Bin-pack Writes Operation into multiple parquet files, and parallelize writing WriteTasks

pyiceberg/io/pyarrow.py

@@ -1761,54 +1761,67 @@ def data_file_statistics_from_parquet_metadata(
 
 
 def write_file(io: FileIO, table_metadata: TableMetadata, tasks: Iterator[WriteTask]) -> Iterator[DataFile]:
-    task = next(tasks)
-
-    try:
-        _ = next(tasks)
-        # If there are more tasks, raise an exception
-        raise NotImplementedError("Only unpartitioned writes are supported: https://github.com/apache/iceberg-python/issues/208")
-    except StopIteration:
-        pass
-
-    parquet_writer_kwargs = _get_parquet_writer_kwargs(table_metadata.properties)
-
-    file_path = f'{table_metadata.location}/data/{task.generate_data_file_filename("parquet")}'
     schema = table_metadata.schema()
     arrow_file_schema = schema.as_arrow()
+    parquet_writer_kwargs = _get_parquet_writer_kwargs(table_metadata.properties)
 
-    fo = io.new_output(file_path)
     row_group_size = PropertyUtil.property_as_int(
         properties=table_metadata.properties,
         property_name=TableProperties.PARQUET_ROW_GROUP_SIZE_BYTES,
         default=TableProperties.PARQUET_ROW_GROUP_SIZE_BYTES_DEFAULT,
     )
-    with fo.create(overwrite=True) as fos:
-        with pq.ParquetWriter(fos, schema=arrow_file_schema, **parquet_writer_kwargs) as writer:
-            writer.write_table(task.df, row_group_size=row_group_size)
-
-    statistics = data_file_statistics_from_parquet_metadata(
-        parquet_metadata=writer.writer.metadata,
-        stats_columns=compute_statistics_plan(schema, table_metadata.properties),
-        parquet_column_mapping=parquet_path_to_id_mapping(schema),
-    )
-    data_file = DataFile(
-        content=DataFileContent.DATA,
-        file_path=file_path,
-        file_format=FileFormat.PARQUET,
-        partition=Record(),
-        file_size_in_bytes=len(fo),
-        # After this has been fixed:
-        # https://github.com/apache/iceberg-python/issues/271
-        # sort_order_id=task.sort_order_id,
-        sort_order_id=None,
-        # Just copy these from the table for now
-        spec_id=table_metadata.default_spec_id,
-        equality_ids=None,
-        key_metadata=None,
-        **statistics.to_serialized_dict(),
-    )
 
-    return iter([data_file])
+    def write_parquet(task: WriteTask) -> DataFile:
+        file_path = f'{table_metadata.location}/data/{task.generate_data_file_filename("parquet")}'
+        fo = io.new_output(file_path)
+        with fo.create(overwrite=True) as fos:
+            with pq.ParquetWriter(fos, schema=arrow_file_schema, **parquet_writer_kwargs) as writer:
+                writer.write(pa.Table.from_batches(task.record_batches), row_group_size=row_group_size)
+
+        statistics = data_file_statistics_from_parquet_metadata(
+            parquet_metadata=writer.writer.metadata,
+            stats_columns=compute_statistics_plan(schema, table_metadata.properties),
+            parquet_column_mapping=parquet_path_to_id_mapping(schema),
+        )
+        data_file = DataFile(
+            content=DataFileContent.DATA,
+            file_path=file_path,
+            file_format=FileFormat.PARQUET,
+            partition=Record(),
+            file_size_in_bytes=len(fo),
+            # After this has been fixed:
+            # https://github.com/apache/iceberg-python/issues/271
+            # sort_order_id=task.sort_order_id,
+            sort_order_id=None,
+            # Just copy these from the table for now
+            spec_id=table_metadata.default_spec_id,
+            equality_ids=None,
+            key_metadata=None,
+            **statistics.to_serialized_dict(),
+        )
+
+        return data_file
+
+    executor = ExecutorFactory.get_or_create()
+    data_files = executor.map(write_parquet, tasks)
+
+    return iter(data_files)
+
+
+def bin_pack_arrow_table(tbl: pa.Table, target_file_size: int) -> Iterator[List[pa.RecordBatch]]:
+    from pyiceberg.utils.bin_packing import PackingIterator
+
+    avg_row_size_bytes = tbl.nbytes / tbl.num_rows
+    target_rows_per_file = target_file_size // avg_row_size_bytes
+    batches = tbl.to_batches(max_chunksize=target_rows_per_file)
+    bin_packed_record_batches = PackingIterator(
+        items=batches,
+        target_weight=target_file_size,
+        lookback=len(batches),  # ignore lookback
+        weight_func=lambda x: x.nbytes,
+        largest_bin_first=False,
+    )
+    return bin_packed_record_batches
 
 
 def parquet_files_to_data_files(io: FileIO, table_metadata: TableMetadata, file_paths: Iterator[str]) -> Iterator[DataFile]:

pyiceberg/table/__init__.py

@@ -215,6 +215,9 @@ class TableProperties:
 
     PARQUET_BLOOM_FILTER_COLUMN_ENABLED_PREFIX = "write.parquet.bloom-filter-enabled.column"
 
