Java Multithreading and Parallel Programming Masterclass - [Project] - Simulating a MapReduce Job with Threads - Part 1

This lecture introduces the concept of big data and distributed processing, focusing on the MapReduce programming model. It explains how data is distributed across clusters using HDFS and describes the three main steps of a MapReduce job: map, shuffle, and reduce operations. A word count example is used to illustrate these concepts. The lecture also simulates a MapReduce job using threads to demonstrate the importance of synchronization in parallel processing.

What is the main purpose of the MapReduce programming model?

In the context of MapReduce, what is the role of a distributed file system?

What are the three main steps involved in a MapReduce job?

Describe the key-value format required for the intermediate results in the map operation.

Explain the significance of the shuffle operation in a MapReduce job.

How does the reduce operation function in a MapReduce job?

What is the purpose of partitioning input data in a MapReduce job?