Redis Cloud Pricing | RedisCustomer case studies Read stories Optimizing Pokémon GO with a Redis Enterprise cluster See more
Redisサーãƒã®CPUè² è·å¯¾ç–パターン - ゆã†ã†ãブãƒã‚°
Redisã¯å¤šå½©ãªãƒ‡ãƒ¼ã‚¿æ§‹é€ ã‚’ã‚‚ã¤1インメモリDBã§ã‚ã‚Šã€æ˜¨ä»Šã®Webアプリケーションã®ãƒ‡ãƒ¼ã‚¿ã‚¹ãƒˆã‚¢ã®ä¸€ã¤ã¨ã—ã¦ã€åºƒã利用ã•ã‚Œã¦ã„る。 ã—ã‹ã—ã€ä¸€æ–¹ã§ã€æ€§èƒ½æ”¹å–„ã®ãŸã‚ã®æ‰‹æ³•ã‚’体系的ã«ã¾ã¨ã‚ãŸè³‡æ–™ãŒè¦‹å½“ãŸã‚‰ãªã„ã¨æ„Ÿã˜ã¦ã„ãŸã€‚ 実際ã€æœ€åˆã«CPUè² è·ãŒå•é¡Œã«ãªã£ãŸã¨ãã«ã©ã†ã—ãŸã‚‚ã®ã‹ã¨æ‚©ã¿ã€èª¿æŸ»ã¨è©¦è¡ŒéŒ¯èª¤ã‚’ç¹°ã‚Šè¿”ã—ãŸã€‚ ãã“ã§ã€ã“ã®è¨˜äº‹ã§ã¯ã€è‡ªåˆ†ã®çµŒé¨“を基ã«ã€Redisサーãƒã®CPUè² è·å¯¾ç–を「CPUè² è·å‰Šæ¸›ã€ã€Œã‚¹ã‚±ãƒ¼ãƒ«ã‚¢ãƒƒãƒ—ã€ã€Œã‚¹ã‚±ãƒ¼ãƒ«ã‚¢ã‚¦ãƒˆã€ã«åˆ†é¡žã—ã€ãƒ‘ターンã¨ã—ã¦ã¾ã¨ã‚る。 背景 Redisã®CPUè² è·å¯¾ç–パターン CPUè² è·å‰Šæ¸› multiコマンド Redisパイプライニング Luaスクリプティング Redisモジュール(夢) スケールアップ スケールアウト å‚照用スレーブ 垂直分割 水平分割 Redis Clusterã«ã‚ˆã‚‹æ°´å¹³åˆ†å‰² ãã®ä»– スライド資料 ã‚ã¨ãŒã å‚考資料 背景 R
Benchmarking Memcached and Redis Clients
Entrepreneur, Automotive Junkie and Founder @ Web Foundation As some of you may know, I’m crazy about speed. So when I saw that people were happily using Predis as their choice of PHP client for Redis, I was a bit confused. Why use a client written in PHP for something that should be ‘fast’ like Redis? That kind of defeats the purpose - unless you don’t really care about response times and scalabi
デブサミ2016「大è¦æ¨¡Redisサーãƒç¸®å°åŒ–ã®æˆ¦ã„ã€è¬›æ¼”メモ #devsumi - å…ƒRX-7ä¹—ã‚Šã®é©å½“ãªæ—¥ã€…
メモメモ。泥è‡ã„話ã§é¢ç™½ã‹ã£ãŸã§ã™ã€‚ 「大è¦æ¨¡Redisサーãƒç¸®å°åŒ–ã®æˆ¦ã„〠駒井 ç¥äºº æ° (æ ª)アカツ゠ゲームã®ã‚µãƒ¼ãƒã‚µã‚¤ãƒ‰æ©Ÿèƒ½é–‹ç™ºã€ã‚¤ãƒ³ãƒ•ãƒ©ã®è¨è¨ˆæ§‹ç¯‰ãƒ»ä¿å®ˆé‹ç”¨ Redisã¨ã¯ インメモリDB 5種類ã®ã‚ーãƒãƒªãƒ¥ãƒ¼ã®ãƒ‡ãƒ¼ã‚¿åž‹ ファイル永続化オプション システムã®å•é¡Œç‚¹ EC2サーãƒãŒ20å°ã«å¯¾ã—ã¦ã€AWSã®ElasticCache(Redis)ãŒ64å°ã‚ã£ãŸ ãªãœ64å°ã‚ã£ãŸã‹ã¨ã„ã†ã¨ã€ãƒªãƒªãƒ¼ã‚¹ç›´å¾Œã«Redisã®è² è·å•é¡ŒãŒã‚ã‚Šã€8å° => 64å°ã«ãªã£ãŸ 調査ã™ã‚‹ã¨keys("")を実行ã—ã¦ã„る箇所ãŒã‚ã£ãŸ 当然ãŠé‡‘ãŒã‹ã‹ã‚‹(cache.m3.large * 64å° = ç´„135万円/月) 冗長化ã—ã‚“ã©ã„ã—ã€è¨å®šãƒ•ã‚¡ã‚¤ãƒ«ã®è¨˜è¼‰ã‚‚辛ㄠã®ã§ã€ç¸®å°åŒ–ã¨å†—長化ã®å¯¾å‡¦ã‚’ã—ãŸã„ ç¾çŠ¶æ•´ç† æ ¼ç´ã•ã‚Œã¦ã„るデータ ãƒ•ãƒ¬ãƒ³ãƒ‰æƒ…å ±ã€ã‚»ãƒ¼ãƒ«æƒ…å ±ã€ãƒ©ãƒ³ã‚ãƒ³ã‚°æƒ…å ± ã‚ーã®ä»¶æ•° 1サーãƒã«8DBã€1DBã‚ãŸ
Twitterã®ã‚ャッシュを支ãˆã‚‹Redis - ワザノム| wazanova
https://www.youtube.com/watch?v=rP9EKvWt0zo 1 comment | 0 points | by WazanovaNews â– comment by Jshiike | ç´„1æ™‚é–“å‰ Twitterã®Yao YuãŒã€å¤§è¦æ¨¡ã‚µãƒ¼ãƒ“スã®ã‚ャッシュã«ãŠã„ã¦Redisを活用ã™ã‚‹å–組ã¿ã«ã¤ã„ã¦ç´¹ä»‹ã—ã¦ã„ã¾ã™ã€‚ 1) Redisを採用ã—ã¦ã„ã‚‹ç†ç”± ã‚ャッシュã ã‘ã§ã€ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã¨ã—ã¦ã¯åˆ©ç”¨ã—ã¦ã„ãªã„。 主ãªã¨ã“ã‚ã§ã¯ã€Twitterã®ã‚¿ã‚¤ãƒ ラインã§åˆ©ç”¨ã—ã¦ã„る。ホーム画é¢ã§ã‚ã‚Œã€ãƒ¦ãƒ¼ã‚¶ç”»é¢ã§ã‚ã‚Œã€ã‚¿ã‚¤ãƒ ラインã¯Tweetã®ã‚¤ãƒ³ãƒ‡ãƒƒã‚¯ã‚¹ãªã®ã§ã€key/valueストア型ã®Redisを利用ã™ã‚‹ã‚±ãƒ¼ã‚¹ã¨ã—ã¦æœ€é©ã€‚ 以å‰ã¯memcachedを使ã£ã¦ã„ãŸãŒã€å•é¡Œã«ãªã£ãŸã®ã¯ã€ã‚¿ã‚¤ãƒ ラインã§ãŠãã‚‹read/writeã¯ã€ï¼ˆãƒ¦ãƒ¼ã‚¶ãŒé–²è¦§ã—ã¦ã„る範囲ã«è¿½åŠ åæ˜ ã™ã‚‹ã¨ã„ã†ã“ã¨ãªã®
Redis ã«ã‚ˆã‚‹åˆ†æ•£ãƒãƒƒã‚¯ — Redis Documentation (Japanese Translation)
Redis ã«ã‚ˆã‚‹åˆ†æ•£ãƒãƒƒã‚¯Â¶ ç•°ãªã‚‹ãƒ—ãƒã‚»ã‚¹åŒå£«ãŒã€ç›¸äº’ã«æŽ’ä»–çš„ãªæ–¹æ³•ã§å…±æœ‰ãƒªã‚½ãƒ¼ã‚¹ã«å¯¾ã—ã¦æ“作を実行ã™ã‚‹ã€ã¨ã„ã†ç’°å¢ƒã«ãŠã„ã¦ã€åˆ†æ•£ãƒãƒƒã‚¯ã¯éžå¸¸ã«å½¹ã«ç«‹ã¡ã¾ã™ã€‚ Redis を使ã£ãŸ DLM (Distributed Lock Manager) ã®å®Ÿè£…ã«ã¤ã„ã¦ã¯ã€å¤šãã®ãƒ©ã‚¤ãƒ–ラリやブãƒã‚°ãƒã‚¹ãƒˆãŒã‚ã‚Šã¾ã™ã€‚ã—ã‹ã—ã€ãƒ©ã‚¤ãƒ–ラリã”ã¨ã«ç•°ãªã‚‹ã‚¢ãƒ—ãƒãƒ¼ãƒãŒã¨ã‚‰ã‚Œã¦ãŠã‚Šã€ã¾ãŸãã®å¤šãã¯ã€ã‚ˆã‚Šè¤‡é›‘ãªãƒ‡ã‚¶ã‚¤ãƒ³ã¨æ¯”較ã™ã‚‹ã¨ã‚·ãƒ³ãƒ—ルã§ã€ãã®ã¶ã‚“ä¿è¨¼ã•ã‚Œã‚‹å†…容ãŒä½Žã„アプãƒãƒ¼ãƒã¨ãªã£ã¦ã„ã¾ã™ã€‚ ã“ã®ãƒšãƒ¼ã‚¸ã¯ã€Redis 上ã§åˆ†æ•£ãƒãƒƒã‚¯ã‚’実装ã™ã‚‹ã«ã‚ãŸã‚Šã€æ¨™æº–çš„ãªã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’æä¾›ã™ã‚‹ã“ã¨ã‚’目指ã™ã‚‚ã®ã§ã™ã€‚ç§ãŸã¡ã¯ã“ã“㧠Redlock ã¨å‘¼ã¶ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’æä¾›ã—ã¾ã™ã€‚ã“れ㯠DLM 実装ã®ä¸€ç¨®ã§ã€ã‚ˆã見ã‹ã‘るよã†ãªã€ã²ã¨ã¤ã®ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã‚’使ã†ã‚¢ãƒ—ãƒãƒ¼ãƒã‚ˆã‚Šã‚‚安全ã§ã‚ã‚‹ã€ã¨ç§ãŸã¡ã¯è€ƒãˆã¦ã„ã¾ã™ã€‚コミュニティãŒã“ã®
Heroku Key-Value Store Now Available in Public Beta
Developers increasingly need a variety of datastores for their projects -- no one database can serve all the needs of a modern, scalable application. For example, an e-commerce app might store its valuable transaction data in a relational database while user session information is stored in a key-value store because it changes often and needs to be accessed quickly. This is a common pattern across
Introducing Dynomite
Introduction & OverviewNetflix has long been a proponent of the microservices model. This model offers higher-availability, resiliency to failure and loose coupling. The downside to such an architecture is the potential for a latent user experience. Every time a customer loads up a homepage or starts to stream a movie, there are a number of microservices involved to complete that request. Most of
