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Write your first MapReduce program in 20 minutes by Michael Nielsen on January 2, The slow revolution Some revolutions are marked by a single, spectacular event: Such a revolution is happening right now in computing.
Microprocessor clock speeds have stagnated since about Major chipmakers such as Intel and AMD continue to wring out improvements in speed by improving on-chip caching and other clever techniques, but they are gradually hitting the point of diminishing returns.
Instead, as transistors continue to shrink in size, the chipmakers are packing multiple processing units onto a single chip.
Most computers shipped today use multi-core microprocessors, i. The result is a revolution in software development. As this movement happens, software development, so long tailored to single-processor models, is seeing a major shift in some its basic paradigms, to make the use of multiple processors natural and simple for programmers.
This movement to multiple processors began decades ago. Projects such as the Connection Machine demonstrated the potential of massively parallel computing in the s. In the s, scientists became large-scale users of parallel computing, using parallel computing to simulate things like nuclear explosions and the dynamics of the Universe.
Those scientific applications were a bit like the early scientific computing of the late s and s: One of the organizations driving this shift is Google. Google is one of the largest users of multiple processor computing in the world, with its entire computing cluster containing hundreds of thousands of commodity machines, located in data centers around the world, and linked using commodity networking components.
If, for example, you allocate a large number of machines in the cluster to a given MapReduce job, the job runs in a highly parallelized way. What exactly is MapReduce? All the details — parallelization, distribution of data, tolerance of machine failures — are hidden away from the programmer, inside the library.
Instead, we can get away with a toy implementation just a few lines of Python!
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By using this single-machine toy library we can learn how to develop for MapReduce. The programs we develop will run essentially unchanged when, in later posts, we improve the MapReduce library so that it can run on a cluster of machines. What it is, however, is an excellent illustration of the basic ideas of MapReduce.
You can create some simple examples texts by cutting and pasting from the following: The quick brown fox jumped over the lazy grey dogs. That's one small step for a man, one giant leap for mankind.
Mary had a little lamb, Its fleece was white as snow; And everywhere that Mary went, The lamb was sure to go. The MapReduce job will process this input dictionary in two phases: The output of the map phase is just the list formed by concatenating the list of intermediate keys and values for all of the different input keys and values.
I said above that the function mapper is supplied by the programmer. In the wordcount example, what mapper does is takes the input key and input value — a filename, and a string containing the contents of the file — and then moves through the words in the file.
For each word it encounters, it returns the intermediate key and value word,1indicating that it found one occurrence of word. Furthermore, the same key gets repeated multiple times, because words like the appear more than once in the text.
This, incidentally, is the reason we use a Python list for the output, and not a Python dictionary, for in a dictionary the same key can only be used once. What the MapReduce library now does in preparation for the reduce phase is to group together all the intermediate values which have the same key.
In our example the result of doing this is the following intermediate dictionary: The reduce phase now commences. This is done by the following code: Repeated intermediate keys then have their values grouped into a list, and the user-defined function reducer is applied to the intermediate key and list of intermediate values.
The results are returned as a list. Obviously, on a single machine an implementation of the MapReduce library is pretty trivial! The payoff is that with enough improvement to the library we can with essentially no change use our wordcount.
What the MapReduce library does, then, is provide an approach to developing in a distributed environment where many simple tasks like wordcount remain simple for the programmer. Important but boring tasks like parallelization, getting the right data into the right places, dealing with the failure of computers and networking components, and even coping with racks of computers being taken offline for maintenance, are all taken care of under the hood of the library.
That means taking familiar tasks — things like computing PageRank — and figuring out how they can be done within the MapReduce framework. This is not only fun in its own right, it will also make us better MapReduce programmers, in the same way as understanding the innards of an operating system for example can make you a better application programmer.
One of the most striking things about MapReduce is how restrictive it is.Write reports, create joint project proposals, keep track of meeting notes, and more.
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