Redis is fast - I'll cache in Postgres
There are books & many articles online, like this one arguing for using Postgres for everything. I thought I’d take a look at one use case - using Postgres instead of Redis for caching. I work with APIs quite a bit, so I’d build a super simple HTTP server that responds with data from that cache. I’d start from Redis as this is something I frequently encounter at work, switch it out to Postgres usi
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The “Build Your Own Redis†Book is Completed | Blog | build-your-own.org
Read it here. Introduction Needless to say, the Redis project is quite a success. It’s an important component in backend applications. Redis could be considered one of the building blocks of modern computing. There are not many projects that fit the such role and stood the test of time. Here are some examples that meet my criteria of the “building blockâ€: NGINX, SQLite, PostgreSQL, Kafka, Linux ke
仮想メモリ技術仕様 — redis 2.0.3 documentation
仮想メモリ技術仕様¶ ã“ã®ãƒ‰ã‚ュメントã§ã¯ã€Redisã®ä»®æƒ³ãƒ¡ãƒ¢ãƒªã‚µãƒ–システムã®å†…部ã®è©³ç´°ã‚’説明ã—ã¾ã™ã€‚ã“ã®ãƒ‰ã‚ュメントã¯ãƒ¦ãƒ¼ã‚¶ã®ãŸã‚ã®ã‚‚ã®ã§ã¯ãªãã€ä»®æƒ³ãƒ¡ãƒ¢ãƒªå®Ÿè£…ã‚’ç†è§£ã—ãŸã„人やã€æ‰‹ã‚’åŠ ãˆãŸã„プãƒã‚°ãƒ©ãƒžãƒ¼ã®ãŸã‚ã®ã‚‚ã®ã§ã™ã€‚ ã‚ーvs値: スワップアウトã¨ã¯ä½•ã‹ï¼ŸÂ¶ VMサブシステムã®ç›®æ¨™ã¯ã€Redisã®ã‚ªãƒ–ジェクトをメモリã‹ã‚‰ãƒ‡ã‚£ã‚¹ã‚¯ã«ç§»ã™ã“ã¨ã«ã‚ˆã£ã¦ã€ãƒ¡ãƒ¢ãƒªã‚’空ã‘ã‚‹ã“ã¨ã«ã‚ã‚Šã¾ã™ã€‚Redisã¯å€¤ã®ã‚ªãƒ–ジェクトã®ã¿ã‚’転é€ã—ã¾ã™ã€‚ã“ã®æ¦‚念をç†è§£ã—ã‚„ã™ãã™ã‚‹ãŸã‚ã«ã€ DEBUG コマンドを使用ã—ã¦ã€Redisã®å†…部ã§ã¯ã€ã©ã®ã‚ˆã†ã«ã‚ーã¨å€¤ãŒã²ã‚‚ã¥ã‘られã¦ã„ã‚‹ã®ã‹ã‚’確èªã—ã¦ã¿ã¾ã—ょã†ã€‚ redis> set foo bar OK redis> debug object foo Key at:0x100101d00 refcount:1, value at:0x100101ce0 refcoun
S3ã¯å·¨å¤§ãªKVSãªã®ã§Railsã®Cache storeã¨ã—ã¦ã‚‚使ãˆã‚‹ | ã†ãªã™ã‘ã¨ã‚ã‚Œã“ã‚Œ
