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

Erasure encoding in Hadoop 3.x


HDFS achieves fault-tolerance by replicating each block three times by default. However, in big clusters, the replication factor can be more. The purpose of replication is to handle data loss against machine failure, providing data locality for the MapReduce job, and so on. The replication takes more storage space, which means that if our replication factor is three, HDFS will take an extra 200% space to store file. In short, storing 1 GB of data will require 3 GB of memory. This also causes metadata memory on NameNode.

HDFS introduced erasure coding (EC) for storing data by taking less storage space. Now, data is labelled based on their usage access pattern, and after the conditions for erasure coding have been satisfied, data will be applicable for erasure coding. The term Data Temperature is used to identify the data usage pattern. The different types of data are as follows:

  • Hot data: By default, all data is considered HOT. Data that is accessed more than...