====== MapReduce Tutorial : Running multiple Hadoop jobs in one source file ====== The Java API offers possibility to submit multiple Hadoop job in one source file. A job can be submitted either using * [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html#waitForCompletion(boolean)|job.waitForCompletion]] -- the job is submitted and the method waits for it to finish (successfully or not). * [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html#submit()|job.submit]] -- the job is submitted and the method immediately returns. In this case, the state of the submitted job can be accessed using [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html#isComplete()|job.isComplete]] and [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html#isSuccessful()|job.isSuccessful]] ===== Exercise 1 ===== Improve the [[.:step-25#exercise|sorting exercise]] to handle [[.:step-13#nonuniform-data|nonuniform keys distribution]]. As in the [[.:step-13#nonuniform-data|Perl solution]], run two Hadoop jobs (using one Java source file) -- first samples the input and creates separator, second does the real sorting. ===== Exercise 2 ===== Implement the [[.:step-15|K-means clustering exercise]] in Java. Instead of an controlling script, use the Java class itself to execute the Hadoop job as many times as necessary. ----

[[step-26|Step 26]]: Compression and job configuration.

[[.|Overview]]

[[step-28|Step 28]]: Custom data types.