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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]):
if primary_keys is not None:
if projection is not None:
key_columns = []
for pk in primary_keys:
key_column = f"_KEY_{pk}"
if key_column not in projection:
key_columns.append(key_column)
system_columns = ["_SEQUENCE_NUMBER", "_VALUE_KIND"]
projection = key_columns + system_columns + projection
# 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=projection,
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