The Search Dilemma: Part 1

Have you ever thought what if a search engine (like Google, Bing or Yahoo) took hours to answer your search queries? Well, neither do I. But I presume that most people would be angry and just stop using them. This assumption is corroborated by a 2009 study[1] that revealed that a delay of 2 seconds in delivering search results may impact companies’ revenue in over 4% per user; in other words, slow answers equals to less cash flow.

Big companies have many ways to address this (quality-of-service) issue and make this response time faster: the most obvious of them is simply deploying faster processors, more memory caches and upgrading network speed for distributed computing. However, this approach is not really the most efficient as there are financial (deploying more servers cost money) and spatial (your datacenter has limited space) constraints. Jeff Dean[2] shows some manners to circumvent these constraints and maximize the system’s efficiency while guaranteeing the same quality-of-service for all users. I’ll discuss one of them here.

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