A Fast Immutable Map in Go
Daniel Lemire가 Go 언어용으로 개발한 'constmap' 라이브러리는 불변(immutable) 문자열-uint64 맵을 매우 빠르고 메모리 효율적으로 구현한다. 기존 Go 내장 맵보다 조회 속도가 약 3배 빠르고 메모리 사용량은 6분의 1 수준으로 줄였다. 이 자료구조는 키 집합이 고정되어 있고 모든 조회 키가 집합에 포함된 경우에 적합하며, 바이너리 퓨즈 필터를 활용해 캐시 적중률을 높여 성능을 개선한다. 대규모 데이터셋에서 메모리 사용량 절감과 빠른 조회가 필요한 상황에 유용하다.
https://lemire.me/blog/2026/03/29/a-fast-immutable-map-in-go/
#golang #immutablemap #binaryfusefilter #datastructure #performance
A Fast Immutable Map in Go
Consider the following problem. You have a large set of strings, maybe millions. You need to map these strings to 8-byte integers (uint64). These integers are given to you. If you are working in Go, the standard solution is to create a map. The construction is trivial, something like the following loop. m := make(map[string]uint64, … Continue reading A Fast Immutable Map in Go
Simon Willison (@simonw)
Redis에 새로운 데이터 타입인 배열(arrays)이 추가되었고, 인덱스로 접근할 수 있으며 텍스트 grep 검색 메커니즘도 함께 제공된다고 소개한다. Redis 기능 확장 측면에서 주목할 만한 업데이트다.
https://x.com/simonw/status/2051310232022986772
#redis #database #datastructure #opensource #search
Simon Willison (@simonw) on X
New Redis data type just dropped - arrays, accessible by index, with a new text grep search mechanism
🎩✨ Behold the majestic skiplist: the data structure nobody asked for but everyone pretends to need! 🤡 This article bravely navigates a sea of
#buzzwords and sales pitches, leaving readers wondering if the true purpose of skiplists is to skip the point entirely. 🚀
https://antithesis.com/blog/2026/skiptrees/ #skiplist #datastructure #techhumor #programming #satire #HackerNews #ngated
What are skiplists good for?
An exotic data structure and a whole lot of JOINs.
Alpha Equivalent Hash Consing with Thinnings
Another step on a journey to a thinning egraph.
My memory isn't getting worse, it's just using exponential decay
https://github.com/friendlymatthew
Index, Count, Offset, Size
Wherein we make progress towards solving one of the most vexing problems of Computer Science — naming things.
Python Draws Binary Tree Numbers #datastructure
La matematica delle strutture dati racconta una verità semplice: non esiste un modo perfetto per archiviare informazioni.
Ogni sistema è un compromesso tra velocità, memoria e ordine. A volte serve struttura, altre volte un po’ di disordine funziona meglio. Un equilibrio dinamico, in cui l’efficienza nasce proprio dalla varietà delle soluzioni.
https://www.quantamagazine.org/why-theres-no-single-best-way-to-store-information-20260116/
#computerscience #datastructure #data #algorithm #algoritmi #informatica
Why There’s No Single Best Way To Store Information | Quanta Magazine
The math of data structures helps us understand how different storage systems come with different trade-offs between resources such as time and memory.
📜 Discover the Fascinating History of Arrays!
💡 Arrays are everywhere today - from simple algorithms of searching and sorting to technologies like GPUs and AI models.
❓But, one may wonder - what was it like when they were first used?
🔎 Want to know the story behind it?
👉 Check out the article titled: "Story of First Array"
🔗 https://priyabrata-paul-blog.hashnode.dev/story-of-the-first-array
#Science #Computing #DataStructure #Array #History
Latest drop from our group 🥳 Paper by Daniel Jünger, Kevin Kristensen, Yunsong Wang, Xiangyao Yu, and Bertil Schmidt
#Bloomfilters are a fundamental #DataStructure for approximate membership queries, with applications ranging from #databases to #genomics.
https://arxiv.org/abs/2512.15595
#GPU #CUDA #bigdata #bioinformatics #datastructures
Optimizing Bloom Filters for Modern GPU Architectures
Bloom filters are a fundamental data structure for approximate membership queries, with applications ranging from data analytics to databases and genomics. Several variants have been proposed to accommodate parallel architectures. GPUs, with massive thread-level parallelism and high-bandwidth memory, are a natural fit for accelerating these Bloom filter variants potentially to billions of operations per second. Although CPU-optimized implementations have been well studied, GPU designs remain underexplored. We close this gap by exploring the design space on GPUs along three dimensions: vectorization, thread cooperation, and compute latency.
Our evaluation shows that the combination of these optimization points strongly affects throughput, with the largest gains achieved when the filter fits within the GPU's cache domain. We examine how the hardware responds to different parameter configurations and relate these observations to measured performance trends. Crucially, our optimized design overcomes the conventional trade-off between speed and precision, delivering the throughput typically restricted to high-error variants while maintaining the superior accuracy of high-precision configurations. At iso error rate, the proposed method outperforms the state-of-the-art by $11.35\times$ ($15.4\times$) for bulk filter lookup (construction), respectively, achieving above $92\%$ of the practical speed-of-light across a wide range of configurations on a B200 GPU. We propose a modular CUDA/C++ implementation, which will be openly available soon.
sayzard
N-gated Hacker News
Philip Zucker
Jan 
Hacker News
Python Peak
John Selection
Priyabrata Paul
N. Metzinger