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)

Basic aggregation

Data aggregation is the process of gathering and expressing data in a summary to get more information about particular groups based on specific conditions. HQL offers several built-in aggregate functions, such as max(...), min(...), and avg(...). It also supports advanced aggregation using keywords such as GROUPING SETS, ROLLUP, and CUBE, and different types of window function.

The basic built-in aggregate functions are usually used with the GROUP BY clause. If there is no GROUP BY clause specified, it aggregates over the whole row (all columns) by default. Besides aggregate functions, all columns selected must also be included in the GROUP BY clause. The following are a few examples involving the built-in aggregate functions:

  1. Aggregation without GROUP BY columns:
      > SELECT 
> count(*) as rowcnt1,
> count(1) as rowcnt2 -- same to...