We will start this book with the basics of Spark SQL and its role in Spark applications. After the initial familiarization with Spark SQL, we will focus on using Spark SQL to execute tasks that are common to all big data projects, such as working with various types of data sources, exploratory data analysis, and data munging. We will also see how Spark SQL and SparkR can be leveraged to accomplish typical data science tasks at scale.
With the DataFrame/Dataset API and the Catalyst optimizer at the heart of Spark SQL, it is no surprise that it plays a key role in all applications based on the Spark technology stack. These applications include large-scale machine learning pipelines, large-scale graph applications, and emerging Spark-based deep learning applications. Additionally, we will present Spark SQL-based Structured Streaming applications that are deployed in complex production environments as continuous applications.
We will also review performance tuning in Spark SQL applications, including cost-based optimization (CBO) introduced in Spark 2.2. Finally, we will present application architectures that leverage Spark modules and Spark SQL in real-world applications. More specifically, we will cover key architectural components and patterns in large-scale Spark applications that architects and designers will find useful as building blocks for their own specific use cases.
Chapter 1, Getting Started with Spark SQL, gives you an overview of Spark SQL while getting you comfortable with the Spark environment through hands-on sessions.
Chapter 2, Using Spark SQL for Processing Structured and Semistructured Data, will help you use Spark to work with a relational database (MySQL), NoSQL database (MongoDB), semistructured data (JSON), and data storage formats commonly used in the Hadoop ecosystem (Avro and Parquet).
Chapter 3, Using Spark SQL for Data Exploration, demonstrates the use of Spark SQL to explore datasets, perform basic data quality checks, generate samples and pivot tables, and visualize data with Apache Zeppelin.
Chapter 4, Using Spark SQL for Data Munging, uses Spark SQL for performing some basic data munging/wrangling tasks. It also introduces you to a few techniques to handle missing data, bad data, duplicate records, and so on.
Chapter 5, Using Spark SQL in Streaming Applications, provides a few examples of using Spark SQL DataFrame/Dataset APIs to build streaming applications. Additionally, it also shows how to use Kafka in structured streaming applications.
Chapter 6, Using Spark SQL in Machine Learning Applications, focuses on using Spark SQL in machine learning applications. In this chapter, we will mainly explore the key concepts in feature engineering and implement machine learning pipelines.
Chapter 7, Using Spark SQL in Graph Applications, introduces you to GraphFrame applications. It provides examples of using Spark SQL DataFrame/Dataset APIs to build graph applications and apply the various graph algorithms into your graph applications.
Chapter 8, Using Spark SQL with SparkR, covers the SparkR architecture and SparkR DataFrames API. It provides code examples for using SparkR for Exploratory Data Analysis (EDA) and data munging tasks, data visualization, and machine learning.
Chapter 9, Developing Applications with Spark SQL, helps you build Spark applications using a mix of Spark modules. It presents examples of applications that combine Spark SQL with Spark Streaming, Spark Machine Learning, and so on.
Chapter 10, Using Spark SQL in Deep Learning Applications, introduces you to deep learning in Spark. It covers the basic concepts of a few popular deep learning models before you delve into working with BigDL and Spark.
Chapter 11, Tuning Spark SQL Components for Performance, presents you with the foundational concepts related to tuning a Spark application, including data serialization using encoders. It also covers the key aspects of the cost-based optimizer introduced in Spark 2.2 to optimize Spark SQL execution automatically.
Chapter 12, Spark SQL in Large-Scale Application Architectures, teaches you to identify the use cases where Spark SQL can be used in large-scale application architectures to implement typical functional and non-functional requirements.
This book is based on Spark 2.2.0 (pre-built for Apache Hadoop 2.7 or later) and Scala 2.11.8. For one or two subsections, Spark 2.1.0 has also been used due to the unavailability of certain libraries and reported bugs (when used with Apache Spark 2.2). The hardware and OS specifications include minimum 8 GB RAM (16 GB strongly recommended), 100 GB HDD, and OS X 10.11.6 or later (or appropriate Linux versions recommended for Spark development).
If you are a developer, engineer, or an architect and want to learn how to use Apache Spark in a web-scale project, then this is the book for you. It is assumed that you have prior knowledge of SQL querying. Basic programming knowledge with Scala, Java, R, or Python is all you need to get started with this book.
In this book, you will find several text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and terminal commands as follows: "The model is trained by calling the fit() method on the training Dataset. "
A block of code is set as follows:
scala> val inDiaDataDF = spark.read.option("header", true).csv("file:///Users/aurobindosarkar/Downloads/dataset_diabetes/diabetic_data.csv").cache()Any command-line input or output is written as follows:
head -n 8000 input.txt > val.txt tail -n +8000 input.txt > train.txt
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."
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