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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

Installing GraphFrames

Under the hood of GraphFrames are two Spark DataFrames: one for the vertices and other one for the edges. GraphFrames might be thought of as the next generation of Spark's GraphX library, with some major improvements over the latter:

GraphFrames leverages the performance optimizations and simplicity of the DataFrame API.
By using the DataFrame API, GraphFrames can be interacted with through Python, Java, and Scala APIs. In contrast, GraphX was only available through the Scala interface.

You can find the latest information on GraphFrames within the GraphFrames overview at https://graphframes.github.io/.

Getting ready

We require a working installation of Spark. This means that you would have followed the steps outlined in Chapter 1, Installing and Configuring Spark. As a reminder, to start the PySpark shell for your local Spark cluster, you can run the following command:

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

Where n is the number of cores.

How to do it...

If you are running your...

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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.

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