![#89: Conflict-free Replicated Data Types](https://cdn-ak-scissors.b.st-hatena.com/image/square/a2e1c9bf9314d4f72e7a56ee5c22b4523fde8326/height=288;version=1;width=512/https%3A%2F%2Fmisreadingchat.files.wordpress.com%2F2022%2F05%2Fmisreading-chat-cover.jpg%3Fw%3D200)
We're excited to announce that Google Cloud Platform (GCP) now features a cutting-edge new congestion control algorithm, TCP BBR, which achieves higher bandwidths and lower latencies for internet traffic. This is the same BBR that powers TCP traffic from google.com and that improved YouTube network throughput by 4 percent on average globally — and by more than 14 percent in some countries. BBR all
概要 インターネットに晒されているWebサービスでは TV等で紹介されたことによる大量流入 悪意ある人物からの攻撃 クライアントのバグに依る大量リクエスト など、本来想定していた以上のトラフィックが来ることはよくあります。 単純にシステムを構築すると大規模トラフィックに対応できずシステムがスローダウンしてしまうため、何かしらrate limitをかけておいた方が良いです。 ただしrate limitと一口に入っても色々あるため、今回は主なrate limitアルゴリズムを紹介します。 Leaky bucket Leaky bucketはデータ転送レートを一定にする(=上限を設定する)アルゴリズムです。 下の図のように、様々な流量の水流がそのバケツに流れ込んでも小さな穴からは一定の水流が流れ出す仕組みです。 ref: What is the difference between token
Haskell Advent Calendar 2018 20日目 単調増加する自然数の列を、最低限のビット数で表現するための興味深いテクニックと、Haskellによる実装を紹介する。 Elias-Fano encoding この手法は、簡潔データ構造に分類されるもの一つであるが、厳密には条件を満たさないため疑似簡潔データ構造と呼ばれる。1970年代、Peter EliasとRobert Mario Fanoによって独立して発見された。 例題として1, 1, 4, 10, 17, 22, 23, 30という列をエンコードしてみよう。まず、それぞれの数を上位3ビットと下位2ビットに分割する。列の長さをNとしたとき、上位のビット数はとする。 上位ビットの列は000 000 001 010 100 101 101 111となる。これをヒストグラムのようにして23個のビンに分ける。 000: 2個
Robert Sedgewick is the founding chair and the William O. Baker Professor in the Department of Computer Science at Princeton University. He was a member of the board of directors of Adobe Systems from 1990 to 2016, served on the faculty at Brown University from 1975 to 1985, and has held visiting research positions at Xerox PARC, IDA, and INRIA. His research expertise is in algorithm science, data
A few days ago, the Computerphile YouTube channel put up a video about infinite lists in Haskell (Haran 2018). It’s pretty basic, but finishes up with a definition of an infinite list of prime numbers. The definition was something like this: primes = sieve [2..] sieve (p:ps) = p : sieve [ x | x <- ps, mod x p /= 0 ] This really demonstrates the elegance of list comprehensions coupled with lazy eva
High-speed memory caches present in modern computer architectures favor data structures with good locality of reference, i.e. the property by which elements accessed in sequence are located in memory addresses close to each other. This is the rationale behind classes such as Boost.Container flat associative containers, that emulate the functionality of standard C++ node-based associative container
Hash table is probably the most commonly used data structure in software industry. Most implementations focus on its speed instead of memory usage, yet small memory footprint has significant impact on large in-memory tables and database hash indexes. In this post, I”ll provide a step by step guide for writing a modern hash table that optimize for both speed and memory efficiency. I’ll also give so
the morning paper a random walk through Computer Science research, by Adrian Colyer Made delightfully fast by strattic Optimizing Hash-Array Mapped Tries for Fast and Lean Immutable JVM Collections – Steinforder & Vinju, 2015 You’d think that the collection classes in modern JVM-based languages would be highly efficient at this point in time – and indeed they are. But the wonderful thing is that t
Since Chris Okasaki's 1998 book "Purely functional data structures", I haven't seen too many new exciting purely functional data structures appear; I can name just a few: IntMap (also invented by Okasaki in 1998, but not present in that book) Finger trees (and their generalization over monoids) There are also some interesting ways of implementing already known datastructures, such as using "nested
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