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courses:mapreduce-tutorial [2012/01/25 00:54] straka |
courses:mapreduce-tutorial [2012/01/27 23:31] straka |
* [[.: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. |
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**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.** | |
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=== MapReduce extended === | === MapReduce extended === |
Setup, cleanup | 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. |
Multiple reducers + Partitions | * [[.:mapreduce-tutorial:Step 8]]: Multiple mappers, reducers and partitioning. |
Combiners, perl inplace | * [[.:mapreduce-tutorial:Step 9]]: Hadoop properties. |
Work dir | * [[.:mapreduce-tutorial:Step 10]]: Combiners. |
Hadoop properties | * [[.:mapreduce-tutorial:Step 11]]: Initialization and cleanup of MR tasks, performance of combiners. |
| * [[.:mapreduce-tutorial:Step 12]]: Additional output from mappers and reducers. |
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| === 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 |
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| ===== Day 2 ===== |
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| Today we will be using the [[http://hadoop.apache.org/common/docs/r1.0.0/api/index.html|Java API]]. |
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| === Environment === |
| * [[.:mapreduce-tutorial:Step 21]]: Preparing the environment. |
| * [[.:mapreduce-tutorial:Step 22]]: Optional -- Setting Eclipse. |
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| === Java Hadoop basics ==== |
| * [[.:mapreduce-tutorial:Step 23]]: Predefined formats and types. |
| * [[.:mapreduce-tutorial:Step 24]]: Mappers, running Java Hadoop jobs. |
| * [[.:mapreduce-tutorial:Step 25]]: Reducers, combiners and partitioners. |
| * [[.:mapreduce-tutorial:Step 26]]: Job configuration, counters and job context. |
| * [[.:mapreduce-tutorial:Step 27]]: Reusing Mapper and Reducer code. |
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| === Exercises === |
| * Is [[.:mapreduce-tutorial:Step 13]], [[.:mapreduce-tutorial:Step 14]] and [[.:mapreduce-tutorial:Step 15]] enough? |
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N-grams | === Advanced topics === |
K-means and Iterations | * Custom input format -- WholeFile and WholeFileAsPath |
| * Custom data type -- Pair<A, B> |
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===== Other ===== | ===== Other ===== |
* [[user:majlis:hadoop|Further information]] | * [[user:majlis:hadoop|Further information]] |
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