#databend search results
๐ #Databend evolved into a ๐ช๐ฃ๐๐๐๐๐ ๐ข๐ช๐ก๐ฉ๐๐ข๐ค๐๐๐ก ๐๐๐ฉ๐๐๐๐จ๐ Pure #Rust kernel = blazing performance & safety One Snowflake-compatible SQL interface seamlessly handles: โข BI Analytics - Traditional SQL workloads โข AI/Vector Search - Embeddings & semanticโฆ
#Databend Optimizer = One brain for all data ๐ง Analytics. JSON. Vector. Geo. ๐ Learn how it works: docs.databend.com/guides/how-datโฆ
Advanced Indexes in Databend Community Edition #Databend v1.2.835 brings Aggregating, Inverted (full-text), Ngram, and Vector indexes to Community Editionโaccelerating rollups, text analytics, and vector retrieval in one engine. As a #Rust-native project, we stay committed toโฆ
![dustrial's tweet image. Magda [ns]
#glitch #databend #art #vector](https://pbs.twimg.com/media/F7F6s4KaYAAmrCU.jpg)
"It is the Divine Language, the ancient language, spoken throughout the Universe before time was time.." #glitch #databend #notepad++ #leeloo #art
redone the test for the cold run, as #databend had a regression in the last update, happy to see an open source DB engine that finish SF100 cold run using only 32 GB of RAM and 4 cores
New Update; Open Source Python DB Engines #Databend, #DuckDB and #Datafusion Parquet generated by Spark with V-Order applied.
In Databend, If it ๐๐๐ง ๐๐ ๐ฏ๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐, ๐ฐ๐ ๐ฏ๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐ ๐ข๐ญ! ๐ก #Databend's optimizer trace provides complete visibility into query optimization. Track every optimizer step, every optimizer rule, understand exactly how they work - zero black boxes in ourโฆ
๐ #Databend is now available in DBeaver 24.3.1! ๐ Huge thanks to rad-pat from the community. ๐ You can now find it as a new connection type under: ๐๐๐ฌ ๐ฟ๐๐ฉ๐๐๐๐จ๐ ๐พ๐ค๐ฃ๐ฃ๐๐๐ฉ๐๐ค๐ฃ โ ๐ผ๐ฃ๐๐ก๐ฎ๐ฉ๐๐๐๐ก โ ๐ฟ๐๐ฉ๐๐๐๐ฃ๐ Check out the release:โฆ
gurubase is an really helpful AI-powered Q&A assistants for tech docs! Awesome & Amazing @gurubaseio #gurubase #databend
After finishing a presentation on #Databend, I received a speaker certificate from #KubeSphere. I hope that one day I can become an expert in K8s.๐
Hey, folks! Just received a cool new hoodie from #Databend. ๐ฅฐAnyone else want to get their hands on one?
ไธๅบๅนณๅนณๆ ๅฅ็ๆฉ่ถ๏ผ็ซๅจๆทฑๅค็ช็ถๅ็ฐๆฏๆจๆฒน่ไผ๐คฃ็ผ๏ผๅฆไธๅฏ่จ @xx01cyx @leiysky @BugenZhao @DylanGalois @Adbean2020 #RisingWave x #Databend Meetup ๅนฒ่ดงๅพๅคๅพๅ ๅฎ๏ผไธๆฌก่ฟๆฅ๐ฅฐ
Advanced Indexes in Databend Community Edition #Databend v1.2.835 brings Aggregating, Inverted (full-text), Ngram, and Vector indexes to Community Editionโaccelerating rollups, text analytics, and vector retrieval in one engine. As a #Rust-native project, we stay committed toโฆ
#Databend Optimizer = One brain for all data ๐ง Analytics. JSON. Vector. Geo. ๐ Learn how it works: docs.databend.com/guides/how-datโฆ
๐ #Databend v1.2.833 is out! Highlights from recent updates: โจ Geo & virtual columns enabled by default โ๏ธ Smarter S3 intelligent tiering support ๐ง Dynamic schema support for the script engine ๐ง Better expression parsing & bug fixes ๐ Experimental aggregate final withโฆ
๐ #Databend evolved into a ๐ช๐ฃ๐๐๐๐๐ ๐ข๐ช๐ก๐ฉ๐๐ข๐ค๐๐๐ก ๐๐๐ฉ๐๐๐๐จ๐ Pure #Rust kernel = blazing performance & safety One Snowflake-compatible SQL interface seamlessly handles: โข BI Analytics - Traditional SQL workloads โข AI/Vector Search - Embeddings & semanticโฆ
Snowflake 60% of TopK queries are actually "LIMIT 0" - metadata only! #Databend optimize these to EmptyResultScan directly
Exactly why we built these optimizations into #Databend. - The footer metadata issue was killing us on S3 - had to externalize it and do async prefetching - Our parallel column readers with merged IO make parquet actually work well in cloud storage - Plus we extended parquet withโฆ
Is Parquet the bottleneckโor are our workloads the problem? S3-first, NVMe, embeddings/images, random accessโฆ New formats like @lancedb or Vortex rethink layout (O(1) lookups, SIMD blocks). Pick formats by workload, not habit. Here is a deep dive:
Yes! That's exactly what I solved for #Databend. The trick: cascading compile-time validation through the entire dependency chain: #[derive(FrozenAPI)] // โ All deps must derive this struct C { pub a: i32 } #[derive(FrozenAPI)] struct B { pub c: C } #[frozen_api("hash123")]โฆ
๐๐๐๐จ #๐๐ช๐จ๐ฉ ๐ก๐๐ข๐๐ฉ๐๐ฉ๐๐ค๐ฃ ๐๐ช๐จ๐ฉ ๐๐๐ฉ ๐ข๐ (and AI can't solve it either) I have structs with transitive dependencies: #[frozen_api = "hash123"] // โ This hash represents A's "API signature" struct A { pub b: B, } struct B { pub c: C, } struct C {โฆ
