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
Free Learning
Arrow right icon
Learning PySpark
Learning PySpark

Learning PySpark: Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

eBook
€20.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR
Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Table of content icon View table of contents Preview book icon Preview Book

Learning PySpark

Chapter 2. Resilient Distributed Datasets

Resilient Distributed Datasets (RDDs) are a distributed collection of immutable JVM objects that allow you to perform calculations very quickly, and they are the backbone of Apache Spark.

As the name suggests, the dataset is distributed; it is split into chunks based on some key and distributed to executor nodes. Doing so allows for running calculations against such datasets very quickly. Also, as already mentioned in Chapter 1, Understanding Spark, RDDs keep track (log) of all the transformations applied to each chunk to speed up the computations and provide a fallback if things go wrong and that portion of the data is lost; in such cases, RDDs can recompute the data. This data lineage is another line of defense against data loss, a complement to data replication.

The following topics are covered in this chapter:

  • Internal workings of an RDD
  • Creating RDDs
  • Global versus local scopes
  • Transformations
  • Actions

Internal workings of an RDD

RDDs operate in parallel. This is the strongest advantage of working in Spark: Each transformation is executed in parallel for enormous increase in speed.

The transformations to the dataset are lazy. This means that any transformation is only executed when an action on a dataset is called. This helps Spark to optimize the execution. For instance, consider the following very common steps that an analyst would normally do to get familiar with a dataset:

  1. Count the occurrence of distinct values in a certain column.
  2. Select those that start with an A.
  3. Print the results to the screen.

As simple as the previously mentioned steps sound, if only items that start with the letter A are of interest, there is no point in counting distinct values for all the other items. Thus, instead of following the execution as outlined in the preceding points, Spark could only count the items that start with A, and then print the results to the screen.

Let's break this example down in code...

Creating RDDs

There are two ways to create an RDD in PySpark: you can either .parallelize(...) a collection (list or an array of some elements):

data = sc.parallelize(
    [('Amber', 22), ('Alfred', 23), ('Skye',4), ('Albert', 12), 
     ('Amber', 9)])

Or you can reference a file (or files) located either locally or somewhere externally:

data_from_file = sc.\    
    textFile(
        '/Users/drabast/Documents/PySpark_Data/VS14MORT.txt.gz',
        4)

Note

We downloaded the Mortality dataset VS14MORT.txt file from (accessed on July 31, 2016) ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/mortality/mort2014us.zip; the record schema is explained in this document http://www.cdc.gov/nchs/data/dvs/Record_Layout_2014.pdf. We selected this dataset on purpose: The encoding of the records will help us to explain how to use UDFs to transform your data later in this chapter. For your convenience, we also host the file here: http:/...

Global versus local scope

One of the things that you, as a prospective PySpark user, need to get used to is the inherent parallelism of Spark. Even if you are proficient in Python, executing scripts in PySpark requires shifting your thinking a bit.

Spark can be run in two modes: Local and cluster. When you run Spark locally your code might not differ to what you are currently used to with running Python: Changes would most likely be more syntactic than anything else but with an added twist that data and code can be copied between separate worker processes.

However, taking the same code and deploying it to a cluster might cause a lot of head-scratching if you are not careful. This requires understanding how Spark executes a job on the cluster.

In the cluster mode, when a job is submitted for execution, the job is sent to the driver (or a master) node. The driver node creates a DAG (see Chapter 1, Understanding Spark) for a job and decides which executor (or worker) nodes will run specific tasks...

Transformations

Transformations shape your dataset. These include mapping, filtering, joining, and transcoding the values in your dataset. In this section, we will showcase some of the transformations available on RDDs.

Note

Due to space constraints we include only the most often used transformations and actions here. For a full set of methods available we suggest you check PySpark's documentation on RDDs http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.

Since RDDs are schema-less, in this section we assume you know the schema of the produced dataset. If you cannot remember the positions of information in the parsed dataset we suggest you refer to the definition of the extractInformation(...) method on GitHub, code for Chapter 03.

The .map(...) transformation

It can be argued that you will use the .map(...) transformation most often. The method is applied to each element of the RDD: In the case of the data_from_file_conv dataset, you can think of this as a transformation...

Internal workings of an RDD


RDDs operate in parallel. This is the strongest advantage of working in Spark: Each transformation is executed in parallel for enormous increase in speed.