よãã‚ã‹ã‚‹Linuxå¸¯åŸŸåˆ¶é™ | GREE Engineering
矢å£ã§ã™ã€‚ ã¿ãªã•ã‚“ã¯Linuxã®tcã¨ã„ã†æ©Ÿèƒ½ã‚’ã”å˜çŸ¥ã§ã—ょã†ã‹ã€‚é€ä¿¡ã™ã‚‹ãƒ‘ケットã®å¸¯åŸŸåˆ¶å¾¡ã‚’è¡Œã†ã“ã¨ãŒã§ãる大変強力ãªæ©Ÿèƒ½ã§ã€ã‚°ãƒªãƒ¼ã§ã‚‚ã„ãã¤ã‹ã®ç”¨é€”ã§ä½¿ç”¨ã•ã‚Œã¦ã„ã¾ã™ã€‚ 具体的ãªäº‹ä¾‹ã®ä¸€ã¤ã¯Redisã§ã™ã€‚Redisã§ã¯replicationã‚’æ–°è¦ã«é–‹å§‹ã™ã‚‹éš›ã‚„failoverãŒç™ºç”Ÿã—masterãŒåˆ‡ã‚Šæ›¿ã‚ã£ãŸéš›(特ã«2.6ç³»)ã«ã‚¹ãƒˆã‚¢ã•ã‚Œã¦ã„る全データãŒè»¢é€ã•ã‚Œã¾ã™ã€‚ã—ã‹ã—帯域制é™ã‚’ã‹ã‘る機能ãŒãªã„ãŸã‚ã€ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯å¸¯åŸŸã‚’圧迫ã—ã¦ã—ã¾ã†å±é™ºæ€§ãŒã‚ã‚Šã¾ã™ã€‚ã¾ãŸé€šå¸¸ã®ã‚¯ãƒ©ã‚¤ã‚¢ãƒ³ãƒˆã¨ã®é€šä¿¡ã§ã‚‚大é‡ã®ã‚¯ã‚¨ãƒªã«ã‚ˆã‚Šäºˆæƒ³ä»¥ä¸Šã®å¸¯åŸŸã‚’使用ã—ã¦ã—ã¾ã†å¯èƒ½æ€§ãŒã‚ã‚Šã¾ã™ã€‚ã“ã®ã‚ˆã†ãªå ´åˆã«tcを用ã„ã‚‹ã“ã¨ã§Redisã®ä½¿ç”¨ã™ã‚‹å¸¯åŸŸã‚’コントãƒãƒ¼ãƒ«ã§ãã¾ã™ã€‚ ã“ã®ã‚ˆã†ã«æœ‰ç”¨ãªtcã§ã™ãŒæ®‹å¿µãªã“ã¨ã«æ—¥æœ¬èªž/英語ã¨ã‚‚ã«ã‚ã‹ã‚Šã‚„ã™ã„解説や詳細ãªæƒ…å ±ã¯å¤šãã‚ã‚Šã¾ã›ã‚“。 ç§ã‚‚社内ã«ãŠã„ã¦ä½¿ã‚ã‚Œã¦ã„ãŸtcã®è¨å®šã«å•é¡ŒãŒ
#isucon 2014ã«fujiwara組ã§å‡ºå ´ã—ã¦äºˆé¸2日目暫定1ä½ã‚’å–ã‚Šã¾ã—ãŸ
ã©ã†ã‚‚ã“ã‚“ã«ã¡ã¯ã€ã‚Œã‚‚ã‚“ã§ã™ã€‚今年もISUCONã®å£ç¯€ãŒã‚„ã£ã¦ãã¾ã—ãŸã。昨年ã¯fujiwaraã•ã‚“ã®å³è…•ã¨ã—ã¦å‡ºé¡Œæ¥ã‚’ã‚„ã‚Šã¾ã—ã¦ã€ä»Šå¹´ã¯ãã®ã¾ã¾fujiwara組ã¨ã—ã¦äºˆé¸ã«å‡ºå ´ã—ã¾ã—ãŸã€‚ çµæžœã¯æ—¢å ±ã®é€šã‚Šã§ã™ãŒã€ã²ã¨ã¾ãš2日目ã§æš«å®š1ä½ã®ã‚¹ã‚³ã‚¢ã‚’出ã—ã¦ã„ã¾ã™ã€‚67782ã§ã™ã€‚ã§ã¯å•é¡Œã®æ„Ÿæƒ³ã¨ã‹ã‚„ã£ãŸã“ã¨ã‚’振り返ã£ã¦ã„ãã¾ã™ã€‚ã‚ã€é•·ã„ã®ã§ãƒžã‚¸ãƒ¡ã«èªã‚“ã らèªã‚€ã®ã«ãŸã¶ã‚“10分ãらã„ã‹ã‹ã‚Šã¾ã™ã‚ˆã€‚ ã¾ãŸã€ãƒãƒ¼ãƒ メンãƒãƒ¼ã®fujiwara, handlenameã®ãƒ–ãƒã‚°ã‚‚出ãã‚ã„ã¾ã—ãŸã®ã§ãã¡ã‚‰ã‚‚åˆã‚ã›ã¦ãŠèªã¿ãã ã•ã„。 #isucon 4ã«å‚åŠ ã—ã¦äºˆé¸2日目暫定1ä½ã«ãªã‚Šã¾ã—㟠(@fujiwara) #isucon 4ã«fujiwara組ã¨ã—ã¦å‚åŠ ã—ã¾ã—㟠(@handlename) å‰æ—¥ã¾ã§ 金曜日ã®ãŠæ˜¼ã«ãƒãƒ¼ãƒ ã§ãƒ©ãƒ³ãƒã—ã¦åˆ†æ‹…を確èªã€‚fujiwaraã•ã‚“ãŒã€Œä»Šå›žã¯å®Ÿè£…ãŒé€Ÿãã†ãªhand
