Duplicate from open-index/hacker-news

Co-authored-by: Duc-Tam Nguyen <tamnd@users.noreply.huggingface.co>

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: odc-by
+task_categories:
+- text-generation
+- feature-extraction
+- text-classification
+- question-answering
+language:
+- en
+pretty_name: Hacker News - Complete Archive
+size_categories:
+- 10M<n<100M
+tags:
+- hacker-news
+- forum
+- text
+- parquet
+- community
+- tech
+- comments
+- live-updated
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/*/*.parquet
+- config_name: today
+  data_files:
+  - split: train
+    path: today/**/*.parquet
+---
+
+# Hacker News - Complete Archive
+
+> Every Hacker News item since 2006, live-updated every 5 minutes
+
+## Table of Contents
+
+- [What is it?](#what-is-it)
+- [What is being released?](#what-is-being-released)
+- [Breakdown by today](#breakdown-by-today)
+- [Breakdown by year](#breakdown-by-year)
+- [How to download and use this dataset](#how-to-download-and-use-this-dataset)
+- [Dataset statistics](#dataset-statistics)
+- [Content breakdown](#content-breakdown)
+- [Community insights](#community-insights)
+- [How it works](#how-it-works)
+- [Dataset card](#dataset-card-for-hacker-news---complete-archive)
+  - [Dataset summary](#dataset-summary)
+  - [Dataset structure](#dataset-structure)
+  - [Dataset creation](#dataset-creation)
+  - [Considerations for using the data](#considerations-for-using-the-data)
+- [Additional information](#additional-information)
+
+## What is it?
+
+This dataset contains the complete [Hacker News](https://news.ycombinator.com) archive: every story, comment, Ask HN, Show HN, job posting, and poll ever submitted to the site. Hacker News is one of the longest-running and most influential technology communities on the internet, operated by [Y Combinator](https://www.ycombinator.com) since 2007. It has become the de facto gathering place for founders, engineers, researchers, and technologists to share and discuss what matters in technology.
+
+The archive currently spans from **2006-10** to **2026-03-28 16:10 UTC**, with **47,484,304 items** committed. New items are fetched every 5 minutes and committed directly as individual Parquet files through an automated live pipeline, so the dataset stays current with the site itself.
+
+We believe this is one of the most complete and regularly updated mirrors of Hacker News data available on Hugging Face. The data is stored as monthly Parquet files sorted by item ID, making it straightforward to query with DuckDB, load with the `datasets` library, or process with any tool that reads Parquet.
+
+## What is being released?
+
+The dataset is organized as one Parquet file per calendar month, plus 5-minute live files for today's activity. Every 5 minutes, new items are fetched from the source and committed directly as a single Parquet block. At midnight UTC, the entire current month is refetched from the source as a single authoritative Parquet file, and today's individual 5-minute blocks are removed from the `today/` directory.
+
+```
+data/
+  2006/2006-10.parquet       first month with HN data
+  2006/2006-12.parquet
+  2007/2007-01.parquet
+  ...
+  2026/2026-03.parquet   most recent complete month
+  2026/2026-03.parquet  current month, ongoing til 2026-03-27
+today/
+  2026/03/28/00/00.parquet  5-min live blocks (YYYY/MM/DD/HH/MM.parquet)
+  2026/03/28/00/05.parquet
+  ...
+  2026/03/28/16/10.parquet  most recent committed block
+stats.csv                    one row per committed month
+stats_today.csv              one row per committed 5-min block
+```
+
+Along with the Parquet files, we include `stats.csv` which tracks every committed month with its item count, ID range, file size, fetch duration, and commit timestamp. This makes it easy to verify completeness and track the pipeline's progress.
+
+## Breakdown by today
+
+The chart below shows items committed to this dataset by hour today (**2026-03-28**, **4,801 items** across **17 hours**, last updated **2026-03-28 16:15 UTC**).
