_schema
+                ],
+                metadata=target_schema.metadata,
+            )
+
         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=self.dictionary_columns,
         ).to_record_batches(self.plan_files())
 
         return pa.RecordBatchReader.from_batches(

tests/io/test_pyarrow.py

@@ -3152,6 +3152,168 @@ def _expected_batch(unit: str) -> pa.RecordBatch:
     assert _expected_batch("ns" if format_version > 2 else "us").equals(actual_result)
 
 
+def test_task_to_record_batches_dictionary_columns(tmpdir: str) -> None:
+    """dictionary_columns causes the column to be read as DictionaryArray, saving memory."""
+    arrow_table = pa.table(
+        {"json_col": pa.array(["large-json-1", "large-json-2", "large-json-1"], type=pa.string())},
+        schema=pa.schema([pa.field("json_col", pa.string(), nullable=True, metadata={PYARROW_PARQUET_FIELD_ID_KEY: "1"})]),
+    )
+    data_file = _write_table_to_data_file(f"{tmpdir}/test_dictionary_columns.parquet", arrow_table.schema, arrow_table)
+    table_schema = Schema(NestedField(1, "json_col", StringType(), required=False))
+
+    batches = list(
+        _task_to_record_batches(
+            PyArrowFileIO(),
+            FileScanTask(data_file),
+            bound_row_filter=AlwaysTrue(),
+            projected_schema=table_schema,
+            table_schema=table_schema,
+            projected_field_ids={1},
+            positional_deletes=None,
+            case_sensitive=True,
+            dictionary_columns=("json_col",),
+        )
+    )
+
+    assert len(batches) == 1, "Expected exactly one record batch"
+    col = batches[0].column("json_col")
+    assert pa.types.is_dictionary(col.type), (
+        f"Expected DictionaryArray for 'json_col' when dictionary_columns is set, got {col.type}"
+    )
+
+
+def test_task_to_record_batches_no_dictionary_columns_by_default(tmpdir: str) -> None:
+    """Without dictionary_columns, string columns are returned as plain StringArray — default unchanged."""
+    arrow_table = pa.table(
+        {"json_col": pa.array(["a", "b", "c"], type=pa.string())},
+        schema=pa.schema([pa.field("json_col", pa.string(), nullable=True, metadata={PYARROW_PARQUET_FIELD_ID_KEY: "1"})]),
+    )
+    data_file = _write_table_to_data_file(f"{tmpdir}/test_no_dictionary_default.parquet", arrow_table.schema, arrow_table)
+    table_schema = Schema(NestedField(1, "json_col", StringType(), required=False))
+
+    batches = list(
+        _task_to_record_batches(
+            PyArrowFileIO(),
+            FileScanTask(data_file),
+            bound_row_filter=AlwaysTrue(),
+            projected_schema=table_schema,
+            table_schema=table_schema,
+            projected_field_ids={1},
+            positional_deletes=None,
+            case_sensitive=True,
+            # dictionary_columns intentionally omitted — must not change behavior
+        )
+    )
+
+    assert len(batches) == 1, "Expected exactly one record batch"
+    col = batches[0].column("json_col")
+    assert not pa.types.is_dictionary(col.type), f"Expected plain StringArray by default, got {col.type}"
+
+
+def test_arrow_scan_to_table_with_dictionary_columns(tmpdir: str) -> None:
+    """ArrowScan.to_table() with dictionary_columns: named column is DictionaryArray, others are not."""
+    import pyarrow.parquet as pq
+
+    arrow_schema = pa.schema(
+        [
+            pa.field("id", pa.int32(), metadata={PYARROW_PARQUET_FIELD_ID_KEY: "1"}),
+            pa.field("json_col", pa.string(), nullable=True, metadata={PYARROW_PARQUET_FIELD_ID_KEY: "2"}),
+        ]
+    )
+    arrow_table = pa.table(
+        {
+            "id": pa.array([1, 2, 3], type=pa.int32()),
+            "json_col": pa.array(['{"x": 1}', '{"x": 2}', '{"x": 1}'], type=pa.string()),
+        },
+        schema=arrow_schema,
+    )
+    filepath = f"{tmpdir}/test_e2e_dictionary.parquet"
+    with pq.ParquetWriter(filepath, arrow_schema) as writer:
+        writer.write_table(arrow_table)
+
+    iceberg_schema = Schema(
+        NestedField(1, "id", IntegerType(), required=False),