+    WRITE_TARGET_FILE_SIZE_BYTES = "write.target-file-size-bytes"
+    WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT = 512 * 1024 * 1024  # 512 MB
+
     DEFAULT_WRITE_METRICS_MODE = "write.metadata.metrics.default"
     DEFAULT_WRITE_METRICS_MODE_DEFAULT = "truncate(16)"
 
@@ -2486,7 +2489,7 @@ def _add_and_move_fields(
 class WriteTask:
     write_uuid: uuid.UUID
     task_id: int
-    df: pa.Table
+    record_batches: List[pa.RecordBatch]
     sort_order_id: Optional[int] = None
 
     # Later to be extended with partition information
@@ -2521,17 +2524,27 @@ def _dataframe_to_data_files(
     Returns:
         An iterable that supplies datafiles that represent the table.
     """
-    from pyiceberg.io.pyarrow import write_file
+    from pyiceberg.io.pyarrow import bin_pack_arrow_table, write_file
 
     if len([spec for spec in table_metadata.partition_specs if spec.spec_id != 0]) > 0:
         raise ValueError("Cannot write to partitioned tables")
 
     counter = itertools.count(0)
     write_uuid = write_uuid or uuid.uuid4()
 
+    target_file_size = PropertyUtil.property_as_int(
+        properties=table_metadata.properties,
+        property_name=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES,
+        default=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT,
+    )
+
     # This is an iter, so we don't have to materialize everything every time
     # This will be more relevant when we start doing partitioned writes
-    yield from write_file(io=io, table_metadata=table_metadata, tasks=iter([WriteTask(write_uuid, next(counter), df)]))
+    yield from write_file(
+        io=io,
+        table_metadata=table_metadata,
+        tasks=iter([WriteTask(write_uuid, next(counter), batches) for batches in bin_pack_arrow_table(df, target_file_size)]),  # type: ignore
+    )
 
 
 def _parquet_files_to_data_files(table_metadata: TableMetadata, file_paths: List[str], io: FileIO) -> Iterable[DataFile]:

tests/conftest.py

@@ -30,7 +30,7 @@
 import socket
 import string
 import uuid
-from datetime import datetime
+from datetime import date, datetime
 from pathlib import Path
 from random import choice
 from tempfile import TemporaryDirectory
@@ -1987,3 +1987,60 @@ def spark() -> SparkSession:
     )
 
     return spark
+
+
+TEST_DATA_WITH_NULL = {
+    'bool': [False, None, True],
+    'string': ['a', None, 'z'],
+    # Go over the 16 bytes to kick in truncation
+    'string_long': ['a' * 22, None, 'z' * 22],
+    'int': [1, None, 9],
+    'long': [1, None, 9],
+    'float': [0.0, None, 0.9],
+    'double': [0.0, None, 0.9],
+    'timestamp': [datetime(2023, 1, 1, 19, 25, 00), None, datetime(2023, 3, 1, 19, 25, 00)],
+    'timestamptz': [datetime(2023, 1, 1, 19, 25, 00), None, datetime(2023, 3, 1, 19, 25, 00)],
+    'date': [date(2023, 1, 1), None, date(2023, 3, 1)],
+    # Not supported by Spark
+    # 'time': [time(1, 22, 0), None, time(19, 25, 0)],
+    # Not natively supported by Arrow
+    # 'uuid': [uuid.UUID('00000000-0000-0000-0000-000000000000').bytes, None, uuid.UUID('11111111-1111-1111-1111-111111111111').bytes],
+    'binary': [b'\01', None, b'\22'],
+    'fixed': [
+        uuid.UUID('00000000-0000-0000-0000-000000000000').bytes,
+        None,
+        uuid.UUID('11111111-1111-1111-1111-111111111111').bytes,
+    ],
+}
+
+
+@pytest.fixture(scope="session")
+def pa_schema() -> "pa.Schema":
+    import pyarrow as pa
+
+    return pa.schema([
+        ("bool", pa.bool_()),
+        ("string", pa.string()),
+        ("string_long", pa.string()),
+        ("int", pa.int32()),
+        ("long", pa.int64()),
+        ("float", pa.float32()),
+        ("double", pa.float64()),
+        ("timestamp", pa.timestamp(unit="us")),
+        ("timestamptz", pa.timestamp(unit="us", tz="UTC")),
+        ("date", pa.date32()),
+        # Not supported by Spark
+        # ("time", pa.time64("us")),
+        # Not natively supported by Arrow
+        # ("uuid", pa.fixed(16)),
+        ("binary", pa.large_binary()),
+        ("fixed", pa.binary(16)),
+    ])
+
+
+@pytest.fixture(scope="session")
+def arrow_table_with_null(pa_schema: "pa.Schema") -> "pa.Table":
+    import pyarrow as pa
+
+    """PyArrow table with all kinds of columns"""
+    return pa.Table.from_pydict(TEST_DATA_WITH_NULL, schema=pa_schema)

tests/integration/test_writes.py

@@ -37,7 +37,7 @@
 from pyiceberg.catalog.sql import SqlCatalog
 from pyiceberg.exceptions import NoSuchTableError
 from pyiceberg.schema import Schema
-from pyiceberg.table import Table, _dataframe_to_data_files
+from pyiceberg.table import Table, TableProperties, _dataframe_to_data_files
 from pyiceberg.typedef import Properties
 from pyiceberg.types import (
     BinaryType,
@@ -383,6 +383,47 @@ def get_current_snapshot_id(identifier: str) -> int:
     assert tbl.current_snapshot().snapshot_id == get_current_snapshot_id(identifier)  # type: ignore
 