+
+```
+  00:00  ███████████████████░░░░░░░░░░░  315
+  01:00  ██████████████████░░░░░░░░░░░░  309
+  02:00  █████████████████░░░░░░░░░░░░░  289
+  03:00  ███████████████████░░░░░░░░░░░  312
+  04:00  █████████████░░░░░░░░░░░░░░░░░  223
+  05:00  ████████████░░░░░░░░░░░░░░░░░░  202
+  06:00  ████████████░░░░░░░░░░░░░░░░░░  199
+  07:00  ████████████░░░░░░░░░░░░░░░░░░  202
+  08:00  ███████████████░░░░░░░░░░░░░░░  248
+  09:00  ████████████████░░░░░░░░░░░░░░  264
+  10:00  ██████████████░░░░░░░░░░░░░░░░  232
+  11:00  █████████████████░░░░░░░░░░░░░  282
+  12:00  ████████████████████░░░░░░░░░░  329
+  13:00  █████████████████████████░░░░░  409
+  14:00  ███████████████████████░░░░░░░  390
+  15:00  ██████████████████████████████  489
+  16:00  ██████░░░░░░░░░░░░░░░░░░░░░░░░  107
+```
+
+## Breakdown by year
+
+The chart below shows items committed to this dataset by year.
+
+```
+  2006  █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  62
+  2007  █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  93.8K
+  2008  ██░░░░░░░░░░░░░░░░░░░░░░░░░░░░  320.9K
+  2009  ███░░░░░░░░░░░░░░░░░░░░░░░░░░░  608.4K
+  2010  ██████░░░░░░░░░░░░░░░░░░░░░░░░  1.0M
+  2011  ████████░░░░░░░░░░░░░░░░░░░░░░  1.4M
+  2012  ██████████░░░░░░░░░░░░░░░░░░░░  1.6M
+  2013  █████████████░░░░░░░░░░░░░░░░░  2.0M
+  2014  ███████████░░░░░░░░░░░░░░░░░░░  1.8M
+  2015  █████████████░░░░░░░░░░░░░░░░░  2.0M
+  2016  ████████████████░░░░░░░░░░░░░░  2.5M
+  2017  █████████████████░░░░░░░░░░░░░  2.7M
+  2018  ██████████████████░░░░░░░░░░░░  2.8M
+  2019  ████████████████████░░░░░░░░░░  3.1M
+  2020  ████████████████████████░░░░░░  3.7M
+  2021  ███████████████████████████░░░  4.2M
+  2022  █████████████████████████████░  4.4M
+  2023  ██████████████████████████████  4.6M
+  2024  ████████████████████████░░░░░░  3.7M
+  2025  █████████████████████████░░░░░  3.9M
+  2026  ███████░░░░░░░░░░░░░░░░░░░░░░░  1.1M
+```
+
+## How to download and use this dataset
+
+You can load the full dataset, a specific year, or even a single month. The dataset uses the standard Hugging Face Parquet layout, so it works out of the box with DuckDB, the `datasets` library, `pandas`, and `huggingface_hub`.
+
+### Using DuckDB
+
+DuckDB can read Parquet files directly from Hugging Face without downloading anything first. This is the fastest way to explore the data:
+
+The `type` column is stored as a small integer: `1` = story, `2` = comment, `3` = poll, `4` = pollopt, `5` = job. The `"by"` column (author username) must be quoted in DuckDB because `by` is a reserved keyword.
+
+```sql
+-- Top 20 highest-scored stories of all time
+SELECT id, title, "by", score, url, time
+FROM read_parquet('hf://datasets/open-index/hacker-news/data/*/*.parquet')
+WHERE type = 1 AND title != ''
+ORDER BY score DESC
+LIMIT 20;
+```
+
+```sql
+-- Monthly submission volume for a specific year
+SELECT
+    strftime(time, '%Y-%m') AS month,
+    count(*) AS items,
+    count(*) FILTER (WHERE type = 1) AS stories,
+    count(*) FILTER (WHERE type = 2) AS comments
+FROM read_parquet('hf://datasets/open-index/hacker-news/data/2024/*.parquet')
+GROUP BY month
+ORDER BY month;
+```
+
+```sql
+-- Most discussed stories by total comment count
+SELECT id, title, "by", score, descendants AS comments, url
+FROM read_parquet('hf://datasets/open-index/hacker-news/data/2025/*.parquet')
+WHERE type = 1 AND descendants > 0
+ORDER BY descendants DESC
+LIMIT 20;
+```
+
+```sql
+-- Who posts the most Ask HN questions?
