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courses:mapreduce-tutorial [2012/01/27 00:44]
straka 		courses:mapreduce-tutorial [2012/02/05 20:01] (current)
straka
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   * [[.:mapreduce-tutorial:Introduction]]   * [[.:mapreduce-tutorial:Introduction]]
  
 +===== Overview =====
 +  * [[.:mapreduce-tutorial:Hadoop job overview]]
 +  * [[.:mapreduce-tutorial:Managing a Hadoop cluster]]
 +  * [[.:mapreduce-tutorial:Running jobs]]
 +  * [[.:mapreduce-tutorial:Perl API]], [[http://hadoop.apache.org/common/docs/r1.0.0/api/index.html|Java API]]
 +  * [[.:mapreduce-tutorial:Making your job configurable]]
 +  * [[.:mapreduce-tutorial:If things go wrong]]
  
 ===== Day 1 ===== ===== Day 1 =====
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 === MapReduce extended === === MapReduce extended ===
-From now on, it is best to run MR jobs using a one-machine cluster. Running the scripts locally without any cluster has several disadvantages, most notably having only one reducer per job. 
   * [[.:mapreduce-tutorial:Step 8]]: Multiple mappers, reducers and partitioning.   * [[.:mapreduce-tutorial:Step 8]]: Multiple mappers, reducers and partitioning.
   * [[.:mapreduce-tutorial:Step 9]]: Hadoop properties.   * [[.:mapreduce-tutorial:Step 9]]: Hadoop properties.
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 === Advanced MapReduce exercises === === Advanced MapReduce exercises ===
 Exercises in this section can be made in any order, but it is recommended to try solving all of them. The [[.:mapreduce-tutorial:Perl API|Perl API reference]] may come handy. Exercises in this section can be made in any order, but it is recommended to try solving all of them. The [[.:mapreduce-tutorial:Perl API|Perl API reference]] may come handy.
-  * [[.:mapreduce-tutorial:Step 13]]: Sorting +  * [[.:mapreduce-tutorial:Step 13]]: Sorting. 
-  * [[.:mapreduce-tutorial:Step 14]]: N-gram language model +  * [[.:mapreduce-tutorial:Step 14]]: N-gram language model. 
-  * [[.:mapreduce-tutorial:Step 15]]: K-means clustering+  * [[.:mapreduce-tutorial:Step 15]]: K-means clustering. 
 + 
 +=== Beyond MapReduce === 
 +  * [[.:mapreduce-tutorial:Step 16]]: Implementing iterative MapReduce jobs faster using All-Reduce.
  
 ===== Day 2 ===== ===== Day 2 =====
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 === Java Hadoop basics ==== === Java Hadoop basics ====
   * [[.:mapreduce-tutorial:Step 23]]: Predefined formats and types.   * [[.:mapreduce-tutorial:Step 23]]: Predefined formats and types.
-  * [[.:mapreduce-tutorial:Step 24]]: Mappers, running Java Hadoop jobs.+  * [[.:mapreduce-tutorial:Step 24]]: Mappers, running Java Hadoop jobs, counters.
   * [[.:mapreduce-tutorial:Step 25]]: Reducers, combiners and partitioners.   * [[.:mapreduce-tutorial:Step 25]]: Reducers, combiners and partitioners.
-  * [[.:mapreduce-tutorial:Step 26]]: Counters, compression. +  * [[.:mapreduce-tutorial:Step 26]]: Compression and job configuration. 
-  * [[.:mapreduce-tutorial:Step 27]]: Reusing Mapper and Reducer code. +  * [[.:mapreduce-tutorial:Step 27]]: Running multiple Hadoop jobs in one source file.
- +
-=== Exercises ===+
  
 === Advanced topics === === Advanced topics ===
-  * Custom input format -- WholeFile and WholeFileAsPath +  * [[.:mapreduce-tutorial:Step 28]]: Custom data types. 
-  * Custom data type -- Pair<A, B>+  * [[.:mapreduce-tutorial:Step 29]]: Custom sorting and grouping comparators. 
 +  * [[.:mapreduce-tutorial:Step 30]]: Custom input formats. 
 + 
 +=== Beyond MapReduce === 
 +  * [[.:mapreduce-tutorial:Step 31]]: Implementing iterative MapReduce jobs faster using All-Reduce.
  
 ===== Other ===== ===== Other =====
   * [[user:majlis:hadoop|Further information]]   * [[user:majlis:hadoop|Further information]]
  

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