| """Convert pdata.world's nightly bulk files (gzipped JSON lines) to Parquet |
| for the Hugging Face dataset. Usage: build.py BULK_DIR OUT_DIR |
| |
| Line 1 of each bulk file is a manifest; it is written to OUT_DIR/manifests/ |
| and the rows to OUT_DIR/data/<kind>.parquet with an explicit schema, so a |
| column never changes type between monthly snapshots. |
| """ |
|
|
| import gzip |
| import json |
| import sys |
| from datetime import datetime |
| from pathlib import Path |
|
|
| import pyarrow as pa |
| import pyarrow.parquet as pq |
|
|
| TS = pa.timestamp("us", tz="UTC") |
| STR_LIST = pa.list_(pa.string()) |
|
|
| SCHEMAS = { |
| "markets-open": pa.schema([ |
| ("source", pa.string()), ("id", pa.string()), ("event_id", pa.string()), |
| ("event_title", pa.string()), ("question", pa.string()), ("label", pa.string()), |
| ("categories", STR_LIST), ("outcomes", STR_LIST), ("outcome_prices", pa.list_(pa.float64())), |
| ("probability", pa.float64()), ("prob_24h_change", pa.float64()), |
| ("volume", pa.float64()), ("volume_24hr", pa.float64()), |
| ("liquidity", pa.float64()), ("open_interest", pa.float64()), |
| ("start_date", TS), ("end_date", TS), ("status", pa.string()), |
| ("countries", STR_LIST), ("updated_at", TS), ("url", pa.string()), ("pdata_url", pa.string()), |
| ]), |
| "events-open": pa.schema([ |
| ("source", pa.string()), ("id", pa.string()), ("title", pa.string()), |
| ("categories", STR_LIST), ("start_date", TS), ("end_date", TS), |
| ("volume", pa.float64()), ("volume_24hr", pa.float64()), ("open_interest", pa.float64()), |
| ("mutually_exclusive", pa.bool_()), ("market_count", pa.int64()), ("is_group", pa.bool_()), |
| ("parent_event_id", pa.string()), ("synthetic", pa.bool_()), |
| ("updated_at", TS), ("url", pa.string()), ("pdata_url", pa.string()), |
| ]), |
| "resolved": pa.schema([ |
| ("source", pa.string()), ("id", pa.string()), ("title", pa.string()), |
| ("categories", STR_LIST), ("closed_time", TS), |
| ("markets", pa.list_(pa.struct([ |
| ("id", pa.string()), ("question", pa.string()), ("result", pa.string()), |
| ("probability", pa.float64()), ("volume", pa.float64()), |
| ]))), |
| ("url", pa.string()), ("pdata_url", pa.string()), ("pruned_at", TS), |
| ]), |
| } |
|
|
|
|
| def convert(bulk: Path, out: Path, kind: str) -> tuple[int, dict]: |
| schema = SCHEMAS[kind] |
| ts_cols = [f.name for f in schema if f.type == TS] |
| with gzip.open(bulk / f"{kind}.jsonl.gz", "rt") as f: |
| manifest = json.loads(next(f))["_manifest"] |
| rows = [] |
| for line in f: |
| r = json.loads(line) |
| for c in ts_cols: |
| if r.get(c) is not None: |
| r[c] = datetime.fromisoformat(r[c]) |
| rows.append(r) |
| extra = set().union(*(r.keys() for r in rows)) - set(schema.names) |
| if extra: |
| raise SystemExit(f"{kind}: fields not in the schema: {sorted(extra)}") |
| table = pa.Table.from_pylist(rows, schema=schema) |
| pq.write_table(table, out / "data" / f"{kind}.parquet", compression="zstd") |
| (out / "manifests" / f"{kind}.json").write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + "\n") |
| return table.num_rows, manifest |
|
|
|
|
| if __name__ == "__main__": |
| bulk, out = Path(sys.argv[1]), Path(sys.argv[2]) |
| for kind in SCHEMAS: |
| n, m = convert(bulk, out, kind) |
| size = (out / "data" / f"{kind}.parquet").stat().st_size |
| print(f"{kind}: {n:,} rows, {size/1e6:.1f} MB, as_of {m['as_of']}") |
|
|
