Profiling Ruby With Google’s Perftools - igvita.com
http://www.igvita.com/2009/06/13/profiling-ruby-with-googles-perftools/
Advanced Messaging & Routing with AMQP - igvita.com
http://www.igvita.com/2009/10/08/advanced-messaging-routing-with-amqp/

Cet article explique ce qu'est AMQP et comment l'utiliser en Ruby.

The flexibility of the message and the exchange model is what makes AMQP such a powerful tool. Whenever a publisher generates a message, he can mark it as 'persistent' which will guarantee delivery through the broker - if there is an attached queue, it will accumulate messages until the consumer requests them. However, if you're streaming transient data (access logs, for example), you can also disable message persistence and not worry about overwhelming your broker. That's how you achieve 'exactly-once' vs 'at least once' semantics.

Your font is too damn small.

If you're considering doing large scale analysis of structured data (access logs, for example), there are dozens of enterprise-level solutions ranging from specialized streaming databases, to the more mundane data warehousing solutions with star topologies and column store semantics. Google, facing the same problem, developed a system called Sawzall, which leverages their existing Map-Reduce clusters for large scale parallel data analysis by adding a DSL for easy manipulation of data.

Map/Reduce Toolkit by NY Times engineers is a great example of a Ruby DSL on top of the Hadoop Streaming interface. Specifically aimed at simplifying their internal log processing jobs, it exposes just the necessary bits for handling the access log inputs and provides a number of predefined reduce steps: unique, counter, etc. For example, to get a list of all unique visitor IP's, the entire program consists of: