Datasets:
Download scripts/govdata/common/store.py from ethux/belastingdienst-dataset: direct link, hf CLI and curl.
- Browser
- Download file 3.66 kB
-
https://huggingface.co/datasets/ethux/belastingdienst-dataset/resolve/main/scripts/govdata/common/store.py
- Command line
-
hf download hf://datasets/ethux/belastingdienst-dataset/scripts/govdata/common/store.py
-
curl -L -o store.py https://huggingface.co/datasets/ethux/belastingdienst-dataset/resolve/main/scripts/govdata/common/store.py
3.66 kB
| import zlib | |
| from collections import defaultdict | |
| from dataclasses import dataclass | |
| from pathlib import Path | |
| import pyarrow as pa | |
| import pyarrow.compute as pc | |
| import pyarrow.parquet as pq | |
| def shard_of(value: str, n: int) -> int: | |
| return zlib.crc32(value.encode()) % n | |
| def shard_name(config: str, s: int, n: int) -> str: | |
| return f"{config}-{s:05d}-of-{n:05d}.parquet" | |
| def read_shard(directory: Path, config: str, s: int, n: int) -> pa.Table | None: | |
| path = Path(directory) / shard_name(config, s, n) | |
| return pq.read_table(path) if path.exists() else None | |
| def write_shard(table: pa.Table, directory: Path, config: str, s: int, n: int) -> Path: | |
| directory = Path(directory) | |
| directory.mkdir(parents=True, exist_ok=True) | |
| path = directory / shard_name(config, s, n) | |
| pq.write_table(table, path, compression="zstd") | |
| return path | |
| def read_index(raw_dir: Path, key_col: str, mod_col: str) -> dict[str, str]: | |
| out: dict[str, str] = {} | |
| for f in sorted(Path(raw_dir).glob("*.parquet")): | |
| t = pq.read_table(f, columns=[key_col, mod_col]) | |
| out.update(zip(t.column(key_col).to_pylist(), t.column(mod_col).to_pylist())) | |
| return out | |
| class Diff: | |
| fetch: list[str] | |
| removed: set[str] | |
| added: int | |
| changed: int | |
| unchanged: int | |
| def diff(current: dict[str, str], previous: dict[str, str]) -> Diff: | |
| added = [k for k in current if k not in previous] | |
| changed = [k for k in current if k in previous and previous[k] != current[k]] | |
| return Diff( | |
| fetch=added + changed, | |
| removed=set(previous) - set(current), | |
| added=len(added), | |
| changed=len(changed), | |
| unchanged=len(current) - len(added) - len(changed), | |
| ) | |
| class Staging: | |
| """Buffers fetched rows to per-shard Parquet files so a shard is read back without scanning the rest.""" | |
| def __init__(self, directory: Path, schema: pa.Schema, shard_col: str, n_shards: int, flush_rows: int = 100): | |
| self.dir = Path(directory) | |
| self.dir.mkdir(parents=True, exist_ok=True) | |
| self.schema = schema | |
| self.shard_col = shard_col | |
| self.n = n_shards | |
| self.flush_rows = flush_rows | |
| self.buffers: dict[int, list[dict]] = defaultdict(list) | |
| self.seq = 0 | |
| def add(self, row: dict) -> None: | |
| s = shard_of(row[self.shard_col], self.n) | |
| self.buffers[s].append(row) | |
| if len(self.buffers[s]) >= self.flush_rows: | |
| self._flush_shard(s) | |
| def flush(self) -> None: | |
| for s in list(self.buffers): | |
| self._flush_shard(s) | |
| def _flush_shard(self, s: int) -> None: | |
| rows = self.buffers.pop(s, []) | |
| if not rows: | |
| return | |
| self.seq += 1 | |
| pq.write_table(pa.Table.from_pylist(rows, schema=self.schema), self.dir / f"{s:05d}-{self.seq:07d}.parquet") | |
| def read_shard(self, s: int) -> pa.Table: | |
| files = sorted(self.dir.glob(f"{s:05d}-*.parquet")) | |
| if not files: | |
| return self.schema.empty_table() | |
| return pa.concat_tables(pq.read_table(f).cast(self.schema) for f in files) | |
| def merge_shard( | |
| previous: pa.Table | None, fetched: pa.Table, removed: set[str], key_col: str, schema: pa.Schema | |
| ) -> pa.Table: | |
| parts = [] | |
| if previous is not None and previous.num_rows: | |
| drop = pa.array(sorted(set(removed) | set(fetched.column(key_col).to_pylist())), pa.string()) | |
| keep = pc.invert(pc.is_in(previous.column(key_col), value_set=drop)) | |
| parts.append(previous.filter(keep).select(schema.names).cast(schema)) | |
| parts.append(fetched.select(schema.names).cast(schema)) | |
| return pa.concat_tables(parts).sort_by(key_col) | |