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 to go from Unresolved Logical Execution Plan to Resolved Logical Execution Plan


The ULEP basically reflects the structure of an AST. So again, the AST is generated from the user's code implemented either on top of the relational API of DataFrames and Datasets or using SQL, or all three. This AST can be easily transformed into a ULEP. But, of course, a ULEP can't be executed. The first thing that is checked is if the referred relations exist in the catalog. This means all table names and fields expressed in the SQL statement or relational API have to exist. If the table (or relation) exists, the column names are verified. In addition, the column names that are referred to multiple times are given an alias in order to read them only once. This is already a first stage optimization taking place here. Finally, the data types of the columns are determined in order to check if the operations expressed on top of the columns are valid. So for example taking the sum of a string doesn't work and...