File size: 4,735 Bytes
c686331 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | #!/usr/bin/env python3
"""Split a large parquet file into smaller parquet shards."""
from __future__ import annotations
import argparse
import os
from pathlib import Path
import pyarrow as pa
import pyarrow.parquet as pq
def parse_size(value: str) -> int:
units = {
"b": 1,
"kb": 1024,
"mb": 1024**2,
"gb": 1024**3,
"tb": 1024**4,
}
text = value.strip().lower()
for suffix, multiplier in sorted(units.items(), key=lambda item: len(item[0]), reverse=True):
if text.endswith(suffix):
number = float(text[: -len(suffix)].strip())
return int(number * multiplier)
return int(float(text))
def shard_name(index: int) -> str:
return f"train-{index:05d}.parquet"
def split_parquet(
source: Path,
output_dir: Path,
target_size: int,
batch_size: int,
compression: str,
) -> None:
data_dir = output_dir / "data"
data_dir.mkdir(parents=True, exist_ok=False)
parquet_file = pq.ParquetFile(source)
schema = parquet_file.schema_arrow
source_rows = parquet_file.metadata.num_rows
source_row_groups = parquet_file.metadata.num_row_groups
print(f"source={source}", flush=True)
print(f"source_rows={source_rows}", flush=True)
print(f"source_row_groups={source_row_groups}", flush=True)
print(f"target_size_bytes={target_size}", flush=True)
print(f"batch_size={batch_size}", flush=True)
writer: pq.ParquetWriter | None = None
current_path: Path | None = None
shard_index = 0
shard_rows = 0
total_rows = 0
def open_writer() -> None:
nonlocal writer, current_path, shard_rows, shard_index
current_path = data_dir / shard_name(shard_index)
writer = pq.ParquetWriter(current_path, schema=schema, compression=compression)
shard_rows = 0
print(f"opened_shard={current_path}", flush=True)
def close_writer() -> None:
nonlocal writer, current_path, shard_rows, shard_index
if writer is None or current_path is None:
return
writer.close()
size = current_path.stat().st_size
print(
f"closed_shard={current_path} rows={shard_rows} size_bytes={size}",
flush=True,
)
writer = None
current_path = None
shard_index += 1
shard_rows = 0
try:
open_writer()
for row_group_index in range(source_row_groups):
for batch in parquet_file.iter_batches(
batch_size=batch_size,
row_groups=[row_group_index],
use_threads=True,
):
if writer is None:
open_writer()
table = pa.Table.from_batches([batch], schema=schema)
writer.write_table(table)
rows = table.num_rows
shard_rows += rows
total_rows += rows
if current_path is not None and current_path.exists():
current_size = current_path.stat().st_size
print(
"progress "
f"row_group={row_group_index + 1}/{source_row_groups} "
f"total_rows={total_rows}/{source_rows} "
f"current_shard={current_path.name} "
f"current_size_bytes={current_size}",
flush=True,
)
if current_size >= target_size:
close_writer()
close_writer()
except Exception:
if writer is not None:
writer.close()
raise
print(f"completed total_rows={total_rows} shards={shard_index}", flush=True)
if total_rows != source_rows:
raise RuntimeError(f"row count mismatch: wrote {total_rows}, expected {source_rows}")
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--source", required=True, type=Path)
parser.add_argument("--output-dir", required=True, type=Path)
parser.add_argument("--target-size", default="10GB")
parser.add_argument("--batch-size", default=8, type=int)
parser.add_argument("--compression", default="snappy")
args = parser.parse_args()
if not args.source.is_file():
raise FileNotFoundError(args.source)
if args.output_dir.exists():
raise FileExistsError(f"Output directory already exists: {args.output_dir}")
split_parquet(
source=args.source,
output_dir=args.output_dir,
target_size=parse_size(args.target_size),
batch_size=args.batch_size,
compression=args.compression,
)
if __name__ == "__main__":
main()
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