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An aggregate-based MapReduce program that counts the words in the input files An aggregate-based MapReduce program that computes the histogram of the words in the input files A MapReduce program that counts the matches of a regular expression in the input A job that performs a join over sorted, equally partitioned datasets A job that counts words from several files A MapReduce tile-laying program to find solutions to pentomino problems A MapReduce program that estimates pi using the Monte Carlo method A MapReduce program that writes 10GB of random textual data per node A MapReduce program that writes 10GB of random data per node A job that sleeps at each map and reduce task A MapReduce program that sorts the data written by the random writer A sudoku solver A MapReduce program that counts the words in the input files

The pi example estimates pi using the Monte Carlo method. The web site http://www.chem. unl.edu/zeng/joy/mclab/mcintro.html provides a good discussion of this technique. The number of samples is the number of points randomly set in the square. The larger this value, the more accurate the calculation of pi. For the sake of simplicity, we are going to make a very poor estimate of pi by using very few operations. The pi program takes two integer arguments: the number of maps and the number of samples per map. The total number of samples used in the calculation is the number of maps times the number of samples per map. The map task generates a random point in a 1 1 area. For each sample where X2+Y2 <=1, the point is inside; otherwise, the point is outside. The map outputs a key of 1 or 0 and a value of 1 for a point that is inside or outside the circle, diameter 1. The reduce task sums the number of inside points and the number of outside points. The ratio between this is, in the limit, pi.

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Although specifying mapping documents directly in the code is one approach, another is to use the <mapping> element in the hibernate.cfg.xml XML configuration file. The <mapping> element has three possible attributes: jar, resource, and file, which map to the addJar(), addResource(), and addFile() methods on the Configuration object: < xml version="1.0" encoding="utf-8" > <!DOCTYPE hibernate-configuration SYSTEM "http://hibernate.sourceforge.net/hibernate-configuration-3.0.dtd"> <hibernate-configuration> <session-factory> <mapping jar="hibernate-mappings.jar"/> <mapping resource="com/apress/hibernate/User.hbm.xml"/> </session-factory> </hibernate-configuration> Whether you use the XML configuration file or directly specify the mapping files in code is up to you we suggest you use whichever you are less likely to forget to do.

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In this example, to help the job run quicker and with less output, you will choose 2 maps, with 10 samples each, for a total of 20 samples. To run the example, change the working directory of your shell to HADOOP_HOME (via cd ${HADOOP_HOME}) and enter the following: jason@cloud9:~/src/hadoop-0.19.0$ hadoop jar hadoop-0.19.0-examples.jar pi 2 10 The bin/hadoop jar command submits jobs to the cluster. The command-line arguments are processed in three steps, with each step consuming some of the command-line arguments. We'll see this in detail in 5, but for now the hadoop-0.19.0-examples.jar file contains the main class for the application. The next three arguments are passed to this class.

Your output will look something like that shown in Listing 2-3. Listing 2-3. Output from the Sample Pi Program Number of Maps = 2 Samples per Map = 10 Wrote input for Map #0 Wrote input for Map #1 Starting Job jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId= mapred.FileInputFormat: Total input paths to process : 2 mapred.FileInputFormat: Total input paths to process : 2 mapred.JobClient: Running job: job_local_0001 mapred.FileInputFormat: Total input paths to process : 2 mapred.FileInputFormat: Total input paths to process : 2 mapred.MapTask: numReduceTasks: 1 mapred.MapTask: io.sort.mb = 100 mapred.MapTask: data buffer = 79691776/99614720 mapred.MapTask: record buffer = 262144/327680 mapred.JobClient: map 0% reduce 0% mapred.MapTask: Starting flush of map output mapred.MapTask: bufstart = 0; bufend = 32; bufvoid = 99614720 mapred.MapTask: kvstart = 0; kvend = 2; length = 327680 mapred.LocalJobRunner: Generated 1 samples mapred.MapTask: Index: (0, 38, 38) mapred.MapTask: Finished spill 0 mapred.LocalJobRunner: Generated 1 samples. mapred.TaskRunner: Task 'attempt_local_0001_m_000000_0' done. mapred.TaskRunner: Saved output of task 'attempt_local_0001_m_000000_0' to file:/home/jason/src/hadoop-0.19.0/test-mini-mr/outmapred. MapTask: numReduceTasks: 1 mapred.MapTask: io.sort.mb = 100 mapred.JobClient: map 0% reduce 0% mapred.LocalJobRunner: Generated 1 samples mapred.MapTask: data buffer = 79691776/99614720

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