Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (18 chapters)
Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface

Chapter 8. Operating in Clustered Mode

We have covered the functional and exciting aspects of Spark and how it provides a whole range of APIs in the areas of streaming, machine learning and graph. However, we have not gone into the details of how does Spark work across multiple machines, which is necessary for working with bigger data sets. Spark framework is a cluster agnostic framework and can run on multiple clusters. In this chapter we are going to cover the following key topics:

  • What is a Cluster?
  • What is Standalone cluster mode?
  • Deploying Spark on a Standalone cluster
  • What is YARN?
  • Deploying Spark on YARN
  • What is Mesos?
  • Deploying Spark on Mesos
  • Optimization

I believe that while it is important to focus on the cool aspects of a data application like which algorithm to choose, how to build a data pipeline and so on, it is equal important to deploy the application so that the value from effort spent on building the application can be realized.

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