In the previous sections, we talked about the YARN architecture and its components. The Resource Manager has two major components; namely, the application manager and the scheduler. The Resource Manager scheduler is responsible for allocating the required resources to an application based on schedule policies. Before YARN, Hadoop used to allocate slots for map and reduce tasks from available memory, which restricts reduce tasks to run on slots allocated for map tasks and the other way around. YARN does not define map and reduce slots initially. Based on a request, it launches containers for tasks. This means that if any free container is available, it will be used for map or reduce tasks. As previously discussed in this chapter, the scheduler will not perform monitoring or status tracking for the any application. The scheduler receives...
Mastering Hadoop 3
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Mastering Hadoop 3
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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)
Preface
Journey to Hadoop 3
Deep Dive into the Hadoop Distributed File System
YARN Resource Management in Hadoop
Internals of MapReduce
Section 2: Hadoop Ecosystem
SQL on Hadoop
Real-Time Processing Engines
Widely Used Hadoop Ecosystem Components
Section 3: Hadoop in the Real World
Designing Applications in Hadoop
Real-Time Stream Processing in Hadoop
Machine Learning in Hadoop
Hadoop in the Cloud
Hadoop Cluster Profiling
Section 4: Securing Hadoop
Who Can Do What in Hadoop
Network and Data Security
Monitoring Hadoop
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