๐คฟDeep Dive into #SeaTunnel #Databend Sink Connector #CDC Implementation #bigdata #OpenSource #dataengineer cloudsjhan.github.io/2025/08/18/Deeโฆ
๐ The #Databend documentation website now features a handy "Copy Page" button that exports docs as Markdown - perfect for LLMs! docs.databend.com
๐ Introducing ๐ช๐ผ๐ฟ๐ธ๐น๐ผ๐ฎ๐ฑ ๐๐ฟ๐ผ๐๐ฝ in #Databend! Stop worrying about resource conflicts and OOM crashes! โ Isolate workloads with guaranteed resource quotas โ Zero system downtime from memory issues โ Simple user-based resource management โ Enterprise-gradeโฆ
In Databend, If it ๐๐๐ง ๐๐ ๐ฏ๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐, ๐ฐ๐ ๐ฏ๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐ ๐ข๐ญ! ๐ก #Databend's optimizer trace provides complete visibility into query optimization. Track every optimizer step, every optimizer rule, understand exactly how they work - zero black boxes in ourโฆ
Currently, this approach is still in the experimental phase. The complete process for a single query takes approximately 10-20 seconds [about ~10s for optimization and 10s for verification]. We hope to implement this in #Databend soon. We welcome professors or students interestedโฆ
๐๐ค๐ฌ ๐ฟ๐๐ฉ๐๐๐๐ฃ๐'๐จ ๐๐ช๐๐ง๐ฎ ๐๐ฅ๐ฉ๐๐ข๐๐ฏ๐๐ง ๐๐๐ ๐๐จ ๐ผ๐ฃ๐๐ก๐ฎ๐ฉ๐๐๐จ ๐๐๐จ๐ฉ Curious how #Databend delivers efficient query performance? Our optimizer transforms SQL into smart execution plans through a sophisticated pipeline. Let's explore the technologyโฆ
๐ #Databend v1.2.725 is now available! โจ 63 exciting new features ๐ง 59 thoughtful bug fixes Highlights: โจ Hybrid cache for improved performance โจ Enhanced query-level memory management โจ Bendsave for backup and restore โจ Auto vacuum capability Full changelog:โฆ
๐ Oops-proof your data operations with #Databend Inspired by Git's version control, #Databend lets you: โข ๐ฅ๐ฒ๐ฐ๐ผ๐๐ฒ๐ฟ ๐ฑ๐ฟ๐ผ๐ฝ๐ฝ๐ฒ๐ฑ ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ & ๐๐ฎ๐ฏ๐น๐ฒ๐ โข ๐ฅ๐ฒ๐๐ฒ๐ฟ๐ ๐ฏ๐ฎ๐ฑ ๐ฑ๐ฎ๐๐ฎ ๐ฐ๐ต๐ฎ๐ป๐ด๐ฒ๐ โข ๐ฅ๐ผ๐น๐น ๐ฏ๐ฎ๐ฐ๐ธ ๐๐ฐ๐ต๐ฒ๐บ๐ฎโฆ
๐ #Databend Feb '25 update is here! โ Easy migration from MySQL โ Flexible spill storage (local disks or S3) โ Powerful SQL like ATTACH TABLE and more! Dive in for details ๐ medium.com/@databend/dataโฆ
towardsdev.com
Databend Monthly (Feb 2025)
Weโre excited to bring you the latest updates, new features, and improvements for February 2025 in Databend!
Modern data warehouses evolve beyond storage-compute separation to include metadata decoupling. With #Databend, metadata lives in S3, enabling: โจ Flexible data sharing: Attach Databend table anywhere ๐ Column-level security: Share specific columns only Learn more:โฆ
"It is the Divine Language, the ancient language, spoken throughout the Universe before time was time.." #glitch #databend #notepad++ #leeloo #art
๐ #Databend evolved into a ๐ช๐ฃ๐๐๐๐๐ ๐ข๐ช๐ก๐ฉ๐๐ข๐ค๐๐๐ก ๐๐๐ฉ๐๐๐๐จ๐ Pure #Rust kernel = blazing performance & safety One Snowflake-compatible SQL interface seamlessly handles: โข BI Analytics - Traditional SQL workloads โข AI/Vector Search - Embeddings & semanticโฆ
๐ You can try using a standalone #Databend to analyze data in @ApacheIceberg . Here's a correlation between the number of passengers and the distance of the trip: (Although this feature is not fully implemented yet, it can work. Please stay tuned for updates on our progress.)
โด๏ธAll good people are asleep and dreaming. #glitch #databend #LeonBonnat #crucifixion #code #netart
redone the test for the cold run, as #databend had a regression in the last update, happy to see an open source DB engine that finish SF100 cold run using only 32 GB of RAM and 4 cores
๐ #Databend window function spill performance has improvedโa 68% speed boost! github.com/datafuselabs/dโฆ
New Update; Open Source Python DB Engines #Databend, #DuckDB and #Datafusion Parquet generated by Spark with V-Order applied.
After finishing a presentation on #Databend, I received a speaker certificate from #KubeSphere. I hope that one day I can become an expert in K8s.๐
Hey, folks! Just received a cool new hoodie from #Databend. ๐ฅฐAnyone else want to get their hands on one?
Indeed, #Databend has implemented an effective caching mechanism that leverages S3/R2 storage. It maintains the result cache, storing it on S3/R2.If the data hasn't been updated and the query is identical to a previous one, it retrieves the result file directly from the cache:
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