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 Timeline server in Hadoop 3.x


The job history server in MapReduce provides the information about all the current and historical MapReduce. jobs details. The job history server was only able to capture the information about MapReduce jobs and it was not able to capture YARN level events and metrics. As we know, YARN has a capability to run applications other than MapReduce and thus, there was a need to have a YARN-specific application that can capture information about all the applications. The YARN Timeline server is responsible for retrieving current as well as historic information about applications. The metrics and information collected through a YARN Timeline server are generic in nature and hence have a common structure that helps in debugging the logs and capturing other metrics for any specific use. The Timeline server captures two types of information, which are as follows:

  • Application information: The application is submitted to the queue by the user and each application can...