open-index/hacker-news-text-search

Hacker News text + substring patterns

Sampled comments and stories from the full year 2025 of the public Hacker News archive, paired with small curated dictionaries of substring patterns and precomputed match labels. The intended use is testing text-search and substring-matching code on real, messy English text: multi-byte characters, HTML entities, embedded URLs, mixed casing, CVE identifiers, version strings, and the long tail of forum slang.

Layout at a glance

• text_pool/comments.{parquet,csv} - 3,398,696 comments, the canonical text store. Columns: text_id, ts, parent, by, text.
• text_pool/stories.{parquet,csv} - 310,384 stories. Columns: story_id, ts, title, url, by, score.
• pattern_pool/{topics,categories,domains,bans,allow}.{parquet,csv}
  • curated pattern dictionaries reused across workloads.
• One directory per workload (see table below). Each carries patterns.{parquet,csv}, labels.{parquet,csv}, meta.json, and (for the HQ multi-relation queries) query.sql. The text side is not re-emitted: every workload joins back to text_pool/ so the baseline is unified across the dataset - resolve labels.text_id against text_pool/comments.parquet (or text_pool/stories.parquet for workloads marked stories in the table below).

Both Parquet and CSV are shipped side by side: the HF dataset viewer reads the Parquet files (faster, typed); the CSVs make raw wget / curl downloads trivial for tooling that does not speak Parquet.

Source and license

Pulled from the open-index/hacker-news mirror, partitions data/2025/*.parquet (full year 2025). After filtering deleted=0 AND dead=0 and stripping HTML, the slice carries 3,709,080 rows: 3,398,696 comments and 310,384 stories.

Redistributed under CC BY-SA 4.0, matching the upstream mirror. Attribution: Hacker News (Y Combinator) and the open-index maintainers of the upstream mirror.