+        NestedField(2, "json_col", StringType(), required=False),
+    )
+    data_file = DataFile.from_args(
+        content=DataFileContent.DATA,
+        file_path=filepath,
+        file_format=FileFormat.PARQUET,
+        partition={},
+        record_count=3,
+        file_size_in_bytes=100,
+    )
+    data_file.spec_id = 0
+
+    result = ArrowScan(
+        TableMetadataV2(
+            location="file://a/b/",
+            last_column_id=2,
+            format_version=2,
+            schemas=[iceberg_schema],
+            partition_specs=[PartitionSpec()],
+        ),
+        PyArrowFileIO(),
+        iceberg_schema,
+        AlwaysTrue(),
+        dictionary_columns=("json_col",),
+    ).to_table(tasks=[FileScanTask(data_file)])
+
+    assert pa.types.is_dictionary(result.schema.field("json_col").type), (
+        f"Expected DictionaryArray for 'json_col', got {result.schema.field('json_col').type}"
+    )
+    assert not pa.types.is_dictionary(result.schema.field("id").type), "Non-listed column 'id' should NOT be dictionary-encoded"
+
+
+def test_arrow_scan_to_record_batches_preserves_dictionary_encoding(tmpdir: str) -> None:
+    """ArrowScan.to_record_batches() must preserve DictionaryArray — not decode back to plain string."""
+    import pyarrow.parquet as pq
+
+    arrow_schema = pa.schema(
+        [
+            pa.field("json_col", pa.string(), nullable=True, metadata={PYARROW_PARQUET_FIELD_ID_KEY: "1"}),
+        ]
+    )
+    arrow_table = pa.table(
+        {"json_col": pa.array(['{"a": 1}', '{"b": 2}'], type=pa.string())},
+        schema=arrow_schema,
+    )
+    filepath = f"{tmpdir}/test_batch_reader_dict.parquet"
+    with pq.ParquetWriter(filepath, arrow_schema) as writer:
+        writer.write_table(arrow_table)
+
+    iceberg_schema = Schema(NestedField(1, "json_col", StringType(), required=False))
+    data_file = DataFile.from_args(
+        content=DataFileContent.DATA,
+        file_path=filepath,
+        file_format=FileFormat.PARQUET,
+        partition={},
+        record_count=2,
+        file_size_in_bytes=100,
+    )
+    data_file.spec_id = 0
+
+    batches = list(
+        ArrowScan(
+            TableMetadataV2(
+                location="file://a/b/",
+                last_column_id=1,
+                format_version=2,
+                schemas=[iceberg_schema],
+                partition_specs=[PartitionSpec()],
+            ),
+            PyArrowFileIO(),
+            iceberg_schema,
+            AlwaysTrue(),
+            dictionary_columns=("json_col",),
+        ).to_record_batches(tasks=[FileScanTask(data_file)])
+    )
+
+    assert len(batches) >= 1, "Expected at least one record batch"
+    col = batches[0].column("json_col")
+    assert pa.types.is_dictionary(col.type), f"DictionaryArray must be preserved through to_record_batches, got {col.type}"
+
+
 def test_parse_location_defaults() -> None:
     """Test that parse_location uses defaults."""
 

tests/table/test_init.py

@@ -274,6 +274,25 @@ def test_table_scan_select(table_fixture: Table) -> None:
     assert scan.select("a", "c").select("a").selected_fields == ("a",)
 
 
+def test_table_scan_dictionary_columns_default(table_v2: Table) -> None:
+    scan = table_v2.scan()
+    assert scan.dictionary_columns is None, "dictionary_columns should default to None"
+
+
+def test_table_scan_dictionary_columns_set(table_v2: Table) -> None:
+    scan = table_v2.scan(dictionary_columns=("json_col", "other_col"))
+    assert scan.dictionary_columns == ("json_col", "other_col"), "dictionary_columns should be stored on the scan"
+
+
+def test_table_scan_dictionary_columns_preserved_on_update(table_v2: Table) -> None:
+    scan = table_v2.scan(dictionary_columns=("json_col",))
+    updated = scan.update(limit=10)
+    assert updated.dictionary_columns == ("json_col",), (
+        "dictionary_columns must survive .update() — TableScan.update() uses inspect.signature "
+        "so DataScan.__init__ must declare and store it"
+    )
+
+
 def test_table_scan_row_filter(table_v2: Table) -> None:
     scan = table_v2.scan()
     assert scan.row_filter == AlwaysTrue()