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pyarrow_dataset_reader.py
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71 lines (59 loc) · 2.72 KB
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################################################################################
# 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.
################################################################################
from typing import Optional, List
import pyarrow.dataset as ds
from pypaimon import Predicate
from pypaimon.pynative.common.row.internal_row import InternalRow
from pypaimon.pynative.reader.core.columnar_row_iterator import ColumnarRowIterator
from pypaimon.pynative.reader.core.file_record_iterator import FileRecordIterator
from pypaimon.pynative.reader.core.file_record_reader import FileRecordReader
from pypaimon.pynative.util.predicate_converter import convert_predicate
class PyArrowDatasetReader(FileRecordReader[InternalRow]):
"""
A PyArrowDatasetReader that reads data from a dataset file using PyArrow,
and filters it based on the provided predicate and projection.
"""
def __init__(self, format, file_path, batch_size, projection,
predicate: Predicate, primary_keys: List[str], fields: List[str]):
if primary_keys is not None:
# TODO: utilize predicate to improve performance
predicate = None
if predicate is not None:
predicate = convert_predicate(predicate)
self._file_path = file_path
self.dataset = ds.dataset(file_path, format=format)
self.scanner = self.dataset.scanner(
columns=fields,
filter=predicate,
batch_size=batch_size
)
self.batch_iterator = self.scanner.to_batches()
def read_batch(self) -> Optional[FileRecordIterator[InternalRow]]:
try:
record_batch = next(self.batch_iterator, None)
if record_batch is None:
return None
return ColumnarRowIterator(
self._file_path,
record_batch
)
except Exception as e:
print(f"Error reading batch: {e}")
raise
def close(self):
pass