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Let's see how to use an alternative map 
produced framework, again in Python, a 

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lightweight one, called mincemeat.python. 
it turns out that this framework is very 

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similar to Octo, but much, much faster. 
In practice, you should use this for your 

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assignment instead of Octo when you'll 
run it on large volume of text. 

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The code is literally the same for the 
example that we used earlier with Octo. 

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The only difference is that mincemeat 
requires you to have code to start the 

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server within your functions or within 
your source file, as opposed to starting 

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the server outside. 
And then essentially this sets up a 

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server. 
sets of the data, so sets of the mapping 

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function, the reduce function, which are 
exactly the same as before. 

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And this statement runs the server. 
You have to give it a password which 

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you'll call when you call it as a client. 
And then you print the results. 

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So this is how the let's see it work. 
You say Python work contact Python, so 

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this actually starts the server. 
And this would, you would just use one 

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client this time. 
You call it with the mincemeat code 

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instead of word conduct by directly. 
and you give it a password and then you 

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call it and tell it where the server is. 
And you can see that it ran very fast. 

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Much, much faster, in fact than the Octo 
example that we ran in the previous 

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segment. 
You can verify that outputs are the same. 

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The output here is not sorted. 
you can sort the result and figure it 

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out. 
verify for yourself, if you have the 

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patience to go implementation of both 
Octo and mincemeat. 

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Why exactly Octo is slower and mincemeat 
is faster, it's kind of interesting and 

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might give some clues when you in, 
examine the detailed architectures of 

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real industrial strength MapReduce 
frameworks like Hadoop going forward 

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after this course.