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Open-sourcing F14 for faster, more memory-efficient hash tables
Open-sourcing F14 for faster, more memory-efficient hash tables Hash tables provide a fast way to maintain a set of keys or map keys to values, even if the keys are objects, like strings. They are such a ubiquitous tool in computer science that even incremental improvements can have a large impact. The potential for optimization led to a proliferation of hash table implementations inside Facebook,
Writing a damn fast hash table with tiny memory footprints - Carpe diem (Felix's blog)
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, Iâ€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
moco(beta)'s backup: [翻訳] Using Finite State Transducers in Lucene
Lucene/Solr Advent Calendar 2014 22æ—¥ã‚ã§ã™ã€‚ 第15回Solr勉強会ã§ã€ã”ã軽ãLucene FST ã®ç´¹ä»‹ã‚’ã—ãŸã®ã§ã™ãŒã€è‡ªåˆ†ã‚‚ã¾ã ã¾ã 勉強ä¸ã§ã™ã€‚ã¨ã„ã†ã“ã¨ã§ Lucene 開発者㮠Mike ã•ã‚“ã«ã€ã ã‚ã‚‚ã¨ã§ã€Œãƒ–ãƒã‚°ã®FST 関連ã®ã‚¨ãƒ³ãƒˆãƒªã‚’翻訳ã—ã¦ã„ã„ã§ã™ã‹ï¼Ÿã€ã¨ãƒ¡ãƒ¼ãƒ«ã‚’é€ã£ã¦ã¿ãŸã¨ã“ã‚ã€ã€Œã„ã„よï¼ã€ã¨å¿«è«¾ã—ã¦ã‚‚らãˆãŸã®ã§ã€ã“ã“ã«æŽ²è¼‰ã—ã¾ã™ã€‚ —- 関連ã™ã‚‹è©±é¡Œã§ã™ãŒã€ Golang ã§æ›¸ã‹ã‚ŒãŸå½¢æ…‹ç´ 解æžå™¨ kagome を作ã£ã¦ã„ã‚‹ @ikawaha ã•ã‚“ãŒã€Lucene を絡ã‚ã¦çŒ«ã«ã‚‚ã‚ã‹ã‚‹ï¼ˆã‹ã‚‚ã—ã‚Œãªã„） FST ã®è§£èª¬ã‚’書ã„ã¦ãã ã•ã„ã¾ã—ãŸã€‚ã‚ã‚ŠãŒã¨ã†ã‚ã‚ŠãŒã¨ã†ï¼ˆï¼›Ï‰ï¼›ï¼‰ï¼ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã®èª¬æ˜Žã‹ã‚‰ Double Array ã¨ã®æ¯”較ã¾ã§è‡³ã‚Œã‚Šå°½ãã›ã‚Šãªã®ã§ã‚ã‚ã›ã¦ã©ã†ãžã€‚ Luceneã§ä½¿ã‚ã‚Œã¦ã‚‹FSTを実装ã—ã¦ã¿ãŸï¼ˆæ£è¦è¡¨ç¾ãƒžãƒƒãƒï¼šVM
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Java8ã‹ã‚‰ã¯HashMapã®æ€§èƒ½ã®ãŸã‚ã«Comparableを実装ã—ã¦ãŠã„ãŸæ–¹ãŒã„ã„ - interprism's blog
ã“ã‚“ã«ã¡ã¯ã€andoã§ã™ã€‚ ã¤ã„ã«Java8ãŒãƒªãƒªãƒ¼ã‚¹ã•ã‚ŒãŸã®ã§ã•ã£ããインストールã—ã¦ã¿ã¾ã—ãŸã€‚ Java8ã«ãªã£ã¦ãƒ©ãƒ ダå¼ã‚’始ã‚ã€å¤šãã®æ©Ÿèƒ½ãŒè¿½åŠ ã•ã‚ŒãŸã®ã§ã™ãŒã€æ—¢å˜æ©Ÿèƒ½ã«ã¤ã„ã¦ã‚‚性能改善ãŒè¡Œã‚ã‚Œã¦ã„ã¾ã™ã€‚ 人気ãŒã‚ã‚Šãã†ãªæ–°æ©Ÿèƒ½ã®ç´¹ä»‹ã¯ä»–ã®äººã«ãŠä»»ã›ã—ã¦ã€ä»Šå›žã¯HashMapã®å¤‰æ›´ç‚¹ã«ã¤ã„ã¦ç¢ºèªã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ ã¯ãŸã—ã¦æ—¢å˜ã®ãƒ—ãƒã‚°ãƒ©ãƒ ã¯Java8ã§å®Ÿè¡Œã™ã‚‹ã ã‘ã§ã€ãã®æ©æµã‚’享å—ã§ãã‚‹ã®ã§ã—ょã†ã‹ã€‚ java.util.HashMap HashMapã¨ã„ãˆã°ä½¿ç”¨é »åº¦1ã€2を争ã†ã‚³ãƒ¬ã‚¯ã‚·ãƒ§ãƒ³ã‚¯ãƒ©ã‚¹ã§ãƒ‡ãƒ¼ã‚¿ã®æ¤œç´¢ã€è¿½åŠ ãŒO(1)ã§è¡Œãˆã€ 辞書的ã«ä½¿ãˆã‚‹äº‹ã‹ã‚‰ç°¡æ˜“çš„ãªDTOã‚„ã‚ャッシュã€ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®ãƒ¬ã‚³ãƒ¼ãƒ‰æ§‹é€ ã€ã•ã‚‰ã«ã¯Listã§ã™ã‚€ã¨ã“ã‚ã§ã•ãˆæ•°å€¤ã‚’ã‚ーã«ã—ã¦ä½¿ã†å…µã‚‚ã„ã‚‹ã¨ã„ã†ãらã„良ãも悪ãも色々使ãˆã¾ã™ã€‚ ãã‚ŒãŒé€Ÿããªã‚‹ã®ã§ã‚ã‚Œã°ã€æ—¢å˜ã®ãƒ—ãƒã‚°ãƒ©ãƒ も速ããªã‚‹ã¯ãšã€ã¨ã„ã†ã“ã¨ã§ã•ã£
