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Apache Hive Essentials

You're reading from   Apache Hive Essentials Immerse yourself on a fantastic journey to discover the attributes of big data by using Hive

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783558575
Length 208 pages
Edition 1st 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 Selection and Scope 5. Data Manipulation 6. Data Aggregation and Sampling 7. Performance Considerations 8. Extensibility Considerations 9. Security Considerations 10. Working with Other Tools Index

Advanced aggregation – ROLLUP and CUBE


The ROLLUP statement enables a SELECT statement to calculate multiple levels of aggregations across a specified group of dimensions. The ROLLUP statement is a simple extension to the GROUP BY clause with high efficiency and minimal overhead to a query. Compared to GROUPING SETS that creates specified levels of aggregations, ROLLUP creates n+1 levels of aggregations, where n is the number of grouping columns. First, it calculates the standard aggregate values specified in the GROUP BY clause. Then, it creates higher-level subtotals, moving from right to left through the list of combinations of grouping columns, as shown in the following example:

GROUP BY a,b,c WITH ROLLUP

This is equivalent to the following:

GROUP BY a,b,c GROUPING SETS ((a,b,c),(a,b),(a),())

The CUBE statement takes a specified set of grouping columns and creates aggregations for all of their possible combinations. If n columns are specified for CUBE, there will be 2n combinations of...

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