PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes

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Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using gra
PySpark sQl recipes Raju Kumar Mishra Sundar Rajan Raman Bangalore, Karnataka, India Chennai, Tamil Nadu, India ISBN-13(pbk):978-148424334-3 ISBN-13( electronic):978-1-4842-4335-0 hts:// doi. org/10.1007/978-1-4842-4335-0 Library of Congress Control Number: 2019934769 Copyright o 2019 by Raju Kumar Mishra and Sundar Rajan Raman This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty express or implied, with respect to the material contained herein Managing Director, Apress Media LLC: Welmoed Spahr Acquisitions Editor: Celestin Suresh John Development Editor: Matthew Moodie Coordinating Editor: Aditee Mirashi Cover designed by eStudio calamar CoverimagedesignedbyFreepik(www.freepik.com Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax(201)348-4505 e-mailorders-ny@springer-sbm.com,orvisitwww.springeronline.comApressMediaLlcisa California LLC and the sole member (owner) is Springer Science +Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation Forinformationontranslationspleasee-mailrights@apress.comorvisithttp://www.apress com/rights-permissions Apress titles may be purchased in bulk for academic, corporate, or promotional use e Book versions and licenses are also available for most titles. For more information reference our print andebookBulkSaleswebpageathttp://www.apress.com/bulk-sale Any source code or other supplementary material referenced by the author in this book is available toreadersonGithubviathebooksproductpagelocatedatwww.apress.com/978-1-4842-4334-3 Formoredetailedinformationpleasevisithttp://www.apress.com/source-code Printed on acid-free paper To the almighty, who guides me in every aspect of my life And to my mother, Smt. Savitri Mishra, and my lovely wife, Smt. smita rani pathak Table of contents About the authors About the technical reviewer ■■口■■ XIX Acknowledgments ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ Introduction mm xxii Chapter 1: Introduction to PySpark SQL Introduction to Big data 2 Volume velocity Variety 3 veracity.mme....... 3 Introduction to Hadoop. Introduction to hdfs Introduction to Mapreduce .mmmmmm.m..mmmmmmm..m. 6 Introduction to Apache Hive 7 Introduction to Apache Pig.……9 Introduction to Apache Kafka Producer………1 Broker Consumer Introduction to Apache Spark 12 TABLE OF CONTENTS PySpark SQL: An Introduction. Introduction to data SparkSession Structured Streaming Catalyst Optimizer Introduction to Cluster Managers.nn mnat.. 18 Introduction to PostgreSQL Introduction to MongoDB ntroduction to cassandra………22 Chapter 2: Installation m meeBBRRERERaID ■■■ 23 Recipe 2-1. Install Hadoop on a Single Machine.m...... 24 Problem g. 24 Solution 24 How It Works 24 Recipe 2-2. Install Spark on a Single Machine Problem Solution…138 How It Works Recipe 2-3. Use the PySpark Shell Problem 重面B日重B面B重 41 Solution m, 41 How It Works Recipe 2-4. Install Hive on a single Machine 42 Problem 42 Solution 42 How It Works 43 TABLE OF CONTENTS Recipe 2-5. Install PostgreSQL 47 Problem 47 Solution 47 How It Works…47 Recipe 2-6. Configure the Hive Metastore on PostgreSQL Problem 49 Soluti0n…………………………………aaaa49 How It Works 49 Recipe 2-7. Connect PySpark to Hive.m. 57 Problem………………57 Solution,,…58 How It Works 58 Recipe 2-8. Install MySQL 58 Problem…58 Solution mmmmm. 58 How It Works 59 Recipe 2-9. Install MongoDB ,11260 Problem 60 So| ution…………………………aaaa60 How It Works…,,………60 Recipe 2-10. Install Cassandra.mm......mm...62 Problem Solution 1 63 How It Works 63 TABLE OF CONTENTS Chapter3:0 in PySpark SQL,,,,,…,…,,,m65 Recipe 3-1. Read a cSv File n,66 Problem 66 Solution 66 How It Works Recipe 3-2. Read a JsoN File.mmcmammmsmcmmnmnmmnmmmmmn 71 Problem Solution……171 How It Works 72 Recipe 3-3. Save a DataFrame as a cSv File.aaamananmacnnn 73 Problem 73 Solution How it works∴ 74 Recipe 3-4. Save a dataframe as a json File ...mmmmmmmmmmm. 75 Problem Solution 重面B日重B面B重 How It Works 75 Recipe 3-5. Read ORC Files.m. 76 Prob|em… Solution How It Works 国DDDD面D重DDD面D重 78 Recipe 3-6. Read a Parquet File n. 78 Problem.…78 Solution 78 How It Works Recipe 3-7. Save a dataframe as an oRc File ..mmmmmmmmmmmmmmmmmm.m. 80 Prob|em….80 Sout0n,………………180 How It Works TABLE OF CONTENTS Recipe 3-8. Save a Data Frame as a Parquet File 81 Problem 81 Solution 81 How It Works…81 Recipe 3-9. Read Data from MySQL. 82 Prob|em…82 Sout0n,…………………………aa.82 HoW[ t Works………82 Recipe 3-10. Read Data from PostgresQl.mamen 84 Problem………………84 Solution,…84 How It Works 85 Recipe 3-11. Read Data from Cassandra Problem…86 Solution Recipe3-12. Read Data from MongoDE6……… How It Works 87 88 Problem 88 Solution…………8 HoW| t Works…90 Recipe3-13. Save a DataFrame to MySQL……,.,, 91 Problem 91 Solution 1 91 How It Works… 91 Recipe 3-14. Save a DataFrame to PostgreSQL …93 Problem u. Solution 93 HoW| t Works… 94 TABLE OF CONTENTS Recipe 3-15. Save DataFrame Contents to MongoDB 95 Problem m.. Solution 95 How It Works…96 Recipe 3-16. Read Data from Apache Prob|em.…97 So| tion∴ How It works …100 Chapter 4: Operations on PySpark SQL Data Frames mmmmmmm 101 Recipe 4-1. Transform Values in a Column of a DataFrame ,102 Problem m, 102 Solution 103 How It Works 104 Recipe 4-2. Select Columns from a Data Frame. mmmmanmmmm. 108 Problem 108 Solution L重 108 How It Works…,……………109 Recipe 4-3. Filter Rows from a DataFrame.am111 Problem 111 Solution 11 How It Works 112 Recipe 4-4. Delete a Column from an Existing DataFrame...am..a.114 Problem …,114 Solution 114 How It works…115

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