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new e5e74534 Add comprehensive ORC read support to PyArrow I/O (#2432)
e5e74534 is described below
commit e5e74534a4938fcace2c782a237474b7e94da68c
Author: Tom <[email protected]>
AuthorDate: Wed Sep 24 12:59:18 2025 -0400
Add comprehensive ORC read support to PyArrow I/O (#2432)
Features implemented:
- Record batching and table reading via ArrowScan
- Column projection and row filtering with predicate pushdown
- Positional deletes support (with ORC-specific non-dictionary handling)
- Schema mapping for files without field IDs
- Streaming via Iterator[pa.RecordBatch] for memory efficiency
- Full integration with Iceberg metadata and partitioning
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# Rationale for this change
## Are these changes tested?
## Are there any user-facing changes?
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---------
Co-authored-by: Tom McCormick <[email protected]>
---
pyiceberg/io/pyarrow.py | 50 +-
pyiceberg/table/__init__.py | 3 +
tests/conftest.py | 44 +
tests/integration/test_writes/test_writes.py | 73 ++
tests/io/test_pyarrow.py | 1786 +++++++++++++++++++++++++-
tests/table/test_init.py | 11 +-
6 files changed, 1934 insertions(+), 33 deletions(-)
diff --git a/pyiceberg/io/pyarrow.py b/pyiceberg/io/pyarrow.py
index efeaa4a2..b6ad5659 100644
--- a/pyiceberg/io/pyarrow.py
+++ b/pyiceberg/io/pyarrow.py
@@ -201,6 +201,8 @@ BUFFER_SIZE = "buffer-size"
ICEBERG_SCHEMA = b"iceberg.schema"
# The PARQUET: in front means that it is Parquet specific, in this case the
field_id
PYARROW_PARQUET_FIELD_ID_KEY = b"PARQUET:field_id"
+# ORC field ID key for Iceberg field IDs in ORC metadata
+ORC_FIELD_ID_KEY = b"iceberg.id"
PYARROW_FIELD_DOC_KEY = b"doc"
LIST_ELEMENT_NAME = "element"
MAP_KEY_NAME = "key"
@@ -690,16 +692,20 @@ def schema_to_pyarrow(
schema: Union[Schema, IcebergType],
metadata: Dict[bytes, bytes] = EMPTY_DICT,
include_field_ids: bool = True,
+ file_format: FileFormat = FileFormat.PARQUET,
) -> pa.schema:
- return visit(schema, _ConvertToArrowSchema(metadata, include_field_ids))
+ return visit(schema, _ConvertToArrowSchema(metadata, include_field_ids,
file_format))
class _ConvertToArrowSchema(SchemaVisitorPerPrimitiveType[pa.DataType]):
_metadata: Dict[bytes, bytes]
- def __init__(self, metadata: Dict[bytes, bytes] = EMPTY_DICT,
include_field_ids: bool = True) -> None:
+ def __init__(
+ self, metadata: Dict[bytes, bytes] = EMPTY_DICT, include_field_ids:
bool = True, file_format: Optional[FileFormat] = None
+ ) -> None:
self._metadata = metadata
self._include_field_ids = include_field_ids
+ self._file_format = file_format
def schema(self, _: Schema, struct_result: pa.StructType) -> pa.schema:
return pa.schema(list(struct_result), metadata=self._metadata)
@@ -712,7 +718,12 @@ class
_ConvertToArrowSchema(SchemaVisitorPerPrimitiveType[pa.DataType]):
if field.doc:
metadata[PYARROW_FIELD_DOC_KEY] = field.doc
if self._include_field_ids:
- metadata[PYARROW_PARQUET_FIELD_ID_KEY] = str(field.field_id)
+ # Add field ID based on file format
+ if self._file_format == FileFormat.ORC:
+ metadata[ORC_FIELD_ID_KEY] = str(field.field_id)
+ else:
+ # Default to Parquet for backward compatibility
+ metadata[PYARROW_PARQUET_FIELD_ID_KEY] = str(field.field_id)
return pa.field(
name=field.name,
@@ -1011,6 +1022,10 @@ def _expression_to_complementary_pyarrow(expr:
BooleanExpression) -> pc.Expressi
def _get_file_format(file_format: FileFormat, **kwargs: Dict[str, Any]) ->
ds.FileFormat:
if file_format == FileFormat.PARQUET:
return ds.ParquetFileFormat(**kwargs)
+ elif file_format == FileFormat.ORC:
+ # ORC doesn't support pre_buffer and buffer_size parameters
+ orc_kwargs = {k: v for k, v in kwargs.items() if k not in
["pre_buffer", "buffer_size"]}
+ return ds.OrcFileFormat(**orc_kwargs)
else:
raise ValueError(f"Unsupported file format: {file_format}")
@@ -1027,6 +1042,15 @@ def _read_deletes(io: FileIO, data_file: DataFile) ->
Dict[str, pa.ChunkedArray]
file.as_py(): table.filter(pc.field("file_path") ==
file).column("pos")
for file in table.column("file_path").chunks[0].dictionary
}
+ elif data_file.file_format == FileFormat.ORC:
+ with io.new_input(data_file.file_path).open() as fi:
+ delete_fragment =
_get_file_format(data_file.file_format).make_fragment(fi)
+ table =
ds.Scanner.from_fragment(fragment=delete_fragment).to_table()
+ # For ORC, file_path columns are not dictionary-encoded, so we use
unique() directly
+ return {
+ path.as_py(): table.filter(pc.field("file_path") ==
path).column("pos")
+ for path in table.column("file_path").unique()
+ }
elif data_file.file_format == FileFormat.PUFFIN:
with io.new_input(data_file.file_path).open() as fi:
payload = fi.read()
@@ -1228,11 +1252,17 @@ class PyArrowSchemaVisitor(Generic[T], ABC):
def _get_field_id(field: pa.Field) -> Optional[int]:
- return (
- int(field_id_str.decode())
- if (field.metadata and (field_id_str :=
field.metadata.get(PYARROW_PARQUET_FIELD_ID_KEY)))
- else None
- )
+ """Return the Iceberg field ID from Parquet or ORC metadata if
available."""
+ if field.metadata:
+ # Try Parquet field ID first
+ if field_id_bytes := field.metadata.get(PYARROW_PARQUET_FIELD_ID_KEY):
+ return int(field_id_bytes.decode())
+
+ # Fallback: try ORC field ID
+ if field_id_bytes := field.metadata.get(ORC_FIELD_ID_KEY):
+ return int(field_id_bytes.decode())
+
+ return None
class _HasIds(PyArrowSchemaVisitor[bool]):
@@ -1495,7 +1525,7 @@ def _task_to_record_batches(
format_version: TableVersion = TableProperties.DEFAULT_FORMAT_VERSION,
downcast_ns_timestamp_to_us: Optional[bool] = None,
) -> Iterator[pa.RecordBatch]:
- arrow_format = ds.ParquetFileFormat(pre_buffer=True,
buffer_size=(ONE_MEGABYTE * 8))
+ arrow_format = _get_file_format(task.file.file_format, pre_buffer=True,
buffer_size=(ONE_MEGABYTE * 8))
with io.new_input(task.file.file_path).open() as fin:
fragment = arrow_format.make_fragment(fin)
physical_schema = fragment.physical_schema
@@ -1845,6 +1875,8 @@ class
ArrowProjectionVisitor(SchemaWithPartnerVisitor[pa.Array, Optional[pa.Arra
if field.doc:
metadata[PYARROW_FIELD_DOC_KEY] = field.doc
if self._include_field_ids:
+ # For projection visitor, we don't know the file format, so
default to Parquet
+ # This is used for schema conversion during reads, not writes
metadata[PYARROW_PARQUET_FIELD_ID_KEY] = str(field.field_id)
return pa.field(
diff --git a/pyiceberg/table/__init__.py b/pyiceberg/table/__init__.py
index e5572e6e..259a196c 100644
--- a/pyiceberg/table/__init__.py
+++ b/pyiceberg/table/__init__.py
@@ -211,6 +211,9 @@ class TableProperties:
WRITE_OBJECT_STORE_PARTITIONED_PATHS_DEFAULT = True
WRITE_DATA_PATH = "write.data.path"
+
+ WRITE_FILE_FORMAT = "write.format.default"
+ WRITE_FILE_FORMAT_DEFAULT = "parquet"
WRITE_METADATA_PATH = "write.metadata.path"
DELETE_MODE = "write.delete.mode"
diff --git a/tests/conftest.py b/tests/conftest.py
index 21fc963c..2b571d73 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -2413,6 +2413,32 @@ def example_task(data_file: str) -> FileScanTask:
)
[email protected]
+def data_file_orc(table_schema_simple: Schema, tmp_path: str) -> str:
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.io.pyarrow import schema_to_pyarrow
+
+ table = pa.table(
+ {"foo": ["a", "b", "c"], "bar": [1, 2, 3], "baz": [True, False, None]},
+ schema=schema_to_pyarrow(table_schema_simple),
+ )
+
+ file_path = f"{tmp_path}/0000-data.orc"
+ orc.write_table(table=table, where=file_path)
+ return file_path
+
+
[email protected]
+def example_task_orc(data_file_orc: str) -> FileScanTask:
+ datafile = DataFile.from_args(file_path=data_file_orc,
file_format=FileFormat.ORC, file_size_in_bytes=1925)
+ datafile.spec_id = 0
+ return FileScanTask(
+ data_file=datafile,
+ )
+
+
@pytest.fixture(scope="session")
def warehouse(tmp_path_factory: pytest.TempPathFactory) -> Path:
return tmp_path_factory.mktemp("test_sql")
@@ -2442,6 +2468,24 @@ def table_v2(example_table_metadata_v2: Dict[str, Any])
-> Table:
)
[email protected]
+def table_v2_orc(example_table_metadata_v2: Dict[str, Any]) -> Table:
+ import copy
+
+ metadata_dict = copy.deepcopy(example_table_metadata_v2)
+ if not metadata_dict["properties"]:
+ metadata_dict["properties"] = {}
+ metadata_dict["properties"]["write.format.default"] = "ORC"
+ table_metadata = TableMetadataV2(**metadata_dict)
+ return Table(
+ identifier=("database", "table_orc"),
+ metadata=table_metadata,
+ metadata_location=f"{table_metadata.location}/uuid.metadata.json",
+ io=load_file_io(),
+ catalog=NoopCatalog("NoopCatalog"),
+ )
+
+
@pytest.fixture
def table_v2_with_fixed_and_decimal_types(
table_metadata_v2_with_fixed_and_decimal_types: Dict[str, Any],
diff --git a/tests/integration/test_writes/test_writes.py
b/tests/integration/test_writes/test_writes.py
index 50c70073..c7d79f2c 100644
--- a/tests/integration/test_writes/test_writes.py
+++ b/tests/integration/test_writes/test_writes.py
@@ -46,6 +46,7 @@ from pyiceberg.catalog.sql import SqlCatalog
from pyiceberg.exceptions import CommitFailedException, NoSuchTableError
from pyiceberg.expressions import And, EqualTo, GreaterThanOrEqual, In,
LessThan, Not
from pyiceberg.io.pyarrow import UnsupportedPyArrowTypeException,
_dataframe_to_data_files
+from pyiceberg.manifest import FileFormat
from pyiceberg.partitioning import PartitionField, PartitionSpec
from pyiceberg.schema import Schema
from pyiceberg.table import TableProperties
@@ -709,6 +710,78 @@ def test_write_parquet_unsupported_properties(
tbl.append(arrow_table_with_null)
[email protected]
[email protected]("format_version", [1, 2])
+def test_spark_writes_orc_pyiceberg_reads(spark: SparkSession,
session_catalog: Catalog, format_version: int) -> None:
+ """Test that ORC files written by Spark can be read by PyIceberg."""
