Book Image

Apache Hive Essentials. - Second Edition

By : Dayong Du
Book Image

Apache Hive Essentials. - Second Edition

By: Dayong Du

Overview of this book

In this book, we prepare you for your journey into big data by frstly introducing you to backgrounds in the big data domain, alongwith the process of setting up and getting familiar with your Hive working environment. Next, the book guides you through discovering and transforming the values of big data with the help of examples. It also hones your skills in using the Hive language in an effcient manner. Toward the end, the book focuses on advanced topics, such as performance, security, and extensions in Hive, which will guide you on exciting adventures on this worthwhile big data journey. By the end of the book, you will be familiar with Hive and able to work effeciently to find solutions to big data problems
Table of Contents (12 chapters)


Views are logical data structures that can be used to simplify queries by hiding the complexities, such as joins, subqueries, and filters. It is called logical because views are only defined in metastore without the footprint in HDFS. Unlike what's in the relational database, views in HQL do not store data or get materialized. Once the view is created, its schema is frozen immediately. Subsequent changes to the underlying tables (for example, adding a column) will not be reflected in the view's schema. If an underlying table is dropped or changed, subsequent attempts to query the invalid view will fail. In addition, views are read-only and may not be used as the target of the LOAD/INSERT/ALTER statements.

The following is an example of a view creation statement:

> CREATE VIEW IF NOT EXISTS employee_skills
> AS
> name, skills_score['DB...