 
+@pytest.mark.integration
+def test_write_bin_pack_data_files(spark: SparkSession, session_catalog: Catalog, arrow_table_with_null: pa.Table) -> None:
+    identifier = "default.write_bin_pack_data_files"
+    tbl = _create_table(session_catalog, identifier, {"format-version": "1"}, [])
+
+    def get_data_files_count(identifier: str) -> int:
+        return spark.sql(
+            f"""
+            SELECT *
+            FROM {identifier}.files
+        """
+        ).count()
+
+    # writes 1 data file since the table is smaller than default target file size
+    assert arrow_table_with_null.nbytes < TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT
+    tbl.overwrite(arrow_table_with_null)
+    assert get_data_files_count(identifier) == 1
+
+    # writes 1 data file as long as table is smaller than default target file size
+    bigger_arrow_tbl = pa.concat_tables([arrow_table_with_null] * 10)
+    assert bigger_arrow_tbl.nbytes < TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT
+    tbl.overwrite(bigger_arrow_tbl)
+    assert get_data_files_count(identifier) == 1
+
+    # writes multiple data files once target file size is overridden
+    target_file_size = arrow_table_with_null.nbytes
+    tbl = tbl.transaction().set_properties({TableProperties.WRITE_TARGET_FILE_SIZE_BYTES: target_file_size}).commit_transaction()
+    assert str(target_file_size) == tbl.properties.get(TableProperties.WRITE_TARGET_FILE_SIZE_BYTES)
+    assert target_file_size < bigger_arrow_tbl.nbytes
+    tbl.overwrite(bigger_arrow_tbl)
+    assert get_data_files_count(identifier) == 10
+
+    # writes half the number of data files when target file size doubles
+    target_file_size = arrow_table_with_null.nbytes * 2
+    tbl = tbl.transaction().set_properties({TableProperties.WRITE_TARGET_FILE_SIZE_BYTES: target_file_size}).commit_transaction()
+    assert str(target_file_size) == tbl.properties.get(TableProperties.WRITE_TARGET_FILE_SIZE_BYTES)
+    assert target_file_size < bigger_arrow_tbl.nbytes
+    tbl.overwrite(bigger_arrow_tbl)
+    assert get_data_files_count(identifier) == 5
+
+
 @pytest.mark.integration
 @pytest.mark.parametrize("format_version", [1, 2])
 @pytest.mark.parametrize(

tests/io/test_pyarrow.py

@@ -64,14 +64,15 @@
     _ConvertToArrowSchema,
     _primitive_to_physical,
     _read_deletes,
+    bin_pack_arrow_table,
     expression_to_pyarrow,
     project_table,
     schema_to_pyarrow,
 )
 from pyiceberg.manifest import DataFile, DataFileContent, FileFormat
 from pyiceberg.partitioning import PartitionSpec
 from pyiceberg.schema import Schema, make_compatible_name, visit
-from pyiceberg.table import FileScanTask, Table
+from pyiceberg.table import FileScanTask, Table, TableProperties
 from pyiceberg.table.metadata import TableMetadataV2
 from pyiceberg.typedef import UTF8
 from pyiceberg.types import (
@@ -1710,3 +1711,25 @@ def test_stats_aggregator_update_max(vals: List[Any], primitive_type: PrimitiveT
         stats.update_max(val)
 
     assert stats.current_max == expected_result
+
+
+def test_bin_pack_arrow_table(arrow_table_with_null: pa.Table) -> None:
+    # default packs to 1 bin since the table is small
+    bin_packed = bin_pack_arrow_table(
+        arrow_table_with_null, target_file_size=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT
+    )
+    assert len(list(bin_packed)) == 1
+
+    # as long as table is smaller than default target size, it should pack to 1 bin
+    bigger_arrow_tbl = pa.concat_tables([arrow_table_with_null] * 10)
+    assert bigger_arrow_tbl.nbytes < TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT
+    bin_packed = bin_pack_arrow_table(bigger_arrow_tbl, target_file_size=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT)
+    assert len(list(bin_packed)) == 1
+
+    # unless we override the target size to be smaller
+    bin_packed = bin_pack_arrow_table(bigger_arrow_tbl, target_file_size=arrow_table_with_null.nbytes)
+    assert len(list(bin_packed)) == 10
+
+    # and will produce half the number of files if we double the target size
+    bin_packed = bin_pack_arrow_table(bigger_arrow_tbl, target_file_size=arrow_table_with_null.nbytes * 2)
+    assert len(list(bin_packed)) == 5