add write target file size config

pyiceberg/io/pyarrow.py

@@ -1758,14 +1758,24 @@ def write_file(table: Table, tasks: Iterator[WriteTask], file_schema: Optional[S
         data_files.append(data_file)
     return iter(data_files)
 
-def bin_pack_arrow_table(tbl: pa.Table) -> Iterator[List[pa.RecordBatch]]:
-    # bin-pack the table into 256 MB chunks
+
+def bin_pack_arrow_table(tbl: pa.Table, table_properties: Properties) -> Iterator[List[pa.RecordBatch]]:
     from pyiceberg.utils.bin_packing import PackingIterator
 
-    splits = tbl.to_batches()
-    target_weight = 2 << 27  # 256 MB
-    bin_packed = PackingIterator(splits, target_weight, lookback=2, weight_func=lambda x: x.nbytes, largest_bin_first=True)
-    return bin_packed
+    target_file_size = PropertyUtil.property_as_int(
+        properties=table_properties,
+        property_name=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES,
+        default=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT,
+    )
+    assert isinstance(target_file_size, int)
+    bin_packed_record_batches = PackingIterator(
+        items=tbl.to_batches(),
+        target_weight=target_file_size,
+        lookback=2,
+        weight_func=lambda x: x.nbytes,
+        largest_bin_first=True,
+    )
+    return bin_packed_record_batches
 
 
 ICEBERG_UNCOMPRESSED_CODEC = "uncompressed"

pyiceberg/table/__init__.py

@@ -156,6 +156,9 @@ 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)"
 
@@ -2367,9 +2370,10 @@ def _dataframe_to_data_files(
     counter = itertools.count(0)
     write_uuid = write_uuid or uuid.uuid4()
 
+    write_tasks = [WriteTask(write_uuid, next(counter), batches) for batches in bin_pack_arrow_table(df, table.properties)]
     # 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(table, iter([WriteTask(write_uuid, next(counter), batches) for batches in bin_pack_arrow_table(df)]), file_schema=file_schema)
+    yield from write_file(table, iter(write_tasks), file_schema=file_schema)
 
 
 class _MergingSnapshotProducer: