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

Lazy persist writes in HDFS


Enterprise adoption of Hadoop is growing day by day. With increased adoption, there are a variety of application types that are using Hadoop for their enterprise goals. One such adoption is for applications that need to deal with data that amounts to only a few GBs. Keeping performance goals in mind with such small records would incur more latency costs when DISK I/O writes are involved during its execution—especially when such volumes of data can easily fit into memory without any DISK I/O. With the release of Hadoop 2.6, provisions for writes have been introduced that will use the off-heap memory of DataNodes. Eventually, data from memory will be flushed out to disk asynchronously. This will remove any expensive Disk I/O and computations for checksum while write operations are initiated from the HDFS client. Such asynchronous writes are called lazy persist writes, where persistence to disk does not happen immediately but asynchronously after some time. HDFS...