In this paper we present the design of a modern course in cluster computing and large-scale data processing. The defining differences between this and previously published designs are its focus on processing very large data sets and its use of Hadoop, an open source Java-based implementation of MapReduce and the Google File System as the platform for programming exercises. Hadoop proved to be a key element for successfully implementing structured lab activities and independent design projects. Through this course, offered at the University of Washington in 2007, we imparted new skills on our students, improving their ability to design systems capable of solving web-scale problems.

This paper introduces interruptible iterators, a language feature that makes expressive iteration abstractions much easier to implement. Iteration abstractions are valuable for software design, as shown by their frequent use in well-designed data structure libraries such as the Java Collections Framework. While Java iterators support iteration abstraction well from the standpoint of client code, they are awkward to implement correctly and efficiently, especially if the iterator needs to support imperative update of the underlying collection, such as removing the current element. Some languages, such as CLU and C# 2.0, support iteration through a limited coroutine mechanism, but these mechanisms do not support imperative updates. Interruptible iterators are more powerful coroutines in which the loop body is able to interrupt the iterator with requests to perform updates. Interrupts are similar to exceptions, but propagate differently and have resumption semantics. Interruptible iterators have been implemented as part of the JMatch programming language, an extended version of Java. A JMatch reimplementation of the Java Collections Framework shows that implementations can be made substantially shorter and simpler; performance results show that this language mechanism can also be implemented efficiently.