File size: 7,367 Bytes
5f87c14 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | #!/usr/bin/env python3
"""Convert a VPD1 SQLite database to a partitioned Parquet dataset."""
from __future__ import annotations
import argparse
import hashlib
import json
import sqlite3
import time
from pathlib import Path
import pyarrow as pa
import pyarrow.dataset as pads
import pyarrow.parquet as pq
PHASE_NAMES = {0: "opening", 1: "middlegame", 2: "endgame"}
COLUMNS = [
"id",
"random_key",
"fen",
"source_member",
"game_number",
"ply",
"result",
"side_to_move",
"piece_count",
"non_pawn_material",
"material_balance",
"legal_moves",
"in_check",
"castling_mask",
"halfmove_clock",
]
SCHEMA = pa.schema(
[
("id", pa.int64()),
("random_key", pa.int64()),
("fen", pa.string()),
("source_member", pa.string()),
("game_number", pa.int64()),
("ply", pa.int16()),
("result", pa.string()),
("side_to_move", pa.int8()),
("piece_count", pa.int8()),
("non_pawn_material", pa.int16()),
("material_balance", pa.int16()),
("legal_moves", pa.int16()),
("in_check", pa.bool_()),
("castling_mask", pa.int8()),
("halfmove_clock", pa.int16()),
]
)
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as source:
while chunk := source.read(8 * 1024 * 1024):
digest.update(chunk)
return digest.hexdigest()
def table_from_rows(rows: list[tuple[object, ...]], schema: pa.Schema) -> pa.Table:
arrays = []
for index, field in enumerate(schema):
values = [row[index] for row in rows]
if pa.types.is_boolean(field.type):
values = [bool(value) for value in values]
arrays.append(pa.array(values, type=field.type))
return pa.Table.from_arrays(arrays, schema=schema)
def convert(args: argparse.Namespace) -> None:
source = Path(args.input).resolve()
output = Path(args.output).resolve()
if output.exists() and any(output.iterdir()):
raise RuntimeError(f"output directory is not empty: {output}")
output.mkdir(parents=True, exist_ok=True)
db = sqlite3.connect(f"file:{source}?mode=ro&immutable=1", uri=True)
metadata = dict(db.execute("SELECT key, value FROM metadata"))
schema = SCHEMA.with_metadata(
{
b"vpd_format": metadata.get("format", "unknown").encode(),
b"source_sha256": sha256(source).encode(),
b"fen_fullmove_normalization": metadata.get(
"fen_fullmove_normalization", "unknown"
).encode(),
}
)
source_total = db.execute("SELECT COUNT(*) FROM positions").fetchone()[0]
splits = [
row[0]
for row in db.execute(
"SELECT DISTINCT source_split FROM positions ORDER BY source_split"
)
]
manifest: dict[str, object] = {
"format": "VPD1-Parquet",
"source": str(source),
"source_sha256": sha256(source),
"compression": args.compression,
"compression_level": args.compression_level,
"row_group_size": args.row_group_size,
"partitioning": ["source_split", "phase"],
"rows": 0,
"partitions": [],
}
started = time.monotonic()
for split in splits:
for phase_id, phase_name in PHASE_NAMES.items():
count = db.execute(
"SELECT COUNT(*) FROM positions WHERE source_split=? AND phase=?",
(split, phase_id),
).fetchone()[0]
if not count:
continue
partition_dir = output / f"source_split={split}" / f"phase={phase_name}"
partition_dir.mkdir(parents=True, exist_ok=True)
parquet_path = partition_dir / "positions.parquet"
query = (
f"SELECT {', '.join(COLUMNS)} FROM positions "
"WHERE source_split=? AND phase=? ORDER BY id"
)
cursor = db.execute(query, (split, phase_id))
written = 0
row_groups = 0
min_id = None
max_id = None
with pq.ParquetWriter(
parquet_path,
schema,
compression=args.compression,
compression_level=args.compression_level,
use_dictionary=["source_member", "result"],
write_statistics=True,
) as writer:
while rows := cursor.fetchmany(args.row_group_size):
table = table_from_rows(rows, schema)
writer.write_table(table, row_group_size=len(rows))
written += len(rows)
row_groups += 1
min_id = rows[0][0] if min_id is None else min_id
max_id = rows[-1][0]
if written != count:
raise RuntimeError(
f"partition count mismatch for {split}/{phase_name}: "
f"expected {count}, wrote {written}"
)
manifest["rows"] += written
manifest["partitions"].append(
{
"source_split": split,
"phase": phase_name,
"rows": written,
"row_groups": row_groups,
"min_id": min_id,
"max_id": max_id,
"bytes": parquet_path.stat().st_size,
"file": str(parquet_path.relative_to(output)),
"sha256": sha256(parquet_path),
}
)
print(
f"wrote {split}/{phase_name}: {written:,} rows, "
f"{parquet_path.stat().st_size / (1024 * 1024):.1f} MiB",
flush=True,
)
db.close()
if manifest["rows"] != source_total:
raise RuntimeError(
f"total mismatch: source has {source_total}, wrote {manifest['rows']}"
)
manifest["elapsed_seconds"] = round(time.monotonic() - started, 3)
# Leading underscore keeps the JSON sidecar out of PyArrow's default
# Parquet dataset discovery while leaving it next to the data it describes.
manifest_path = output / "_manifest.json"
manifest_path.write_text(
json.dumps(manifest, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
dataset = pads.dataset(output, format="parquet", partitioning="hive")
parquet_total = dataset.count_rows()
if parquet_total != source_total:
raise RuntimeError(
f"PyArrow validation mismatch: expected {source_total}, got {parquet_total}"
)
print(
json.dumps(
{
"rows": parquet_total,
"files": len(dataset.files),
"manifest": str(manifest_path),
"elapsed_seconds": manifest["elapsed_seconds"],
},
indent=2,
)
)
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--input", required=True)
parser.add_argument("--output", required=True)
parser.add_argument("--row-group-size", type=int, default=65_536)
parser.add_argument("--compression", default="zstd")
parser.add_argument("--compression-level", type=int, default=6)
convert(parser.parse_args())
if __name__ == "__main__":
main()
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