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

Managing temporary views with the catalog API


Since Apache Spark 2.0, the catalog API is used to create and remove temporary views from an internal meta store. This is necessary if you want to use SQL, because it basically provides the mapping between a virtual table name and a DataFrame or Dataset.

Internally, Apache Spark uses the org.apache.spark.sql.catalyst.catalog.SessionCatalog class to manage temporary views as well as persistent tables.

Temporary views are stored in the SparkSession object, as persistent tables are stored in an external metastore. The abstract base class org.apache.spark.sql.catalyst.catalog.ExternalCatalog is extended for various meta store providers. One already exists for using Apache Derby and another one for the Apache Hive metastore, but anyone could extend this class and make Apache Spark use another metastore as well.