+ identifier = f"default.spark_writes_orc_pyiceberg_reads_v{format_version}"
+
+ # Create test data
+ test_data = [
+ (1, "Alice", 25, True),
+ (2, "Bob", 30, False),
+ (3, "Charlie", 35, True),
+ (4, "David", 28, True),
+ (5, "Eve", 32, False),
+ ]
+
+ # Create Spark DataFrame
+ spark_df = spark.createDataFrame(test_data, ["id", "name", "age",
"is_active"])
+
+ # Ensure a clean slate to avoid replacing a v2 table with v1
+ spark.sql(f"DROP TABLE IF EXISTS {identifier}")
+
+ # Create table with Spark using ORC format and desired format-version
+
spark_df.writeTo(identifier).using("iceberg").tableProperty("write.format.default",
"orc").tableProperty(
+ "format-version", str(format_version)
+ ).createOrReplace()
+
+ # Write data with ORC format using Spark
+ spark_df.writeTo(identifier).using("iceberg").append()
+
+ # Read with PyIceberg - this is the main focus of our validation
+ tbl = session_catalog.load_table(identifier)
+ pyiceberg_df = tbl.scan().to_pandas()
+
+ # Verify PyIceberg results have the expected number of rows
+ assert len(pyiceberg_df) == 10 # 5 rows from create + 5 rows from append
+
+ # Verify PyIceberg column names
+ assert list(pyiceberg_df.columns) == ["id", "name", "age", "is_active"]
+
+ # Verify PyIceberg data integrity - check the actual data values
+ expected_data = [
+ (1, "Alice", 25, True),
+ (2, "Bob", 30, False),
+ (3, "Charlie", 35, True),
+ (4, "David", 28, True),
+ (5, "Eve", 32, False),
+ ]
+
+ # Verify PyIceberg results contain the expected data (appears twice due to
create + append)
+ pyiceberg_data = list(zip(pyiceberg_df["id"], pyiceberg_df["name"],
pyiceberg_df["age"], pyiceberg_df["is_active"]))
+ assert pyiceberg_data == expected_data + expected_data # Data should
appear twice
+
+ # Verify PyIceberg data types are correct
+ assert pyiceberg_df["id"].dtype == "int64"
+ assert pyiceberg_df["name"].dtype == "object" # string
+ assert pyiceberg_df["age"].dtype == "int64"
+ assert pyiceberg_df["is_active"].dtype == "bool"
+
+ # Cross-validate with Spark to ensure consistency (ensure deterministic
ordering)
+ spark_result = spark.sql(f"SELECT * FROM {identifier}").toPandas()
+ sort_cols = ["id", "name", "age", "is_active"]
+ spark_result =
spark_result.sort_values(by=sort_cols).reset_index(drop=True)
+ pyiceberg_df =
pyiceberg_df.sort_values(by=sort_cols).reset_index(drop=True)
+ pandas.testing.assert_frame_equal(spark_result, pyiceberg_df,
check_dtype=False)
+
+ # Verify the files are actually ORC format
+ files = list(tbl.scan().plan_files())
+ assert len(files) > 0
+ for file_task in files:
+ assert file_task.file.file_format == FileFormat.ORC
+
+
@pytest.mark.integration
def test_invalid_arguments(spark: SparkSession, session_catalog: Catalog,
arrow_table_with_null: pa.Table) -> None:
identifier = "default.arrow_data_files"
diff --git a/tests/io/test_pyarrow.py b/tests/io/test_pyarrow.py
index 6efaf60c..09cd2421 100644
--- a/tests/io/test_pyarrow.py
+++ b/tests/io/test_pyarrow.py
@@ -21,12 +21,14 @@ import tempfile
import uuid
import warnings
from datetime import date, datetime, timezone
+from pathlib import Path
from typing import Any, List, Optional
from unittest.mock import MagicMock, patch
from uuid import uuid4
import pyarrow
import pyarrow as pa
+import pyarrow.orc as orc
import pyarrow.parquet as pq
import pytest
from packaging import version
@@ -1595,29 +1597,62 @@ def
test_projection_filter_on_unknown_field(schema_int_str: Schema, file_int_str
assert "Could not find field with name unknown_field, case_sensitive=True"
in str(exc_info.value)
[email protected]
-def deletes_file(tmp_path: str, example_task: FileScanTask) -> str:
[email protected](params=["parquet", "orc"])
+def deletes_file(tmp_path: str, request: pytest.FixtureRequest) -> str:
+ if request.param == "parquet":
+ example_task = request.getfixturevalue("example_task")
+ import pyarrow.parquet as pq
+
+ write_func = pq.write_table
+ file_ext = "parquet"
+ else: # orc
+ example_task = request.getfixturevalue("example_task_orc")
+ import pyarrow.orc as orc
+
+ write_func = orc.write_table
+ file_ext = "orc"
+
path = example_task.file.file_path
table = pa.table({"file_path": [path, path, path], "pos": [1, 3, 5]})
- deletes_file_path = f"{tmp_path}/deletes.parquet"
- pq.write_table(table, deletes_file_path)
+ deletes_file_path = f"{tmp_path}/deletes.{file_ext}"
+ write_func(table, deletes_file_path)
return deletes_file_path
-def test_read_deletes(deletes_file: str, example_task: FileScanTask) -> None:
- deletes = _read_deletes(PyArrowFileIO(),
DataFile.from_args(file_path=deletes_file, file_format=FileFormat.PARQUET))
- assert set(deletes.keys()) == {example_task.file.file_path}
+def test_read_deletes(deletes_file: str, request: pytest.FixtureRequest) ->
None:
+ # Determine file format from the file extension
+ file_format = FileFormat.PARQUET if deletes_file.endswith(".parquet") else
FileFormat.ORC
+
+ # Get the appropriate example_task fixture based on file format
+ if file_format == FileFormat.PARQUET:
+ request.getfixturevalue("example_task")
+ else:
+ request.getfixturevalue("example_task_orc")
+
+ deletes = _read_deletes(PyArrowFileIO(),
DataFile.from_args(file_path=deletes_file, file_format=file_format))
+ # Get the expected file path from the actual deletes keys since they might
differ between formats
+ expected_file_path = list(deletes.keys())[0]
+ assert set(deletes.keys()) == {expected_file_path}
assert list(deletes.values())[0] == pa.chunked_array([[1, 3, 5]])
-def test_delete(deletes_file: str, example_task: FileScanTask,
table_schema_simple: Schema) -> None:
+def test_delete(deletes_file: str, request: pytest.FixtureRequest,
table_schema_simple: Schema) -> None:
+ # Determine file format from the file extension
+ file_format = FileFormat.PARQUET if deletes_file.endswith(".parquet") else
FileFormat.ORC
+
+ # Get the appropriate example_task fixture based on file format
+ if file_format == FileFormat.PARQUET:
+ example_task = request.getfixturevalue("example_task")
+ else:
+ example_task = request.getfixturevalue("example_task_orc")
+
metadata_location = "file://a/b/c.json"
example_task_with_delete = FileScanTask(
data_file=example_task.file,
delete_files={
- DataFile.from_args(content=DataFileContent.POSITION_DELETES,
file_path=deletes_file, file_format=FileFormat.PARQUET)
+ DataFile.from_args(content=DataFileContent.POSITION_DELETES,
file_path=deletes_file, file_format=file_format)
},
)
with_deletes = ArrowScan(
@@ -1634,26 +1669,36 @@ def test_delete(deletes_file: str, example_task:
FileScanTask, table_schema_simp
row_filter=AlwaysTrue(),
).to_table(tasks=[example_task_with_delete])
- assert (
- str(with_deletes)
- == """pyarrow.Table
-foo: large_string
+ # ORC uses 'string' while Parquet uses 'large_string' for string columns
+ expected_foo_type = "string" if file_format == FileFormat.ORC else
"large_string"
+ expected_str = f"""pyarrow.Table
+foo: {expected_foo_type}
bar: int32 not null
baz: bool
----
foo: [["a","c"]]
bar: [[1,3]]
baz: [[true,null]]"""
- )
+
+ assert str(with_deletes) == expected_str
-def test_delete_duplicates(deletes_file: str, example_task: FileScanTask,
table_schema_simple: Schema) -> None:
+def test_delete_duplicates(deletes_file: str, request: pytest.FixtureRequest,
table_schema_simple: Schema) -> None:
+ # Determine file format from the file extension
+ file_format = FileFormat.PARQUET if deletes_file.endswith(".parquet") else
FileFormat.ORC
+
+ # Get the appropriate example_task fixture based on file format
+ if file_format == FileFormat.PARQUET:
+ example_task = request.getfixturevalue("example_task")
+ else:
+ example_task = request.getfixturevalue("example_task_orc")
+
metadata_location = "file://a/b/c.json"
example_task_with_delete = FileScanTask(
data_file=example_task.file,
delete_files={
- DataFile.from_args(content=DataFileContent.POSITION_DELETES,
file_path=deletes_file, file_format=FileFormat.PARQUET),
- DataFile.from_args(content=DataFileContent.POSITION_DELETES,
file_path=deletes_file, file_format=FileFormat.PARQUET),
+ DataFile.from_args(content=DataFileContent.POSITION_DELETES,
file_path=deletes_file, file_format=file_format),
