![]() There are two steps in this phase: splitting and mapping. There is also an optional phase known as the combiner phase. The MapReduce program is executed in three main phases: mapping, shuffling, and reducing. Image Source: CNBlogs Phases of MapReduce The following diagram summarizes how job trackers and task trackers work. Task trackers report the status of each assigned job to the job tracker. Each task tracker consists of a map task and reduces the task. The job tracker schedules jobs that have been submitted by clients. The reduce task plays the role of shuffling and reducing intermediate data into smaller units. The map task plays the role of splitting jobs into job-parts and mapping intermediate data. Image Source: Data Flair How job trackers and task trackers workĮvery job consists of two key components: mapping task and reducing task. The following diagram shows a simplified flow diagram for the MapReduce program. The reducer that will generate a final output stored in the HDFS will process the resulting output. The intermediate data will then be sorted and merged. The developer will write logic that satisfies the requirements of the organization or company. The job-parts will be used for the two main tasks in MapReduce: mapping and reducing. This master will then sub-divide the job into equal sub-parts. In the MapReduce architecture, clients submit jobs to the MapReduce Master.
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