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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

Design optimization


Design optimization covers several data layout and design strategies to improve performance.

Partition tables

Hive partitioning is one of the most effective methods to improve the query performance on larger tables. The query with partition filtering will only load the data in the specified partitions (subdirectories), so it can execute much faster than a normal query that filters by a non-partitioning field. The selection of partition key is always an important factor for performance. It should always be a low cardinal attribute to avoid many subdirectories overhead.

The following are some commonly used dimensions as partition keys:

  • Partitions by date and time: Use date and time, such as year, month, and day (even hours), as partition keys when data is associated with the time dimension

  • Partitions by locations: Use country, territory, state, and city as partition keys when data is location related

  • Partitions by business logics: Use department, sales region, applications...

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