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

Understanding Kubernetes


Kubernetes (K8s) is an orchestrator of containers. It supports various container technologies, but here we'll concentrate on Docker.

So let's have a look at the Kubernetes architecture:

As in nearly every cluster infrastructure, there exists a master node managing the whole of the cluster. So let's have a brief look at the responsibilities of each of its components:

  • API server: The API server provides a means of communication between the Kubernetes cluster and external system administrators. It provides a REST API used by the kubectl command line tool. This API can also be used by other consumers, making it possible to plug-in Kubernetes in existing infrastructures and automated processes.
  • Controller manager: The controller manager is responsible for managing the core controller processes within a Kubernetes cluster.
  • Scheduler: The scheduler is responsible...