Apache Spark has captured the imagination of the analytics and big data developers, and rightfully so. In a nutshell, Spark enables distributed computing on a large scale in the lab or in production. Till now, the pipeline collect-store-transform was distinct from the Data Science pipeline reason-model, which was again distinct from the deployment of the analytics and machine learning models. Now, with Spark and technologies, such as Kafka, we can seamlessly span the data management and data science pipelines. We can build data science models on larger datasets, requiring not just sample data. However, whatever models we build can be deployed into production (with added work from engineering on the "ilities", of course). It is our hope that this book would enable an engineer to get familiar with the fundamentals of the Spark platform as well as provide hands-on experience on some of the advanced capabilities.
Chapter 1, Installing Spark and Setting up your Cluster, discusses some common methods for setting up Spark.
Chapter 2, Using the Spark Shell, introduces the command line for Spark. The Shell is good for trying out quick program snippets or just figuring out the syntax of a call interactively.
Chapter 3, Building and Running a Spark Application, covers Maven and sbt for compiling Spark applications.
Chapter 4, Creating a SparkContext, describes the programming aspects of the connection to a Spark server, for example, the SparkContext.
Chapter 5, Loading and Saving Data in Spark, deals with how we can get data in and out of a Spark environment.
Chapter 6, Manipulating your RDD, describes how to program the Resilient Distributed Datasets, which is the fundamental data abstraction in Spark that makes all the magic possible.
Chapter 7, Spark SQL, deals with the SQL interface in Spark. Spark SQL probably is the most widely used feature.
Chapter 8, Spark with Big Data, describes the interfaces with Parquet and HBase.
Chapter 9, Machine Learning Using Spark MLlib, talks about regression, classification, clustering, and recommendation. This is probably the largest chapter in this book. If you are stranded on a remote island and could take only one chapter with you, this should be the one!
Chapter 10, Testing, talks about the importance of testing distributed applications.
Chapter 11, Tips and Tricks, distills some of the things we have seen. Our hope is that as you get more and more adept in Spark programming, you will add this to the list and send us your gems for us to include in the next version of this book!
Like any development platform, learning to develop systems with Spark takes trial and error. Writing programs, encountering errors, agonizing over pesky bugs are all part of the process. We expect a basic level of programming skills—Python or Java—and experience in working with operating system commands. We have kept the examples simple and to the point. In terms of resources, we do not assume any esoteric equipment for running the examples and developing the code. A normal development machine is enough.
Data scientists and data engineers would benefit more from this book. Folks who have an exposure to big data and analytics will recognize the patterns and the pragmas. Having said that, anyone who wants to understand distributed programming would benefit from working through the examples and reading the book.
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: "While the methods for loading an RDD are largely found in the SparkContext class, the methods for saving an RDD are defined on the RDD classes."
A block of code is set as follows:
//Next two lines only needed if you decide to use the assembly plugin import AssemblyKeys._assemblySettings scalaVersion := "2.10.4" name := "groupbytest" libraryDependencies ++= Seq( "org.spark-project" % "spark-core_2.10" % "1.1.0" )
Any command-line input or output is written as follows:
scala> val inFile = sc.textFile("./spam.data")
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: " Select Source Code from option 2. Choose a package type and either download directly or select a mirror."
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