Workloads

config	kind	text side	description	size
hq1_topic_mentions	hq	comments	HQ1: per-category comment mention count over a topic watchlist; paper analog of the Audio Promo query.	50,000 texts, 10 matches
hq1_topic_mentions_edge	hq_edge	comments	HQ1 LIKE edge: 50k comments x 100 topic patterns	50,000 texts, M=100, 14,774 matches
hq2_domain_topic	hq	comments	HQ2: comments on stories whose URL matches a domain pattern, joined to topic patterns. Two LIKE edges; the engine has to pick a join order.	50,000 texts, 5 matches
hq2_topic_edge	hq_edge	comments	HQ2 topic LIKE edge after URL-domain pushdown: surviving comments x 100 topic patterns	31 texts, M=100, 9 matches
hq2_url_edge	hq_edge	stories	HQ2 URL LIKE edge: 10k stories x 20 domain patterns	9,444 texts, M=20, 1,088 matches
hq3_hot_stories	hq	comments	HQ3: top 50 stories by comment-topic density. LIKE edge feeds a COUNT(*) that drives the ORDER BY.	50,000 texts, 19 matches
hq3_hot_stories_edge	hq_edge	comments	HQ3 LIKE edge: 50k comments x 30 topic patterns (cats 0,4,2)	50,000 texts, M=30, 2,263 matches
hq4_author_distinct	hq	comments	HQ4: authors whose comments touch >= 3 distinct topics. COUNT(DISTINCT topic_id) requires AC to emit pattern identity.	50,000 texts, 1,138 matches
hq4_author_distinct_edge	hq_edge	comments	HQ4 LIKE edge: 50k comments x 100 topic patterns (identical shape to hq1_edge; emitted separately for table layout)	50,000 texts, M=100, 14,774 matches
hq5_timeline	hq	comments	HQ5: daily mention counts per topic over 2025-04. Exercises filter pushdown of ts before the LIKE-join.	50,000 texts, 54 matches
hq5_timeline_edge	hq_edge	comments	HQ5 LIKE edge: 50k comments x 20 topic patterns (cats 2,4). ts filter targets 2025-04 slice.	50,000 texts, M=20, 921 matches
hq6_allow_edge	hq_edge	comments	HQ6 allow LIKE edge: 50k comments x 30 allow patterns	50,000 texts, M=30, 1,837 matches
hq6_ban_allow	hq	comments	HQ6: count comments matching a ban list but not the allow list. Models a trust-and-safety filter; AC can do ban+allow in one pass.	50,000 texts, 1 matches
hq6_bans_edge	hq_edge	comments	HQ6 bans LIKE edge: 50k comments x 31 ban patterns	50,000 texts, M=31, 194 matches
q1_trending_M10	q	comments	Q1: %TOKEN% substring on HN comments; ASCII single-literal patterns scaling in M.	20,000 texts, M=10, 697 matches
q1_trending_M100	q	comments	Q1: %TOKEN% substring on HN comments; ASCII single-literal patterns scaling in M.	20,000 texts, M=100, 146,159 matches
q1_trending_M1k	q	comments	Q1: %TOKEN% substring on HN comments; ASCII single-literal patterns scaling in M.	20,000 texts, M=1000, 554,943 matches
q2_phrase_M100	q	comments	Q2: %a%b% multi-segment phrase patterns on HN comments.	20,000 texts, M=100, 3,078 matches
q2_phrase_M30	q	comments	Q2: %a%b% multi-segment phrase patterns on HN comments.	20,000 texts, M=30, 2,870 matches
q3_positional_adversarial	q	comments	Q3: _ positional constraints; family=adversarial	4,968 texts, M=5, 10,281 matches
q3_positional_cves	q	comments	Q3: _ positional constraints; family=cves	287 texts, M=4, 752 matches
q3_positional_dates	q	comments	Q3: _ positional constraints; family=dates	3,235 texts, M=4, 3,711 matches
q3_positional_versions	q	comments	Q3: _ positional constraints; family=versions	6,316 texts, M=7, 894 matches
q4_unicode	q	comments	Q4: multi-byte UTF-8 literals + _ straddling code-point boundaries.	5,000 texts, M=20, 22 matches
q5_escape	q	comments	Q5: ESCAPE-clause patterns over HTML-entity-bearing comment bodies.	10,000 texts, M=10, 7,308 matches
q6_ilike_ilike	q	comments	Q6: camelCase tech names with ILIKE.	10,000 texts, M=20, 643 matches
q6_ilike_like	q	comments	Q6: camelCase tech names with LIKE.	10,000 texts, M=20, 317 matches
q7_url	q	stories	Q7: anchored & interior URL patterns over story URLs.	20,000 texts, M=10, 2,617 matches
q8_scan_literal_long	q	comments	Q8: single-pattern scan (literal_long).	50,000 texts, M=1, 11 matches
q8_scan_literal_short	q	comments	Q8: single-pattern scan (literal_short).	50,000 texts, M=1, 555 matches
q8_scan_two_seg	q	comments	Q8: single-pattern scan (two_seg).	50,000 texts, M=1, 546 matches
q8_scan_underscore	q	comments	Q8: single-pattern scan (underscore).	50,000 texts, M=1, 54 matches

Two families are present:

• Q-family (q1 ... q8): flat texts x patterns pairs. Single relation on each side. Good for isolating substring matchers from any surrounding SQL.
• HQ-family (hq1 ... hq6): multi-relation SQL queries embedding one or two LIKE joins inside realistic shapes (GROUP BY, TOP-K, HAVING COUNT DISTINCT, EXISTS / NOT EXISTS). hq*_edge configs are the same data flattened to the Q-family layout, exposing only the LIKE edge.

Quick look in Python

from datasets import load_dataset

# Browse the canonical text store.
comments = load_dataset(
    "open-index/hacker-news-text-search", "comments", split="train"
)
print(comments[0])

# Pick one workload's patterns and labels, join against the pool.
patterns = load_dataset(
    "open-index/hacker-news-text-search", "q1_trending_M100",
    split="patterns",
)
labels = load_dataset(
    "open-index/hacker-news-text-search", "q1_trending_M100",
    split="labels",
)
print(len(patterns), "patterns;", len(labels), "matches")

Sampling

Per-workload subsets are picked by BLAKE2b over (workload_id, row_id) so the same workload always picks the same rows across re-runs. No global RNG, no random.sample.

Redactions

A small list of text_ids is excluded from text_pool because the comment body matches a ClamAV exploit-PoC signature (the user quoted a real PoC verbatim). The list is in redactions.json at the dataset root and replicated under meta.json of every workload. As of this revision: 1 row dropped.

Reproducing locally

Code that downloads the upstream parquet, cleans it in DuckDB, and emits this dataset lives at tamnd/hackernews-processing. Re-running the pipeline against the same upstream parquet at the same pinned month reproduces every byte of this dataset.