Add dataset README with schema and row examples
Browse files
README.md
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---
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pretty_name: pr0gramm user comments (sanitized, vacuumed)
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license: other
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language:
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- de
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- en
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task_categories:
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- other
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---
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# pr0gramm-usercomments
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This dataset contains a sanitized SQLite export of pr0gramm item metadata and comments.
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Main file:
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- `pr0gramm-copy-vacuumed.sqlite3`
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Sanitization applied:
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- Removed crawler-specific timing fields (`comments.crawl_time`, `items.crawl_time`, `items.detail_crawl_time`)
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- Removed crawler state/failure tables (`crawl_state`, `item_detail_failures`)
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## Tables
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The database contains:
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- `comments`
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- `items`
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### comments schema
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- `comment_id` INTEGER PRIMARY KEY
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- `item_id` INTEGER NOT NULL
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- `parent_comment_id` INTEGER
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- `comment_time` INTEGER
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- `score` INTEGER
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- `upvotes` INTEGER
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- `downvotes` INTEGER
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- `user_name` TEXT
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- `user_id` INTEGER
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- `user_profile_url` TEXT
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- `body` TEXT
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- `raw_json` TEXT NOT NULL
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### items schema
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- `item_id` INTEGER PRIMARY KEY
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- `promoted` INTEGER
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- `flags` INTEGER
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- `created_at` INTEGER
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- `user_name` TEXT
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- `source_feed` TEXT NOT NULL
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- `raw_json` TEXT NOT NULL
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## Example rows
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Example row from `comments`:
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```sql
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comment_id: 83842203
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item_id: 480055
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parent_comment_id: 0
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comment_time: 1776609541
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score: 1
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upvotes: 1
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downvotes: 0
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user_name: "1111111101"
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user_id: NULL
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user_profile_url: "https://pr0gramm.com/user/1111111101"
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body: "Damals war OC noch gut"
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raw_json (truncated): {"id":83842203,"parent":0,"content":"Damals war OC noch gut","created":1776609541,"up":1,"down":0,"confidence":0.206543,"name":"1111111101","mark":10}
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```
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Example row from `items`:
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```sql
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item_id: 6986879
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promoted: 0
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flags: 1
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created_at: 1776453456
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user_name: "Ometen"
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source_feed: "new"
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raw_json (truncated): {"id":6986879,"promoted":0,"userId":132720,"up":1,"down":0,"created":1776453456,"image":"2026/04/17/8804ec2d7b3e5297-h264-ultra_hd.mp4","thumb":"2026/04/17/8804ec2d7b3e5297.jpg",...}
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```
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## Notes
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- Timestamps are stored as Unix seconds.
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- `raw_json` contains API payload snapshots and may include fields not normalized into separate columns.
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