nssalian commented on code in PR #3119: URL: https://github.com/apache/iceberg-python/pull/3119#discussion_r2998897474
########## pyiceberg/io/fileformat.py: ########## @@ -0,0 +1,182 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""File Format API for writing Iceberg data files.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any + +from pyiceberg.io import OutputFile +from pyiceberg.manifest import FileFormat +from pyiceberg.partitioning import PartitionField, PartitionSpec, partition_record_value +from pyiceberg.schema import Schema +from pyiceberg.typedef import Properties, Record + +if TYPE_CHECKING: + import pyarrow as pa + + from pyiceberg.io.pyarrow import StatsAggregator + + +@dataclass(frozen=True) +class DataFileStatistics: + record_count: int + column_sizes: dict[int, int] + value_counts: dict[int, int] + null_value_counts: dict[int, int] + nan_value_counts: dict[int, int] + column_aggregates: dict[int, StatsAggregator] + split_offsets: list[int] + + def _partition_value(self, partition_field: PartitionField, schema: Schema) -> Any: + if partition_field.source_id not in self.column_aggregates: + return None + + source_field = schema.find_field(partition_field.source_id) + iceberg_transform = partition_field.transform + + if not iceberg_transform.preserves_order: + raise ValueError( + f"Cannot infer partition value from parquet metadata for a non-linear Partition Field: " + f"{partition_field.name} with transform {partition_field.transform}" + ) + + transform_func = iceberg_transform.transform(source_field.field_type) + + lower_value = transform_func( + partition_record_value( + partition_field=partition_field, + value=self.column_aggregates[partition_field.source_id].current_min, + schema=schema, + ) + ) + upper_value = transform_func( + partition_record_value( + partition_field=partition_field, + value=self.column_aggregates[partition_field.source_id].current_max, + schema=schema, + ) + ) + if lower_value != upper_value: + raise ValueError( + f"Cannot infer partition value from parquet metadata as there are more than one partition values " + f"for Partition Field: {partition_field.name}. {lower_value=}, {upper_value=}" + ) + + return lower_value + + def partition(self, partition_spec: PartitionSpec, schema: Schema) -> Record: + return Record(*[self._partition_value(field, schema) for field in partition_spec.fields]) + + def to_serialized_dict(self) -> dict[str, Any]: + lower_bounds = {} + upper_bounds = {} + + for k, agg in self.column_aggregates.items(): + _min = agg.min_as_bytes() + if _min is not None: + lower_bounds[k] = _min + _max = agg.max_as_bytes() + if _max is not None: + upper_bounds[k] = _max + return { + "record_count": self.record_count, + "column_sizes": self.column_sizes, + "value_counts": self.value_counts, + "null_value_counts": self.null_value_counts, + "nan_value_counts": self.nan_value_counts, + "lower_bounds": lower_bounds, + "upper_bounds": upper_bounds, + "split_offsets": self.split_offsets, + } + + +class FileFormatWriter(ABC): + """Writes data to a single file in a specific format.""" + + _result: DataFileStatistics | None = None + + @abstractmethod + def write(self, table: pa.Table) -> None: + """Write a batch of data. May be called multiple times.""" + + @abstractmethod + def close(self) -> DataFileStatistics: + """Finalize the file and return statistics.""" + + def result(self) -> DataFileStatistics: + """Return statistics from a previous close() call.""" + if self._result is None: + raise RuntimeError("Writer has not been closed yet") + return self._result + + def __enter__(self) -> FileFormatWriter: + """Enter the context manager.""" + return self + + def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None: + """Exit the context manager, closing the writer and caching statistics.""" + if exc_type is not None: + try: + self._result = self.close() + except Exception: + pass + return + self._result = self.close() + + +class FileFormatModel(ABC): + """Represents a file format's capabilities. Creates writers.""" + + @property + @abstractmethod + def format(self) -> FileFormat: ... + + @abstractmethod + def file_extension(self) -> str: + """Return file extension without dot, e.g. 'parquet', 'orc'.""" + + @abstractmethod + def create_writer( + self, + output_file: OutputFile, + file_schema: Schema, + properties: Properties, + ) -> FileFormatWriter: ... + + +class FileFormatFactory: + """Registry of FileFormatModel implementations.""" + + _registry: dict[FileFormat, FileFormatModel] = {} + + @classmethod + def register(cls, model: FileFormatModel) -> None: + cls._registry[model.format] = model + + @classmethod + def get(cls, file_format: FileFormat) -> FileFormatModel: + if file_format not in cls._registry: + raise ValueError(f"No writer registered for {file_format}. Available: {list(cls._registry.keys())}") + return cls._registry[file_format] Review Comment: I implemented the FileFormatFactory as the Python equivalent of Java's FormatModelRegistry, keyed by FileFormat alone since Python only has Arrow (vs Java needing (FileFormat, Class<?>) for Spark/Flink/Generic). Let me know if you think it's worth adding a property-based override. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
