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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 2. Getting Hadoop Up and Running FREE CHAPTER 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 – defining the schema


Let's now create this simplified UFO schema in a single Avro schema file.

Create the following as ufo.avsc:

{ "type": "record",
  "name": "UFO_Sighting_Record",
  "fields" : [
    {"name": "sighting_date", "type": "string"},
    {"name": "city", "type": "string"},
    {"name": "shape", "type": ["null", "string"]}, 
    {"name": "duration", "type": "float"}
] 
}

What just happened?

As can be seen, Avro uses JSON in its schemas, which are usually saved with the .avsc extension. We create here a schema for a format that has four fields, as follows:

  • The Sighting_date field of type string to hold a date of the form yyyy-mm-dd

  • The City field of type string that will contain the city's name where the sighting occurred

  • The Shape field, an optional field of type string, that represents the UFO's shape

  • The Duration field gives a representation of the sighting duration in fractional minutes

With the schema defined, we will now create some sample data.

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