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Fast Data Processing with Spark 2
Fast Data Processing with Spark 2

Fast Data Processing with Spark 2: Accelerate your data for rapid insight , Third Edition

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Profile Icon Karau Profile Icon Krishna Sankar
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€32.99
Paperback Oct 2016 274 pages 3rd Edition
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€8.99 €26.99
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€32.99
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Arrow left icon
Profile Icon Karau Profile Icon Krishna Sankar
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€32.99
Paperback Oct 2016 274 pages 3rd Edition
eBook
€8.99 €26.99
Paperback
€32.99
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Free Trial
Renews at €18.99p/m
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€8.99 €26.99
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Fast Data Processing with Spark 2

Chapter 2. Using the Spark Shell

In this chapter, we will cover the following topics related to the Spark shell:

  • Running the Spark shell
  • Loading a simple text file
  • Interactively loading data from S3
  • Running the Spark shell in Python

The Spark shell

The Spark shell is an excellent tool for rapid prototyping with Spark. It works with Scala and Python. It allows you to interact with the Spark cluster and as a result of which, the full API is under your command. It can be great for debugging, just trying things out, or interactively exploring new Datasets or approaches.

The previous chapter should have gotten you to the point of having a Spark instance running; now all you need to do is start your Spark shell and point it at your running instance with the command given in the table we're soon going to check out.

For local mode, Spark will start an instance when you invoke the Spark shell or start a Spark program from an IDE. So, a local installation on a Mac or Linux PC/laptop is sufficient to start exploring the Spark shell. Not having to spin up a real cluster to do the prototyping is an important and useful feature of Spark. The Quick Start guide at http://spark.apache.org/docs/latest/quick-start.html is a good...

Loading a simple text file

Let's download a Dataset and do some experimentation. One of the (if not the best) books for machine learning is The Elements of Statistical Learning, Trevor Hastie, Jerome H. Friedman, Robert Tibshirani, Springer. The book site has an interesting set of Datasets. Let's grab the spam Dataset using the following command:

wget http://www-stat.stanford.edu/~tibs/ElemStatLearn/ datasets/spam.data

Alternatively, you can find the spam Dataset from the GitHub link at https://github.com/xsankar/fdps-v3.

Note

All the examples assume that you have downloaded the repository in the fdps-v3 directory in your home folder, that is, ~/fdps-v3/. Please adjust the directory name if you have downloaded them somewhere else.

Now, load it as a text file into Spark with the following commands inside your Spark shell:

scala> val inFile = sc.textFile("data/spam.data")
scala> inFile.count()

This loads the spam.data file into Spark with each line being a separate entry...

Interactively loading data from S3

Now let's try another exercise with the Spark shell. As part of Amazon's EMR Spark support, they have handily provided some sample data of Wikipedia traffic statistics in S3, in the format that Spark can use. To access the data, you first need to set your AWS access credentials as shell params. For instructions on signing up for EC2 and setting up the shell parameters, see the Running Spark on EC2 with the scripts section in Chapter 1, Installing Spark and Setting Up Your Cluster (S3 access requires additional keys such as fs.s3n.awsAccessKeyId/awsSecretAccessKey or the use of the s3n://user:pw@ syntax). You can also set the shell parameters as AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. We will leave the AWS configuration out of this discussion, but it needs to be completed.

Tip

This is a slightly advanced topic and needs a few S3 configurations (which we won't cover here). The Stack Overflow has two good links on this, namely http://stackoverflow...

Summary

In this chapter, you learned how to start the Spark shell (Scala and Python) and load data.

Now that you've seen how Spark's interactive console works, it's time to see how to build Spark jobs in a more traditional and persistent environment in the subsequent chapters.

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Key benefits

  • A quick way to get started with Spark – and reap the rewards
  • From analytics to engineering your big data architecture, we’ve got it covered
  • Bring your Scala and Java knowledge – and put it to work on new and exciting problems

Description

When people want a way to process big data at speed, Spark is invariably the solution. With its ease of development (in comparison to the relative complexity of Hadoop), it’s unsurprising that it’s becoming popular with data analysts and engineers everywhere. Beginning with the fundamentals, we’ll show you how to get set up with Spark with minimum fuss. You’ll then get to grips with some simple APIs before investigating machine learning and graph processing – throughout we’ll make sure you know exactly how to apply your knowledge. You will also learn how to use the Spark shell, how to load data before finding out how to build and run your own Spark applications. Discover how to manipulate your RDD and get stuck into a range of DataFrame APIs. As if that’s not enough, you’ll also learn some useful Machine Learning algorithms with the help of Spark MLlib and integrating Spark with R. We’ll also make sure you’re confident and prepared for graph processing, as you learn more about the GraphX API.

Who is this book for?

This book is for developers with little to no knowledge of Spark, but with a background in Scala/Java programming. It’s recommended that you have experience in dealing and working with big data and a strong interest in data science.

What you will learn

  • Install and set up Spark in your cluster
  • Prototype distributed applications with Spark s interactive shell
  • Perform data wrangling using the new DataFrame APIs
  • Get to know the different ways to interact with Spark s distributed representation of data (RDDs)
  • Query Spark with a SQL-like query syntax
  • See how Spark works with big data
  • Implement machine learning systems with highly scalable algorithms
  • Use R, the popular statistical language, to work with Spark
  • Apply interesting graph algorithms and graph processing with GraphX
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Table of Contents

12 Chapters
1. Installing Spark and Setting Up Your Cluster Chevron down icon Chevron up icon
2. Using the Spark Shell Chevron down icon Chevron up icon
3. Building and Running a Spark Application Chevron down icon Chevron up icon
4. Creating a SparkSession Object Chevron down icon Chevron up icon
5. Loading and Saving Data in Spark Chevron down icon Chevron up icon
6. Manipulating Your RDD Chevron down icon Chevron up icon
7. Spark 2.0 Concepts Chevron down icon Chevron up icon
8. Spark SQL Chevron down icon Chevron up icon
9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists Chevron down icon Chevron up icon
10. Spark with Big Data Chevron down icon Chevron up icon
11. Machine Learning with Spark ML Pipelines Chevron down icon Chevron up icon
12. GraphX Chevron down icon Chevron up icon
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