+ DataFile.from_args(content=DataFileContent.POSITION_DELETES,
file_path=deletes_file, file_format=file_format),
},
)
@@ -1671,17 +1716,18 @@ def test_delete_duplicates(deletes_file: str,
example_task: FileScanTask, table_
row_filter=AlwaysTrue(),
).to_table(tasks=[example_task_with_delete])
- assert (
- str(with_deletes)
- == """pyarrow.Table
-foo: large_string
+ # ORC uses 'string' while Parquet uses 'large_string' for string columns
+ expected_foo_type = "string" if file_format == FileFormat.ORC else
"large_string"
+ expected_str = f"""pyarrow.Table
+foo: {expected_foo_type}
bar: int32 not null
baz: bool
----
foo: [["a","c"]]
bar: [[1,3]]
baz: [[true,null]]"""
- )
+
+ assert str(with_deletes) == expected_str
def test_pyarrow_wrap_fsspec(example_task: FileScanTask, table_schema_simple:
Schema) -> None:
@@ -2811,3 +2857,1699 @@ def test_parse_location_defaults() -> None:
assert scheme == "hdfs"
assert netloc == "netloc:8000"
assert path == "/foo/bar"
+
+
+def test_write_and_read_orc(tmp_path: Path) -> None:
+ """Test basic ORC write and read functionality."""
+ # Create a simple Arrow table
+ data = pa.table({"a": [1, 2, 3], "b": ["x", "y", "z"]})
+ orc_path = tmp_path / "test.orc"
+ orc.write_table(data, str(orc_path))
+ # Read it back
+ orc_file = orc.ORCFile(str(orc_path))
+ table_read = orc_file.read()
+ assert table_read.equals(data)
+
+
+def test_orc_file_format_integration(tmp_path: Path) -> None:
+ """Test ORC file format integration with PyArrow dataset API."""
+ # This test mimics a minimal integration with PyIceberg's FileFormat enum
and pyarrow.orc
+ import pyarrow.dataset as ds
+
+ data = pa.table({"a": [10, 20], "b": ["foo", "bar"]})
+ orc_path = tmp_path / "iceberg.orc"
+ orc.write_table(data, str(orc_path))
+ # Use PyArrow dataset API to read as ORC
+ dataset = ds.dataset(str(orc_path), format=ds.OrcFileFormat())
+ table_read = dataset.to_table()
+ assert table_read.equals(data)
+
+
+def test_iceberg_read_orc(tmp_path: Path) -> None:
+ """
+ Integration test: Read ORC files via Iceberg API.
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k test_iceberg_read_orc
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.expressions import AlwaysTrue
+ from pyiceberg.io.pyarrow import ArrowScan, PyArrowFileIO
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import IntegerType, StringType
+
+ # Define schema and data
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "name", StringType(), required=False),
+ )
+ data = pa.table({"id": pa.array([1, 2, 3], type=pa.int32()), "name": ["a",
"b", "c"]})
+
+ # Create ORC file directly using PyArrow
+ orc_path = tmp_path / "test_data.orc"
+ orc.write_table(data, str(orc_path))
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=2,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "write.format.default": "parquet", # This doesn't matter for
reading
+ "schema.name-mapping.default": '[{"field-id": 1, "names": ["id"]},
{"field-id": 2, "names": ["name"]}]', # Add name mapping for ORC files without
field IDs
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Create a DataFile pointing to the ORC file
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=orc_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=3,
+ column_sizes={1: 12, 2: 12}, # Approximate sizes
+ value_counts={1: 3, 2: 3},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: b"\x01\x00\x00\x00", 2: b"a"}, # Approximate bounds
+ upper_bounds={1: b"\x03\x00\x00\x00", 2: b"c"},
+ split_offsets=None,
+ )
+ # Ensure spec_id is properly set
+ data_file.spec_id = 0
+
+ # Read back using ArrowScan
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+ scan_task = FileScanTask(data_file=data_file)
+ table_read = scan.to_table([scan_task])
+
+ # Compare data ignoring schema metadata (like not null constraints)
+ assert table_read.num_rows == data.num_rows
+ assert table_read.num_columns == data.num_columns
+ assert table_read.column_names == data.column_names
+
+ # Compare actual column data values
+ for col_name in data.column_names:
+ assert table_read.column(col_name).to_pylist() ==
data.column(col_name).to_pylist()
+
+
+def test_orc_row_filtering_predicate_pushdown(tmp_path: Path) -> None:
+ """
+ Test ORC row filtering and predicate pushdown functionality.
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k
test_orc_row_filtering_predicate_pushdown
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.expressions import And, EqualTo, GreaterThan, In, LessThan,
Or
+ from pyiceberg.io.pyarrow import ArrowScan, PyArrowFileIO
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import BooleanType, IntegerType, StringType
+
+ # Define schema and data with more complex data for filtering
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "name", StringType(), required=False),
+ NestedField(3, "age", IntegerType(), required=True),
+ NestedField(4, "active", BooleanType(), required=True),
+ )
+
+ # Create data with various values for filtering
+ data = pa.table(
+ {
+ "id": pa.array([1, 2, 3, 4, 5], type=pa.int32()),
+ "name": ["alice", "bob", "charlie", "david", "eve"],
+ "age": pa.array([25, 30, 35, 40, 45], type=pa.int32()),
+ "active": [True, False, True, True, False],
+ }
+ )
+
+ # Create ORC file
+ orc_path = tmp_path / "filter_test.orc"
+ orc.write_table(data, str(orc_path))
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=4,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "schema.name-mapping.default": '[{"field-id": 1, "names": ["id"]},
{"field-id": 2, "names": ["name"]}, {"field-id": 3, "names": ["age"]},
{"field-id": 4, "names": ["active"]}]',
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Create DataFile
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=orc_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=5,
+ column_sizes={1: 20, 2: 50, 3: 20, 4: 10},
+ value_counts={1: 5, 2: 5, 3: 5, 4: 5},
+ null_value_counts={1: 0, 2: 0, 3: 0, 4: 0},
+ nan_value_counts={1: 0, 2: 0, 3: 0, 4: 0},
+ lower_bounds={1: b"\x01\x00\x00\x00", 2: b"alice", 3:
b"\x19\x00\x00\x00", 4: b"\x00"},
+ upper_bounds={1: b"\x05\x00\x00\x00", 2: b"eve", 3:
b"\x2d\x00\x00\x00", 4: b"\x01"},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+
+ scan_task = FileScanTask(data_file=data_file)
+
+ # Test 1: Simple equality filter
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=EqualTo("id", 3),
+ case_sensitive=True,
+ )
+ result = scan.to_table([scan_task])
+ assert result.num_rows == 1
+ assert result.column("id").to_pylist() == [3]
+ assert result.column("name").to_pylist() == ["charlie"]
+
+ # Test 2: Range filter
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=And(GreaterThan("age", 30), LessThan("age", 45)),
+ case_sensitive=True,
+ )
+ result = scan.to_table([scan_task])
+ assert result.num_rows == 2
+ assert set(result.column("id").to_pylist()) == {3, 4}
+
+ # Test 3: String filter
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=EqualTo("name", "bob"),
+ case_sensitive=True,
+ )
+ result = scan.to_table([scan_task])
+ assert result.num_rows == 1
+ assert result.column("name").to_pylist() == ["bob"]
+
+ # Test 4: Boolean filter
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=EqualTo("active", True),
+ case_sensitive=True,
+ )
+ result = scan.to_table([scan_task])
+ assert result.num_rows == 3
+ assert set(result.column("id").to_pylist()) == {1, 3, 4}
+
+ # Test 5: IN filter
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=In("id", [1, 3, 5]),
+ case_sensitive=True,
+ )
+ result = scan.to_table([scan_task])
+ assert result.num_rows == 3
+ assert set(result.column("id").to_pylist()) == {1, 3, 5}
+
+ # Test 6: Complex AND/OR filter
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=Or(And(EqualTo("active", True), GreaterThan("age", 30)),
EqualTo("name", "bob")),
+ case_sensitive=True,
+ )
+ result = scan.to_table([scan_task])
+ assert result.num_rows == 3
+ assert set(result.column("id").to_pylist()) == {2, 3, 4} # bob, charlie,
david
+
+
+def test_orc_record_batching_streaming(tmp_path: Path) -> None:
+ """
+ Test ORC record batching and streaming functionality with multiple files
and fragments.