Developers Summit 2014 「Play2/Scalaã§ãƒ‰ãƒ¡ã‚¤ãƒ³é§†å‹•è¨è¨ˆã‚’利用ã—ãŸå¤§è¦æ¨¡Webアプリケーションã®ã‚¹ã‚¯ãƒ©ãƒ 開発ã®å‹˜æ‰€ã€
Developers Summit 2014 「Play2/Scalaã§ãƒ‰ãƒ¡ã‚¤ãƒ³é§†å‹•è¨è¨ˆã‚’利用ã—ãŸå¤§è¦æ¨¡Webアプリケーションã®ã‚¹ã‚¯ãƒ©ãƒ 開発ã®å‹˜æ‰€ã€
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23. 例ãˆã°ã“ã‚“ãªã‚³ãƒ¼ãƒ‰ 2011∼ #  ã“ã®ä¸ã¯ãƒˆãƒ©ãƒ³ã‚¶ã‚¯ã‚·ãƒ§ãƒ³å†…ã¨ã„ã†ä»®å®š try: #  プレイヤーã®ä½“⼒力力を消費 player.consume_̲vitality() #  プレイヤーã®çµŒé¨“値アップ player.add_̲experience() except: #  エラー起ããŸã‚‰DBã‚’ãƒãƒ¼ãƒ«ãƒãƒƒã‚¯ transaction.rollback() else: #  å•é¡Œãªã‘ã‚Œã°DBæ›´ï¤æ–°ã€‚経験値ãŒå¢—ãˆã‚‹ã€‚ transaction.commit() 体力→KVS 経験値→DB ※実際ã®ã‚³ãƒ¼ãƒ‰ã¨ã¯ç•°ãªã‚Šã¾ã™ 24. 例ãˆã°ã“ã‚“ãªã‚³ãƒ¼ãƒ‰ 2011∼ #  ã“ã®ä¸ã¯ãƒˆãƒ©ãƒ³ã‚¶ã‚¯ã‚·ãƒ§ãƒ³å†…ã¨ã„ã†ä»®å®š try: #  プレイヤーã®ä½“⼒力力を消費 player.consume_̲vitality() #  プレイヤーã®çµŒé¨“値アップ player.add_̲experience() excep
How HipChat Stores and Indexes Billions of Messages Using ElasticSearch and Redis - High Scalability -
« Sponsored Post: Netflix, Logentries, Host Color, Booking, Apple, ScaleOut, MongoDB, BlueStripe, AiScaler, Aerospike, LogicMonitor, AppDynamics, ManageEngine, Site24x7 | Main | Stuff The Internet Says On Scalability For January 3rd, 2014 » This article is from an interview with Zuhaib Siddique, a production engineer at HipChat, makers of group chat and IM for teams. HipChat started in an unusual
