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PySpark Cookbook
PySpark Cookbook

PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

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PySpark Cookbook

Abstracting Data with RDDs

In this chapter, we will cover how to work with Apache Spark Resilient Distributed Datasets. You will learn the following recipes:

  • Creating RDDs
  • Reading data from files
  • Overview of RDD transformations
  • Overview of RDD actions
  • Pitfalls of using RDDs

Introduction

Resilient Distributed Datasets (RDDs) are collections of immutable JVM objects that are distributed across an Apache Spark cluster. Please note that if you are new to Apache Spark, you may want to initially skip this chapter as Spark DataFrames/Datasets are both significantly easier to develop and typically have faster performance. More information on Spark DataFrames can be found in the next chapter.

An RDD is the most fundamental dataset type of Apache Spark; any action on a Spark DataFrame eventually gets translated into a highly optimized execution of transformations and actions on RDDs (see the paragraph on catalyst optimizer in Chapter 3, Abstracting Data with DataFrames, in the Introduction section). 

Data in an RDD is split into chunks based on a key and then dispersed across all the executor nodes. RDDs are highly resilient, that is, there are able...

Creating RDDs

For this recipe, we will start creating an RDD by generating the data within the PySpark. To create RDDs in Apache Spark, you will need to first install Spark as shown in the previous chapter. You can use the PySpark shell and/or Jupyter notebook to run these code samples.

Getting ready 

We require a working installation of Spark. This means that you would have followed the steps outlined in the previous chapter. As a reminder, to start PySpark shell for your local Spark cluster, you can run this command:

./bin/pyspark --master local[n]

Where n is the number of cores. 

How to do it...

...

Reading data from files

For this recipe, we will create an RDD by reading a local file in PySpark. To create RDDs in Apache Spark, you will need to first install Spark as noted in the previous chapter. You can use the PySpark shell and/or Jupyter notebook to run these code samples. Note that while this recipe is specific to reading local files, a similar syntax can be applied for Hadoop, AWS S3, Azure WASBs, and/or Google Cloud Storage:

Storage type Example
Local files sc.textFile('/local folder/filename.csv')
Hadoop HDFS sc.textFile('hdfs://folder/filename.csv')
AWS S3 (https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-configure.html) sc.textFile('s3://bucket/folder/filename.csv')
Azure WASBs (https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-use-blob-storage) sc.textFile('wasb://bucket/folder/filename...

Overview of RDD transformations

As noted in preceding sections, there are two types of operation that can be used to shape data in an RDD: transformations and actions. A transformation, as the name suggests, transforms one RDD into another. In other words, it takes an existing RDD and transforms it into one or more output RDDs. In the preceding recipes, we had used a map() function, which is an example of a transformation to split the data by its tab-delimiter.

Transformations are lazy (unlike actions). They only get executed when an action is called on an RDD. For example, calling the count() function is an action; more information is available in the following section on actions.

Getting ready

This recipe...

Overview of RDD actions

As noted in preceding sections, there are two types of Apache Spark RDD operations: transformations and actions. An action returns a value to the driver after running a computation on the dataset, typically on the workers. In the preceding recipes, the take() and count() RDD operations are examples of actions.

Getting ready

This recipe will be reading a tab-delimited (or comma-delimited) file, so please ensure that you have a text (or CSV) file available. For your convenience, you can download the airport-codes-na.txt and departuredelays.csv files from learning http://bit.ly/2nroHbh. Ensure your local Spark cluster can access this file (~/data/flights/airport...

Pitfalls of using RDDs

The key concern associated with using RDDs is that they can take a lot of time to master. The flexibility of running functional operators such as map, reduce, and shuffle allows you to perform a wide variety of transformations against your data. But with this power comes great responsibility, and it is potentially possible to write code that is inefficient, such as the use of GroupByKey; more information can be found in Avoid GroupByKey at https://databricks.gitbooks.io/databricks-spark-knowledge-base/content/best_practices/prefer_reducebykey_over_groupbykey.html.

