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courses:mapreduce-tutorial [2012/01/25 14:47]
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courses:mapreduce-tutorial [2012/02/05 20:01] (current)
straka |
| * [[.: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 ===== |
| * [[.:mapreduce-tutorial:Step 6]]: Running on cluster. | * [[.:mapreduce-tutorial:Step 6]]: Running on cluster. |
| * [[.:mapreduce-tutorial:Step 7]]: Dynamic Hadoop cluster for several computations. | * [[.:mapreduce-tutorial:Step 7]]: Dynamic Hadoop cluster for several computations. |
| |
| **From now on, run all examples using a one-machine cluster. Running the scripts locally without any cluster has several disadvantages, most notably having only one reducer per job.** | |
| |
| === MapReduce extended === | === MapReduce extended === |
| * [[.:mapreduce-tutorial:Step 8]]: Multiple mappers, reducers and partitioning. | * [[.:mapreduce-tutorial:Step 8]]: Multiple mappers, reducers and partitioning. |
| Hadoop properties | * [[.:mapreduce-tutorial:Step 9]]: Hadoop properties. |
| * sorting | * [[.:mapreduce-tutorial:Step 10]]: Combiners. |
| Combiners | * [[.:mapreduce-tutorial:Step 11]]: Initialization and cleanup of MR tasks, performance of combiners. |
| setup, cleanup, perl inplace | * [[.:mapreduce-tutorial:Step 12]]: Additional output from mappers and reducers. |
| Work dir | |
| | === 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. |
| | * [[.:mapreduce-tutorial:Step 13]]: Sorting. |
| | * [[.:mapreduce-tutorial:Step 14]]: N-gram language model. |
| | * [[.:mapreduce-tutorial:Step 15]]: K-means clustering. |
| | |
| | === Beyond MapReduce === |
| | * [[.:mapreduce-tutorial:Step 16]]: Implementing iterative MapReduce jobs faster using All-Reduce. |
| | |
| | ===== Day 2 ===== |
| | |
| | Today we will be using the [[http://hadoop.apache.org/common/docs/r1.0.0/api/index.html|Java API]]. |
| | |
| | === Environment === |
| | * [[.:mapreduce-tutorial:Step 21]]: Preparing the environment. |
| | * [[.:mapreduce-tutorial:Step 22]]: Optional -- Setting Eclipse. |
| | |
| | === Java Hadoop basics ==== |
| | * [[.:mapreduce-tutorial:Step 23]]: Predefined formats and types. |
| | * [[.:mapreduce-tutorial:Step 24]]: Mappers, running Java Hadoop jobs, counters. |
| | * [[.:mapreduce-tutorial:Step 25]]: Reducers, combiners and partitioners. |
| | * [[.:mapreduce-tutorial:Step 26]]: Compression and job configuration. |
| | * [[.:mapreduce-tutorial:Step 27]]: Running multiple Hadoop jobs in one source file. |
| | |
| | === Advanced topics === |
| | * [[.:mapreduce-tutorial:Step 28]]: Custom data types. |
| | * [[.:mapreduce-tutorial:Step 29]]: Custom sorting and grouping comparators. |
| | * [[.:mapreduce-tutorial:Step 30]]: Custom input formats. |
| |
| N-grams | === Beyond MapReduce === |
| K-means and Iterations | * [[.:mapreduce-tutorial:Step 31]]: Implementing iterative MapReduce jobs faster using All-Reduce. |
| |
| ===== Other ===== | ===== Other ===== |
| * [[user:majlis:hadoop|Further information]] | * [[user:majlis:hadoop|Further information]] |
| |
