Book Image

Hadoop Beginner's Guide

Book Image

Hadoop Beginner's Guide

Overview of this book

Data is arriving faster than you can process it and the overall volumes keep growing at a rate that keeps you awake at night. Hadoop can help you tame the data beast. Effective use of Hadoop however requires a mixture of programming, design, and system administration skills."Hadoop Beginner's Guide" removes the mystery from Hadoop, presenting Hadoop and related technologies with a focus on building working systems and getting the job done, using cloud services to do so when it makes sense. From basic concepts and initial setup through developing applications and keeping the system running as the data grows, the book gives the understanding needed to effectively use Hadoop to solve real world problems.Starting with the basics of installing and configuring Hadoop, the book explains how to develop applications, maintain the system, and how to use additional products to integrate with other systems.While learning different ways to develop applications to run on Hadoop the book also covers tools such as Hive, Sqoop, and Flume that show how Hadoop can be integrated with relational databases and log collection.In addition to examples on Hadoop clusters on Ubuntu uses of cloud services such as Amazon, EC2 and Elastic MapReduce are covered.
Table of Contents (19 chapters)
Hadoop Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
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...