+SELECT "by", count(*) AS posts
+FROM read_parquet('hf://datasets/open-index/hacker-news/data/*/*.parquet')
+WHERE type = 1 AND title LIKE 'Ask HN:%'
+GROUP BY "by"
+ORDER BY posts DESC
+LIMIT 20;
+```
+
+```sql
+-- Track how often a topic appears on HN over time
+SELECT
+    extract(year FROM time) AS year,
+    count(*) AS mentions
+FROM read_parquet('hf://datasets/open-index/hacker-news/data/*/*.parquet')
+WHERE type = 1 AND lower(title) LIKE '%rust%'
+GROUP BY year
+ORDER BY year;
+```
+
+```sql
+-- Top linked domains, year over year
+SELECT
+    extract(year FROM time) AS year,
+    regexp_extract(url, 'https?://([^/]+)', 1) AS domain,
+    count(*) AS stories
+FROM read_parquet('hf://datasets/open-index/hacker-news/data/*/*.parquet')
+WHERE type = 1 AND url != ''
+GROUP BY year, domain
+QUALIFY row_number() OVER (PARTITION BY year ORDER BY stories DESC) <= 5
+ORDER BY year, stories DESC;
+```
+
+### Using `datasets`
+
+```python
+from datasets import load_dataset
+
+# Stream the full history without downloading everything first
+ds = load_dataset("open-index/hacker-news", split="train", streaming=True)
+for item in ds:
+    print(item["id"], item["type"], item["title"])
+
+# Load a specific year into memory
+ds = load_dataset(
+    "open-index/hacker-news",
+    data_files="data/2024/*.parquet",
+    split="train",
+)
+print(f"{len(ds):,} items in 2024")
+
+# Load today's live blocks (updated every 5 minutes)
+ds = load_dataset(
+    "open-index/hacker-news",
+    name="today",
+    split="train",
+    streaming=True,
+)
+```
+
+### Using `huggingface_hub`
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download only 2024 data (about 1.5 GB)
+snapshot_download(
+    "open-index/hacker-news",
+    repo_type="dataset",
+    local_dir="./hn/",
+    allow_patterns="data/2024/*",
+)
+```
+
+For faster downloads, install `pip install huggingface_hub[hf_transfer]` and set `HF_HUB_ENABLE_HF_TRANSFER=1`.
+
+### Using the CLI
+
+```bash
+# Download a single month
+huggingface-cli download open-index/hacker-news \
+    data/2024/2024-01.parquet \
+    --repo-type dataset --local-dir ./hn/
+```
+
+### Using pandas + DuckDB
+
+```python
+import duckdb
+
+conn = duckdb.connect()
+
+# Score distribution: what does a "typical" HN story look like?
+# type=1 is story (stored as integer: 1=story, 2=comment, 3=poll, 4=pollopt, 5=job)
+df = conn.sql("""
+    SELECT
+        percentile_disc(0.50) WITHIN GROUP (ORDER BY score) AS p50,
+        percentile_disc(0.90) WITHIN GROUP (ORDER BY score) AS p90,
+        percentile_disc(0.99) WITHIN GROUP (ORDER BY score) AS p99,
+        percentile_disc(0.999) WITHIN GROUP (ORDER BY score) AS p999
+    FROM read_parquet('hf://datasets/open-index/hacker-news/data/*/*.parquet')
+    WHERE type = 1
+""").df()
+print(df)
+```
+
+## Dataset statistics
+
+You can query the per-month statistics directly from the `stats.csv` file included in the dataset:
+
+```sql
+SELECT * FROM read_csv_auto('hf://datasets/open-index/hacker-news/stats.csv')
+ORDER BY year, month;
+```
+
+The `stats.csv` file tracks each committed month with the following columns:
+
+| Column | Description |
+|--------|-------------|
+| `year`, `month` | Calendar month |
+| `lowest_id`, `highest_id` | Item ID range covered by this file |
+| `count` | Number of items in the file |
+| `dur_fetch_s` | Seconds to fetch from the data source |
+| `dur_commit_s` | Seconds to commit to Hugging Face |
+| `size_bytes` | Parquet file size on disk |
+| `committed_at` | ISO 8601 timestamp of when this month was committed |
+
+## Content breakdown
+
+Hacker News has five item types. The vast majority of content is comments, followed by stories (which include Ask HN, Show HN, and regular link submissions). Jobs, polls, and poll options make up a small fraction.
+
+| Type | Count | Share |
+|------|------:|------:|
+| comment | 41,346,149 | 87.2% |
+| story | 6,044,061 | 12.7% |
+| job | 18,072 | 0.0% |
+| poll | 2,240 | 0.0% |
+| pollopt | 15,449 | 0.0% |
+
+Of all stories submitted to Hacker News, **84.8%** link to an external URL. The rest are text-only posts: Ask HN questions, Show HN launches, and other self-posts where the discussion itself is the content.
+
+The average story generates **23.9 comments** in its discussion thread. The most-discussed story of all time received 9,275 comments, which gives a sense of how deep conversations can go on particularly controversial or interesting topics.
+
+### Story scores
+
+Scores on Hacker News follow a steep power law. Most stories receive only a few points, but a small number break out and reach the front page with hundreds or thousands of upvotes.
+
+| Metric | Value |
+|--------|------:|
+| Average score | 1.5 |
+| Median score | 0 |
+| Highest score ever | 6,015 |
+| Stories with 100+ points | 175,906 |
+| Stories with 1,000+ points | 2,169 |
+
+The median score of 0 reflects the fact that many stories are submitted but never gain traction. However, the long tail is where things get interesting: over 6,044,061 stories have been submitted, and the top 0.03% (those with 1,000+ points) represent the content that defined conversations across the technology industry.
+
+### Most-shared domains
+
+The domains most frequently linked from Hacker News stories tell a clear story about what the community values. GitHub dominates, reflecting HN's deep roots in open source and software development. Major publications like the New York Times and Ars Technica show the community's interest in journalism and long-form analysis.
+
+| # | Domain | Stories |
+|--:|--------|--------:|
+| 1 | github.com | 197,959 |
+| 2 | www.youtube.com | 134,903 |
+| 3 | medium.com | 124,561 |
+| 4 | www.nytimes.com | 77,719 |
+| 5 | en.wikipedia.org | 54,439 |
+| 6 | techcrunch.com | 54,190 |
+| 7 | twitter.com | 50,590 |
+| 8 | arstechnica.com | 47,090 |
+| 9 | www.theguardian.com | 44,337 |
+| 10 | www.bloomberg.com | 37,826 |
+
+### Most active story submitters
+
+These are the users who have submitted the most stories over the lifetime of Hacker News. Many of them have been active for over a decade, consistently curating and sharing content with the community.
