Master MapReduce: Principles, Process, and 7 Hands‑On Examples
This tutorial quickly introduces the MapReduce model, explains its core principles and execution flow, and guides you through seven practical examples—from basic WordCount to custom serialization, partitioning, joins, and friend‑recommendation—while providing test data and an optional ready‑made Hadoop environment for hands‑on practice.
1. Tutorial Content
1) Objective
Help you quickly understand the working mechanism and development method of MapReduce.
The tutorial aims to solve the following questions:
What are the basic principles of MapReduce?
What is the execution process of MapReduce?
What are the core workflow details of MapReduce?
How to develop MapReduce programs? (Master through seven examples)
Test data files used in the program examples are provided for direct download.
If you prefer not to set up a Hadoop environment yourself, you can download the environment supplied with this tutorial; the practical section explains how to use it.
After completing the learning and practice, you will have a clear understanding of MapReduce's working principles and programming approach.
2) Content Outline
MapReduce basic principles
MapReduce introductory example – WordCount
Analysis of MapReduce execution process
Example 1 – Custom object serialization
Example 2 – Custom partitioning
Example 3 – Find the record with the maximum amount in each order group
Example 4 – Merging multiple small files
Example 5 – Grouped output to multiple files
MapReduce core workflow summary
Example 6 – Join operation
Example 7 – Compute common friends between users
2. Download Method
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