Pairy : ãƒãƒ£ãƒƒãƒˆãƒ‡ãƒ¼ã‚¿ã‚’ Redis ã‹ã‚‰ Amazon DynamoDB ã«å…¨ç§»è¡Œã—ãŸè©±ï¼ˆï¼‘） - Tech Blog
CTOã®æ¤Žåアマドã§ã™ã€‚ 今回ã¯ã€Pairyã®ãƒãƒ£ãƒƒãƒˆãƒ‡ãƒ¼ã‚¿ã‚’å…¨ã¦Redisã‹ã‚‰Amazon DynamoDBã«ç§»è¡Œã—ãŸè©±ã‚’ã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ 我々㌠2012å¹´6月㫠カップル専用アプリ Pairy をリリースã—ãŸæ™‚ã«ã¯ã€ データベース㯠MySQL 㨠Redis ã®ä¸¡æ–¹ã‚’利用ã™ã‚‹ã¨ã“ã‚ã§å§‹ã‚ã¾ã—ãŸã€‚ Redis ã®å½¹å‰²ã¯ï¼š 1. MySQLレスãƒãƒ³ã‚¹ã®ã‚ャッシュ 2. プッシュ通知ç‰ã®ã‚ュー 3. ãƒãƒ£ãƒƒãƒˆã®ãƒ‡ãƒ¼ã‚¿ã‚’å…¨ä¿ç®¡ サービスãƒãƒ¼ãƒ³ãƒç›´å¾Œã¯ã¾ã Appサーãƒãƒ¼(EC2)１å°ã¨ã€MySQL & Redisを両方走らã›ã¦ã‚‹ DBサーãƒãƒ¼(EC2)１å°ã§å分ã ã¨ã„ã†åˆ¤æ–ã§ã€ã—ã°ã‚‰ãã¯ãã‚“ãªæ§‹æˆã§ã‚„ã£ã¦ã¾ã—ãŸã€‚（S3ãªã©ã¯çœç•¥ï¼‰ ã—ã‹ã—ã€ã„ã–サービスãŒæˆé•·ã—ã¦ãã‚‹ã¨ã‚‚ã¡ã‚ã‚“ MySQL & Redis を１å°ã§ã¾ã‹ãªã†ã®ã¯ã‚ツããªã‚Šã€MySQL 㨠Redis を別々ã®EC2インスタン
Redis勉強会資料(2015/06 update)
Redis勉強会ã®è³‡æ–™ã§ã™ã€‚2013å¹´ã«åˆç‰ˆã‚’アップã—ã¾ã—ãŸã€‚2015å¹´6月ã«Redis-Clusterã®å†…容を追記ã—ã¦å†ã‚¢ãƒƒãƒ—ã‚’è¡Œã£ã¦ã„ã¾ã™ã€‚Read less
Vedis - An Embeddable Datastore Engine
Vedis is an embeddable datastore C library built with over 70 commands similar in concept to Redis but without the networking layer since Vedis run in the same process of the host application. Unlike most other datastores (i.e. memcache, Redis), Vedis does not have a separate server process. Vedis reads and writes directly to ordinary disk files. A complete database with multiple collections, is c
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The Right Toolbox
Redis vs postgre sql
Redis on EC2
Redisを使ã†æ™‚ã¯è¦‹ç©ã®äºŒå€ã®å®¹é‡å¿…è¦ã よãã€ã¨ã„ã†è©± - Qiita
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