The MapReduce framework provides Counters as an efficient mechanism for tracking the occurrences of global events within the map and reduces the phases of jobs. For example, a typical MapReduce job will kick off several mapper instances, one for each block of the input data, all running the same code. These instances are part of […]
Conceptually, MapReduce jobs are relatively simple. In the map phase, each input record has a function applied to it, resulting in one or more key-value pairs. The reduce phase receives a group of the key-value pairs and performs some function over that group. Testing mappers and reducers should be as easy as testing any other […]
MapReduce has a feature known as Hadoop Streaming that gives the flexibility to write code in your favorite language other than Java. You can use Ruby, Perl, Python or even quickly write a MapReduce job using shell script.