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

YARN and MapReduce


We have covered enough information about YARN in previous chapters. In this section, we will talk about the execution of MapReduce over YARN. The JobTracker in Hadoop version 1 has a bottleneck due to a scalability limit of 4,000 nodes. Yahoo realizes that their current requirement needs a scaling of up to 20,000 nodes. The latter was certainly not possible due to the legacy architecture of the job tracker. Yahoo then introduced YARN, which broke the function of the job tracker for efficient management. We covered the detail architecture in Chapter 3, YARN Resource Management in Hadoop

The node manager in YARN has enough memory to launch multiple containers. The application master can request any number of containers from the resource manager, which keeps track of the available resources in the YARN cluster. The job type is not limited to MapReduce; instead, YARN can launch any type of application. Let's take a look at the life cycle of a MapReduce application on YARN...