Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
Arrow right icon
View More author details
Toc

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

Exploring the Spark shell


Spark comes bundled with a read–eval–print loop (REPL) shell, which is a wrapper around the Scala shell. Though the Spark shell looks like a command line for simple things, in reality, a lot of complex queries can also be executed using it. A lot of times, the Spark shell is used in the initial development phase and once the code is stabilized, it is written as a class file and bundled as a jar to be run using spark-submit flag. This chapter explores different development environments in which Spark applications can be developed.

How to do it...

Hadoop MapReduce's word count, which takes at least three class files and one configuration file, namely project object model (POM), becomes very simple with the Spark shell. In this recipe, we are going to create a simple one-line text file, upload it to the Hadoop distributed file system (HDFS), and use Spark to count the occurrences of words. Let's see how:

  1. Create the words directory using the following command:
$ mkdir words...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image