Question

Briefly and thoroughly explain how MapReduce algorithms are implemented using Task Parallelism and Data Parallelism

Answer 1

let's talk about what is MapReduce...MapReduce is a processing technique for distributed computing based on java. Which contains two tasks, Map and Reduce.

actually, the 'map' takes some of the data and converts it into another set of data, where individual elements are broken down into tuples. The 'reduce' task takes the output from a map as an input and group those data tuples into a smaller set of tuples.

Map-reduce Is based on a parallel programming framework to process large amounts of data spread across different systems.
Data Parallelism and task Parallelism techniques are used for implementing the map-reduce algorithms...
Data parallelism is the ability to work out multiple independent functioning in any order. In a parallel programming framework, the order of execution does not matter to the results of the equation in commutative operations.
The other goal of parallelism is identifying the logical 'tasks' that can be run in parallel as threads. developers need to implement dependency graphs in Parallel programming techniques, which can become much more as the number of shared information increases.MapReduce authorizes the parallelization across thousands of CPUs.

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