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Apache Hive Essentials

You're reading from   Apache Hive Essentials Essential techniques to help you process, and get unique insights from, big data

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788995092
Length 210 pages
Edition 2nd Edition
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Author (1):
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Dayong Du Dayong Du
Author Profile Icon Dayong Du
Dayong Du
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Toc

Table of Contents (12) Chapters Close

Preface 1. Overview of Big Data and Hive FREE CHAPTER 2. Setting Up the Hive Environment 3. Data Definition and Description 4. Data Correlation and Scope 5. Data Manipulation 6. Data Aggregation and Sampling 7. Performance Considerations 8. Extensibility Considerations 9. Security Considerations 10. Working with Other Tools 11. Other Books You May Enjoy

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...
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