{"slug": "vortex", "name": "Vortex", "category": "files", "category_name": "Files, sync and backup", "description": "", "recommenders": 7, "commenters": 10, "warners": 0, "score": 6.867, "rising": -0.96, "first": "2025-11-12", "latest": "2026-08-18", "why": "You should take a look at https://github.com/vortex-data/vortex!", "why_is_quote": true, "has_verdict": false, "sentiment": {"recommend": 7, "neutral": 3, "compare": 0, "warn": 0}, "spark": [2, 0, 2, 0, 0, 0, 0, 1, 1, 1, 0, 0], "listed": true, "loved": [], "caveat": "", "short_quote": "You should take a look at https://github.com/vortex-data/vortex!", "kind": "", "icon": true, "domain": "vortex.dev", "github_repo": null, "official_url": "https://vortex.dev", "pricing": "", "aliases": ["Vortex"], "verdict": null, "praise": [{"label": "", "summary": "", "quotes": [{"id": 789495872, "item_id": 49343492, "url": "https://news.ycombinator.com/item?id=49343492", "by": "nylonstrung", "date": "2026-08-18", "version": null, "stance": "recommend", "quote": "I love Vortex' Dtypes", "excerpt": "For columnar databases, I love Vortex' Dtypes which lets you attach semantic context in a logical type to what is essentially compressed Arrow https://docs.vortex.dev/concepts/dtypes#logical-types", "context": "", "use_case": "columnar data logical types", "switched_from": "", "story": "Rethinking Database Programming"}, {"id": 785861008, "item_id": 49116313, "url": "https://news.ycombinator.com/item?id=49116313", "by": "westurner", "date": "2026-07-30", "version": null, "stance": "recommend", "quote": "TIL Vortex is Zero-Copy compatible with Arrow", "excerpt": "TigerBeetle has static allocation for their known workload. SIMD is more useful for heavy parallel analytical workloads. There are probably returns from SIMD even for index updates on transactional inserts. SIMD is faster for: vectorized index comparisons, Vectorized Constraint Validation, Multi-Column SIMD Hashing, Masked Bitmaps for Nullable Fields, gather and scatter, string processing, casting to numeric types. I", "context": "", "use_case": "Columnar file format", "switched_from": "", "story": "Postgres rewritten in Rust v0.2, now faster than Postgres and ClickHouse"}, {"id": 778567504, "item_id": 48660469, "url": "https://news.ycombinator.com/item?id=48660469", "by": "ConnorTsui20", "date": "2026-06-24", "version": null, "stance": "recommend", "quote": "You should take a look at https://github.com/vortex-data/vortex!", "excerpt": "You should take a look at https://github.com/vortex-data/vortex!", "context": "", "use_case": "columnar format with random access", "switched_from": "", "story": "F3"}, {"id": 746056752, "item_id": 46628547, "url": "https://news.ycombinator.com/item?id=46628547", "by": "pedrini210", "date": "2026-01-14", "version": null, "stance": "recommend", "quote": "Check the Vortex file format", "excerpt": "Check the Vortex file format (https://vortex.dev/), if you are interested in a distributed SQL engine then you can check SpiralDB (https://spiraldb.com/), I haven\u2019t used this one personally but they created Vortex. If you can drop the \u201cdistributed\u201d part, then plug DuckDB (https://duckdb.org/) and query Parquet (out of the box) or Vortex (https://duckdb.org/docs/stable/core_extensions/vortex.html) with it.", "context": "", "use_case": "", "switched_from": "", "story": "Distributed SQL engine for ultra-wide tables"}, {"id": 743483248, "item_id": 46467703, "url": "https://news.ycombinator.com/item?id=46467703", "by": "bigyabai", "date": "2026-01-02", "version": null, "stance": "recommend", "quote": "you can install Vortex on Linux systems and mod games via that just fine", "excerpt": "FYI, you can install Vortex on Linux systems and mod games via that just fine: https://github.com/pikdum/steam-deck/ (Edit: their native version appears to be in-development, but doesn't currently support non-native titles: https://nexus-mods.github.io/NexusMods.App/ )", "context": "", "use_case": "Game mod management", "switched_from": "", "story": "Linux is good now; to feel like you actually own your PC, put Linux on it"}]}], "caveats": [], "who": [], "tags": [], "use_cases": ["columnar file format", "columnar data logical types", "columnar format with random access", "game mod management", "columnar data storage"], "compared": [{"slug": "duckdb", "name": "DuckDB", "co": 3, "switched_from": 0, "switched_to": 0}, {"slug": "lance", "name": "Lance", "co": 3, "switched_from": 0, "switched_to": 0}], "makers": [{"id": 778397058, "item_id": 48649816, "url": "https://news.ycombinator.com/item?id=48649816", "by": "aduffy", "date": "2026-06-23", "version": null, "stance": "recommend", "quote": "Vortex