11 Common Web Use Cases Solved in Redis - High Scalability -
« Myth: Google Uses Server Farms So You Should Too - Resurrection of the Big-Ass Machines | Main | Sponsored Post: TripAdvisor, eHarmony, NoSQL Now!, Surge, BioWare, Tungsten, deviantART, Aconex, Hadapt, Mathworks, AppDynamics, ScaleOut, Membase, CloudSigma, ManageEngine, Site24x7 » In How to take advantage of Redis just adding it to your stack Salvatore 'antirez' Sanfilippo shows how to solve som
Pinterestã¯ã„ã‹ã«ã‚¹ã‚±ãƒ¼ãƒ©ãƒ“リティã¨æ ¼é—˜ã—ã¦ããŸã®ã‹ï¼ˆå‰ç·¨ï¼‰ã€‚QCon Tokyo 2013
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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
The Anatomy of Search Technology: blekko’s NoSQL database - High Scalability -
This is a guest post (part 2, part 3) by Greg Lindahl, CTO of blekko, the spam free search engine that had over 3.5 million unique visitors in March. Greg Lindahl was Founder and Distinguished Engineer at PathScale, at which he was the architect of the InfiniPath low-latency InfiniBand HCA, used to build tightly-coupled supercomputing clusters. Imagine that you're crazy enough to think about build
Pinterest Architecture Update - 18 Million Visitors, 10x Growth,12 Employees, 410 TB of Data - High Scalability -
« Sponsored Post: Torbit, Infragistics, Velocity, Reality Check Network, Gigaspaces, AiCache, Logic Monitor, Attribution Modeling, New Relic, AppDynamics, CloudSigma, ManageEnine, Site24x7 | Main | Stuff The Internet Says On Scalability For May 18, 2012 » There has been an update on Pinterest: Pinterest growth driven by Amazon cloud scalability since our last post: A Short on the Pinterest Stack f
Big Data Counting: How to count a billion distinct objects using only 1.5KB of Memory - High Scalability -
The table shows that we can count the words with a 3% error rate using only 512 bytes of space. Compare that to a perfect count using a HashMap that requires nearly 10 megabytes of space and you can easily see why cardinality estimators are useful. In applications where accuracy is not paramount, which is true for most web scale and network counting scenarios, using a probabilistic counter can res
How Twitter Stores 250 Million Tweets a Day Using MySQL - High Scalability -
Jeremy Cole, a DBA Team Lead/Database Architect at Twitter, gave a really good talk at the O'Reilly MySQL conference: Big and Small Data at @Twitter, where the topic was thinking of Twitter from the data perspective. One of the interesting stories he told was of the transition from Twitter's old way of storing tweets using temporal sharding, to a more distributed approach using a new tweet store c
7 Years of YouTube Scalability Lessons in 30Â Minutes
« Sponsored Post: Gigaspaces, Nokia, Oracle, Percona Live, AiCache, ElasticHosts, Logic Monitor, Attribution Modeling, New Relic, AppDynamics, CloudSigma, ManageEngine, Site24x | Main | Stuff The Internet Says On Scalability For March 23, 2012 » If you started out building a dating site and instead ended up  building a video sharing site (YouTube) that handles 4 billion views a day, then it’s just
LinkedIn: Creating A Low Latency Change Data Capture System With Databus
This is a guest post by Siddharth Anand, a senior member of LinkedIn's Distributed Data Systems team. Over the past 3 years, I've had the good fortune to work with many emerging NoSQL products in the context of supporting the needs of a high-traffic, customer facing web site. In 2010, I helped Netflix to successfully transition its web scale use-cases from Oracle to SimpleDB, AWS' hosted database
Tumblr Architecture - 15 Billion Page Views a Month and Harder to Scale than Twitter - High Scalability -
With over 15 billion page views a month Tumblr has become an insanely popular blogging platform. Users may like Tumblr for its simplicity, its beauty, its strong focus on user experience, or its friendly and engaged community, but like it they do. Growing at over 30% a month has not been without challenges. Some reliability problems among them. It helps to realize that Tumblr operates at surprisin
7 Stages of Scaling Web Applications
The document discusses the typical 7 stages of scaling a web application as it grows in popularity and usage. Stage 1 involves a simple initial architecture. Stage 2 adds more redundant components to improve performance and availability as usage grows. Stages 3-5 involve significant pain as the application is pushed to its limits, requiring re-architecting and partitioning of databases and service
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High Scalability - High Scalability - Tumblr Architecture - 15 Billion Page Views a Month and Harder to Scale than Twitter ã‚’èªã‚“ã 。ã™ã”ãé¢ç™½ã‹ã£ãŸã€‚ Kindle ã§å¼•ç”¨ã—ãŸã¨ã“ã‚ã‚’ä¸å¿ƒã«ãƒ¡ãƒ¢ã€‚ Tumblr ã®ã‚½ãƒ¼ã‚·ãƒ£ãƒ«ã‚°ãƒ©ãƒ•ã®ç‰¹å¾´ The graph for Tumblr users has hundreds of followers. This is different than any other social network and is what makes Tumblr so challenging to scale. Tumblr ã 㨠follower ãŒæ•°ç™¾äººã„るユーザーã¯ã‚¶ãƒ©ã«ã„る。 follower ã®å¤šã„ユーザー㮠post ã¯å¤šãã®ãƒ¦ãƒ¼ã‚¶
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HowFriendFeedUsesMySqlToStoreSchemaLessData - FriendFeed ã§ã¯ MySQL を使ã„ã©ã®ã‚ˆã†ã«ã‚¹ã‚ーマレスã®ãƒ‡ãƒ¼ã‚¿ã‚’ä¿å˜ã—ã¦ã„ã‚‹ã®ã‹
HowFriendFeedUsesMySqlToStoreSchemaLessData - FriendFeed ã§ã¯ MySQL を使ã„ã©ã®ã‚ˆã†ã«ã‚¹ã‚ーマレスã®ãƒ‡ãƒ¼ã‚¿ã‚’ä¿å˜ã—ã¦ã„ã‚‹ã®ã‹ 目次 ã“ã®è¨˜äº‹ã«ã¤ã„㦠FriendFeed? ã§ã¯ MySQL を使ã„ã©ã®ã‚ˆã†ã«ã‚¹ã‚ーマレスã®ãƒ‡ãƒ¼ã‚¿ã‚’ä¿å˜ã—ã¦ã„ã‚‹ã®ã‹ 背景 概観 詳細 一貫性ã¨åŽŸå性 性能 FriendFeed? ã§ã¯ MySQL を使ã„ã©ã®ã‚ˆã†ã«ã‚¹ã‚ーマレスã®ãƒ‡ãƒ¼ã‚¿ã‚’ä¿å˜ã—ã¦ã„ã‚‹ã®ã‹ ã“ã®è¨˜äº‹ã«ã¤ã„㦠"How FriendFeed? uses MySQL to store schema-less data" ã®æ—¥æœ¬èªžè¨³ã§ã™ http://bret.appspot.com/entry/how-friendfeed-uses-mysql CC 2.5 ã§ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ã•ã‚Œã¦ã„ã¾ã™: http://creativecommons.org/
Registration: Machine learning & data conference | O'Reilly Strata
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GitHub - vitessio/vitess: Vitess is a database clustering system for horizontal scaling of MySQL.
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