+
+| # | User | Stories |
+|--:|------|--------:|
+| 1 | rbanffy | 36,795 |
+| 2 | Tomte | 26,192 |
+| 3 | tosh | 24,082 |
+| 4 | bookofjoe | 20,608 |
+| 5 | mooreds | 20,381 |
+| 6 | pseudolus | 19,917 |
+| 7 | PaulHoule | 19,042 |
+| 8 | todsacerdoti | 18,880 |
+| 9 | ingve | 17,059 |
+| 10 | thunderbong | 15,989 |
+| 11 | jonbaer | 14,169 |
+| 12 | rntn | 13,410 |
+| 13 | doener | 12,827 |
+| 14 | Brajeshwar | 12,411 |
+| 15 | LinuxBender | 11,058 |
+
+## How it works
+
+The pipeline is built in Go and uses [DuckDB](https://duckdb.org) for Parquet conversion. Historical data is sourced from [ClickHouse](https://clickhouse.com); live data is fetched directly from the [HN Firebase API](https://hacker-news.firebaseio.com/v2).
+
+**Historical backfill.** The pipeline iterates through every month from October 2006 to the most recent complete month. For each month, it queries the ClickHouse source with a time-bounded SQL query, exports the result as a Parquet file sorted by `id` using DuckDB with Zstandard compression at level 22, and commits it to this repository along with an updated `stats.csv` and `README.md`. Months already tracked in `stats.csv` are skipped, making the process fully resumable.
+
+**Live polling.** Every 5 minutes, the pipeline calls the HN Firebase API to fetch new items by ID range. Items are grouped into their 5-minute time windows, written as individual Parquet files at `today/YYYY/MM/DD/HH/MM.parquet` using DuckDB, and committed to Hugging Face immediately. Using the HN API directly means live blocks reflect real-time data with no indexing lag.
+
+**Day rollover.** At midnight UTC, the entire current month is refetched from the ClickHouse source in a single query and written as an authoritative Parquet file. Today's individual 5-minute blocks are deleted from the repository in the same atomic commit. Refetching instead of merging ensures the monthly file is always complete and deduplicated, regardless of any local state.
+
+## Thanks
+
+The data in this dataset comes from the [ClickHouse Playground](https://sql.clickhouse.com), a free public SQL endpoint maintained by [ClickHouse, Inc.](https://clickhouse.com) that mirrors the official [Hacker News Firebase API](https://github.com/HackerNewsAPI/HN-API). ClickHouse uses Hacker News as one of their canonical demo datasets. Without their public endpoint, building and maintaining a complete, regularly updated archive like this would not be practical.
+
+The original content is created by the Hacker News community and is operated by [Y Combinator](https://www.ycombinator.com). This is an independent mirror and is not affiliated with or endorsed by Y Combinator or ClickHouse, Inc.
+
+# Dataset card for Hacker News - Complete Archive
+
+## Dataset summary
+
+This dataset is a complete mirror of the [Hacker News](https://news.ycombinator.com) archive, sourced from the [ClickHouse Playground](https://sql.clickhouse.com) which itself mirrors the official [HN Firebase API](https://github.com/HackerNewsAPI/HN-API). The data covers every item ever posted to the site, from the earliest submissions in October 2006 through today.
+
+The dataset is intended for research, analysis, and training. Common use cases include:
+
+- **Language model pretraining and fine-tuning** on high-quality technical discussions
+- **Sentiment and trend analysis** across two decades of technology discourse
+- **Community dynamics research** on one of the internet's most influential forums
+- **Information retrieval** benchmarks using real-world questions and answers
+- **Content recommendation** and ranking model development
+
+## Dataset structure
+
+### Data instances
+
+Here is an example item from the dataset. This is a story submission with a link to an external URL:
+
+```json
+{
+  "id": 1,
+  "deleted": 0,
+  "type": 1,
+  "by": "pg",
+  "time": "2006-10-09T18:21:51+00:00",
+  "text": "",
+  "dead": 0,
+  "parent": 0,
+  "poll": 0,
+  "kids": [15, 234509, 487171],
+  "url": "http://ycombinator.com",
+  "score": 57,
+  "title": "Y Combinator",
+  "parts": [],
+  "descendants": 0,
+  "words": ["y", "combinator"]
+}
+```
+
+And here is a comment, showing how discussion threads are connected via the `parent` field:
+
+```json
+{
+  "id": 15,
