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Spark Cookbook

You're reading from  Spark Cookbook

Product type Book
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Pages 226 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav

Table of Contents (19) Chapters

Spark Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Loading and saving data using the Parquet format


Apache Parquet is a columnar data storage format, specifically designed for big data storage and processing. Parquet is based on record shredding and assembly algorithm in the Google Dremel paper. In Parquet, data in a single column is stored contiguously.

The columnar format gives Parquet some unique benefits. For example, if you have a table with 100 columns and you mostly access 10 columns, in a row-based format you will have to load all 100 columns, as granularity level is at row level. But, in Parquet, you will only load 10 columns. Another benefit is that since all the data in a given column is of the same datatype (by definition), compression is much more efficient.

How to do it...

  1. Open the terminal and create the person data in a local file:

    $ mkdir person
    $ echo "Barack,Obama,53" >> person/person.txt
    $ echo "George,Bush,68" >> person/person.txt
    $ echo "Bill,Clinton,68" >> person/person.txt
    
  2. Upload the person directory...

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