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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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Concepts
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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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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

Sampling

When the data volume is extra large, we may need to find a subset of data to speed up data analysis. This is sampling, a technique used to identify and analyze a subset of data in order to discover patterns and trends in the whole dataset. In HQL, there are three ways of sampling data: random sampling, bucket table sampling, and block sampling.

Random sampling

Random sampling uses the rand() function and LIMIT keyword to get the sampling of data, as shown in the following example. The DISTRIBUTE and SORT keywords are used here to make sure the data is also randomly distributed among mappers and reducers efficiently. The ORDER BY rand() statement can also achieve the same purpose, but the performance is not good:

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