Performance Serveur
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In the spring of 2010, the search team at Twitter started to rewrite our search engine in order to serve our ever-growing traffic, improve the end-user latency and availability of our service, and enable rapid development of new search features. As part of the effort, we launched a new real-time search engine , changing our back-end from MySQL to a real-time version of Lucene . Last week, we launched a replacement for our Ruby-on-Rails front-end: a Java server we call Blender.
Engineering: Twitter Search is Now 3x Faster
The SMAQ stack for big data - O'Reilly Radar
"Big data" is data that becomes large enough that it cannot be processed using conventional methods. Creators of web search engines were among the first to confront this problem. Today, social networks, mobile phones, sensors and science contribute to petabytes of data created daily. To meet the challenge of processing such large data sets, Google created MapReduce.
clouds
Best Practices for building JSON REST Web Services « Building F
Phase 3 – Adding validation | Change your service implementation to add some data validation to the JSON resource which is being received during PUT and POST. Learn how to use HTTP error code to define and transfer exception information. Learn how to handle those exceptions on the client side. The important thing for this phase is to make sure that you know the existing HTTP error codes, reuse them when it makes sense and create new one which are compliant with HTTP when needed.
HighScalability November links
Update 6: Some interesting changes from Twitter's Evan Weaver : everything in RAM now, database is a backup; peaks at 300 tweets/second; every tweet followed by average 126 people; vector cache of tweet IDs; row cache; fragment cache; page cache; keep separate caches; GC makes Ruby optimization resistant so went with Scala; Thrift and HTTP are used internally; 100s internal requests for every external request; rewrote MQ but kept interface the same; 3 queues are used to load balance requests; extensive A/B testing for backwards capability; switched to C memcached client for speed; optimize critical path; faster to get the cached results from the network memory than recompute them locally. Update 5: Twitter on Scala . A Conversation with Steve Jenson, Alex Payne, and Robey Pointer by Bill Venners.
Scaling Twitter: Making Twitter 10000 Percent Faster | High Scal
Map Reduce
Concurrence
ParaVM or full-VM or Cloud ? Xen