Once upon a time we scaled databases by buying ever bigger, faster, and more expensive machines. While this arrangement is great for big iron profit margins, it doesn't work so well for the bank accounts of our heroic system builders who need to scale well past what they can afford to spend on giant database servers. In a extraordinary two article series, Dathan Pattishall, explains his motivation for a revolutionary new database architecture--sharding--that he began thinking about even before he worked at Friendster, and fully implemented at Flickr. Flickr now handles more than 1 billion transactions per day, responding in less then a few seconds and can scale linearly at a low cost.

What is sharding and how has it come to be the answer to large website scaling problems?

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Abstract:
Today’s data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy. Unfortunately, even when deploying the highest-end IP switches/routers, resulting topologies may only support 50% of the aggregate bandwidth available at the edge of the network, while still incurring tremendous cost. Nonuniform bandwidth among data center nodes complicates application design and limits overall system performance.
In this paper, we show how to leverage largely commodity Ethernet switches to support the full aggregate bandwidth of clusters consisting of tens of thousands of elements. Similar to how clusters of commodity computers have largely replaced more specialized SMPs and MPPs, we argue that appropriately architected and interconnected commodity switches may deliver more performance at less cost than available from today’s higher-end solutions. Our approach requires no modifications to the end host network interface, operating system, or applications; critically, it is fully backward compatible with Ethernet, IP, and TCP.

Static files have the advantage of being very fast to serve. Read from disk and display. Simple and fast. Especially when caching proxies are used. The issue is how do you bulk generate the initial files, how do you serve the files, and how do you keep the changed files up to date? This is the process Thomas covers in his excellent article Serving static files with Django and AWS - going fast on a budget", where he explains how he converted 600K thousand previously dynamic pages to static pages for his site Eventseer.net, a service for tracking academic events.

Eventseer.net was experiencing performance problems as search engines crawled their 600K dynamic pages. As a solution you could imagine scaling up, adding more servers, adding sharding, etc etc, all somewhat complicated approaches. Their solution was to convert the dynamic pages to static pages in order to keep search engines from killing the site. As an added bonus non logged-in users experienced a much faster site and were more likely to sign up for the service.

The article does a good job explaining what they did, so I won't regurgitate it all here, but I will cover the highlights and comment on some additional potential features and alternate implementations...

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