focuses on extensibility and performance by replacing all of Parquet's black-box encoders", "excerpt": "> Also, my main gripe with parquet (single table per file) is not even addressed, so, also the name is a bit hyped up. This is really more of an expectation that has been put on file formats by the query engines. Spark/Datafusion/DuckDB wouldn't really know what to do with a multi-table file. > Parquet is unfortunately very good just by virtue of being first, and so widely supported IMO that is not how technology wor", "context": "", "use_case": "", "switched_from": "", "story": "F3"}, {"id": 732820240, "item_id": 45801265, "url": "https://news.ycombinator.com/item?id=45801265", "by": "JustSkyfall", "date": "2025-11-03", "version": null, "stance": "recommend", "quote": "Way too proud of this one :D", "excerpt": "Location: near London Remote: Yes Willing to relocate: Unfortunately not. Technologies: Rust, TypeScript, Svelte, Astro, React. I do have some experience with Rails and can and have shipped features to production, but I'm not as comfortable with it as the other technologies. R\u00e9sum\u00e9/CV: Available on request Email: hi@skyfall.dev Hi! I\u2019m a high-school student looking for a part-time role or internship at a company buil", "context": "high-school student", "use_case": "temporary email service", "switched_from": "", "story": "Ask HN: Who wants to be hired? (November 2025)"}], "threads": [{"url": "https://news.ycombinator.com/item?id=48647799", "title": "F3", "mentions": 2}, {"url": "https://news.ycombinator.com/item?id=49342530", "title": "Rethinking Database Programming", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=49111925", "title": "Postgres rewritten in Rust v0.2, now faster than Postgres and ClickHouse", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=47966254", "title": "Full-Text Search with DuckDB", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=47761997", "title": "Distributed DuckDB Instance", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=47008163", "title": "Show HN: Data Engineering Book \u2013 An open source, community-driven guide", "mentions": 1}], "sentiment_by_month": {"2025-11": {"recommend": 2}, "2025-12": {}, "2026-01": {"recommend": 2}, "2026-02": {"neutral": 1}, "2026-03": {}, "2026-04": {"neutral": 2}, "2026-05": {}, "2026-06": {"recommend": 1}, "2026-07": {"recommend": 1}, "2026-08": {"recommend": 1}, "2026-09": {}, "2026-10": {}}, "versions": [], "version_unnamed": 0, "family_ref": null, "runs_on": null, "lines": [], "quotes": [{"id": 789495872, "item_id": 49343492, "url": "https://news.ycombinator.com/item?id=49343492", "by": "nylonstrung", "date": "2026-08-18", "version": null, "stance": "recommend", "quote": "I love Vortex' Dtypes", "excerpt": "For columnar databases, I love Vortex' Dtypes which lets you attach semantic context in a logical type to what is essentially compressed Arrow https://docs.vortex.dev/concepts/dtypes#logical-types", "context": "", "use_case": "columnar data logical types", "switched_from": "", "story": "Rethinking Database Programming"}, {"id": 785861008, "item_id": 49116313, "url": "https://news.ycombinator.com/item?id=49116313", "by": "westurner", "date": "2026-07-30", "version": null, "stance": "recommend", "quote": "TIL Vortex is Zero-Copy compatible with Arrow", "excerpt": "TigerBeetle has static allocation for their known workload. SIMD is more useful for heavy parallel analytical workloads. There are probably returns from SIMD even for index updates on transactional inserts. SIMD is faster for: vectorized index comparisons, Vectorized Constraint Validation, Multi-Column SIMD Hashing, Masked Bitmaps for Nullable Fields, gather and scatter, string processing, casting to numeric types. I", "context": "", "use_case": "Columnar file format", "switched_from": "", "story": "Postgres rewritten in Rust v0.2, now faster than Postgres and ClickHouse"}, {"id": 778567504, "item_id": 48660469, "url": "https://news.ycombinator.com/item?id=48660469", "by": "ConnorTsui20", "date": "2026-06-24", "version": null, "stance": "recommend", "quote": "You should take a look at https://github.com/vortex-data/vortex!", "excerpt": "You should take a look at https://github.com/vortex-data/vortex!", "context": "", "use_case": "columnar format with random access", "switched_from": "", "story": "F3"}, {"id": 767514097, "item_id": 47969631, "url": "https://news.ycombinator.com/item?id=47969631", "by": "dkdcdev", "date": "2026-04-30", "version": null, "stance": "neutral", "quote": "I\u2019m unfamiliar with Vortex", "excerpt": "I believe as it states that\u2019s only for the core extensions