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 the workings of the Catalyst Optimizer


So how does the optimizer work? The following figure shows the core components and how they are involved in a sequential optimization process:

First of all, it has to be understood that it doesn't matter if a DataFrame, the Dataset API, or SQL is used. They all result in the same Unresolved Logical Execution Plan (ULEP). A QueryPlan is unresolved if the column names haven't been verified and the column types haven't been looked up in the catalog. A Resolved Logical Execution Plan (RLEP) is then transformed multiple times, until it results in an Optimized Logical Execution Plan. LEPs don't contain a description of how something is computed, but only what has to be computed. The optimized LEP is transformed into multiple Physical Execution Plans (PEP) using so-called strategies. Finally, an optimal PEP is selected to be executed using a cost model by taking statistics about the Dataset to be queried into account. Note that the final execution...