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

Increased performance with good old friends


As in Apache SparkSQL for batch processing and, as Apache Spark structured streaming is part of Apache SparkSQL, the Planner (Catalyst) creates incremental execution plans as well for mini batches. This means that the whole streaming model is based on batches. This is the reason why a unified API for streams and batch processing could be achieved. The price we pay is that Apache Spark streaming sometimes has drawbacks when it comes to very low latency requirements (sub-second, in the range of tens of ms). As the name Structured Streaming and the usage of DataFrames and Datasets implies, we are also benefiting from performance improvements due to project Tungsten, which has been introduced in a previous chapter. To the Tungsten engine itself, a mini batch doesn't look considerably different from an ordinary batch. Only Catalyst is aware of the incremental nature of streams. Therefore, as of Apache Spark V2.2, the following operations are not (yet...