Reduce

This idea comes out for an old university-time idea: to write a serverless chat application. Of course, I’m aware of the complications and the problems that using Broadcasting could create, so this problems would be took in consideration by design. But why now? I was thinking of a way to reduce the “office distances”: making easy to connect with a person who works in your own office. I know, it sounds a bit “creepy” to depend on an application to do that. After all you could just stand up and go to the colleague’s desk. And that’s what I’d normally do: nothing impossible. ...

For some reasons I started to play with Apache Hadoop (Core): Hadoop is a software platform that lets one easily write and run applications that process vast amounts of data. Here’s what makes Hadoop especially useful: Scalable: Hadoop can reliably store and process petabytes. Economical: It distributes the data and processing across clusters of commonly available computers. These clusters can number into the thousands of nodes. Efficient: By distributing the data, Hadoop can process it in parallel on the nodes where the data is located. This makes it extremely rapid. Reliable: Hadoop automatically maintains multiple copies of data and automatically redeploys computing tasks based on failures. Hadoop implements MapReduce, using theHadoop Distributed File System (HDFS). MapReduce divides applications into many small blocks of work. HDFS creates multiple replicas of data blocks for reliability, placing them on compute nodes around the cluster. MapReduce can then process the data where it is located. Hadoop has been demonstrated on clusters with 2000 nodes. The current design target is 10,000 node clusters. I followed the Quickstart guide and I can confirm that it works on [en:Mac OS X] too, but I managed only to make it run in “standalone” mode: usefull for first-stage development and debugging. ...