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Sat Mar 28 12:09:46 GMT 2020 scalabilityThere are two key primary ways of scaling web applications which is in practice today. 1) “Vertical Scalability” - Adding resource within the same logical unit to increase capacity. An example of this would be to add CPUs to an existing server, or expanding storage by adding hard drive on an existing RAID/SAN storage. 2) “Horizontal Scalability” - Adding multiple logical units of resources and making them work as a single unit. Most clustering solutions, distributed file systems, load-balancers help you with horizontal scalability. Scalability can be further sub-classified based on the “scalability factor”. 1) If the scalability factor stays constant as you scale. This is called “linear scalability“. 2) But chances are that some components may not scale as well as others. A scalability factor below 1.0 is called “sub-linear scalability“. 3) Though rare, its possible to get better performance (scalability factor) just by adding more components (i/o across multiple disk spindles in a RAID gets better with more spindles). This is called “supra-linear scalability“. 4) If the application is not designed for scalability, its possible that things can actually get worse as it scales. This is called “negative scalability“. http://www.royans.net/arch/2007/09/22/what-is-scalability/ Report of building web application with 55k pageload with rail - http://shanti.railsblog.com[..]mongrels-handled-a-550k-pageview-digging XMPP a IM protocol about scalability - http://www.process-one.net[..]icle/the_aol_xmpp_scalability_challenge/ Presentation and resources of making you website more scalable - http://www.scribd.com[..]9/Real-World-Web-Performance-Scalability http://www.theserverside.com[..]lications&asrc=EM_NLN_3990118&uid=703565 http://www.theserverside.com[..]ionsPart2&asrc=EM_NLN_3990119&uid=703565 Brian Zimmer, architect at travel startup Yapta, highlights some worst practices jeopardizing the growth and scalability of a system: * The Golden Hammer. Forcing a particular technology to work in ways it was not intended is sometimes counter-productive. Using a database to store key-value pairs is one example. Another example is using threads to program for concurrency. * Resource Abuse. Manage the availability of shared resources because when they fail, by definition, their failure is experienced pervasively rather than in isolation. For example, connection management to the database through a thread pool. * Big Ball of Mud. Failure to manage dependencies inhibits agility and scalability. * Everything or Something. In both code and application dependency management, the worst practice is not understanding the relationships and formulating a model to facilitate their management. Failure to enforce diligent control is a contributing scalability inhibiter. * Forgetting to check the time. To properly scale a system it is imperative to manage the time alloted for requests to be handled. * Hero Pattern. One popular solution to the operation issue is a Hero who can and often will manage the bulk of the operational needs. For a large system of many components this approach does not scale, yet it is one of the most frequently-deployed solutions. * Not automating. A system too dependent on human intervention, frequently the result of having a Hero, is dangerously exposed to issues of reproducibility and hit-by-a-bus syndrome. * Monitoring. Monitoring, like testing, is often one of the first items sacrificed when time is tight. http://highscalability.com/scalability-worst-practices Useful Corporate Blogs that Talk About Scalability - http://highscalability.com[..]l-corporate-blogs-talk-about-scalability Overview of mapreduce and how it compare with other distributed programming model -http://natishalom.typepad.com[..]0/is-mapreduce-going-to-main-stream.html Paper of data store at amazon http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html Discuss how haven't sync can cause performance issue - http://www.theserverside.com[..]lications&asrc=EM_NLN_6273194&uid=703565 http://bugs.sun.com/bugdatabase/view_bug.do?bug_id=6423457 Discussion about Cloud Based Memory Architectures - http://highscalability.com[..]ased-memory-architectures-next-big-thing http://highscalability.com[..]alability-and-performance-best-practices Interview with google engineer - http://www.zdnet.co.uk[..]gle-at-scale-everything-breaks-40093061/ Surprisingly youtube is blocking - http://highscalability.com[..]e-scalability-lessons-in-30-minutes.html If we are seeing a sustained arrival rate of requests, greater than our system is capable of processing, then something has to give. Having the entire system degrade is not the ideal service we want to give our customers. A better approach would be to process transactions at our systems maximum possible throughput rate, while maintaining a good response time, and rejecting requests above this arrival rate. - http://mechanical-sympathy.blogspot.com.au[..]apply-back-pressure-when-overloaded.html How twitter scaling - http://www.infoq.com/presentations/Twitter-Analytics How Reddit scaling - http://www.infoq.com/presentations/scaling-reddit How Hotjar scaling - https://www.hotjar.com[..]-while-scaling-hotjars-tech-architecture How infiniteDB prevent locking and IO - http://highscalability.com[..]-scalable-relational-database-manag.html http://highscalability.com[..]ard-way-about-scaling-a-million-use.html http://martin.kleppmann.com[..]2014/03/26/six-things-about-scaling.html The experiences of various big companies, about network issues - http://aphyr.com/posts/288-the-network-is-reliable Stackoverflow, scale without cloud - http://highscalability.com[..]nth-25-servers-and-i.html?SSLoginOk=true How netflix scale - http://highscalability.com[..]ix-what-happens-when-you-press-play.html Scaling to 100k Users - https://alexpareto.com[..]ity/systems/2020/02/03/scaling-100k.html https://www.infoq.cn/article/Tyx5HwaD9OKNX4xzFaFo (google search) (amazon search) second |