listed here: https://duckdb.org/docs/current/core_extensions/overview all are by the DuckDB team except three third-party owners. I\u2019m unfamiliar with Vortex, but presume it\u2019s like LanceDB and MotherDuck with a serious company behind it. and presumably the DuckDB team trusts them not to ship malware in their extension I think it\u2019s a UX trade off that benefits ", "context": "", "use_case": "", "switched_from": "", "story": "Full-Text Search with DuckDB"}, {"id": 764218579, "item_id": 47763661, "url": "https://news.ycombinator.com/item?id=47763661", "by": "jeadie", "date": "2026-04-14", "version": null, "stance": "neutral", "quote": "We ending up building a Sqlite + vortex file alternative", "excerpt": "This is exactly what we found. Ingest rates were tough. We partitioned and ran over multiple duckdb instances too (and wrangled the complexity). We ending up building a Sqlite + vortex file alternative for our use case: https://spice.ai/blog/introducing-spice-cayenne-data-accelerator", "context": "", "use_case": "", "switched_from": "", "story": "Distributed DuckDB Instance"}, {"id": 752339729, "item_id": 47021233, "url": "https://news.ycombinator.com/item?id=47021233", "by": "cpard", "date": "2026-02-14", "version": null, "stance": "neutral", "quote": "vortex[2] is another one", "excerpt": "It's important in a book treating an emerging field (data eng for LLMs) to mention emerging categories related to it such as storage formats purpose built for the full ML lifecycle. Lance[1] (the format, not just LanceDB) is a great example, where you have columnar storage optimized for both analytical operations and vector workloads together with built-in versioning for dataset iteration. Plus (very important) rando", "context": "", "use_case": "", "switched_from": "", "story": "Show HN: Data Engineering Book \u2013 An open source, community-driven guide"}, {"id": 746056752, "item_id": 46628547, "url": "https://news.ycombinator.com/item?id=46628547", "by": "pedrini210", "date": "2026-01-14", "version": null, "stance": "recommend", "quote": "Check the Vortex file format", "excerpt": "Check the Vortex file format (https://vortex.dev/), if you are interested in a distributed SQL engine then you can check SpiralDB (https://spiraldb.com/), I haven\u2019t used this one personally but they created Vortex. If you can drop the \u201cdistributed\u201d part, then plug DuckDB (https://duckdb.org/) and query Parquet (out of the box) or Vortex (https://duckdb.org/docs/stable/core_extensions/vortex.html) with it.", "context": "", "use_case": "", "switched_from": "", "story": "Distributed SQL engine for ultra-wide tables"}, {"id": 743483248, "item_id": 46467703, "url": "https://news.ycombinator.com/item?id=46467703", "by": "bigyabai", "date": "2026-01-02", "version": null, "stance": "recommend", "quote": "you can install Vortex on Linux systems and mod games via that just fine", "excerpt": "FYI, you can install Vortex on Linux systems and mod games via that just fine: https://github.com/pikdum/steam-deck/ (Edit: their native version appears to be in-development, but doesn't currently support non-native titles: https://nexus-mods.github.io/NexusMods.App/ )", "context": "", "use_case": "Game mod management", "switched_from": "", "story": "Linux is good now; to feel like you actually own your PC, put Linux on it"}, {"id": 735823168, "item_id": 45988948, "url": "https://news.ycombinator.com/item?id=45988948", "by": "kipukun", "date": "2025-11-19", "version": null, "stance": "recommend", "quote": "The cuDF interop in the roadmap [1] will be huge for my workloads.", "excerpt": "The cuDF interop in the roadmap [1] will be huge for my workloads. XGBoost has the fastest inference time on GPUs, so a fast path straight from these Vortex files to GPU memory seems promising. [1] https://github.com/vortex-data/vortex/issues/2116", "context": "", "use_case": "Columnar file format", "switched_from": "", "story": "Vortex: An extensible, state of the art columnar file format"}, {"id": 734566672, "item_id": 45910417, "url": "https://news.ycombinator.com/item?id=45910417", "by": "jauntywundrkind", "date": "2025-11-12", "version": null, "stance": "recommend", "quote": "Vortex looks great.", "excerpt": "Really a pity the Nimble alliance was skewered by the lawyers. But nice that there's so many excellent works to improve data storage. Vortex looks great. F3. Many others. https://db.cs.cmu.edu/papers/2025/zeng-sigmod2025.pdf https://news.ycombinator.com/item?id=45437759", "context": "", "use_case": "Columnar data storage", "switched_from": "", "story": "Vortex \u2013 An extensible, state of the art columnar file format"}]}