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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

HPL/SQL

Since Hive v2.0.0, the Hadoop Procedure Language SQL (HPL/SQL) (http://www.hplsql.org/) available to provide store procedure programming in Hive. HPL/SQL supports Hive, Spark SQL, and Impala, and is compatible with Oracle, DB2, MySQL, and TSQL standard. One of its benefits is making the migration of existing database-stored procedures to Hive easy and efficient. Using HPL/SQL does not require Java skills to implement what can be done through UDF mentioned. Compared with UDF, HPL/SQL's performance is a little slower and it is still new for production usage.

The following is an example of creating a stored procedure. HPL/SQL supports the creation of both Function and Procedure:

$ cat getEmpCnt.pl
CREATE PROCEDURE getCount()
BEGIN
DECLARE cnt INT = 0;
SELECT COUNT(*) INTO cnt FROM employee;
PRINT 'Users cnt: ' || cnt;
END;

call getCount(); -- Call a procedure

In order...

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