Packt+ | Advance your knowledge in tech

All Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

PySpark Cookbook

You're reading from PySpark Cookbook

Product type Book

Published in Jun 2018

Publisher Packt

ISBN-13 9781788835367

Pages 330 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Authors (2):

Denny Lee

Tomasz Drabas

View More author details

Table of Contents (13) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Installing and Configuring Spark

2. Abstracting Data with RDDs

3. Abstracting Data with DataFrames

4. Preparing Data for Modeling

5. Machine Learning with MLlib

6. Machine Learning with the ML Module

7. Structured Streaming with PySpark

8. GraphFrames – Graph Theory with PySpark

Index

Performance optimizations

Starting with Spark 2.0, the performance of PySpark using DataFrames was on apar with that of Scala or Java. However, there was one exception: using User Defined Functions (UDFs); if a user defined a pure Python method and registered it as a UDF, under the hood, PySpark would have to constantly switch runtimes (Python to JVM and back). This was the main reason for an enormous performance hit compared with Scala, which does not need to convert the JVM object to a Python object.

Things have changed significantly in Spark 2.3. First, Spark started using the new Apache project. Arrow creates a single memory space used by all environments, thus removing the need for constant copying and converting between objects.

Source: https://arrow.apache.org/img/shared.png

Note

For an overview of Apache Arrow, go to https://arrow.apache.org.

Second, Arrow stores columnar objects in memory giving a big performance boost. Thus, in order to further leverage that, portions of the PySpark...

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

Start free trial

Renews at $15.99/month. Cancel anytime

Authors (2)

Denny Lee

Denny Lee is a Principal Program Manager at Microsoft for the Azure DocumentDB teamMicrosoft's blazing fast, planet-scale managed document store service. He is a hands-on distributed systems and data science engineer with more than 18 years of experience developing Internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premise and cloud environments. He has extensive experience of building greenfield teams as well as turnaround/ change catalyst. Prior to joining the Azure DocumentDB team, Denny worked as a Technology Evangelist at Databricks; he has been working with Apache Spark since 0.5. He was also the Senior Director of Data Sciences Engineering at Concur, and was on the incubation team that built Microsoft's Hadoop on Windows and Azure service (currently known as HDInsight). Denny also has a Masters in Biomedical Informatics from Oregon Health and Sciences University and has architected and implemented powerful data solutions for enterprise healthcare customers for the last 15 years.

See other products by Denny Lee

Tomasz Drabas

Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years' international experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting. Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland while finishing his Master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a Data Analyst for Beyond Analysis Australia and as a Senior Data Analyst/Data Scientist for Vodafone Hutchison Australia among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals. In 2015 he relocated to Seattle to begin his work for Microsoft. While there, he has worked on numerous projects involving solving problems in high-dimensional feature space.

See other products by Tomasz Drabas

Other recommended products

Related to this chapter

Learning PySpark

Learning PySpark

This book will get you to grips with the Spark Python API. You’ll explore how Python can be used with Spark to build scalable and reliable data-intensive applications.

Feb 2017 9 hours 8 minutes

Apache Spark Quick Start Guide

Apache Spark Quick Start Guide

Apache Spark is a ?exible in-memory framework that allows processing of both batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to quickly get started with Apache Spark 2.0 and write efficient big data applications for a variety of use cases.

Jan 2019 5 hours 8 minutes

Hands-On Big Data Analytics with PySpark

Hands-On Big Data Analytics with PySpark

In this book, you'll learn to implement some practical and proven techniques to improve aspects of programming and administration in Apache Spark. Techniques are demonstrated using practical examples and best practices. You will also learn how to use Spark and its Python API to create performant analytics with large-scale data.

Mar 2019 6 hours 4 minutes

Apache Spark 2.x for Java Developers

Apache Spark 2.x for Java Developers

Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone.

Jul 2017 11 hours 40 minutes

Mastering Apache Spark 2.x

Mastering Apache Spark 2.x

Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and more. This book will familiarize you with the newest features in Apache Spark 2.x, and take you through an exciting journey of complex Big Data processing, analytics, streaming analytics as well as advanced machine learning with Apache Spark. During the course of the book, you will leverage different functionalities and modules of Apache Spark such as Spark SQL, Spark MLlib, Spark Streaming, SparkML and more, to build efficient data processing solutions. By the end of this book, you will have all the necessary knowledge to use Apache Spark effectively in your day to day tasks.