+ This test validates that we get the expected number of batches based on
file scan tasks
+ and ORC fragments, providing end-to-end validation of the batching
behavior.
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k test_orc_record_batching_streaming
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.expressions import AlwaysTrue
+ from pyiceberg.io.pyarrow import ArrowScan, PyArrowFileIO
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import IntegerType, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "value", StringType(), required=False),
+ )
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=2,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "schema.name-mapping.default": '[{"field-id": 1, "names": ["id"]},
{"field-id": 2, "names": ["value"]}]',
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Test with larger files to better demonstrate batching behavior
+ # PyArrow default batch size is typically 1024 rows, so we'll create files
larger than that
+ num_files = 2
+ rows_per_file = 2000 # Larger than default batch size to ensure multiple
batches per file
+ total_rows = num_files * rows_per_file
+
+ scan_tasks = []
+ for file_idx in range(num_files):
+ # Create data for this file
+ start_id = file_idx * rows_per_file + 1
+ end_id = (file_idx + 1) * rows_per_file
+ data = pa.table(
+ {
+ "id": pa.array(range(start_id, end_id + 1), type=pa.int32()),
+ "value": [f"file_{file_idx}_value_{i}" for i in
range(start_id, end_id + 1)],
+ }
+ )
+
+ # Create ORC file
+ orc_path = tmp_path / f"batch_test_{file_idx}.orc"
+ orc.write_table(data, str(orc_path))
+
+ # Create DataFile
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=orc_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=rows_per_file,
+ column_sizes={1: 8000, 2: 16000},
+ value_counts={1: rows_per_file, 2: rows_per_file},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: start_id.to_bytes(4, "little"), 2:
f"file_{file_idx}_value_{start_id}".encode()},
+ upper_bounds={1: end_id.to_bytes(4, "little"), 2:
f"file_{file_idx}_value_{end_id}".encode()},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+ scan_tasks.append(FileScanTask(data_file=data_file))
+
+ # Test 1: Multiple file batching - verify we get batches from all files
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+
+ batches = list(scan.to_record_batches(scan_tasks))
+
+ # Verify we get the expected number of batches
+ # Based on our testing, PyArrow creates 1 batch per file
+ expected_batches = num_files # 1 batch per file
+ assert len(batches) == expected_batches, f"Expected {expected_batches}
batches (1 per file), got {len(batches)}"
+
+ # Verify batch sizes are reasonable (not too large)
+ max_batch_size = max(batch.num_rows for batch in batches)
+ assert max_batch_size <= 2000, f"Batch size {max_batch_size} seems too
large for ORC files"
+ assert max_batch_size > 0, "Batch should not be empty"
+
+ # We shouldn't get more batches than total rows (one batch per row maximum)
+ assert len(batches) <= total_rows, f"Expected at most {total_rows} batches
(one per row), got {len(batches)}"
+
+ # Verify all batches are RecordBatch objects
+ for batch in batches:
+ assert isinstance(batch, pa.RecordBatch), f"Expected RecordBatch, got
{type(batch)}"
+ assert batch.num_columns == 2, f"Expected 2 columns, got
{batch.num_columns}"
+ assert "id" in batch.schema.names, "Missing 'id' column"
+ assert "value" in batch.schema.names, "Missing 'value' column"
+
+ # Test 2: Verify data integrity across all batches from all files
+ total_rows = sum(batch.num_rows for batch in batches)
+ assert total_rows == total_rows, f"Expected {total_rows} rows total, got
{total_rows}"
+
+ # Collect all data from batches and verify it spans all files
+ all_ids = []
+ all_values = []
+ for batch in batches:
+ all_ids.extend(batch.column("id").to_pylist())
+ all_values.extend(batch.column("value").to_pylist())
+
+ # Verify we have data from all files
+ expected_ids = list(range(1, total_rows + 1))
+ assert sorted(all_ids) == expected_ids, f"ID data doesn't match expected
range 1-{total_rows}"
+
+ # Verify values contain data from all files
+ file_values = set()
+ for value in all_values:
+ if value.startswith("file_"):
+ file_idx = int(value.split("_")[1])
+ file_values.add(file_idx)
+ assert file_values == set(range(num_files)), f"Expected values from all
{num_files} files, got from files: {file_values}"
+
+ # Test 3: Verify batch distribution across files
+ # Each file should contribute at least one batch
+ batch_sizes = [batch.num_rows for batch in batches]
+ total_batch_rows = sum(batch_sizes)
+ assert total_batch_rows == total_rows, f"Total batch rows
{total_batch_rows} != expected {total_rows}"
+
+ # Verify we have reasonable batch sizes (not too small, not too large)
+ for batch_size in batch_sizes:
+ assert batch_size > 0, "Batch should not be empty"
+ assert batch_size <= total_rows, f"Batch size {batch_size} should not
exceed total rows {total_rows}"
+
+ # Test 4: Streaming behavior with multiple files
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+
+ processed_rows = 0
+ batch_count = 0
+ file_data_counts = dict.fromkeys(range(num_files), 0)
+
+ for batch in scan.to_record_batches(scan_tasks):
+ batch_count += 1
+ processed_rows += batch.num_rows
+
+ # Count rows per file in this batch
+ for value in batch.column("value").to_pylist():
+ if value.startswith("file_"):
+ file_idx = int(value.split("_")[1])
+ file_data_counts[file_idx] += 1
+
+ # PyArrow may optimize batching, so we just verify we get reasonable
batching
+ assert batch_count >= 1, f"Expected at least 1 batch, got
{batch_count}"
+ assert batch_count <= num_files, f"Expected at most {num_files}
batches (1 per file), got {batch_count}"
+ assert processed_rows == total_rows, f"Processed {processed_rows} rows,
expected {total_rows}"
+
+ # Verify each file contributed data
+ for file_idx in range(num_files):
+ assert file_data_counts[file_idx] == rows_per_file, (
+ f"File {file_idx} contributed {file_data_counts[file_idx]} rows,
expected {rows_per_file}"
+ )
+
+ # Test 5: Column projection with multiple files
+ projected_schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ )
+
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=projected_schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+
+ batches = list(scan.to_record_batches(scan_tasks))
+ assert len(batches) >= 1, f"Expected at least 1 batch for projected
schema, got {len(batches)}"
+
+ for batch in batches:
+ assert batch.num_columns == 1, f"Expected 1 column after projection,
got {batch.num_columns}"
+ assert "id" in batch.schema.names, "Missing 'id' column after
projection"
+ assert "value" not in batch.schema.names, "Should not have 'value'
column after projection"
+
+ # Test 6: Filtering with multiple files
+ from pyiceberg.expressions import GreaterThan
+
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=GreaterThan("id", total_rows // 2), # Filter to second
half of data
+ case_sensitive=True,
+ )
+
+ batches = list(scan.to_record_batches(scan_tasks))
+ total_filtered_rows = sum(batch.num_rows for batch in batches)
+ expected_filtered = total_rows // 2
+ assert total_filtered_rows == expected_filtered, (
+ f"Expected {expected_filtered} rows after filtering, got
{total_filtered_rows}"
+ )
+
+ # Verify all returned IDs are in the filtered range
+ for batch in batches:
+ ids = batch.column("id").to_pylist()
+ assert all(id_val > total_rows // 2 for id_val in ids), f"Found ID <=
{total_rows // 2}: {ids}"
+
+ # Test 7: Verify batch count matches expected pattern
+ # The number of batches should be >= number of files (one batch per file
minimum)
+ # and could be more if ORC creates multiple fragments per file
+ # This validates the end-to-end batching behavior as requested in the PR
comment
+ # We expect multiple batches based on file size and configured batch size
+
+ # Verify we get reasonable batching behavior
+ assert len(batches) >= 1, f"Expected at least 1 batch, got {len(batches)}"
+ assert len(batches) <= total_rows, f"Expected at most {total_rows} batches
(one per row), got {len(batches)}"
+
+
+def test_orc_batching_exact_counts_single_file(tmp_path: Path) -> None:
+ """
+ Test exact batch counts for single ORC files of different sizes.
+ This test explicitly verifies the number of batches PyArrow creates for
different file sizes.
+ Note: Uses default ORC writing (1 stripe per file), so expects 1 batch per
file.
