File size: 2,689 Bytes
f93691f | 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 | # /// script
# requires-python = ">=3.10"
# dependencies = [
# "duckdb",
# "huggingface-hub",
# ]
# ///
"""Prep Hacker News stories for atlas visualization.
Filters to stories with titles, adds year column for coloring.
Uses DuckDB to query HF parquet files directly (no full download).
Writes prepped parquet to output path (bucket mount or local).
Usage (as HF Job):
hf jobs uv run --flavor cpu-upgrade \
-v hf://buckets/davanstrien/atlas-data:/output \
-s HF_TOKEN --timeout 1h \
hn-prep.py --output /output/hn-stories/stories.parquet
"""
import argparse
import os
import time
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--output", default="/output/hn-stories/stories.parquet")
parser.add_argument("--max-rows", type=int, default=None,
help="Cap total rows after filtering")
args = parser.parse_args()
import duckdb
start = time.time()
con = duckdb.connect()
con.execute("SET enable_http_metadata_cache=true")
# DuckDB hf:// protocol picks up HF_TOKEN from env automatically
source = "hf://datasets/open-index/hacker-news/data/**/*.parquet"
os.makedirs(os.path.dirname(args.output), exist_ok=True)
print("Querying HN stories from HF parquet files via DuckDB...")
limit_clause = f"LIMIT {args.max_rows}" if args.max_rows else ""
query = f"""
COPY (
SELECT
id,
title,
score,
CAST(year(time) AS VARCHAR) AS year,
CASE
WHEN score <= 5 THEN '0-5'
WHEN score <= 25 THEN '6-25'
WHEN score <= 100 THEN '26-100'
WHEN score <= 500 THEN '101-500'
ELSE '500+'
END AS score_bucket,
"by",
url,
descendants
FROM '{source}'
WHERE type = 1
AND title IS NOT NULL
AND trim(title) != ''
ORDER BY random()
{limit_clause}
) TO '{args.output}' (FORMAT PARQUET)
"""
con.execute(query)
elapsed = time.time() - start
# Check output
result = con.execute(f"SELECT count(*) FROM '{args.output}'").fetchone()
size_mb = os.path.getsize(args.output) / (1024**2)
print(f"\nWrote {result[0]:,} stories to {args.output} ({size_mb:.0f} MB)")
print(f"Total time: {elapsed:.0f}s")
# Quick stats
stats = con.execute(f"""
SELECT min(year) as min_year, max(year) as max_year, count(distinct year) as n_years
FROM '{args.output}'
""").fetchone()
print(f"Year range: {stats[0]} - {stats[1]} ({stats[2]} years)")
if __name__ == "__main__":
main()
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