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 (12 chapters)

Clusters, nodes and daemons


Cluster, node, and daemon is the terminology that we will use throughout this chapter. It is important to build a common understanding of the context around which these terms are used during this chapter.

  • Cluster: A cluster is a group of computers (nodes) that work together in many aspects, and are often viewed as a single system.
  • Node: A node is an individual component in the cluster.
  • Daemon: In multitasking computer operating systems, a daemon is a computer program that runs as a background process, rather than being under control of an interactive user.

So now that we have terminology out of the way, what exactly does Spark need to run on a cluster? How is it managed? Is it a master/slave architecture? All these are key questions and need to be answered to fully understand the way how Spark works on a set of machines that comprise a cluster. Let's refer to the classical Spark architecture diagram (reference spark.apache.org/docs/latest) to understand this in a...