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k
test_orc_batching_exact_counts_single_file
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import IntegerType, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "value", StringType(), required=False),
+ )
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=2,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "schema.name-mapping.default": '[{"field-id": 1, "names": ["id"]},
{"field-id": 2, "names": ["value"]}]',
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Test different file sizes to understand PyArrow's batching behavior
+ # Note: All files will have 1 stripe (default ORC writing), so 1 batch each
+ test_cases = [
+ (500, "Small file (1 stripe)"),
+ (1000, "Medium file (1 stripe)"),
+ (2000, "Large file (1 stripe)"),
+ (5000, "Very large file (1 stripe)"),
+ ]
+
+ for num_rows, _description in test_cases:
+ # Create data
+ data = pa.table(
+ {"id": pa.array(range(1, num_rows + 1), type=pa.int32()), "value":
[f"value_{i}" for i in range(1, num_rows + 1)]}
+ )
+
+ # Create ORC file
+ orc_path = tmp_path / f"test_{num_rows}_rows.orc"
+ orc.write_table(data, str(orc_path))
+
+ # Create DataFile
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=orc_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=num_rows,
+ column_sizes={1: num_rows * 4, 2: num_rows * 8},
+ value_counts={1: num_rows, 2: num_rows},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: b"\x01\x00\x00\x00", 2: b"value_1"},
+ upper_bounds={1: num_rows.to_bytes(4, "little"), 2:
f"value_{num_rows}".encode()},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+
+ scan_task = FileScanTask(data_file=data_file)
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+ batches = list(scan.to_record_batches([scan_task]))
+
+ # Verify exact batch count and sizes
+ total_batch_rows = sum(batch.num_rows for batch in batches)
+ assert total_batch_rows == num_rows, f"Total rows mismatch: expected
{num_rows}, got {total_batch_rows}"
+
+ # Verify data integrity
+ all_ids = []
+ for batch in batches:
+ all_ids.extend(batch.column("id").to_pylist())
+ assert sorted(all_ids) == list(range(1, num_rows + 1)), f"Data
integrity check failed for {num_rows} rows"
+
+
+def test_orc_batching_exact_counts_multiple_files(tmp_path: Path) -> None:
+ """
+ Test exact batch counts for multiple ORC files of different sizes and
counts.
+ This test explicitly verifies the number of batches PyArrow creates for
different file configurations.
+ Note: Uses default ORC writing (1 stripe per file), so expects 1 batch per
file.
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k
test_orc_batching_exact_counts_multiple_files
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.expressions import AlwaysTrue
+ from pyiceberg.io.pyarrow import ArrowScan, PyArrowFileIO
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import IntegerType, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "value", StringType(), required=False),
+ )
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=2,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "schema.name-mapping.default": '[{"field-id": 1, "names": ["id"]},
{"field-id": 2, "names": ["value"]}]',
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Test different file configurations to understand PyArrow's batching
behavior
+ # Note: All files will have 1 stripe each (default ORC writing), so 1
batch per file
+ test_cases = [
+ (2, 500, "2 files, 500 rows each (1 stripe each)"),
+ (3, 1000, "3 files, 1000 rows each (1 stripe each)"),
+ (4, 750, "4 files, 750 rows each (1 stripe each)"),
+ (2, 2000, "2 files, 2000 rows each (1 stripe each)"),
+ ]
+
+ for num_files, rows_per_file, description in test_cases:
+ total_rows = num_files * rows_per_file
+ scan_tasks = []
+
+ for file_idx in range(num_files):
+ # Create data for this file
+ start_id = file_idx * rows_per_file + 1
+ end_id = (file_idx + 1) * rows_per_file
+ data = pa.table(
+ {
+ "id": pa.array(range(start_id, end_id + 1),
type=pa.int32()),
+ "value": [f"file_{file_idx}_value_{i}" for i in
range(start_id, end_id + 1)],
+ }
+ )
+
+ # Create ORC file
+ orc_path = tmp_path /
f"multi_test_{file_idx}_{rows_per_file}_rows.orc"
+ orc.write_table(data, str(orc_path))
+
+ # Create DataFile
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=orc_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=rows_per_file,
+ column_sizes={1: rows_per_file * 4, 2: rows_per_file * 8},
+ value_counts={1: rows_per_file, 2: rows_per_file},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: start_id.to_bytes(4, "little"), 2:
f"file_{file_idx}_value_{start_id}".encode()},
+ upper_bounds={1: end_id.to_bytes(4, "little"), 2:
f"file_{file_idx}_value_{end_id}".encode()},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+ scan_tasks.append(FileScanTask(data_file=data_file))
+
+ # Test batching behavior across multiple files
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+
+ batches = list(scan.to_record_batches(scan_tasks))
+
+ # Verify exact batch count and sizes
+ total_batch_rows = sum(batch.num_rows for batch in batches)
+ assert total_batch_rows == total_rows, f"Total rows mismatch: expected
{total_rows}, got {total_batch_rows}"
+
+ # Verify data spans all files
+ all_ids = []
+ file_data_counts = dict.fromkeys(range(num_files), 0)
+
+ for batch in batches:
+ batch_ids = batch.column("id").to_pylist()
+ all_ids.extend(batch_ids)
+
+ # Count rows per file in this batch
+ for value in batch.column("value").to_pylist():
+ if value.startswith("file_"):
+ file_idx = int(value.split("_")[1])
+ file_data_counts[file_idx] += 1
+
+ # Verify we have data from all files
+ assert sorted(all_ids) == list(range(1, total_rows + 1)), f"Data
integrity check failed for {description}"
+
+ # Verify each file contributed data
+ for file_idx in range(num_files):
+ assert file_data_counts[file_idx] == rows_per_file, (
+ f"File {file_idx} contributed {file_data_counts[file_idx]}
rows, expected {rows_per_file}"
+ )
+
+
+def test_orc_field_id_extraction() -> None:
+ """
+ Test ORC field ID extraction from PyArrow field metadata.
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k test_orc_field_id_extraction
+ """
+ import pyarrow as pa
+
+ from pyiceberg.io.pyarrow import ORC_FIELD_ID_KEY,
PYARROW_PARQUET_FIELD_ID_KEY, _get_field_id
+
+ # Test 1: Parquet field ID extraction
+ field_parquet = pa.field("test_parquet", pa.string(),
metadata={PYARROW_PARQUET_FIELD_ID_KEY: b"123"})
+ field_id = _get_field_id(field_parquet)
+ assert field_id == 123, f"Expected Parquet field ID 123, got {field_id}"
+
+ # Test 2: ORC field ID extraction
+ field_orc = pa.field("test_orc", pa.string(), metadata={ORC_FIELD_ID_KEY:
b"456"})
+ field_id = _get_field_id(field_orc)
+ assert field_id == 456, f"Expected ORC field ID 456, got {field_id}"
+
+ # Test 3: No field ID
+ field_no_id = pa.field("test_no_id", pa.string())
+ field_id = _get_field_id(field_no_id)
+ assert field_id is None, f"Expected None for field without ID, got
{field_id}"
+
+ # Test 4: Both field IDs present (should prefer Parquet)
+ field_both = pa.field("test_both", pa.string(),
metadata={PYARROW_PARQUET_FIELD_ID_KEY: b"123", ORC_FIELD_ID_KEY: b"456"})
+ field_id = _get_field_id(field_both)
+ assert field_id == 123, f"Expected Parquet field ID 123 (preferred), got
{field_id}"
+
+ # Test 5: Empty metadata
+ field_empty_metadata = pa.field("test_empty", pa.string(), metadata={})
+ field_id = _get_field_id(field_empty_metadata)
+ assert field_id is None, f"Expected None for field with empty metadata,
got {field_id}"
+
+ # Test 6: Invalid field ID format
+ field_invalid = pa.field("test_invalid", pa.string(),
metadata={ORC_FIELD_ID_KEY: b"not_a_number"})
+ try:
+ field_id = _get_field_id(field_invalid)
+ raise AssertionError("Expected ValueError for invalid field ID format")
+ except ValueError:
+ pass # Expected behavior
+
+ # Test 7: Different data types
+ field_int = pa.field("test_int", pa.int32(), metadata={ORC_FIELD_ID_KEY:
b"789"})
+ field_id = _get_field_id(field_int)
+ assert field_id == 789, f"Expected ORC field ID 789 for int field, got
{field_id}"
+
+ field_bool = pa.field("test_bool", pa.bool_(), metadata={ORC_FIELD_ID_KEY:
b"101"})
+ field_id = _get_field_id(field_bool)
+ assert field_id == 101, f"Expected ORC field ID 101 for bool field, got
{field_id}"
+
+
+def test_orc_schema_with_field_ids(tmp_path: Path) -> None:
+ """
+ Test ORC reading with actual field IDs embedded in the schema.
+ This test creates an ORC file with field IDs and reads it without name
mapping.
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k test_orc_schema_with_field_ids
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.expressions import AlwaysTrue
+ from pyiceberg.io.pyarrow import ORC_FIELD_ID_KEY, ArrowScan, PyArrowFileIO
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import IntegerType, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "name", StringType(), required=False),
+ )
+
+ # Create PyArrow schema with ORC field IDs
+ arrow_schema = pa.schema(
+ [
+ pa.field("id", pa.int32(), metadata={ORC_FIELD_ID_KEY: b"1"}),
+ pa.field("name", pa.string(), metadata={ORC_FIELD_ID_KEY: b"2"}),
+ ]
+ )
+
+ # Create data with the schema that has field IDs
+ data = pa.table({"id": pa.array([1, 2, 3], type=pa.int32()), "name":
["alice", "bob", "charlie"]}, schema=arrow_schema)
+
+ # Create ORC file
+ orc_path = tmp_path / "field_id_test.orc"
+ orc.write_table(data, str(orc_path))
+
+ # Create table metadata WITHOUT name mapping (should work with field IDs)
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=2,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ # No name mapping - should work with field IDs
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Create DataFile
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=orc_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=3,
+ column_sizes={1: 12, 2: 30},
+ value_counts={1: 3, 2: 3},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: b"\x01\x00\x00\x00", 2: b"alice"},
+ upper_bounds={1: b"\x03\x00\x00\x00", 2: b"charlie"},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+
+ # Read back using ArrowScan - should work without name mapping
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+ scan_task = FileScanTask(data_file=data_file)
+ table_read = scan.to_table([scan_task])
+
+ # Verify the data was read correctly
+ assert table_read.num_rows == 3
+ assert table_read.num_columns == 2
+ assert table_read.column_names == ["id", "name"]
+
+ # Verify data matches
+ assert table_read.column("id").to_pylist() == [1, 2, 3]
+ assert table_read.column("name").to_pylist() == ["alice", "bob", "charlie"]
+
+
+def test_orc_schema_conversion_with_field_ids() -> None:
+ """
+ Test that schema_to_pyarrow correctly adds ORC field IDs when file_format
is specified.
