The job history server in MapReduce provides the information about all the current and historical MapReduce. jobs details. The job history server was only able to capture the information about MapReduce jobs and it was not able to capture YARN level events and metrics. As we know, YARN has a capability to run applications other than MapReduce and thus, there was a need to have a YARN-specific application that can capture information about all the applications. The YARN Timeline server is responsible for retrieving current as well as historic information about applications. The metrics and information collected through a YARN Timeline server are generic in nature and hence have a common structure that helps in debugging the logs and capturing other metrics for any specific use. The Timeline server captures two types of information, which...
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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