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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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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

Buckets

Besides partition, the bucket is another technique to cluster datasets into more manageable parts to optimize query performance. Different from a partition, a bucket corresponds to segments of files in HDFS. For example, the employee_partitioned table from the previous section uses year and month as the top-level partition. If there is a further request to use employee_id as the third level of partition, it creates many partition directories. For instance, we can bucket the employee_partitioned table using employee_id as a bucket column. The value of this column will be hashed by a user-defined number of buckets. The records with the same employee_id will always be stored in the same bucket (segment of files). The bucket columns are defined by CLUSTERED BY keywords. It is quite different from partition columns since partition columns refer to the directory, while bucket...

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