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--- courses:mapreduce-tutorial:running-jobs [2012/02/05 19:37]
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+++ courses:mapreduce-tutorial:running-jobs [2012/02/05 21:24]
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-====== MapReduce Tutorial : ======
+====== MapReduce Tutorial : Running jobs ======
+The input of a Hadoop job is either a file, or a directory. In latter case all files in the directory are processed.
+The output of a Hadoop job must be a directory, which does not exist.
+===== Running jobs =====
+| ^ Command ^
+^ Run Perl script ''script.pl'' | ''perl script.pl'' //options// |
+^ Run Java job ''job.jar'' | ''/net/projects/hadoop/bin/hadoop job.jar'' //options// |
+The options are the same for Perl and java:
+| ^ Options ^
+^ Run locally | ''input output'' |
+^ Run using specified jobtracker | ''-jt jobtracker:port input output'' |
+^ Run job in dedicated cluster | ''-c number_of_machines input output'' |
+^ Run job in dedicated cluster and after it finishes, \\ wait for //W// seconds before stopping the cluster | ''-c number_of_machines -w W_seconds input output'' |
+^ Run using //R// reducers \\ (//R//>1 not working when running locally)| ''-r R input output'' |
+^ Run using //M// mappers | ''`/net/projects/hadoop/bin/compute-splitsize input M` input output'' |
+===== Running multiple jobs =====
+There are several ways of running multiple jobs:
+  * Java only: Create multiple ''Job'' instances and call ''submit'' or ''waitForCompletion'' multiple times
+  * Create a cluster using ''/net/projects/hadoop/bin/hadoop-cluster'', parse the jobtracker:port using ''head -1'' and run the jobs using ''-jt jobtracker:port''
+  * Create a shell script running multiple jobs using ''-jt HADOOP_JOBTRACKER''. Then run it using ''//net/projects/hadoop/bin/hadoop-cluster -c machines script.sh''.

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