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# Map_reduce.word_count_map_reduce "./musepages/";; - : int = 166 # Map_reduce.top_n_words_map_reduce 10 "./musepages/";; - : (string * int) list = [("in", 34); ("for", 34); ("that", 46); ("I", 50); ("a", 63); ("is", 68); ("and", 79); ("of", 92); ("to", 95); ("the", 146)] These functions might take some time to complete, especially in directories with many files. Running the code on a directory of text files reveals that there is an average of 166 words in each file and that the word the is the most commonly found word, showing up 146 times. The word in is the tenth most common word, showing up a scant 34 times. The preceding code could be implemented in any programming language. However, it is the expressive power of functional programming that enables it to be expressed easily in OCaml and in only 87 lines of code. The last point to make about important aspects of functional programming is the idea of lazy evaluation of data. Haskell, for example, is a lazy language and it does not evaluate all function arguments before the function is evaluated. ML and its derivates are strict all function arguments are evaluated before the function. Lazy evaluation is not, strictly speaking, a requirement for functional programming even though it is often a part of it. OCaml, like the other ML languages, does not support lazy evaluation.

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In short: less code and fewer bugs. This may seem like a very strong statement to make, so hold on a moment before rushing to judgment. Functional programs are often shorter in terms of lines of code (LoC) than either structured or OOP implementations. As shown at the Win32 Shootout (found at http://dada.perl.it/shootout), there were a total of 640 lines of OCaml code versus 1068 lines of C and 798 lines of Java (which translates to roughly 40 percent and 20 percent shorter, respectively). If you can agree that the number of bugs in any given program is proportional to the number of LoC, you can argue that a shorter program is (by definition) a less-buggy program.

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For Ajax and web developers, one of the most exciting additions to Visual Studio 2005 is the introduction of visualizers. How many times have you had to drill down into a DataSet just to find a value for one of the columns It takes an exhaustive amount of effort and time just to find a simple value. Wouldn t it be great if you could have a more representational display of the DataSet s current data That is where visualizers step in. You ll find the visualizer icon (a magnifying glass) if you hover over a value that is visualizer friendly, as you see in Figure 17-2.

You should see output similar to that shown in Figure 9-1.

s Note The Win32 Shootout was inspired by the original Shootout created by Doug Bagley. He took a set

Note that the SQL has the data already loaded into it (as specified with SQL INSERT statements), so the script does not need to handle the data insertion directly. Next, you need some code to analyze our data. The code shown in Listing 1-4 does just that.

If you click the drop-down next to the magnifying glass, you ll find a list of possible compatible visualizers for the data type that you re hovering over, as shown in Figure 17-3.

of problems (the Sieve of Erasthenes, word count, and so on) and tested implementations of many programming languages. He reported their sizes, execution speed, and other variables in an attempt to provide an unbiased comparison between languages. The results achieved are still hotly debated.

Listing 1-4. Analyzing Player Wins (player_wins.rb)

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