+  "deleted": 0,
+  "type": 2,
+  "by": "sama",
+  "time": "2006-10-09T19:51:01+00:00",
+  "text": "\"the way to get good software is to find ...",
+  "dead": 0,
+  "parent": 1,
+  "poll": 0,
+  "kids": [17],
+  "url": "",
+  "score": 0,
+  "title": "",
+  "parts": [],
+  "descendants": 0,
+  "words": []
+}
+```
+
+### Data fields
+
+Every Parquet file shares the same schema, matching the [HN API](https://github.com/HackerNewsAPI/HN-API) item format:
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | uint32 | Unique item ID, monotonically increasing across the entire site |
+| `deleted` | uint8 | 1 if the item was soft-deleted by its author or by moderators, 0 otherwise |
+| `type` | int8 | Item type as an integer: `1`=story, `2`=comment, `3`=poll, `4`=pollopt, `5`=job |
+| `by` | string | Username of the author who created this item. Note: `by` is a reserved word in DuckDB and must be quoted as `"by"` |
+| `time` | timestamp | When the item was created, in UTC |
+| `text` | string | HTML body text. Used for comments, Ask HN posts, job listings, and polls |
+| `dead` | uint8 | 1 if the item was flagged or killed by moderators, 0 otherwise |
+| `parent` | uint32 | The ID of the parent item. For comments, this points to either a story or another comment |
+| `poll` | uint32 | For poll options (`pollopt`), the ID of the associated poll |
+| `kids` | list\<uint32\> | Ordered list of direct child item IDs (typically comments) |
+| `url` | string | The external URL for link stories. Empty for text posts and comments |
+| `score` | int32 | The item's score (upvotes minus downvotes) |
+| `title` | string | Title text for stories, jobs, and polls. Empty for comments |
+| `parts` | list\<uint32\> | For polls, the list of associated poll option item IDs |
+| `descendants` | int32 | Total number of comments in the entire discussion tree below this item |
+| `words` | list\<string\> | Tokenized words extracted from the title and text fields |
+
+### Data splits
+
+The `default` configuration includes all historical monthly Parquet files. If you only need today's latest items, use the `today` configuration which includes only the 5-minute live blocks for the current day.
+
+You can also load individual years or months by specifying `data_files`:
+
+```python
+# Load just January 2024
+ds = load_dataset("open-index/hacker-news", data_files="data/2024/2024-01.parquet", split="train")
+
+# Load all of 2024
+ds = load_dataset("open-index/hacker-news", data_files="data/2024/*.parquet", split="train")
+```
+
+## Dataset creation
+
+### Curation rationale
+
+Hacker News is one of the richest sources of technical discussion on the internet, but accessing the full archive programmatically has historically required either scraping the Firebase API item-by-item or working with incomplete third-party dumps. This dataset provides the complete archive in a standard, efficient format that anyone can query without setting up infrastructure.
+
+By publishing on Hugging Face with Parquet files, the data becomes immediately queryable with DuckDB (via `hf://` paths), streamable with the `datasets` library, and downloadable in bulk. The 5-minute live update pipeline means researchers always have access to near-real-time data.
+
+### Source data
+
+All data is sourced from the [ClickHouse Playground](https://sql.clickhouse.com), a public SQL endpoint maintained by ClickHouse that mirrors the official Hacker News Firebase API. The ClickHouse mirror is widely used for analytics demonstrations and contains the complete dataset.
+
+The pipeline queries the ClickHouse endpoint month-by-month, exports each month as a Parquet file using DuckDB with Zstandard compression at level 22, and commits it to this Hugging Face repository. Already-committed months are tracked in `stats.csv` and skipped on subsequent runs, making the process fully resumable.
+
+### Data processing steps
+
+The pipeline runs in three modes:
+
+1. **Historical backfill.** Iterates through every month from October 2006 to the most recent complete month. For each month, it runs a SQL query against the ClickHouse source, writes the result as a Parquet file sorted by `id`, and commits it to Hugging Face along with an updated `stats.csv` and `README.md`.