ConcurrentHashMapを使ã†ãªã‚‰atomicãªãƒ¡ã‚½ãƒƒãƒ‰ã‚’使ãŠã† - kencharosã®æ—¥è¨˜
ConcurrentHashMapを使ã†ã¨ãã®æ³¨æ„点 タイトルã®é€šã‚Šã§ã™ã€‚ ConcurrentHashMapã¯åŒæœŸã‚’å–ã‚Šã¤ã¤ã€ãƒ‘フォーマンスも優れãŸã¨ã¦ã‚‚優秀ãªã‚„ã¤ã§ã€æœ€è¿‘Webアプリケーションã§ã€ã‚ャッシュã¿ãŸã„ãªã‚‚ã®ã‚’作るã¨ãã«ä½¿ã„ã¾ã—ãŸã€‚ ã¨ã¯ã„ãˆåŒæœŸã‚’å–ã‚‹ã¨ã¯ã„ã£ã¦ã‚‚以下ã®ã‚ˆã†ãªã‚ーã®å˜åœ¨ã‚’確èªã—ã¦ã€putã™ã‚‹ã‚ˆã†ãªè¤‡åˆã‚¢ã‚¯ã‚·ãƒ§ãƒ³ã®å‘¼ã³å‡ºã—(ã„ã‚ゆるcheck-then-act)ã§ã¯åŒæœŸã•ã‚Œã¾ã›ã‚“。(別ã«ã“ã‚Œã¯ã€å¾“æ¥ã®HahMapã¨ã‹Collections.syncronizedMapã¨ã‹ã§ã‚‚åŒã˜ã§ã™) // atomicã§ã¯ãªã„æ“作。 if(!map.containsKey(key)) { map.put(key, value); } 以下ãŒå®Ÿè¨¼ã‚³ãƒ¼ãƒ‰ã§ã€å®Ÿè¡Œã™ã‚‹ã¨é«˜ã„確率ã§è¤‡æ•°ã‚¹ãƒ¬ãƒƒãƒ‰ã§åŒä¸€ã‚ーã¸ã®putã‚’è¡Œã„ã¾ã™ã€‚ (今回ã®ã‚±ãƒ¼ã‚¹ã ã¨ã‚ã¾ã‚Šå•é¡Œã«ãªã‚‰ãªã„ã§ã™ãŒã€valueã‚’
3 billion items in Java Map with 16 GB RAM
3 billion items in Java Map with 16 GB RAM One rainy evening I meditated about memory managment in Java and how effectively Java collections utilise memory. I made simple experiment, how much entries can I insert into Java Map with 16 GB of RAM? Goal of this experiment is to investigate internal overhead of collections. So I decided to use small keys and small values. All tests were made on Linux
Robin Hood Hashing should be your default Hash Table implementation
A Random Walk Through Geek-Space Brain dumps and other ramblings from Sebastian Sylvan Robin Hood Hashing should be your default Hash Table implementation 8/May 2013 There’s a neat variation on open-addressing based hash tables called Robin Hood hashing. This technique isn’t very well-known, but it makes a huge practical difference because it both improves performance and space utilization compare
GitHub - stephenc/high-scale-lib: A Mavenized fork of http://high-scale-lib.sourceforge.net/
IF YOU ARE LOOKING for the drop-in replacement for java.util.Hashtable, it's in the lib directory, lib/java_util_hashtable.jar. It needs to be in your bootclasspath. Example: java -Xbootclasspath/p:lib/java_util_hashtable.jar my_java_app_goes_here --- A collection of Concurrent and Highly Scalable Utilities. These are intended as direct replacements for the java.util.* or java.util.concurrent.* co
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War Story: Speeding up Block-based Table Lookup
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Map<D,B>
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