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    <p>Hadoop streaming is advantageous for those cases when the developer do not have the much knowhow of Java and can write Mapper/Reducer in any scripting language faster.</p> <p>When compared to custom jar jobs, a streaming Job would also have the additional overhead of starting a scripting(Python/Ruby/Perl) VM. This leads to a lot of inter-process communication, resulting in reduced efficiency of the jobs in most of the cases. </p> <p>Using Hadoop streaming brings with it restrictions on the input/output formats. There are times when you would like to create custom input/output formats, using custom jars would be the natural choice. Also using Java one can over-ride/extend many of hadoop's functionalities to one's need/choice.</p> <p>Quoting from an answer <a href="https://stackoverflow.com/questions/9569009/what-are-the-pros-and-cons-of-running-a-job-in-hadoop-using-various-languages">here</a>:</p> <blockquote> <p>Hadoop do has capability to work with MR jobs created in other languages - it is called streaming. This model only allow us to define mapper and reducer with some restrictions not present in java. In the same time - input/output formats and other plugins do have to be written as java classes So I would define decision making as following: </p> <ul> <li>Use Java, unless you have serious codebase you need to resue in Your MR job. </li> <li>Consider to use python when you need to create some simple ad hoc jobs.</li> </ul> </blockquote> <p>As for streaming only available for <code>mapred</code> API, it doesn't make sense. While using streaming mappers/reducers are written in another languages, so no point worrying about which API hadoop internally will use in order to execute them.</p>
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