def read_pieces(serialized_pieces: str) -> Iterator[pa.Table]:
# Implicitly trigger S3 subsystem initialization by importing
# pyarrow.fs.
import pyarrow.fs # noqa: F401
# Deserialize after loading the filesystem class.
try:
_register_parquet_file_fragment_serialization()
pieces: List[
"pyarrow._dataset.ParquetFileFragment"
] = cloudpickle.loads(serialized_pieces)
finally:
_deregister_parquet_file_fragment_serialization()
# Ensure that we're reading at least one dataset fragment.
assert len(pieces) > 0
from pyarrow.dataset import _get_partition_keys
ctx = DatasetContext.get_current()
output_buffer = BlockOutputBuffer(
block_udf=_block_udf, target_max_block_size=ctx.target_max_block_size
)
logger.debug(f"Reading {len(pieces)} parquet pieces")
use_threads = reader_args.pop("use_threads", False)
for piece in pieces:
part = _get_partition_keys(piece.partition_expression)
batches = piece.to_batches(
use_threads=use_threads,
columns=columns,
schema=schema,
batch_size=PARQUET_READER_ROW_BATCH_SIZE,
**reader_args,
)
for batch in batches:
table = pyarrow.Table.from_batches([batch], schema=schema)
if part:
for col, value in part.items():
table = table.set_column(
table.schema.get_field_index(col),
col,
pa.array([value] * len(table)),
)
# If the table is empty, drop it.
if table.num_rows > 0:
output_buffer.add_block(table)
if output_buffer.has_next():
yield output_buffer.next()
output_buffer.finalize()
if output_buffer.has_next():
yield output_buffer.next()
def read_files(
read_paths: List[str],
fs: Union["pyarrow.fs.FileSystem", _S3FileSystemWrapper],
) -> Iterable[Block]:
logger.debug(f"Reading {len(read_paths)} files.")
if isinstance(fs, _S3FileSystemWrapper):
fs = fs.unwrap()
ctx = DatasetContext.get_current()
output_buffer = BlockOutputBuffer(
block_udf=_block_udf, target_max_block_size=ctx.target_max_block_size
)
for read_path in read_paths:
compression = open_stream_args.pop("compression", None)
if compression is None:
import pyarrow as pa
try:
# If no compression manually given, try to detect
# compression codec from path.
compression = pa.Codec.detect(read_path).name
except (ValueError, TypeError):
# Arrow's compression inference on the file path
# doesn't work for Snappy, so we double-check ourselves.
import pathlib
suffix = pathlib.Path(read_path).suffix
if suffix and suffix[1:] == "snappy":
compression = "snappy"
else:
compression = None
if compression == "snappy":
# Pass Snappy compression as a reader arg, so datasource subclasses
# can manually handle streaming decompression in
# self._read_stream().
reader_args["compression"] = compression
reader_args["filesystem"] = fs
elif compression is not None:
# Non-Snappy compression, pass as open_input_stream() arg so Arrow
# can take care of streaming decompression for us.
open_stream_args["compression"] = compression
with self._open_input_source(fs, read_path, **open_stream_args) as f:
for data in read_stream(f, read_path, **reader_args):
output_buffer.add_block(data)
if output_buffer.has_next():
yield output_buffer.next()
output_buffer.finalize()
if output_buffer.has_next():
yield output_buffer.next()
def read_files(
read_paths: List[str],
fs: Union["pyarrow.fs.FileSystem", _S3FileSystemWrapper],
) -> Iterable[Block]:
logger.debug(f"Reading {len(read_paths)} files.")
if isinstance(fs, _S3FileSystemWrapper):
fs = fs.unwrap()
ctx = DatasetContext.get_current()
output_buffer = BlockOutputBuffer(
block_udf=_block_udf, target_max_block_size=ctx.target_max_block_size
)
for read_path in read_paths:
with fs.open_input_stream(read_path, **open_stream_args) as f:
for data in read_stream(f, read_path, **reader_args):
output_buffer.add_block(data)
if output_buffer.has_next():
yield output_buffer.next()
output_buffer.finalize()
if output_buffer.has_next():
yield output_buffer.next()