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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 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 (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Programmatically specifying the schema

There are a few cases where case classes might not work; one of these cases is where case classes cannot take more than 22 fields. Another case can be that you do not know about the schema beforehand. In this approach, data is loaded as an RDD of the Row objects. The schema is created separately using the StructType and StructField objects, which represent a table and a field, respectively. The schema is applied to the Row RDD to create a DataFrame.

How to do it...

  1. Start the Spark shell or Databricks Cloud Scala notebook:
        $ spark-shell  
  1. Import the Spark SQL datatypes and Row objects:
        scala> import org.apache.spark.sql._
scala> import org.apache.spark.sql.types._
  1. Create the schema using the...
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