Jul 2017 11 hours 48 minutes

Apache Spark 2.x Cookbook

Apache Spark 2.x Cookbook

Apache Spark has become the hottest platform and sought after skill set when it comes to the fields of Big Data, Analytics and Data Science. Apache Spark 2.x comes with series of new improvements in the areas of performance, scalability, operational and production readiness for structured processing of massive datasets. This book brings in a systematic way of getting a practical hands on to using its improved programming APIs, expanded SQL functionalities and implement distributed machine learning applications with Spark ML. Through the course of chapters, you will have explored the power of Spark DataFrames/Datasets, harness MLLib for Data mining, analyze complex problems with iterative or multi-stage Spark scripts and other associated toolsets such as Spark SQL, Spark Streaming and GraphX .

May 2017 9 hours 48 minutes

Hands-On Deep Learning with Apache Spark

Hands-On Deep Learning with Apache Spark

Deep Learning is a subset of Machine Learning where data sets with several layers of complexity can be processed. This book teaches you the different techniques using which deep learning solutions can be implemented at scale, on Apache Spark. This will help you gain experience of implementing your deep learning models in many real-world use cases.

Jan 2019 10 hours 44 minutes

Apache Spark 2.x Machine Learning Cookbook

Apache Spark 2.x Machine Learning Cookbook

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

Sep 2017 22 hours 12 minutes

Learning Apache Spark 2

Learning Apache Spark 2

Apache Spark is one of the most popular Big Data processing frameworks today, delivering speed, accuracy and real-time results – all in one solution. With this book, you will delve into the world of Apache Spark and learn about the new features introduced in Spark 2, along with the architecture and the associated concepts. A comprehensive guide to Apache Spark 2 for beginners, this book covers everything you need to know to get up and running with Big Data processing, machine learning and stream processing with Apache Spark, and allows you to easily understand each of these concepts through real-world examples.

Mar 2017 11 hours 52 minutes

Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics

Over the last few years, Scala has been adopted increasingly, especially in the field of data science and analytics, along with Apache Spark, which is built on Scala and is widely used in the field of analytics. With this book, you’ll learn how to leverage the power of both Scala and Spark to make sense of big data.

Jul 2017 26 hours 32 minutes

Machine Learning with Spark

Machine Learning with Spark

Spark ML is the machine learning module of Spark. It uses in-memory RDDs to process machine learning models faster for clustering, classification, and regression.

Apr 2017 17 hours 44 minutes

Big Data Analytics with Hadoop 3

Big Data Analytics with Hadoop 3

Apache Hadoop is the most popular platform for big data processing to build powerful analytics solutions. This book shows you how to do just that, with the help of practical examples. You will be well-versed with the analytical capabilities of Hadoop ecosystem with Apache Spark and Apache Flink to perform big data analytics by the end of this book.

May 2018 16 hours 4 minutes

Personalised recommendations for you

Based on your interests and search pattern

Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.

Aug 2023 7 hours 40 minutes

Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

Dec 2023 12 hours 0 minutes

Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

Dec 2023 12 hours 0 minutes

Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

Dec 2023 12 hours 0 minutes

Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

Dec 2023 12 hours 0 minutes

Mastering Tableau 2023

Mastering Tableau 2023

This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.

Aug 2023 22 hours 48 minutes

Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

Sep 2023 8 hours 36 minutes

Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

Sep 2023 8 hours 36 minutes

Data Engineering with AWS

Data Engineering with AWS

Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.

Oct 2023 21 hours 12 minutes

Modern Data Architecture on AWS

Modern Data Architecture on AWS

Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.

Aug 2023 14 hours 0 minutes

Practical Guide to Applied Conformal Prediction in Python

Practical Guide to Applied Conformal Prediction in Python

Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.

Dec 2023 8 hours 0 minutes

TinyML Cookbook

TinyML Cookbook

With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.

Nov 2023 22 hours 8 minutes