• Part 1: Monday January 30, 14:00-17:00, lab SU2
• Part 2: Tuesday January 31, 14:00-17:00, lab SU2

• Introduction

Today we will be using the Perl API (there is no need to study it now, the tutorial will explain it).

• Step 1: Setting the environment.

• Step 2: Input and output format, testing data.
• Step 3: Basic mapper.
• Step 4: Counters.
• Step 5: Basic reducer.

• Step 6: Running on cluster.
• Step 7: Dynamic Hadoop cluster for several computations.

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.

• Step 8: Multiple mappers, reducers and partitioning.
• Step 9: Hadoop properties.
• Step 10: Properties of reducers, combiners.
• Step 11: Initialization and cleanup of MR tasks.
• Step 12: Additional output from mappers and reducers.

• Step 13: Sorting
• Step 14: N-gram language model
• Step 15: K-means algorithm

• Further information