Which techniques is used to optimize MapReduce jobs?

6 Best MapReduce Job Optimization Techniques

1. Proper configuration of your cluster.
2. LZO compression usage.
3. Proper tuning of the number of MapReduce tasks.
4. Combiner between Mapper and Reducer.
5. Usage of most appropriate and compact writable type for data.
6. Reusage of Writables.

What are the three main phases of a MapReduce job?

MapReduce program executes in three stages, namely map stage, shuffle stage, and reduce stage. Map stage − The map or mapper’s job is to process the input data.

What are the steps in a MapReduce job submission?

Steps of MapReduce Job Execution flow

1. Input Files. In input files data for MapReduce job is stored.
2. InputFormat. After that InputFormat defines how to split and read these input files.
3. InputSplits.
4. RecordReader.
5. Mapper.
6. Combiner.
7. Partitioner.
8. Shuffling and Sorting.

How do I tune a MapReduce job performance?

The best thumb rule for memory tuning to maximize the performance is to ensure that the MapReduce jobs do not trigger swapping. That means use as much memory as you can without triggering swapping. Softwares like Cloudera Manager, Nagios, or Ganglia can be used for monitoring the swap memory usage.

What are the important phases involved in MapReduce program?

The MapReduce program is executed in three main phases: mapping, shuffling, and reducing. There is also an optional phase known as the combiner phase.

What does a MapReduce job comprise of?

The MapReduce framework consists of a single master JobTracker and one slave TaskTracker per cluster-node. The master is responsible for scheduling the jobs’ component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves execute the tasks as directed by the master.

How do I run a MapReduce program?

Your answer

1. Now for exporting the jar part, you should do this:
2. Now, browse to where you want to save the jar file. Step 2: Copy the dataset to the hdfs using the below command: hadoop fs -put wordcountproblem​
3. Step 4: Execute the MapReduce code:
4. Step 8: Check the output directory for your output.

What custom object should you implement to reduce IO in MapReduce?

Which custom object should you implement to reduce IO in MapReduce? A combiner does not have a predefined interface and it must implement the Reducer interface’s reduce() method. A combiner operates on each map output key. It must have the same output key-value types as the Reducer class.