Based on Hadoop, MapReduce equips users with potent distributed data-processing tools
These two steps, the map function and the reduce function, comprise what Amazon MapReduce refers to as a "job flow." Admittedly, this is an oversimplification, because job flows involve other configuration parameters (such as where you get the input data and where you put the output), and you can define additional steps in the process, but that's the basic idea.
As a result, a programmer building a Hadoop-powered MapReduce system can focus on the comparatively simple job of crafting the individual functions that process single key/value pairs at a time. Hadoop does the legwork of carving the input data into initial key/value pairs; starting multiple map function instances; feeding them input data; gathering, sorting, and ordering the intermediate key/value pairs; launching reduce instances; feeding them the properly arranged intermediate data; and -- finally -- delivering the output. And all the while, Hadoop monitors the progress map and reduce tasks, as well as restarts "dead" ones automatically. Whuf.
Hadoop in the cloud
To access Amazon's Elastic MapReduce, your first stop is your Amazon Web Services account page (assuming you have an account with AWS), where you must sign up for the Elastic MapReduce service. Then, head on over to the AWS Management Console and log in. You'll find that the AWS Console -- which had been a control panel for Amazon's EC2 only -- displays a new Amazon Elastic MapReduce tab. Click the tab, and you are transferred to the Job Flows page, from which you can monitor the status of current job flows, as well as examine details of previous (terminated) job flows.
To define a new job flow, click the Create New Job Flow button. This sends you through a series of windows in step-by-step fashion. You fill in textboxes to define the location of your input data, where you want your output data, and paths to your map and reduce function. All of these locations must exist in Amazon S3 buckets. In the case of the output data, the location will exist when the job flow concludes. Consequently, it's a good idea to have a utility for transferring data to and from S3 on hand. I recommend the excellent S3Fox Organizer.
Amazon Elastic MapReduce allows for two kinds of job flows: custom jar and streaming. A custom jar-style job flow expects your map and reduce functions to be in compiled Java classes stored in Java JAR files. The Hadoop framework is Java-based, so a custom jar job flow provides the better performance. On the other hand, a streaming-type job flow lets you write your map and reduce functions in non-Java languages such as Python, Ruby, Perl, and others. The functions of a streaming job flow read the input data from stdin, and send the output to stdout. So, data flows in and out of the functions as strings, and -- by convention -- a tab separates the key and value of each input line.
Once you've specified the whereabouts of your job flow's components, you identify the quantity and processing power of the EC2 instances on which the job will execute. You can select up to 20 EC2 instances; any more than that, and you have to fill out a special request form. Your choice of compute instances ranges from Small to Extra Large High CPU. Check the Amazon documentation for a complete description of the power of a CPU instance.
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