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Traditionally, if you wanted to distribute a large task across several racks of machine-room servers, then you faced two quite different problems. First, of course, you had to write code that could be assigned a small part of the problem and solve it, and then write code that could assemble the various answers from each node back into one big answer to the original question. But, finally, you would also have wound up writing a lot of code that had little to do with your problem at all: the scripts that would push your code out to all of the servers in the cluster, then run it, and then finally collect the data back together using the network or a shared file system. The idea of a map-reduce system is to eliminate that last step in distributing a large computation, and to offer a framework that will distribute data and execute code without your having to worry about the underlying distribution mechanisms. Most frameworks also implement precautions that are often not present in homemade parallel computations, like the ability to seamlessly re-submit tasks to other nodes if some of the cluster servers fail during a particular computation. In fact, some map-reduce frameworks will happily let you unplug and reboot machines for routine maintenance even while the

Figure 5-7 illustrates the following about the values of the actual and formal parameters at various stages in the execution of the method: Before the method call, variables A1 and A2, which will be used as the actual parameters, are already on the stack. By the beginning of the method, the system has allocated space on the stack for the formal parameters, and copied the values from the actual parameters. Since A1 is a reference type, the reference is copied, resulting in both the actual and formal parameters referring to the same object in the heap. Since A2 is a value type, the value is copied, producing an independent data item. At the end of the method, both f2 and the field of object f1 have been incremented by 5. After method execution, the formal parameters are popped off the stack. The value of A2, the value type, is unaffected by the activity in the method. The value of A1, the reference type, however, has been changed by the activity in the method.

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An add method is called with four parameters. The first two are the most important parameters; the last two refer to images that represent the block and are not required for the block to run in the harness. The first parameter, twitter, is the name of the variable that will hold the object created in the second parameter. TwitterClass is the object representing the block and is passed into the second parameter. The function TwitterClass is created to represent the block (from Listing 8-1).

cluster is busy with a computation, and will quietly work around the unavailable nodes without disturbing the actual application in the least Note that there are two quite different reasons for distributing a computation One kind of task simply requires a lot of CPU In this case, the cluster nodes do not start off holding any data relevant to the problem; they have to be loaded with both their data set and code to run against it But another kind of task involves a large data set that is kept permanently distributed across the nodes, making them asymmetric workers who are each, so to speak, the expert on some particular slice of the data.

The second type of parameter is called a reference parameter. When using a reference parameter, you must use the ref modifier in both the declaration and the invocation of the method. The actual parameter must be a variable, which must be assigned to before being used as the actual parameter. If it is a reference type variable, it can be assigned a reference or the value null. For example, the following code illustrates the syntax of the declaration and invocation. Include the ref modifier. void MyMethod( ref int val ) { ... } int y = 1; MyMethod ( ref y ); Include the ref modifier. MyMethod ( ref 3+5 ); Must use a variable.

This approach could be used, for example, by an organization that has saved years of web logs across dozens of machines, and wants to perform queries where each machine in the cluster computes some particular tally, or looks for some particular pattern, in the few months of data for which it is uniquely responsible Although a map-reduce framework might superficially resemble the Beowulf clusters pioneered at NASA in the 1990s, it imposes a far more specific semantics on the phases of computation than did the generic message-passing libraries that tended to power Beowulf s Instead, a map-reduce framework takes responsibility for both distributing tasks and assembling an answer, by imposing structure on the processing code submitted by programmers: The task under consideration needs to be broken into two pieces, one called the map operation, and the other reduce.

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The two operations bear some resemblance to the Python built-in functions of that name (which Python itself borrowed from the world of functional programming); imagine how one might split across several servers the tasks of summing the squares of many integers: >>> >>> [0, >>> >>> 385 squares = map(lambda n: n*n, range(11)) squares 1, 4, 9, 16, 25, 36, 49, 64, 81, 100] import operator reduce(operatoradd, squares).

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