January 2017
Intermediate to advanced
350 pages
7h 8m
English
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Chapter 7. Large-Scale MapReduce
In this chapter, we will consider how to write MapReduce jobs, how to design a large-scale MapReduce using HBase, how the internals of it work, and how to optimize the HBase framework to do it. In doing so, we will discuss the following:
- MapReduce frameworks
- When to use MapReduce and when not to
- Case study with example code and explanations
Introduction
HBase provides various ways to leverage the potential of MapReduce based on the stack and the architecture you are going to use.
Before we start, let's do a quick revisit to the components, which will be used in MapReduce:
- Record reader
- Mapper
- Combiner
- Practitioner
- Shuffle and sort
- Reduce
- Output format
- Record reader: The core responsibility of a record reader is to analyze the ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.