Book Image

Apache Hadoop 3 Quick Start Guide

By : Hrishikesh Vijay Karambelkar
Book Image

Apache Hadoop 3 Quick Start Guide

By: Hrishikesh Vijay Karambelkar

Overview of this book

Apache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS. The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring. You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster.
Table of Contents (10 chapters)

High availability of Hadoop

We have seen the architecture of Apache Hadoop in a Chapter 1, Hadoop 3.0 - Background and Introduction. In this section, we will go through the High Availability (HA) feature of Apache Hadoop, given the fact that HDFS supports high availability through its replication factor. However, in earlier Apache Hadoop 1.X, NameNode was the single point of failure due to it being a central gateway for accessing data blocks. Similarly, Resource Manager is responsible for managing resources for MapReduce and YARN applications. We will study both of these points with respect to high availability.

High availability for NameNode

We have understood the challenges faced with Hadoop 1.x, so now let's understand...