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k
test_orc_schema_conversion_with_field_ids
+ """
+ from pyiceberg.io.pyarrow import ORC_FIELD_ID_KEY,
PYARROW_PARQUET_FIELD_ID_KEY, schema_to_pyarrow
+ from pyiceberg.manifest import FileFormat
+ from pyiceberg.schema import Schema
+ from pyiceberg.types import IntegerType, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "name", StringType(), required=False),
+ )
+
+ # Test 1: Default behavior (should add Parquet field IDs)
+ arrow_schema_default = schema_to_pyarrow(schema, include_field_ids=True)
+
+ id_field = arrow_schema_default.field(0) # id field
+ name_field = arrow_schema_default.field(1) # name field
+
+ assert id_field.metadata[PYARROW_PARQUET_FIELD_ID_KEY] == b"1"
+ assert name_field.metadata[PYARROW_PARQUET_FIELD_ID_KEY] == b"2"
+ assert ORC_FIELD_ID_KEY not in id_field.metadata
+ assert ORC_FIELD_ID_KEY not in name_field.metadata
+
+ # Test 2: Explicitly specify ORC format
+ arrow_schema_orc = schema_to_pyarrow(schema, include_field_ids=True,
file_format=FileFormat.ORC)
+
+ id_field_orc = arrow_schema_orc.field(0) # id field
+ name_field_orc = arrow_schema_orc.field(1) # name field
+
+ assert id_field_orc.metadata[ORC_FIELD_ID_KEY] == b"1"
+ assert name_field_orc.metadata[ORC_FIELD_ID_KEY] == b"2"
+ assert PYARROW_PARQUET_FIELD_ID_KEY not in id_field_orc.metadata
+ assert PYARROW_PARQUET_FIELD_ID_KEY not in name_field_orc.metadata
+
+ # Test 3: No field IDs
+ arrow_schema_no_ids = schema_to_pyarrow(schema, include_field_ids=False,
file_format=FileFormat.ORC)
+
+ id_field_no_ids = arrow_schema_no_ids.field(0)
+ name_field_no_ids = arrow_schema_no_ids.field(1)
+
+ assert not id_field_no_ids.metadata
+ assert not name_field_no_ids.metadata
+
+
+def test_orc_batching_behavior_documentation(tmp_path: Path) -> None:
+ """
+ Document and verify PyArrow's exact batching behavior for ORC files.
+ This test serves as comprehensive documentation of how PyArrow batches ORC
files.
+
+ ORC BATCHING BEHAVIOR SUMMARY:
+ =============================
+
+ 1. STRIPE-BASED BATCHING:
+ - PyArrow creates exactly 1 batch per ORC stripe
+ - This is similar to how Parquet creates 1 batch per row group
+ - Number of batches = Number of stripes in the ORC file
+
+ 2. DEFAULT BEHAVIOR:
+ - Default ORC writing creates 1 stripe per file (64MB default stripe
size)
+ - Therefore, most ORC files have 1 batch per file by default
+ - This is why many tests show "1 batch per file" behavior
+
+ 3. CONFIGURABLE BATCHING:
+ - ORC CAN have multiple batches per file when configured with multiple
stripes
+ - Use stripe_size parameter when writing ORC files to control batching
+ - stripe_size < 200KB: PyArrow ignores the parameter, uses default 1024
rows per stripe
+ - stripe_size >= 200KB: PyArrow respects the parameter and creates
stripes accordingly
+
+ 4. PYARROW CONFIGURATION:
+ - PyIceberg sets buffer_size=8MB for both Parquet and ORC
+ - Parquet: Gets the 8MB buffer_size parameter (ds.ParquetFileFormat
supports it)
+ - ORC: Ignores the 8MB buffer_size parameter (ds.OrcFileFormat doesn't
support it)
+ - This means ORC uses PyArrow's default batching behavior (based on
stripes)
+
+ 5. KEY DIFFERENCES FROM PARQUET:
+ - Parquet: 1 FileScanTask → 1 File → 1 PyArrow Fragment → N Batches
(based on row groups)
+ - ORC: 1 FileScanTask → 1 File → 1 PyArrow Fragment → N Batches (based
on stripes)
+ - Both formats support multiple batches per file when configured
properly
+ - The difference is in default configuration, not fundamental behavior
+
+ 6. TESTING IMPLICATIONS:
+ - Tests using default ORC writing will show 1 batch per file
+ - Tests using custom stripe_size >= 200KB will show multiple batches
per file
+ - Always verify the actual number of stripes in ORC files when testing
batching
+
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k
test_orc_batching_behavior_documentation
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.expressions import AlwaysTrue
+ from pyiceberg.io.pyarrow import ArrowScan, PyArrowFileIO
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import IntegerType, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "value", StringType(), required=False),
+ )
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=2,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "schema.name-mapping.default": '[{"field-id": 1, "names": ["id"]},
{"field-id": 2, "names": ["value"]}]',
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Test cases that document the exact behavior (using default ORC writing =
1 stripe per file)
+ test_cases = [
+ # (file_count, rows_per_file, expected_batches, description)
+ (1, 100, 1, "Single small file (1 stripe)"),
+ (1, 1000, 1, "Single medium file (1 stripe)"),
+ (1, 5000, 1, "Single large file (1 stripe)"),
+ (2, 500, 2, "Two small files (1 stripe each)"),
+ (3, 1000, 3, "Three medium files (1 stripe each)"),
+ (4, 750, 4, "Four small files (1 stripe each)"),
+ (2, 2000, 2, "Two large files (1 stripe each)"),
+ ]
+
+ for file_count, rows_per_file, expected_batches, description in test_cases:
+ total_rows = file_count * rows_per_file
+ scan_tasks = []
+
+ for file_idx in range(file_count):
+ # Create data for this file
+ start_id = file_idx * rows_per_file + 1
+ end_id = (file_idx + 1) * rows_per_file
+ data = pa.table(
+ {
+ "id": pa.array(range(start_id, end_id + 1),
type=pa.int32()),
+ "value": [f"file_{file_idx}_value_{i}" for i in
range(start_id, end_id + 1)],
+ }
+ )
+
+ # Create ORC file
+ orc_path = tmp_path /
f"doc_test_{file_idx}_{rows_per_file}_rows.orc"
+ orc.write_table(data, str(orc_path))
+
+ # Create DataFile
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=orc_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=rows_per_file,
+ column_sizes={1: rows_per_file * 4, 2: rows_per_file * 8},
+ value_counts={1: rows_per_file, 2: rows_per_file},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: start_id.to_bytes(4, "little"), 2:
f"file_{file_idx}_value_{start_id}".encode()},
+ upper_bounds={1: end_id.to_bytes(4, "little"), 2:
f"file_{file_idx}_value_{end_id}".encode()},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+ scan_tasks.append(FileScanTask(data_file=data_file))
+
+ # Test batching behavior
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+
+ batches = list(scan.to_record_batches(scan_tasks))
+
+ # Verify exact batch count
+ assert len(batches) == expected_batches, f"Expected {expected_batches}
batches, got {len(batches)} for {description}"
+
+ # Verify total rows
+ total_batch_rows = sum(batch.num_rows for batch in batches)
+ assert total_batch_rows == total_rows, f"Total rows mismatch: expected
{total_rows}, got {total_batch_rows}"
+
+ # Verify data integrity
+ all_ids = []
+ for batch in batches:
+ all_ids.extend(batch.column("id").to_pylist())
+ assert sorted(all_ids) == list(range(1, total_rows + 1)), f"Data
integrity check failed for {description}"
+
+
+def test_parquet_vs_orc_batching_behavior_comparison(tmp_path: Path) -> None:
+ """
+ Compare Parquet vs ORC batching behavior to document the key differences.
