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Mastering Hadoop 3

Mastering Hadoop 3

By : Wong, Singh, Kumar
5 (1)
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Mastering Hadoop 3

Mastering Hadoop 3

5 (1)
By: Wong, Singh, 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 (21 chapters)
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1
Section 1: Introduction to Hadoop 3
6
Section 2: Hadoop Ecosystem
10
Section 3: Hadoop in the Real World
16
Section 4: Securing Hadoop

Resource Manager high availability

Resource Manager (RM) is the single point of failure in a YARN cluster as every request from a client goes through it. The Resource Manager also acts as a central system to allocate resources for various tasks. The failure of the resource manager will lead to failure of YARN and thus a client cannot obtain any information about the YARN cluster or a client cannot submit any application for execution. Therefore, it is important to implement high availability of Resource Manager to prevent any cluster failure. The following are a few important considerations for high availability:

  • Resource Manager state: It is very important to persist a resource manager state, which if stored in memory may be lost upon resource manager failure. If the state of the Resource Manager is available even after failure, we can restart the Resource Manager from...
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