Asked by: Anahit Shamovtechnology and computing programming languages
What are the main configuration parameters that user need to specify to run MapReduce job?
Last Updated: 1st May, 2020
- Job's input locations in the distributed file system.
- Job's output location in the distributed file system.
- Input format of data.
- Output format of data.
- Class containing the map function.
- Class containing the reduce function.
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Similarly one may ask, what are the main configuration parameters in a MapReduce program?
The main configuration parameters in “MapReduce” framework are:
- Input location of Jobs in the distributed file system.
- Output location of Jobs in the distributed file system.
- The input format of data.
- The output format of data.
- The class which contains the map function.
- The class which contains the reduce function.
Also Know, what are the parameters of mappers and reducers? The four parameters for mappers are:
- LongWritable (input)
- text (input)
- text (intermediate output)
- IntWritable (intermediate output)
Similarly, what are the main components of MapReduce job?
- Main driver class which provides job configuration parameters.
- Mapper class which must extend org. apache. hadoop. mapreduce. Mapper class and provide implementation for map () method.
- Reducer class which should extend org. apache. hadoop. mapreduce. Reducer class.
What is partitioner and how it helps in MapReduce job process?
Partitioner in MapReduce job execution controls the partitioning of the keys of the intermediate map-outputs. With the help of hash function, key (or a subset of the key) derives the partition. Records as having the same key value go into the same partition (within each mapper).