+
+ Key differences:
+ - PARQUET: 1 FileScanTask → 1 File → 1 PyArrow Fragment → N Batches (based
on row groups)
+ - ORC: 1 FileScanTask → 1 File → 1 PyArrow Fragment → N Batches (based on
stripes)
+
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k
test_parquet_vs_orc_batching_behavior_comparison
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+ import pyarrow.parquet as pq
+
+ from pyiceberg.expressions import AlwaysTrue
+ from pyiceberg.io.pyarrow import ArrowScan, PyArrowFileIO
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import IntegerType, NestedField, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "value", StringType(), required=False),
+ )
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=2,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "schema.name-mapping.default": '[{"field-id": 1, "names": ["id"]},
{"field-id": 2, "names": ["value"]}]',
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Test Parquet with different row group sizes
+ parquet_test_cases = [
+ (1000, "Small row groups"),
+ (2000, "Medium row groups"),
+ (5000, "Large row groups"),
+ ]
+
+ for row_group_size, _description in parquet_test_cases:
+ # Create data
+ data = pa.table({"id": pa.array(range(1, 10001), type=pa.int32()),
"value": [f"value_{i}" for i in range(1, 10001)]})
+
+ # Create Parquet file with specific row group size
+ parquet_path = tmp_path / f"parquet_test_{row_group_size}.parquet"
+ pq.write_table(data, str(parquet_path), row_group_size=row_group_size)
+
+ # Create DataFile
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(parquet_path),
+ file_format=FileFormat.PARQUET,
+ partition=Record(),
+ file_size_in_bytes=parquet_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=10000,
+ column_sizes={1: 40000, 2: 80000},
+ value_counts={1: 10000, 2: 10000},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: b"\x01\x00\x00\x00", 2: b"value_1"},
+ upper_bounds={1: b"\x10'\x00\x00", 2: b"value_10000"},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+ scan_task = FileScanTask(data_file=data_file)
+
+ # Test batching behavior
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+
+ batches = list(scan.to_record_batches([scan_task]))
+ expected_batches = 10000 // row_group_size # Number of row groups
+
+ # Verify exact batch count based on row groups
+ assert len(batches) == expected_batches, (
+ f"Expected {expected_batches} batches for
row_group_size={row_group_size}, got {len(batches)}"
+ )
+
+ # Verify total rows
+ total_rows = sum(batch.num_rows for batch in batches)
+ assert total_rows == 10000, f"Expected 10000 total rows, got
{total_rows}"
+
+ orc_test_cases = [
+ (1000, "Small file"),
+ (5000, "Medium file"),
+ (10000, "Large file"),
+ ]
+
+ for file_size, _description in orc_test_cases:
+ # Create data
+ data = pa.table(
+ {"id": pa.array(range(1, file_size + 1), type=pa.int32()),
"value": [f"value_{i}" for i in range(1, file_size + 1)]}
+ )
+
+ # Create ORC file
+ orc_path = tmp_path / f"orc_test_{file_size}.orc"
+ orc.write_table(data, str(orc_path))
+
+ # Create DataFile
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=orc_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=file_size,
+ column_sizes={1: file_size * 4, 2: file_size * 8},
+ value_counts={1: file_size, 2: file_size},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: b"\x01\x00\x00\x00", 2: b"value_1"},
+ upper_bounds={1: file_size.to_bytes(4, "little"), 2:
f"value_{file_size}".encode()},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+ scan_task = FileScanTask(data_file=data_file)
+
+ # Test batching behavior
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+
+ batches = list(scan.to_record_batches([scan_task]))
+
+ # Verify ORC creates 1 batch per file (with default stripe
configuration)
+ # Note: This is because default ORC writing creates 1 stripe per file
+ assert len(batches) == 1, (
+ f"Expected 1 batch for ORC file with {file_size} rows (default
stripe config), got {len(batches)}"
+ )
+
+ # Verify total rows
+ total_rows = sum(batch.num_rows for batch in batches)
+ assert total_rows == file_size, f"Expected {file_size} total rows, got
{total_rows}"
+
+
+def test_orc_stripe_size_batch_size_compression_interaction(tmp_path: Path) ->
None:
+ """
+ Test that demonstrates how stripe size, batch size, and compression
interact
+ to affect ORC batching behavior.
+
+ This test shows:
+ 1. How stripe_size affects the number of stripes (and therefore batches)
+ 2. How batch_size affects the number of stripes when stripe_size is small
+ 3. How compression affects both stripe count and file size
+ 4. The relationship between uncompressed target size and actual file size
+
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k
test_orc_stripe_size_batch_size_compression_interaction
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import IntegerType, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "value", StringType(), required=False),
+ )
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=2,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "schema.name-mapping.default": '[{"field-id": 1, "names": ["id"]},
{"field-id": 2, "names": ["value"]}]',
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Create test data
+ data = pa.table({"id": pa.array(range(1, 10001), type=pa.int32()),
"value": [f"value_{i}" for i in range(1, 10001)]})
+
+ # Test different combinations
+ test_cases = [
+ # (stripe_size, batch_size, compression, description)
+ (200000, None, "uncompressed", "200KB stripe, no batch limit,
uncompressed"),
+ (200000, 1000, "uncompressed", "200KB stripe, 1000 batch,
uncompressed"),
+ (200000, None, "snappy", "200KB stripe, no batch limit, snappy"),
+ (100000, None, "uncompressed", "100KB stripe, no batch limit,
uncompressed"),
+ (500000, None, "uncompressed", "500KB stripe, no batch limit,
uncompressed"),
+ (None, 1000, "uncompressed", "No stripe limit, 1000 batch,
uncompressed"),
+ (None, 2000, "uncompressed", "No stripe limit, 2000 batch,
uncompressed"),
+ ]
+
+ for stripe_size, batch_size, compression, description in test_cases:
+ # Create ORC file with specific parameters
+ orc_path = tmp_path / f"orc_test_{hash(description)}.orc"
+
+ write_kwargs: dict[str, Any] = {"compression": compression}
+ if stripe_size is not None:
+ write_kwargs["stripe_size"] = stripe_size
+ if batch_size is not None:
+ write_kwargs["batch_size"] = batch_size
+
+ orc.write_table(data, str(orc_path), **write_kwargs)
+
+ # Analyze the ORC file
+ file_size = orc_path.stat().st_size
+ orc_file = orc.ORCFile(str(orc_path))
+ actual_stripes = orc_file.nstripes
+
+ # Assert basic file properties
+ assert file_size > 0, f"ORC file should have non-zero size for
{description}"
+ assert actual_stripes > 0, f"ORC file should have at least one stripe
for {description}"
+
+ # Assert stripe count expectations based on stripe_size and compression
+ if stripe_size is not None:
+ # With stripe_size specified, we expect multiple stripes for small
sizes
+ # But compression can make the data small enough to fit in one
stripe
+ if stripe_size <= 200000 and compression == "uncompressed": #
200KB or smaller, uncompressed
+ assert actual_stripes > 1, (
+ f"Expected multiple stripes for small
stripe_size={stripe_size} in {description}, got {actual_stripes}"
+ )
+ else: # Larger stripe sizes or compressed data might result in
single stripe
+ assert actual_stripes >= 1, (
+ f"Expected at least 1 stripe for stripe_size={stripe_size}
in {description}, got {actual_stripes}"
+ )
+ else:
+ # Without stripe_size, we expect at least 1 stripe
+ assert actual_stripes >= 1, f"Expected at least 1 stripe for
{description}, got {actual_stripes}"
+
+ # Test PyArrow batching
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=file_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=10000,
+ column_sizes={1: 40000, 2: 80000},
+ value_counts={1: 10000, 2: 10000},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: b"\x01\x00\x00\x00", 2: b"value_1"},
+ upper_bounds={1: b"\x10'\x00\x00", 2: b"value_10000"},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+
+ # Test batching behavior
+ scan_task = FileScanTask(data_file=data_file)
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+ batches = list(scan.to_record_batches([scan_task]))
+
+ # Assert batching behavior
+ assert len(batches) > 0, f"Should have at least one batch for
{description}"
+ assert len(batches) == actual_stripes, (
+ f"Number of batches should match number of stripes for
{description}: {len(batches)} batches vs {actual_stripes} stripes"
+ )
+
+ # Assert data integrity
+ total_rows = sum(batch.num_rows for batch in batches)
+ assert total_rows == 10000, f"Total rows should be 10000 for
{description}, got {total_rows}"
+
+ # Assert compression effect
+ if compression == "snappy":
+ # Snappy compression should result in smaller file size than
uncompressed
+ uncompressed_size = len(data) * 15 # Rough estimate of
uncompressed size
+ assert file_size < uncompressed_size, (
+ f"Snappy compression should reduce file size for
{description}: {file_size} vs {uncompressed_size}"
+ )
+ elif compression == "uncompressed":
+ # Uncompressed should be larger than snappy
+ assert file_size > 0, f"Uncompressed file should have size > 0 for
{description}"
+
+
+def test_orc_near_perfect_stripe_size_mapping(tmp_path: Path) -> None:
+ """
+ Test that demonstrates near-perfect 1:1 mapping between stripe size and
actual file size.
+
+ This test shows how to achieve ratios of 0.9+ (actual/target) by using:
+ 1. Large stripe sizes (2-5MB)
+ 2. Large datasets (50K+ rows)
+ 3. Data that is hard to compress (large random strings)
+ 4. uncompressed compression setting
+
+ This is the closest we can get to having stripe size directly map to
number of batches
+ without significant ORC encoding overhead.
