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

Opportunistic containers in Hadoop 3.x


Containers are allocated to nodes by the scheduler only when there is sufficient unallocated resources at a node. YARN guarantees that once the application master dispatches a container to a node, the execution will immediately start. The execution of a container will only be completed if there is no violation of fairness or capacity, which means until some other containers ask for preemption of resources from the node, the container is guaranteed to run to completion.

The current container execution design allows an efficient task execution but it has two primary limitations, which are as follows:

  • Heartbeat delay: The Node Manager at regular intervals sends heartbeats to its resource manager and the heartbeat request also contains the resource metrics of a Node Manager. If any container running on a Node Manager finishes its execution then the information is sent as part of request in the next heartbeat, which means that the Resource Manager knows that...