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

HDFS high availability in Hadoop 3.x


With Hadoop 2.0, active and standby NameNodes were introduced. At any point, out of two NameNodes, one will always be in active state and other will be in standby state. The active NameNode is the one that's responsible for any client requests in the cluster. Standby NameNodes are slave nodes whose responsibility is to keep its state in sync with the active NameNode so that it can provide fast failover in the event of failover. However, what if one of the NameNodes fails? In that case, the NameNode would become non-HA. This means that NameNodes can only tolerate up to one failure. This behavior is the opposite of the core fault -tolerant behavior of Hadoop, which certainly can accommodate more than one failure of DataNodes in a cluster. Keeping that in mind, provisions of more than one standby NameNode was introduced in Hadoop 3. The behavior of additional standby NameNodes will still be the same as any other standby NameNode. They will have their own...