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Learning Hadoop 2

You're reading from   Learning Hadoop 2 Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783285518
Length 382 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction FREE CHAPTER 2. Storage 3. Processing – MapReduce and Beyond 4. Real-time Computation with Samza 5. Iterative Computation with Spark 6. Data Analysis with Apache Pig 7. Hadoop and SQL 8. Data Lifecycle Management 9. Making Development Easier 10. Running a Hadoop Cluster 11. Where to Go Next Index

Fundamentals of Apache Pig


The primary interface to program Apache Pig is Pig Latin, a procedural language that implements ideas of the dataflow paradigm.

Pig Latin programs are generally organized as follows:

  • A LOAD statement reads data from HDFS

  • A series of statements aggregates and manipulates data

  • A STORE statement writes output to the filesystem

  • Alternatively, a DUMP statement displays the output to the terminal

The following example shows a sequence of statements that outputs the top 10 hashtags ordered by the frequency, extracted from the dataset of tweets:

tweets = LOAD 'tweets.json' 
  USING JsonLoader('created_at:chararray, 
    id:long, 
    id_str:chararray, 
    text:chararray');

hashtags = FOREACH tweets {
  GENERATE FLATTEN(
    REGEX_EXTRACT(
      text, 
      '(?:\\s|\\A|^)[##]+([A-Za-z0-9-_]+)', 1)
    ) as tag;
}

hashtags_grpd = GROUP hashtags BY tag;
hashtags_count = FOREACH hashtags_grpd {
  GENERATE 
    group, 
    COUNT(hashtags) as occurrencies; 
}
hashtags_count_sorted...
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