S3 is a Key-Value store Amazon S3 ã¯ã€ä¸€æ„ã®ã‚ー値を使用ã—ã¦ã€å¿…è¦ãªæ•°ã®ã‚ªãƒ–ジェクトをä¿å˜ã§ãるオブジェクトストアã§ã™ã€‚ Amazon S3 オブジェクトã®æ¦‚è¦ - Amazon Simple Storage Service Amazon S3ã®åŸºç¤ŽæŠ€è¡“ã¯ã€å˜ç´”ãªKVS（Key-Value型データストア）ã§ã—ã‹ã‚ã‚Šã¾ã›ã‚“。 Amazon S3ã«ãŠã‘る「フォルダã€ã¨ã„ã†å¹»æƒ³ã‚’ã¶ã¡å£Šã—ã€ãã®å®Ÿä½“を明らã‹ã«ã™ã‚‹ | DevelopersIO Amazon S3ã®å®Ÿä½“ã¯Key-Value storeã¨ã„ã†äº‹å®Ÿã¯ã€æ—¢ã«ã”å˜çŸ¥ã®æ–¹ã€…ã«ã¨ã£ã¦ã¯ä½•ã‚’今更ã¨ã„ã†ã‚ˆã†ãªã“ã¨ã§ã¯ã‚ã‚Šã¾ã™ãŒã€ãã‚Œã§ã‚‚åˆã‚ã¦èžãã¨ãã«ã¯é©šã‹ã•ã‚ŒãŸã‚‚ã®ã§ã™ã€‚ ã•ã¦ã€Key-Value storeã¨èžã„ã¦ä¸€èˆ¬çš„ã«é¦´æŸ“ã¿ãŒæ·±ã„ã®ã¯Redisã§ã—ょã†ã€‚ãã—ã¦ã€Railsã«ãŠã‘ã‚‹Redisã®å½¹å‰²ã¨ã—ã¦Cac
11 Common Web Use Cases Solved in Redis - High Scalability -
In How to take advantage of Redis just adding it to your stack Salvatore 'antirez' Sanfilippo shows how to solve some common problems in Redis by taking advantage of its unique data structure handling capabilities. Common Redis primitives like LPUSH, and LTRIM, and LREM are used to accomplish tasks programmers need to get done, but that can be hard or slow in more traditional stores. A very useful
Redisã®BitCountã¨HyperLogLogを使用ã—ãŸè¶…高速Unique User数集計
Redis ã® BitCount 機能㨠HyperLogLog を利用ã—ã¦ãƒ¦ãƒ‹ãƒ¼ã‚¯ãƒ¦ãƒ¼ã‚¶æ•° （ Unique User Cardinality ） をミリ秒å˜ä½ã§ã‚«ã‚¦ãƒ³ãƒˆã™ã‚‹ã€‚ BitCount ã§ã®è¨ˆæ¸¬ã«ã¯ Linear Counting ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ を用ã„ã€HyperLogLog ã«ã¤ã„ã¦ã¯ã€MinHash ã®æ´¾ç”Ÿã§ã‚ã‚‹ Base-2 Ranks ã®è€ƒãˆæ–¹ã‚‚説明ã™ã‚‹ã€‚ é‡è¤‡çŽ‡ã®è¨ˆç®—方法や AND OR ã®è€ƒãˆæ–¹ã€ã¾ãŸã€BigData ã«å¯¾ã—ã¦ã©ã®ã‚ˆã†ãªã‚µãƒ¼ãƒæ§‹æˆã§å‡¦ç†ã™ã‚‹ã¹ãã‹ã®æ¡ˆã‚‚æ示ã™ã‚‹ã€‚
memcachedã¨Redisã®ç”Ÿå˜æˆ¦ç•¥ã€ã¨ã„ã†ã‹memory allocation戦略 - æ„›ã¨å‹‡æ°—ã¨ç¼¶ãƒ“ール