Generally, you will typically have slower performance when using RDDs compared to Spark DataFrames, as noted in the following diagram:

Source: Introducing DataFrames in Apache Spark for Large Scale Data Science at https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark...
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Key benefits

  • Perform effective data processing, machine learning, and analytics using PySpark
  • Overcome challenges in developing and deploying Spark solutions using Python
  • Explore recipes for efficiently combining Python and Apache Spark to process data

Description

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.

Who is this book for?

The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.

What you will learn

  • Configure a local instance of PySpark in a virtual environment
  • Install and configure Jupyter in local and multi-node environments
  • Create DataFrames from JSON and a dictionary using pyspark.sql
  • Explore regression and clustering models available in the ML module
  • Use DataFrames to transform data used for modeling
  • Connect to PubNub and perform aggregations on streams

Product Details

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Publication date : Jun 29, 2018
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781788835367
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Product Details

Publication date : Jun 29, 2018
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781788835367
Category :
Languages :
Concepts :

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Table of Contents

8 Chapters
Installing and Configuring Spark Chevron down icon Chevron up icon
Abstracting Data with RDDs Chevron down icon Chevron up icon
Abstracting Data with DataFrames Chevron down icon Chevron up icon
Preparing Data for Modeling Chevron down icon Chevron up icon
Machine Learning with MLlib Chevron down icon Chevron up icon
Machine Learning with the ML Module Chevron down icon Chevron up icon
Structured Streaming with PySpark Chevron down icon Chevron up icon
GraphFrames – Graph Theory with PySpark Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.7
(3 Ratings)
5 star 0%
4 star 0%
3 star 0%
2 star 66.7%
1 star 33.3%
Dimitri Shvorob Oct 02, 2020
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Wishing to learn Spark, I signed up for Databricks Associate Spark Developer certification exam - Python flavor - and ordered off Amazon a number of Spark books, avoiding Scala-based titles, and older titles pre-dating the DataFrame API. I ended up with the following list:"Learning PySpark" by Drabas and Lee, published by Packt in 2017"Frank Kane's Taming Big Data with Apache Spark and Python" by (no surprise) Kane, Packt, 2017"Data Analytics with Spark Using Python" by Aven, Addison Wesley, 2018"PySpark Cookbook" by (once again) Drabas and Lee, Packt, 2018"Developing Spark Applications with Python" by Morera and Campos, self-published in 2019"PySpark Recipes" by Mishra, Apress, 2017"Learning Spark" by Damjil et al., O'Reilly, 2020"Beginning Apache Spark Using Azure Databricks" by Ilijason, Apress, 2020"Spark: The Definitive Guide" by Chambers and Zaharia, O'Reilly, 2018Databricks themselves point to "Learning Spark" and "Spark: The Definitive Guide" as preparation aids, so I started with these, skimming both books - and strongly preferring "The Definitive Guide" - and then took a look at the others."PySpark Cookbook" is an easy "pass". It is not as low-quality as the books by Mishra or by Morera and Campo, but it is still a low-quality, low-value-added affair of the type routinely churned out by Packt. Much of the page count is spent on setup matters, where directions may be out of date - then when we get to Spark, a lot of space is taken up by the old RDD interface. Strikingly, Spark SQL gets all of 3 pages (pp. 117-119). Chapter 4 has some more interesting content - several non-trivial data-manipulation tasks that actually merit the "recipe" label - but with that, "core" Spark content ends, and the authors get into streaming, ML and graphs. It's important to remember that Packt pages have less text than pages of books from other publishers: here, 300 "Packt pages" translate to maybe 150 "normal" pages, and that is not a lot.Skip this book, and consider the Databricks-based introduction by Ilijason and the comprehensive but very accessible reference by Chambers and Zaharia.
Amazon Verified review Amazon
mmays Apr 17, 2022
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Pretty good text, and I like the approach the author takes, but the Kindle version is really awful for the illegible graphics. I've tried them on a Kindle reader, Kindle cloud in a browser, copy and paste, no joy, they are just too small and illegible if magnified.
Amazon Verified review Amazon
Victor Tkachenko Jul 06, 2018
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This is a plagiary. Guys simply copied all info from the Wiki and trying to make money on it.Shame. No explanation of the code as far as I concern. Don't buy it, You can get more info from Googling...
Amazon Verified review Amazon
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