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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Apache Spark 2. Developing Applications with Spark FREE CHAPTER 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 data from HDFS

HDFS is the most widely used big data storage system. One of the reasons for the wide adoption of HDFS is schema-on-read. What this means is that HDFS does not put any restriction on data when data is being written. Any and all kinds of data are welcome and can be stored in a raw format. This feature makes it ideal storage for raw unstructured data and semi-structured data.

When it comes to reading data, even unstructured data needs to be given some structure to make sense. Hadoop uses InputFormat to determine how to read the data. Spark provides complete support for Hadoop's InputFormat so anything that can be read by Hadoop can be read by Spark as well.

The default InputFormat is TextInputFormat. TextInputFormat takes the byte offset of a line as a key and the content of a line as a value. Spark uses the sc.textFile method to read using TextInputFormat. It ignores the byte offset and creates an RDD of strings.

Sometimes the filename itself contains useful information...

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