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--- courses:mapreduce-tutorial:step-7 [2012/01/24 19:05]
straka vytvořeno
+++ courses:mapreduce-tutorial:step-7 [2013/02/08 14:36] (current)
popel Milan improved our Hadoop
@@ Line 1: / Line 1: @@
-====== MapReduce Tutorial : ======
+====== MapReduce Tutorial : Dynamic Hadoop cluster for several computations ======
+When multiple Hadoop jobs should be executed, it is better to reuse the cluster instead of allocating a new one for every computation.
+A cluster can be created using
+  /net/projects/hadoop/bin/hadoop-cluster -c number_of_machines -w sec_to_wait_after_all_jobs_completed
+The syntax is the same as in ''perl script.pl run''.
+The associated SGE job name is HadoopCluster. The running job can be stopped by either removing ''HadoopCluster.c$SGE_JOBID'' file or deleting the SGE job using ''qdel''.
+===== Using a running cluster =====
+Running cluster is identified by its master. When running a Hadoop job using Perl API, existing cluster can be used by
+  perl script.pl -jt cluster_master:9001 ...
+===== Running Hadoop jobs from now on =====
+From now on, it is best to run MR jobs using a one-machine cluster -- create a one-machine cluster using ''hadoop-cluster'' for 3h (10800s) and run jobs using ''-jt cluster_master''. Running the scripts locally without any cluster has several disadvantages, most notably having only one reducer per job.
+===== Example =====
+Try running the same script {{:courses:mapreduce-tutorial:step-6.txt|step-7-wordcount.pl}} as in the last step, this time by creating the cluster and submitting the job to it:
+  wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-6.txt' -O 'step-7-wordcount.pl'
+  /net/projects/hadoop/bin/hadoop-cluster -c 1 -w 600
+  # NOW VIEW THE FILE
+  # $EDITOR step-7-wordcount.pl
+  rm -rf step-7-out-sol; perl step-7-wordcount.pl -jt cluster_master:9001 -Dmapred.max.split.size=1000000 /home/straka/wiki/cs-text-medium step-7-out-sol
+  less less step-7-out-sol/part-*
+Remarks:
+  * The reducers seem to start running before the mappers finish. In the web interface, the running time of reducers is divided into thirds:
+    * during the first 33%, the mapper outputs are copied to the machine where reducer runs.
+    * during the second 33%, the (key, value) pairs are sorted.
+    * during the last 33%, the user-defined reducer runs.
+----
+<html>
+<table style="width:100%">
+<tr>
+<td style="text-align:left; width: 33%; "></html>[[step-6|Step 6]]: Running on cluster.<html></td>
+<td style="text-align:center; width: 33%; "></html>[[.|Overview]]<html></td>
+<td style="text-align:right; width: 33%; "></html>[[step-8|Step 8]]: Multiple mappers, reducers and partitioning.<html></td>
+</tr>
+</table>
+</html>

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