Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the features of Spark available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java without stepping out of your comfort zone.
The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the concepts of Java that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark Streaming, machine learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages.
By the end of the book, you will have a solid foundation in implementing the components of the Spark framework in Java to build fast, real-time applications.
Chapter 1, Introduction to Spark, covers the history of big data, its dimensions, and basic concepts of Hadoop and Spark.
Chapter 2, Revisiting Java, refreshes the concepts of core Java and will focus on the newer feature of Java 8 that will be leveraged while developing Spark applications.
Chapter 3, Let Us Spark, serves the purpose of providing an instruction set so that the reader becomes familiar with installing Apache Spark in standalone mode along with its dependencies.
Chapter 4, Understanding the Spark Programming Model, makes progress by explaining the word count problem in Apache Spark using Java and simultaneously setting up an IDE.
Chapter 5, Working with Data and Storage, teaches you how to read/store data in Spark from/to different storage systems.
Chapter 6, Spark on Cluster, discusses the cluster setup process and some popular cluster managers available with Spark in detail. After this chapter, you will be able to execute Spark jobs effectively in distributed mode.
Chapter 7, Spark Programming Model — Advanced, covers partitioning concepts in RDD along with advanced transformations and actions in Spark.
Chapter 8, Working with Spark SQL, discusses Spark SQL and its related concepts such as dataframe, dataset, and UDF. We will also discuss SqlContext and the newly introduced SparkSession.
Chapter 9, Near-Real-Time Processing with Spark Streaming, covers the internals of Spark Streaming, reading streams of data in Spark from various data sources with examples, and newer extensions of stream processing in Spark known as structured streaming.
Chapter 10, Machine Learning Analytics with Spark MLlib, focuses on introducing the concepts of machine learning and then moves on towards its implementation using Apache Spark Mllib libraries. We also discuss some real-world problems using Spark Mllib.
Chapter 11, Learning Spark GraphX, looks into another module of Spark, GraphX; we will discover types of GraphX RDD and various operations associated with them. We will also discuss the use cases of GraphX implementation.
If you want to set up Spark on your local machine, then you can follow the instructions mentioned in Chapter 3, Let Us Spark.
If you are a Java developer interested in learning to use the popular Apache Spark framework, this book is the resource you need to get started. Apache Spark developers who are looking to build enterprise-grade applications in Java will also find this book very useful.
In this book, you will find a number of 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 Twitter handles are shown as follows: "The mode function was not implemented in the numpy package.". Any command-line input or output is written as follows:
>>> import numpy as np
>>> from scipy import stats
>>> data = np.array([4,5,1,2,7,2,6,9,3])
# Calculate Mean
>>> dt_mean = np.mean(data) ;
print ("Mean :",round(dt_mean,2)) New terms and important words are shown in bold.
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