The transformations to the dataset are lazy. This means that any transformation is only executed when an action on a dataset is called. This helps Spark to optimize the execution. For instance, consider the following very common steps that an analyst would normally do to get familiar with a dataset:

  1. Count the occurrence of distinct values in a certain column.

  2. Select those that start with an A.

  3. Print the results to the screen.

As simple as the previously mentioned steps sound, if only items that start with the letter A are of interest, there is no point in counting distinct values for all the other items. Thus, instead of following the execution as outlined in the preceding points, Spark could only count the items that start with A, and then print the results to the screen.

Let's break this example down in code. First...

Creating RDDs


There are two ways to create an RDD in PySpark: you can either .parallelize(...) a collection (list or an array of some elements):

data = sc.parallelize(
    [('Amber', 22), ('Alfred', 23), ('Skye',4), ('Albert', 12), 
     ('Amber', 9)])

Or you can reference a file (or files) located either locally or somewhere externally:

data_from_file = sc.\    
    textFile(
        '/Users/drabast/Documents/PySpark_Data/VS14MORT.txt.gz',
        4)

Note

We downloaded the Mortality dataset VS14MORT.txt file from (accessed on July 31, 2016) ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/mortality/mort2014us.zip; the record schema is explained in this document http://www.cdc.gov/nchs/data/dvs/Record_Layout_2014.pdf. We selected this dataset on purpose: The encoding of the records will help us to explain how to use UDFs to transform your data later in this chapter. For your convenience, we also host the file here: http://tomdrabas.com/data/VS14MORT.txt.gz

The last parameter in sc.textFile...

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0
  • Develop and deploy efficient, scalable real-time Spark solutions
  • Take your understanding of using Spark with Python to the next level with this jump start guide

Description

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

Who is this book for?

If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.

What you will learn

  • Learn about Apache Spark and the Spark 2.0 architecture
  • Build and interact with Spark DataFrames using Spark SQL
  • Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively
  • Read, transform, and understand data and use it to train machine learning models
  • Build machine learning models with MLlib and ML
  • Learn how to submit your applications programmatically using spark-submit
  • Deploy locally built applications to a cluster
Estimated delivery fee Deliver to France

Premium delivery 7 - 10 business days

€10.95
(Includes tracking information)

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 27, 2017
Length: 274 pages
Edition : 1st
Language : English
ISBN-13 : 9781786463708
Category :
Languages :

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR
Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Estimated delivery fee Deliver to France

Premium delivery 7 - 10 business days

€10.95
(Includes tracking information)

Product Details

Publication date : Feb 27, 2017
Length: 274 pages
Edition : 1st
Language : English
ISBN-13 : 9781786463708
Category :
Languages :

Packt Subscriptions

See our plans and pricing
Modal Close icon
€18.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
€189.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
€264.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total 115.97
Learning PySpark
€36.99
Python Machine Learning, Second Edition
€32.99
Mastering Spark for Data Science
€45.99
Total 115.97 Stars icon
Banner background image

Table of Contents

12 Chapters
1. Understanding Spark Chevron down icon Chevron up icon
2. Resilient Distributed Datasets Chevron down icon Chevron up icon
3. DataFrames Chevron down icon Chevron up icon
4. Prepare Data for Modeling Chevron down icon Chevron up icon
5. Introducing MLlib Chevron down icon Chevron up icon
6. Introducing the ML Package Chevron down icon Chevron up icon
7. GraphFrames Chevron down icon Chevron up icon
8. TensorFrames Chevron down icon Chevron up icon
9. Polyglot Persistence with Blaze Chevron down icon Chevron up icon
10. Structured Streaming Chevron down icon Chevron up icon
11. Packaging Spark Applications Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9
(194 Ratings)
5 star 39.2%
4 star 32%
3 star 13.9%
2 star 7.2%
1 star 7.7%
Filter icon Filter
Top Reviews

Filter reviews by




Priyanka Prakash Nair Oct 10, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
Daniel Xoconostle Luna Jan 18, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
Sourav Sinha Oct 27, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
Mohamed Amir Sohail K Jun 05, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
Ravi Mar 12, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is the delivery time and cost of print book? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact [email protected] with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at [email protected] using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on [email protected] with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on [email protected] within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on [email protected] who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on [email protected] within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela