Both sides previous revision
Previous revision
Next revision
|
Previous revision
|
courses:mapreduce-tutorial [2012/01/29 22:00] straka Deleted a dot. |
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 extended === | === MapReduce extended === |
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. | |
* [[.: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. |
* [[.: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 ===== |
=== 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 and job configuration. | * [[.:mapreduce-tutorial:Step 26]]: Compression and job configuration. |
| * [[.:mapreduce-tutorial:Step 27]]: Running multiple Hadoop jobs in one source file. |
| |
=== Advanced topics === | === Advanced topics === |
* [[.:mapreduce-tutorial:Step 27]]: Custom data types. | * [[.:mapreduce-tutorial:Step 28]]: Custom data types. |
* [[.:mapreduce-tutorial:Step 28]]: Running multiple Hadoop jobs in one class. | * [[.:mapreduce-tutorial:Step 29]]: Custom sorting and grouping comparators. |
* [[.:mapreduce-tutorial:Step 29]]: Custom input formats. | * [[.:mapreduce-tutorial:Step 30]]: Custom input formats. |
| |
=== Beyond MapReduce === | === Beyond MapReduce === |
* [[.:mapreduce-tutorial:Step 30]]: Implementing iterative MapReduce jobs faster using All-Reduce. | * [[.:mapreduce-tutorial:Step 31]]: Implementing iterative MapReduce jobs faster using All-Reduce. |
| |
===== Other ===== | ===== Other ===== |
* [[user:majlis:hadoop|Further information]] | * [[user:majlis:hadoop|Further information]] |
| |