ã¡ã‚‡ã£ã¨memcached & Redisã«ã¤ã„ã¦èª¿ã¹ãŸã®ã§ã‚も。 ã¡ãªã¿ã«ã€ç”Ÿå˜æˆ¦ç•¥ã£ã¦è¨€è‘‰ã¯æœ€è¿‘Twitterã§ã‚ˆã見るã‹ã‚‰ä½¿ã£ã¦ã¿ãŸã ã‘ã§ã€å®Ÿéš›ã«ä½•ã‹ã¯çŸ¥ã‚Šã¾ã›ã‚“。æŒã‹ä½•ã‹ã‹ãªã€‚ ã¡ãªã¿ã«è¦‹ã¦ã„るソースã«ã¤ã„ã¦ã¯ã€memcachedã¯1.4.6ã€Redisã¯ç¾æ™‚点ã§ã®gitã®æœ€æ–°(多分)。 memcachedã«é–¢ã—ã¦ã€ç‰¹å®šã®ã‚µã‚¤ã‚ºã®chunkを管ç†ã™ã‚‹slab classã£ã¦ã„ã†ã‚‚ã®ãŒã‚るよーんã€ã¨ã‹ã¯èª¬æ˜Žã™ã‚‹ã¨ã‚ã‚“ã©ãã•ã„ã®ã§é£›ã°ã—ã¾ã™ã€‚↓ã®è¨˜äº‹ã¨ã‹ã«æ›¸ã„ã¦ã‚ã‚Šã¾ã™ã€‚ http://gihyo.jp/dev/feature/01/memcached/0002?page=1 memcached 起動時ã®-LオプションãŒä»˜ã„ã¦ã‚‹å ´åˆã€åˆã‚ã«å…¨éƒ¨mallocã—ã¡ã‚ƒã†ã€‚付ã„ã¦ã„ãªã„ && DONT_PREALLOC_SLABSãŒdefineã•ã‚Œã¦ã„ã‚‹å ´åˆã¯chunkã®preallocate
YouPorn - Targeting 200 Million Views a Day and Beyond - High Scalability -
Update: Here's the video of the talk. Erick Pickup, lead developer at YouPorn.com, presented their architecture in a talk titled Building a Website To Scale given at the ConFoo conference. Â As you might expect, YouPorn is a beast, streaming three full DVDs of video every second, handing 300K queries every second, and generating up to 15GBs of log data per hour. Unfortunately, all we have are the s
Instagram ã®ã‚¹ã‚±ãƒ¼ãƒ«æ£æ”»æ³• -- Kosei Kitahara's Blog
Instagram ãŒã©ã“ã«è²·åŽã•ã‚ŒãŸã¨ã‹ã¯ä»–ã®ãƒ‹ãƒ¥ãƒ¼ã‚¹ã‚µã‚¤ãƒˆã«ãŠä»»ã›ã—ã¦ã€Django アプリケーションをæ£æ”»æ³•ã§ã‚¹ã‚±ãƒ¼ãƒ«ã—㦠"æˆåŠŸ" ã—ã¦ã‚‹ã®ãŒã¨ã¦ã‚‚興味深ã„ã§ã™ã€‚ç¾æ™‚点㧠Instagram Engineering ã§ç´¹ä»‹ã•ã‚Œã¦ã„ã‚‹ã“ã¨ã¨ TechCrunch ã«ã‚‚掲載ã•ã‚ŒãŸã‚¹ãƒ©ã‚¤ãƒ‰ã‹ã‚‰å€‹äººçš„ãªãƒ¡ãƒ¢ã¨ã—ã¦ã¾ã¨ã‚ã¦ã¿ã¾ã—ãŸã€‚ Instagram ã®å“²å¦ã¯ シンプルã§ã‚ã‚‹ã“ã¨ ã‚ªãƒšãƒ¬ãƒ¼ã‚·ãƒ§ãƒ³è² è·ã‚’最å°åŒ–ã™ã‚‹ã“㨠ã™ã¹ã¦è£…å‚™ ã¨ã®ã“ã¨ã€‚ Instagram ã¯ä»¥ä¸‹ã® OSS, サービスã§æ§‹ç¯‰ã•ã‚Œã¦ã„るよã†ã§ã™ã€‚ >>> OS / ホスティング Ubuntu Linux 11.04 ã‚’ Amazon EC2 ã«ãƒ›ã‚¹ãƒ†ã‚£ãƒ³ã‚°ã€‚以å‰ã®ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã¯é«˜ãƒˆãƒ©ãƒ•ã‚£ãƒƒã‚¯ã«ãªã‚‹ã¨å›ºã¾ã‚‹å•é¡ŒãŒã‚ã£ãŸã‚ˆã†ã§ã™ã€‚é‹ç”¨ã¯ 3 人。EC2 ã«ãƒ›ã‚¹ãƒ†ã‚£ãƒ³ã‚°ã—ã¦ã„ã‚‹ç†ç”±ã¯ã€èª¿æŸ»çµæžœã«ã‚ˆã‚‹ã‚‚ã®ã§ã¯ãªãã€"ã¾ã 進化途ä¸ã ã‹
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