+
+2. **Live polling.** After the historical backfill completes, the pipeline polls the [HN Firebase API](https://hacker-news.firebaseio.com/v2) every 5 minutes for new items. It fetches all items with IDs greater than the last committed watermark, groups them into 5-minute time windows by item timestamp, and writes each window as a `today/YYYY/MM/DD/HH/MM.parquet` file committed to Hugging Face immediately. The HN API provides real-time data with no indexing lag.
+
+3. **Day rollover.** At midnight UTC, the entire current month is refetched from the ClickHouse source in a single query and written as a fresh, authoritative Parquet file. Today's individual 5-minute blocks are deleted from the repository in the same atomic commit. This approach is more reliable than merging local blocks — the result is always complete and deduplicated, sourced directly from the origin.
+
+All Parquet files use **Zstandard compression at level 22** and are sorted by `id` for efficient range scans. No filtering, deduplication, or transformation is applied to the data beyond what the source provides.
+
+### Personal and sensitive information
+
+This dataset contains usernames (`by` field) and user-generated text content (`text`, `title` fields) as they appear on the public Hacker News website. No additional PII processing has been applied. The data reflects what is publicly visible on [news.ycombinator.com](https://news.ycombinator.com).
+
+If you find content in this dataset that you believe should be removed, please open a discussion on the Community tab.
+
+## Considerations for using the data
+
+### Social impact
+
+By providing the complete Hacker News archive in an accessible format, we hope to enable research into online community dynamics, technology trends, and the evolution of technical discourse. The dataset can serve as training data for language models that need to understand technical discussions, or as a benchmark for information retrieval and recommendation systems.
+
+### Discussion of biases
+
+Hacker News has a well-documented set of community biases. The user base skews heavily toward software engineers, startup founders, and technology enthusiasts based in the United States. Topics related to Silicon Valley, programming languages, startups, and certain political viewpoints tend to receive disproportionate attention and engagement.
+
+The moderation system (flagging, vouching, and moderator intervention) shapes what content survives and what gets killed. Stories and comments that violate community norms are flagged as `dead`, but this moderation reflects the values of the existing community rather than any objective standard.
+
+We have not applied any additional filtering or quality scoring to the data. All items, including deleted and dead items, are preserved exactly as they appear in the source.
+
+### Known limitations
+
+- **`type` is an integer.** The item type is stored as a TINYINT enum: `1`=story, `2`=comment, `3`=poll, `4`=pollopt, `5`=job. When writing DuckDB queries, use `WHERE type = 1` for stories rather than `WHERE type = 'story'`.
+- **`by` is a reserved keyword in DuckDB.** Always quote it with double quotes: `"by"`.
+- **`deleted` and `dead` are integers.** They are stored as 0/1 rather than booleans.
+- **Comment text is HTML.** The `text` field contains raw HTML as stored by HN, not plain text. You may need to strip tags depending on your use case.
+- **Deleted items have sparse fields.** When an item is deleted, most fields become empty, but the `id` and `deleted` flag are preserved.
+- **Scores are point-in-time snapshots.** The score reflects the value at the time the ClickHouse mirror last synced, not necessarily the final score.
+- **No user profiles.** This dataset contains items only, not user profiles (karma, bio, etc.).
+- **Code content is HTML-escaped.** Code snippets in comments use HTML entities and `<code>` tags rather than Markdown formatting.
+
+## Additional information
+
+### Licensing
+
+The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0**. The original content is subject to the rights of its respective authors. Hacker News data is provided by [Y Combinator](https://www.ycombinator.com).
+
+This is an independent community mirror. It is not affiliated with or endorsed by Y Combinator.
+
+### Contact
+
+For questions, feedback, or issues, please open a discussion on the [Community tab](https://huggingface.co/datasets/open-index/hacker-news/discussions).
+
+*Last updated: 2026-03-28 16:15 UTC*

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