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The driver, shown in Listing 9-11, builds on our base class, utils/MainProgramShell.java, and defines only a small number of methods. This example relies on there being only a single reduce task, as the default partitioner will cause this job to fail. In our next design iteration, we will write a custom partitioner. All of the examples in this chapter are structured to run on small machines, so the reduce sort space has been reduced from 100MB to 10MB, using the following line: conf.setInt("io.sort.mb", 10); The values for input and output are set by the use of the command-line flags --input and --output, respectively. The setup follows the general rule for using the chain, and allocates dummyConf to use as the private configuration object for the chained map and reduce tasks. The framework serializes the contents in each call to the ChainMapper methods, making it safe to clear dummyConf and reuse it. Listing 9-11. The Job Setup, BruteForceMapReduceDriver.java super.customSetup(conf); conf.setJobName("BruteForceRangeMapReduce"); conf.setNumReduceTasks(1); conf.setInt("io.sort.mb", 10); conf.setInputFormat( KeyValueTextInputFormat.class); for( String input : inputs) { if (verbose) { LOG.info("Adding input path " + input); } FileInputFormat.addInputPaths(conf, input); } if (verbose) { LOG.info( "Setting output path " + output); } FileOutputFormat.setOutputPath(conf, new Path(output)); conf.setOutputFormat(TextOutputFormat.class); JobConf dummyConf = new JobConf(false); ChainMapper.addMapper(conf, ApacheLogTransformMapper.class, Text.class, Text.class, Text.class, Text.class, false, dummyConf); dummyConf.clear(); ChainMapper.addMapper(conf, KeyValidatingMapper.class, Text.class, Text.class, Text.class, Text.class, false, dummyConf); dummyConf.clear(); ChainReducer.setReducer(conf, ReducerForStandardComparator.class, Text.class, Text.class, Text.class, Text.class, false, dummyConf); dummyConf.clear(); ChainReducer.addMapper(conf, TranslateBackToIPMapper.class, Text.class, Text.class, Text.class, Text.class, false, dummyConf);

If you look back at the programs we ve worked on so far, you ll notice that all of them are linear: the program is interpreted a piece at a time, jumping for function calls and ending when there is no more code to be interpreted Take a look at the following code, for example: var sum = function(num1, num2){ return num1 + num2; }; sum(2, 3); The interpreter first executes our function expression, setting the value of the sum variable as a function that takes two arguments and adds them Then, the interpreter executes the invocation sum(2, 3), calling the sum function to add the two values and then returning the result After the last line, our program is done: there is no more code to be interpreted, and our program therefore exits and finishes execution.

The map and reduce methods used do not modify the passed-in key or value objects; therefore, the chaining framework is being formed to pass keys and values by reference. The second-to-last argument, false, in the ChainMapper.addMapper and ChainMapper.setReducer methods forces this behavior. All of the mappers and reducers expect Text objects for the input key and value, and output Text. In an updated version of chaining, in which the key and value objects implement WritableComparable and Writable, passing TextKeyHelperWithSeparators objects for the key would probably be significantly more efficient.

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While there are hundreds of simple programs like this, most programs aren t so linear Most user applications, such as word processors or painting programs, never really exit In fact, barring crashes or explicitly closing the program, most applications will run continuously This behavior is different from the programs we ve seen thus far, which exit as soon as they re done with all their instructions Like that bunny on TV, those applications just keep going and going But how exactly do these applications keep themselves from exiting We know that programs exit naturally when there are no more instructions to interpret No matter how long our programs are, there still comes a point when the interpreter will run out of instructions and exit, so the length of code isn t what s keeping these applications alive.

The biggest plus of this design is that it is simple and took about a day to put together. The biggest disadvantages are that all of the data must pass through the mapper and be sorted, and that only a single reduce task may be used. Given that the total number of networks is relatively bounded, if the incoming log records are batched in smaller sizes, this job will run reasonably well and reasonably fast. Without a custom partitioner, this job cannot be made to run with multiple reduce tasks.