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Hadoop Beginner's Guide

You're reading from   Hadoop Beginner's Guide Get your mountain of data under control with Hadoop. This guide requires no prior knowledge of the software or cloud services ‚Äì just a willingness to learn the basics from this practical step-by-step tutorial.

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Product type Paperback
Published in Feb 2013
Publisher Packt
ISBN-13 9781849517300
Length 398 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (19) Chapters Close

Hadoop Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. What It's All About FREE CHAPTER 2. Getting Hadoop Up and Running 3. Understanding MapReduce 4. Developing MapReduce Programs 5. Advanced MapReduce Techniques 6. When Things Break 7. Keeping Things Running 8. A Relational View on Data with Hive 9. Working with Relational Databases 10. Data Collection with Flume 11. Where to Go Next Pop Quiz Answers Index

Time for action – performing a join


Joins are a very frequently used tool in SQL, though sometimes appear a little intimidating to those new to the language. Essentially a join allows rows in multiple tables to be logically combined together based on a conditional statement. Hive has rich support for joins which we will now examine.

  1. Create the following as join.hql:

    SELECT t1.sighted, t2.full_name
    FROM ufodata t1 JOIN states t2
    ON (LOWER(t2.abbreviation) = LOWER(SUBSTR( t1.sighting_location, (LENGTH(t1.sighting_location)-1)))) 
    LIMIT 5 ;
  2. Execute the query:

    $ hive -f join.hql
    

    You will receive the following response:

    OK
    20060930  Alaska
    20051018  Alaska
    20050707  Alaska
    20100112  Alaska
    20100625  Alaska
    Time taken: 33.255 seconds
    

What just happened?

The actual join query is relatively straightforward; we want to extract the sighted date and location for a series of records but instead of the raw location field, we wish to map this into the full state name. The HiveQL file we created performs...

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