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

Fair scheduler


In fair scheduling, all applications get almost an equal amount of the available resources. In fair scheduler, when the first application is submitted to YARN, it will assign all the available resources to the application. Now in any scenario, if the new application is submitted to the scheduler, the scheduler will start allocating resources to the new application until both the applications have almost an equal amount of resources for their execution. Unlike the two schedulers discussed before, the fair scheduler prevents applications from resource starvation and assures that the applications in the queue get the required memory for execution. The distribution of the minimum and maximum share resources are calculated by the scheduling queue by using the configuration provided in the fair scheduler. The application will get the amount of resources configured for the queue where the application is submitted and if a new application is submitted to the same queue, the total...