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What is the difference between Hadoop Map Reduce and Google Map Reduce?
Is it just Hadoop provides standardization for map reduce and others? what else is amongst the diff.
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Definition. HDFS is a Distributed File System that reliably stores large files across machines in a large cluster. In contrast, MapReduce is a software framework for easily writing applications which process vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner.
MapR is a business software distribution company that provides access to different Big Data workloads such as Apache Hadoop and Apache Spark. MapReduce is a programming paradigm of Apache Hadoop. It was developed by Google. MapReduce is the processing layer of the Hadoop architecture.
The primary difference between Spark and MapReduce is that Spark processes and retains data in memory for subsequent steps, whereas MapReduce processes data on disk. As a result, for smaller workloads, Spark's data processing speeds are up to 100x faster than MapReduce.
The technology is unable to handle the amounts of data Google wants to analyze these days, however. Urs Hölzle, senior vice president of technical infrastructure at the Mountain View, California-based giant, said it got too cumbersome once the size of the data reached a few petabytes.
Google MapReduce and Hadoop are two different implementations (instances) of the MapReduce framework/concept. Hadoop is open source , Google MapReduce is not and actually there are not so many available details about it.
Since they work with large data sets, they have to rely on distributed file systems. Hadoop uses as a standard distributed file system the HDFS (Hadoop Distributed File Systems) while Google MapReduce uses GFS (Google File System)
Hadooop is implemented in java. Google MapReduce seems to be in C++.
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Google has exposed Map Reduce functionality via their BigQuery webservice. It works like Hadoop with Hive (i.e. using a SQL-like language which generates Map Reduce jobs in the background.) An example, using the browser-based query tool for Big Query is shown below. As is typical with Google's release of their technologies as public offerings, internal details are not exposed, nor can you tune or adjust settings. You simply use the API to call the web service and use Google's infrastructure to return the results to your application.
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you can see the following link for this:
http://www.linuxforu.com/2011/03/mapreduce-more-power-less-code-hadoop/
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