Introduction

With the recent advancements in cluster computing coupled with the rise of big data, the field of machine learning has been pushed to the forefront of computing. The need for an interactive platform that enables data science at scale has long been a dream that is now a reality.

The following three areas together have enabled and accelerated interactive data science at scale:

• Apache Spark: A unified technology for data science that combines a fast compute engine and fault-tolerant data structures into a well-designed and integrated offering
• Machine learning: A field of artificial intelligence enables machines to mimic some of the tasks originally reserved exclusively for the human brain
• Scala: A modern JVM-based language that on traditional languages, but unites functional and object-oriented concepts without the verboseness of other languages

First, we need to set up the development environment, which will consist of the following components:

• Spark
• IntelliJ community edition IDE
• Scala

The recipes in this chapter will give you detailed instructions for installing and configuring the IntelliJ IDE, Scala plugin, and Spark. After the development environment is set up, we'll proceed to run one of the Spark ML sample codes to test the setup.

Apache Spark

Apache Spark is as the de facto platform and trade for big data analytics and as a complement to the Hadoop paradigm. Spark enables a data scientist to work in the manner that is most conducive to their workflow right out of the box. Spark's approach is to process the workload in a completely distributed manner without the need for MapReduce (MR) or repeated writing of the intermediate results to a disk.

Spark provides an easy-to-use distributed framework in a unified technology stack, which has made it the platform of choice for data science projects, which more often than not require an iterative algorithm that eventually merges toward a solution. These algorithms, due to their inner workings, generate a large amount of intermediate results that need to go from one stage to the next during the intermediate steps. The need for an interactive tool with a robust native distributed machine learning library (MLlib) rules out a disk-based for most of the data science projects.

Spark has a different approach toward cluster computing. It solves the problem as a technology stack rather than as an ecosystem. A large number of centrally managed libraries combined with a lightning-fast compute engine that can support fault-tolerant data structures has poised Spark to take over Hadoop as the preferred big data platform for analytics.

Spark has a modular approach, as depicted in the following diagram:

Machine learning

The aim of learning is to produce machines and devices that can mimic human intelligence and automate some of the tasks that have been traditionally reserved for a human brain. Machine learning algorithms are designed to go through very large data sets in a relatively short time and approximate answers that would have taken a human much longer to process.

The field of machine learning can be classified into many forms and at a high level, it can be classified as supervised and unsupervised learning. Supervised learning algorithms are a class of ML algorithms that use a training set (that is, labeled data) to compute a probabilistic distribution or graphical model that in turn allows them to classify the new data points without further human intervention. Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.

Out of the box, Spark offers a rich set of ML algorithms that can be deployed on large datasets without any further coding. The following figure depicts Spark's MLlib as a mind map. Spark's MLlib is designed to take advantage of parallelism while having fault-tolerant distributed data structures. Spark refers to such data structures as Resilient Distributed Datasets or RDDs:

Scala

Scala is a modern language that is emerging as an alternative to traditional programming languages such as Java and C++. Scala is a JVM-based language not only offers a concise syntax without the traditional boilerplate code, but also incorporates both object-oriented and programming into an extremely crisp and extraordinarily powerful type-safe language.

Scala takes a flexible and expressive approach, which makes it perfect for interacting with Spark's MLlib. The fact that Spark itself is written in Scala provides a strong evidence that the Scala language is a full-service programming language that can be used to create sophisticated system code with heavy performance needs.

Scala builds on Java's tradition by addressing some of its shortcomings, while avoiding an all-or-nothing approach. Scala code compiles into Java bytecode, which in turn makes it possible to coexist with rich Java libraries interchangeably. The ability to use Java libraries with Scala and vice versa provides continuity and a rich environment for software engineers to build modern and complex machine learning systems without being fully disconnected from the Java tradition and code base.

Scala fully supports a feature-rich functional programming paradigm with standard support for lambda, currying, type interface, immutability, lazy evaluation, and a pattern-matching paradigm reminiscent of Perl without the cryptic syntax. Scala is an excellent match for machine learning programming due to its support for algebra-friendly data types, anonymous functions, covariance, contra-variance, and higher-order functions.

Here's a hello world program in Scala:

object HelloWorld extends App { 
   println("Hello World!") 
 } 

Compiling and running HelloWorld in Scala looks like this:

The Apache Spark Machine Learning Cookbook takes a practical approach by offering a multi-disciplinary view with the developer in mind. This book focuses on the interactions and cohesiveness of machine learning, Apache Spark, and Scala. We also take an extra step and teach you how to set up and run a comprehensive development environment familiar to a developer and provide code snippets that you have to run in an interactive shell without the modern facilities that an IDE provides:

Software versions and libraries used in this book

The following table a detailed list of versions and libraries used in this book. If you follow the installation instructions covered in this chapter, it will include most of the items listed here. Any other JAR or library files that may be required for specific recipes are covered via additional installation instructions in the respective recipes:

Core systems	Version
Spark	2.0.0
Java	1.8
IntelliJ IDEA	2016.2.4
Scala-sdk	2.11.8

Miscellaneous JARs that will be required are as follows:

Miscellaneous JARs	Version
bliki-core	3.0.19
breeze-viz	0.12
Cloud9	1.5.0
Hadoop-streaming	2.2.0
JCommon	1.0.23
JFreeChart	1.0.19
lucene-analyzers-common	6.0.0
Lucene-Core	6.0.0
scopt	3.3.0
spark-streaming-flume-assembly	2.0.0
spark-streaming-kafka-0-8-assembly	2.0.0

 

We have additionally tested all the recipes in this book on Spark 2.1.1 and found that the programs executed as expected. It is recommended for learning purposes you use the software versions and libraries listed in these tables.

To stay current with the rapidly changing Spark landscape and documentation, the API links to the Spark documentation mentioned throughout this book point to the latest version of Spark 2.x.x, but the API references in the recipes are explicitly for Spark 2.0.0.

All the documentation links provided in this book will point to the latest documentation on Spark's website. If you prefer to look for documentation for a specific version of Spark (for example, Spark 2.0.0), look for relevant documentation on the Spark website using the following URL:

https://spark.apache.org/documentation.html

We've made the code as simple as possible for clarity purposes rather than demonstrating the advanced features of Scala.