Book Image

Mastering Apache Spark 2.x - Second Edition

Book Image

Mastering Apache Spark 2.x - Second Edition

Overview of this book

Apache Spark is an in-memory, cluster-based Big Data processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and more. This book will take your knowledge of Apache Spark to the next level by teaching you how to expand Spark’s functionality and build your data flows and machine/deep learning programs on top of the platform. The book starts with a quick overview of the Apache Spark ecosystem, and introduces you to the new features and capabilities in Apache Spark 2.x. You will then work with the different modules in Apache Spark such as interactive querying with Spark SQL, using DataFrames and DataSets effectively, streaming analytics with Spark Streaming, and performing machine learning and deep learning on Spark using MLlib and external tools such as H20 and Deeplearning4j. The book also contains chapters on efficient graph processing, memory management and using Apache Spark on the cloud. By the end of this book, you will have all the necessary information to master Apache Spark, and use it efficiently for Big Data processing and analytics.
Table of Contents (21 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
10
Deep Learning on Apache Spark with DeepLearning4j and H2O

Using Kubernetes for provisioning containerized Spark applications


So what's in it for Apache Spark here? Let's assume we have a set of powerful nodes in our local data center. What is the advantage of using Kubernetes for deployment over just installing Apache Spark on bare metal? Let's take the question the other way round. Let's have a look at the disadvantages of using Kubernetes in this scenario. Actually, there is no disadvantage at all.

Note

The link http://domino.research.ibm.com/library/cyberdig.nsf/papers/0929052195DD819C85257D2300681E7B/$File/rc25482.pdf is a 2014 paper of IBM Research, stating that performance within a Docker container is nearly identical to bare metal.

So this means that the only disadvantage is the effort you invest in installing and maintaining Kubernetes. But what you gain are the following:

  • Easy installation and updates of Apache Spark and other additional software packages (such as Apache Flink, Jupyter, or Zeppelin)
  • Easy switching between different versions...