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

Chapter 8. Apache SparkML

So now that you've learned a lot about MLlib, why another ML API? First of all, it is a common task in data science to work with multiple frameworks and ML libraries as there are always advantages and disadvantages; mostly, it is a trade-off between performance and functionality. R, for instance, is the king when it comes to functionality--there exist more than 6000 R add-on packages. However, R is also one of the slowest execution environments for data science. SparkML, on the other hand, currently has relatively limited functionality but is one of the fastest libraries. Why is this so? This brings us to the second reason why SparkML exists.

The duality between RDD on the one hand and DataFrames and Datasets on the other is like a red thread in this book and doesn't stop influencing the machine learning chapters. As MLlib is designed to work on top of RDDs, SparkML works on top of DataFrames and Datasets, therefore making use of all the new performance benefits...