Book Image

Mastering Hadoop 3

By : Chanchal Singh, Manish Kumar
Book Image

Mastering Hadoop 3

By: Chanchal Singh, Manish Kumar

Overview of this book

Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines.
Table of Contents (23 chapters)
Title Page
Dedication
About Packt
Foreword
Contributors
Preface
Index

MapReduce workflow in the Hadoop framework


The MapReduce execution goes through various steps and each step has scope for a little optimization. In the previous sections, we have covered the components of the MapReduce framework and now we will briefly look into the MapReduce execution flow, which will help us understand how each component interacts with each other. The following diagram gives a brief overview about the MapReduce execution flow. We have divided the diagram into smaller parts so that each step looks easier to understand. The step numbers are mentioned over arrow connectors and the last arrow in the diagram connects to the following diagram in the section: 

We will explain the different steps of the MapReduce internal flow here as follows:

  1. The InputFormat is the starting point of any MapReduce application. It is defined in the job configuration in the Driver class of the application, for example, job.setInputFormatClass(TextInputFormat.class). The InputFormat helps in understanding...