This chapter covered development of a MapReduce job, highlighting some of the issues and approaches you are likely to face frequently. In particular, we learned how Hadoop Streaming provides a means to use scripting languages to write map and reduce tasks, and how using Streaming can be an effective tool for early stages of job prototyping and initial data analysis.
We also learned that writing tasks in a scripting language can provide the additional benefit of using command-line tools to directly test and debug the code. Within the Java API, we looked at the ChainMapper class that provides an efficient way of decomposing a complex map task into a series of smaller, more focused ones.
We then saw how the Distributed Cache provides a mechanism for efficient sharing of data across all nodes. It copies files from HDFS onto the local filesystem on each node, providing local access to the data. We also learned how to add job counters by defining a Java enumeration for the counter group...