+
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k
test_orc_near_perfect_stripe_size_mapping
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.expressions import AlwaysTrue
+ from pyiceberg.io.pyarrow import ArrowScan, PyArrowFileIO
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.types import NestedField, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", StringType(), required=True), # Large string
field
+ )
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=1,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "schema.name-mapping.default": '[{"field-id": 1, "names":
["id"]}]',
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Create large dataset with hard-to-compress data
+ data = pa.table(
+ {
+ "id": pa.array(
+ [
+
f"very_long_string_value_{i:06d}_with_lots_of_padding_to_make_it_harder_to_compress_{i
* 7919 % 100000:05d}_more_padding_{i * 7919 % 100000:05d}"
+ for i in range(1, 50001)
+ ]
+ ) # 50K rows
+ }
+ )
+
+ # Test with large stripe sizes that should give us multiple stripes
+ test_cases = [
+ (2000000, "2MB stripe size"),
+ (3000000, "3MB stripe size"),
+ (4000000, "4MB stripe size"),
+ (5000000, "5MB stripe size"),
+ ]
+
+ for stripe_size, _description in test_cases:
+ # Create ORC file with specific stripe size
+ orc_path = tmp_path / f"orc_perfect_test_{stripe_size}.orc"
+ orc.write_table(data, str(orc_path), stripe_size=stripe_size,
compression="uncompressed")
+
+ # Analyze the ORC file
+ file_size = orc_path.stat().st_size
+ orc_file = orc.ORCFile(str(orc_path))
+ actual_stripes = orc_file.nstripes
+
+ # Assert basic file properties
+ assert file_size > 0, f"ORC file should have non-zero size for
stripe_size={stripe_size}"
+ assert actual_stripes > 0, f"ORC file should have at least one stripe
for stripe_size={stripe_size}"
+
+ # Assert that larger stripe sizes result in fewer stripes
+ # With 50K rows of large strings, we expect multiple stripes for
smaller stripe sizes
+ if stripe_size <= 3000000: # 3MB or smaller
+ assert actual_stripes > 1, f"Expected multiple stripes for
stripe_size={stripe_size}, got {actual_stripes}"
+ else: # Larger stripe sizes might result in single stripe
+ assert actual_stripes >= 1, f"Expected at least 1 stripe for
stripe_size={stripe_size}, got {actual_stripes}"
+
+ # Test PyArrow batching
+ from pyiceberg.manifest import DataFile
+ from pyiceberg.typedef import Record
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=file_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=50000,
+ column_sizes={1: 5450000},
+ value_counts={1: 50000},
+ null_value_counts={1: 0},
+ nan_value_counts={1: 0},
+ lower_bounds={1: b"very_long_string_value_000001_"},
+ upper_bounds={1: b"very_long_string_value_050000_"},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+
+ # Test batching behavior
+ scan_task = FileScanTask(data_file=data_file)
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+ batches = list(scan.to_record_batches([scan_task]))
+
+ # Assert batching behavior
+ assert len(batches) > 0, f"Should have at least one batch for
stripe_size={stripe_size}"
+ assert len(batches) == actual_stripes, (
+ f"Number of batches should match number of stripes for
stripe_size={stripe_size}: {len(batches)} batches vs {actual_stripes} stripes"
+ )
+
+ # Assert data integrity
+ total_rows = sum(batch.num_rows for batch in batches)
+ assert total_rows == 50000, f"Total rows should be 50000 for
stripe_size={stripe_size}, got {total_rows}"
+
+ # Assert compression ratio is reasonable (uncompressed should be close
to raw data size)
+ raw_data_size = data.nbytes
+ compression_ratio = raw_data_size / file_size if file_size > 0 else 0
+ assert compression_ratio > 0.5, (
+ f"Compression ratio should be reasonable for
stripe_size={stripe_size}: {compression_ratio:.2f}"
+ )
+ assert compression_ratio < 2.0, (
+ f"Compression ratio should not be too high for
stripe_size={stripe_size}: {compression_ratio:.2f}"
+ )
+
+
+def test_orc_stripe_based_batching(tmp_path: Path) -> None:
+ """
+ Test ORC stripe-based batching to demonstrate that ORC can have multiple
batches per file.
+ This corrects the previous understanding that ORC always has 1 batch per
file.
+
+ This test uses hardcoded expected values based on observed behavior with
10,000 rows:
+ - 200KB stripe size: 5 stripes of [2048, 2048, 2048, 2048, 1808] rows
+ - 400KB stripe size: 2 stripes of [7168, 2832] rows
+ - 600KB stripe size: 1 stripe of [10000] rows
+
+ To run just this test:
+ pytest tests/io/test_pyarrow.py -k test_orc_stripe_based_batching
+ """
+ import pyarrow as pa
+ import pyarrow.orc as orc
+
+ from pyiceberg.expressions import AlwaysTrue
+ from pyiceberg.io.pyarrow import ArrowScan, PyArrowFileIO
+ from pyiceberg.manifest import DataFileContent, FileFormat
+ from pyiceberg.partitioning import PartitionSpec
+ from pyiceberg.schema import Schema
+ from pyiceberg.table import FileScanTask
+ from pyiceberg.table.metadata import TableMetadataV2
+ from pyiceberg.typedef import Record
+ from pyiceberg.types import IntegerType, NestedField, StringType
+
+ # Define schema
+ schema = Schema(
+ NestedField(1, "id", IntegerType(), required=True),
+ NestedField(2, "value", StringType(), required=False),
+ )
+
+ # Create table metadata
+ table_metadata = TableMetadataV2(
+ location=f"file://{tmp_path}/test_location",
+ last_column_id=2,
+ format_version=2,
+ schemas=[schema],
+ partition_specs=[PartitionSpec()],
+ properties={
+ "schema.name-mapping.default": '[{"field-id": 1, "names": ["id"]},
{"field-id": 2, "names": ["value"]}]',
+ },
+ )
+ io = PyArrowFileIO()
+
+ # Test ORC with different stripe configurations (stripe_size in bytes)
+ # Note: PyArrow ORC ignores stripe_size < 200KB, so we use larger values
+ # Expected values are hardcoded based on observed behavior with 10,000 rows
+ test_cases = [
+ (200000, "Small stripes (200KB)", 5, [2048, 2048, 2048, 2048, 1808]),
+ (400000, "Medium stripes (400KB)", 2, [7168, 2832]),
+ (600000, "Large stripes (600KB)", 1, [10000]),
+ ]
+
+ for stripe_size, _description, expected_stripes, expected_stripe_sizes in
test_cases:
+ # Create data
+ data = pa.table({"id": pa.array(range(1, 10001), type=pa.int32()),
"value": [f"value_{i}" for i in range(1, 10001)]})
+
+ # Create ORC file with specific stripe size (in bytes)
+ orc_path = tmp_path / f"orc_stripe_test_{stripe_size}.orc"
+ orc.write_table(data, str(orc_path), stripe_size=stripe_size)
+
+ # Check ORC metadata
+ orc_file = orc.ORCFile(str(orc_path))
+ actual_stripes = orc_file.nstripes
+ actual_stripe_sizes = [orc_file.read_stripe(i).num_rows for i in
range(actual_stripes)]
+
+ # Create DataFile
+ from pyiceberg.manifest import DataFile
+
+ data_file = DataFile.from_args(
+ content=DataFileContent.DATA,
+ file_path=str(orc_path),
+ file_format=FileFormat.ORC,
+ partition=Record(),
+ file_size_in_bytes=orc_path.stat().st_size,
+ sort_order_id=None,
+ spec_id=0,
+ equality_ids=None,
+ key_metadata=None,
+ record_count=10000,
+ column_sizes={1: 40000, 2: 80000},
+ value_counts={1: 10000, 2: 10000},
+ null_value_counts={1: 0, 2: 0},
+ nan_value_counts={1: 0, 2: 0},
+ lower_bounds={1: b"\x01\x00\x00\x00", 2: b"value_1"},
+ upper_bounds={1: b"\x10'\x00\x00", 2: b"value_10000"},
+ split_offsets=None,
+ )
+ data_file.spec_id = 0
+
+ # Test batching behavior
+ scan_task = FileScanTask(data_file=data_file)
+ scan = ArrowScan(
+ table_metadata=table_metadata,
+ io=io,
+ projected_schema=schema,
+ row_filter=AlwaysTrue(),
+ case_sensitive=True,
+ )
+ batches = list(scan.to_record_batches([scan_task]))
+
+ # CRITICAL: Verify we get multiple batches for a single file (when
stripe size is small enough)
+ if expected_stripes > 1:
+ assert len(batches) > 1, f"Expected multiple batches for single
file, got {len(batches)} batches"
+ assert actual_stripes > 1, f"Expected multiple stripes for single
file, got {actual_stripes} stripes"
+
+ # Verify exact batch count matches expected
+ assert len(batches) == expected_stripes, f"Expected {expected_stripes}
batches, got {len(batches)}"
+
+ # Verify batch sizes match expected stripe sizes
+ batch_sizes = [batch.num_rows for batch in batches]
+ assert batch_sizes == expected_stripe_sizes, (
+ f"Batch sizes {batch_sizes} don't match expected stripe sizes
{expected_stripe_sizes}"
+ )
+
+ # Verify actual ORC metadata matches expected
+ assert actual_stripes == expected_stripes, f"Expected
{expected_stripes} stripes, got {actual_stripes}"
+ assert actual_stripe_sizes == expected_stripe_sizes, (
+ f"Expected stripe sizes {expected_stripe_sizes}, got
{actual_stripe_sizes}"
+ )
+
+ # Verify total rows
+ total_rows = sum(batch.num_rows for batch in batches)
+ assert total_rows == 10000, f"Expected 10000 total rows, got
{total_rows}"
diff --git a/tests/table/test_init.py b/tests/table/test_init.py
index dea4e069..cd81df4d 100644
--- a/tests/table/test_init.py
+++ b/tests/table/test_init.py
@@ -266,8 +266,15 @@ def test_history(table_v2: Table) -> None:
]
-def test_table_scan_select(table_v2: Table) -> None:
- scan = table_v2.scan()
[email protected](
+ "table_fixture",
+ [
+ pytest.param(pytest.lazy_fixture("table_v2"), id="parquet"),
+ pytest.param(pytest.lazy_fixture("table_v2_orc"), id="orc"),
+ ],
+)
+def test_table_scan_select(table_fixture: Table) -> None:
+ scan = table_fixture.scan()
assert scan.selected_fields == ("*",)
assert scan.select("a", "b").selected_fields == ("a", "b")
assert scan.select("a", "c").select("a").selected_fields == ("a",)
