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

How transparent fault tolerance and exactly-once delivery guarantee is achieved


Apache Spark structured streaming supports full crash fault tolerance and exactly-once delivery guarantee without the user taking care of any specific error handling routines. Isn't this amazing? So how is this achieved?

Note

Full crash fault tolerance and exactly-once delivery guarantee are terms of systems theory. Full crash fault tolerance means that you can basically pull the power plug of the whole data center at any point in time, and no data is lost or left in an inconsistent state. Exactly-once delivery guarantee means, even if the same power plug is pulled, it is guaranteed that each tuple- end-to-end from the data source to the data sink - is delivered - only, and exactly, once. Not zero times and also not more than one time. Of course those concepts must also hold in case a single node fails or misbehaves (for example- starts throttling).

First of all, states between individual batches and offset ranges...