The three major categories of machine roles in a Hadoop deployment are Client machines, Masters nodes, and Slave nodes. The Master nodes oversee the two key functional pieces that make up Hadoop: storing lots of data (HDFS), and running parallel computations on all that data (Map Reduce). The Name Node oversees and coordinates the data storage function (HDFS), while the Job Tracker oversees and coordinates the parallel processing of data using Map Reduce. Slave Nodes make up the vast majority of machines and do all the dirty work of storing the data and running the computations. Each slave runs both a Data Node and Task Tracker daemon that communicate with and receive instructions from their master nodes. The Task Tracker daemon is a slave to the Job Tracker, the Data Node daemon a slave to the Name Node.
Client machines have Hadoop installed with all the cluster settings, but are neither a Master or a Slave. Instead, the role of the Client machine is to load data into the cluster, submit Map Reduce jobs describing how that data should be processed, and then retrieve or view the results of the job when its finished. In smaller clusters (~40 nodes) you may have a single physical server playing multiple roles, such as both Job Tracker and Name Node. With medium to large clusters you will often have each role operating on a single server machine.
In real production clusters there is no server virtualization, no hypervisor layer. That would only amount to unnecessary overhead impeding performance. Hadoop runs best on Linux machines, working directly with the underlying hardware. That said, Hadoop does work in a virtual machine. That’s a great way to learn and get Hadoop up and running fast and cheap. I have a 6-node cluster up and running in VMware Workstation on my Windows 7 laptop.
