| """Load one ForensicBench sector into a PostgreSQL database. |
| |
| Usage: |
| python tools/load_postgres.py --sector energy --dsn "dbname=forensicbench_energy user=postgres" |
| |
| The database must already exist. Tables are created from the parquet schemas. |
| Requires: pandas, pyarrow, psycopg2-binary. |
| """ |
| import argparse, io |
| import pandas as pd |
| import pyarrow.parquet as pq |
| import psycopg2 |
|
|
| PG_TYPES = {"string": "text", "large_string": "text", "int64": "bigint", "int32": "integer", |
| "double": "double precision", "float": "real", "bool": "boolean", |
| "date32[day]": "date"} |
| TABLES = ["je_header", "je_line", "chart_of_accounts", "vendors", "customers", "employees"] |
|
|
| def pg_type(arrow_type) -> str: |
| s = str(arrow_type) |
| if s.startswith("timestamp"): |
| return "timestamp" |
| return PG_TYPES.get(s, "text") |
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--sector", required=True, choices=["energy", "healthcare", "luxurygoods", "manufacturing", "transport"]) |
| ap.add_argument("--dsn", required=True) |
| ap.add_argument("--data-dir", default="data") |
| args = ap.parse_args() |
| conn = psycopg2.connect(args.dsn) |
| cur = conn.cursor() |
| for t in TABLES: |
| path = f"{args.data_dir}/{args.sector}/{t}.parquet" |
| schema = pq.read_schema(path) |
| cols = ", ".join(f'"{f.name}" {pg_type(f.type)}' for f in schema) |
| cur.execute(f'DROP TABLE IF EXISTS {t} CASCADE') |
| cur.execute(f'CREATE TABLE {t} ({cols})') |
| df = pd.read_parquet(path) |
| buf = io.StringIO() |
| df.to_csv(buf, index=False, header=False, na_rep="\\N") |
| buf.seek(0) |
| cur.copy_expert(f"COPY {t} FROM STDIN WITH (FORMAT csv, NULL '\\N')", buf) |
| print(f"{t}: {len(df)} rows") |
| cur.execute("CREATE INDEX IF NOT EXISTS je_line_doc ON je_line (document_id)") |
| cur.execute("CREATE INDEX IF NOT EXISTS je_header_doc ON je_header (document_id)") |
| conn.commit() |
| conn.close() |
|
|
| if __name__ == "__main__": |
| main() |
|
|
