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Learning Spark SQL

By : Sarkar

3.5 (4)

Learning Spark SQL

3.5 (4)

By: Sarkar

Overview of this book

In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems. This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL. It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Getting Started with Spark SQL

Getting Started with Spark SQL

What is Spark SQL?

Introducing SparkSession

Understanding Spark SQL concepts

Using Spark SQL in streaming applications

Summary

Using Spark SQL for Processing Structured and Semistructured Data

Using Spark SQL for Processing Structured and Semistructured Data

Understanding data sources in Spark applications

Using Spark with relational databases

Using Spark with MongoDB (NoSQL database)

Using Spark with JSON data

Using Spark with Avro files

Using Spark with Parquet files

Defining and using custom data sources in Spark

Summary

Using Spark SQL for Data Exploration

Using Spark SQL for Data Exploration

Introducing Exploratory Data Analysis (EDA)

Using Spark SQL for basic data analysis

Visualizing data with Apache Zeppelin

Sampling data with Spark SQL APIs

Using Spark SQL for creating pivot tables

Summary

Using Spark SQL for Data Munging

Using Spark SQL for Data Munging

Introducing data munging

Exploring data munging techniques

Munging textual data

Munging time series data

Dealing with variable length records

Preparing data for machine learning

Summary

Using Spark SQL in Streaming Applications

Using Spark SQL in Streaming Applications

Introducing streaming data applications

Building Spark streaming applications

Using Kafka with Spark Structured Streaming

Writing a receiver for a custom data source

Summary

Using Spark SQL in Machine Learning Applications

Using Spark SQL in Machine Learning Applications

Introducing machine learning applications

Introducing feature engineering

Implementing a Spark ML classification model

Introducing Spark ML tools and utilities

Implementing a Spark ML clustering model

Summary

Using Spark SQL in Graph Applications

Using Spark SQL in Graph Applications

Introducing large-scale graph applications

Exploring graphs using GraphFrames

Analyzing JSON input modeled as a graph

Processing graphs containing multiple types of relationships

Understanding GraphFrame internals

Summary

Using Spark SQL with SparkR

Using Spark SQL with SparkR

Introducing SparkR

Understanding the SparkR architecture

Understanding SparkR DataFrames

Using SparkR for EDA and data munging tasks

Using SparkR for computing summary statistics

Using SparkR for data visualization

Using SparkR for machine learning

Summary

Developing Applications with Spark SQL

Developing Applications with Spark SQL

Introducing Spark SQL applications

Understanding text analysis applications

Understanding themes in document corpuses

Using Naive Bayes classifiers

Developing a machine learning application

Summary

Using Spark SQL in Deep Learning Applications

Using Spark SQL in Deep Learning Applications

Introducing neural networks

Introducing deep learning in Spark

Understanding Supervised learning

Using deep neural networks for language processing

Introducing autoencoders

Summary

Tuning Spark SQL Components for Performance

Tuning Spark SQL Components for Performance

Introducing performance tuning in Spark SQL

Understanding DataFrame/Dataset APIs

Understanding Catalyst optimizations

Visualizing Spark application execution

Cost-based optimizer in Apache Spark 2.2

Understanding multi-way JOIN ordering optimization

Understanding performance improvements using whole-stage code generation

Summary

Spark SQL in Large-Scale Application Architectures

Spark SQL in Large-Scale Application Architectures

Understanding Spark-based application architectures

Understanding the Lambda architecture

Understanding the Kappa Architecture

Design considerations for building scalable stream processing applications

Building robust ETL pipelines using Spark SQL

Implementing a scalable monitoring solution

Deploying Spark machine learning pipelines

Using cluster managers

Summary

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Using Spark SQL for basic data analysis

Interactively, processing and visualizing large data is challenging as the queries can take a long time to execute and the visual interface cannot accommodate as many pixels as data points. Spark supports in-memory computations and a high degree of parallelism to achieve interactivity with large distributed data. In addition, Spark is capable of handling petabytes of data and provides a set of versatile programming interfaces and libraries. These include SQL, Scala, Python, Java and R APIs, and libraries for distributed statistics and machine learning.

For data that fits into a single computer, there are many good tools available, such as R, MATLAB, and others. However, if the data does not fit into a single machine, or if it is very complicated to get the data to that machine, or if a single computer cannot easily process the data, then...

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