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PySpark Cookbook

You're reading from PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788835367

Length 330 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Big Data

Authors (2):

Lee

Drabas

View More author details

Table of Contents (13) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Installing and Configuring Spark FREE CHAPTER

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

Finding the fewest number of connections

When you're flying to many cities, one of the recurring problems is to determine either the shortest path between two cities or the shortest time of travel. From the viewpoint of the airline traveler, the aim is to find the shortest set of flights between two cities. From the viewpoint of the airline, determining how to route their passengers between cities as efficiently as possible increases customer satisfaction and lowers prices (less fuel, wear and tear on equipment, ease for the flight crew, and so on). Within the context of GraphFrames and graph algorithms, one approach would be to use the breadth first search (BFS) algorithm to help us find the shortest path between these airports.

Getting ready

Ensure that you have created the graph GraphFrame from the preceding subsections.

How to do it...

Let's start using our BFS algorithm to determine whether there are any direct flights between SFO and SEA:

subsetOfPaths = graph.bfs(
   fromExpr = "id = ...

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Authors (2)

Lee

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.

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Drabas

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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