Apache Spark 2.x Machine Learning Cookbook

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:

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 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:

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:

Miscellaneous JARs that will be required are as follows:

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.

When you are ready to download and the JDK, access the following link:

http://www.oracle.com/technetwork/java/javase/downloads/index.html

After successful download, follow the on-screen instructions to install the JDK.

When you are ready to and install IntelliJ, access the following link:

https://www.jetbrains.com/idea/download/

At the time of writing, we are using IntelliJ version 15.x or later (for example, version 2016.2.4) to test the examples in the book, but feel free to download the latest version. Once the installation file is downloaded, double-click on the downloaded file (.exe) and begin to install the IDE. Leave all the installation options at the default settings if you do not want to make any changes. Follow the on-screen instructions to complete the installation:

When you are ready to download and install Spark, access the Apache website at this link:

http://spark.apache.org/downloads.html

Go to the Apache website and select the required download parameters, as shown in this screenshot:

Make sure to accept the default choices (click on Next) and proceed with the installation.

We need to be particularly careful when configuring the project structure and global libraries. After we set everything up, we proceed to run the sample ML code provided by the Spark team to verify the setup. Sample code can be found under the Spark directory or can be obtained by downloading the Spark source code with samples.

The following are the steps for configuring IntelliJ to work with Spark MLlib and for running the sample ML code provided by Spark in the examples directory. The examples directory can be found in your home directory for Spark. Use the Scala samples to proceed:

The next step is to download and install the Flume and Kafka JARs. For the purposes of this book, we have used the Maven repo:

Prior to Spark 2.0, we needed library from called Guava for facilitating I/O and for providing a set of rich methods of defining tables and then letting Spark broadcast them across the cluster. Due to dependency issues that were hard to work around, Spark 2.0 no longer uses the Guava library. Make sure you use the Guava library if you are using Spark versions prior to 2.0 (required in version 1.5.2). The library can be accessed at the following URL:

https://github.com/google/guava/wiki

You may want to use Guava version 15.0, which can be found here:

https://mvnrepository.com/artifact/com.google.guava/guava/15.0

If you are using installation instructions from previous blogs, make sure to exclude the Guava library from the installation set.

If there are other third-party libraries or JARs required for the completion of the Spark installation, you can find those in the following repository:

https://repo1.maven.org/maven2/org/apache/spark/

We will first run the logistic regression code from the samples to verify installation. In the next section, we proceed to write our own version of the same program and examine the output in order to understand how it works.

In addition to the university and government sources, there are many other open sources of data that can be used to learn and code your own examples and projects. We will list the data sources and show you how to best obtain and download data for each chapter.

The following is a list of open source data worth exploring if you would like to develop applications in this field:

Other sources for learning data:

There are some datasets (for example, text analytics in Spanish, and gene and IMF data) that might be of some interest to you:

We installed Spark 2.0 in the C:\spark-2.0.0-bin-hadoop2.7\ directory on a Windows machine.

Mac users note that we installed Spark 2.0 in the /Users/USERNAME/spark/spark-2.0.0-bin-hadoop2.7/ directory on a Mac machine.

Place the myFirstSpark20.scala file in the /Users/USERNAME/spark/spark-2.0.0-bin-hadoop2.7/examples/src/main/scala/spark/ml/cookbook/chapter1 directory.

package spark.ml.cookbook.chapter1 

import org.apache.spark.sql.SparkSession 
import org.apache.log4j.Logger 
import org.apache.log4j.Level 

Logger.getLogger("org").setLevel(Level.ERROR) 

val spark = SparkSession 
.builder 
.master("local[*]")
 .appName("myFirstSpark20") 
.config("spark.sql.warehouse.dir", ".") 
.getOrCreate() 

The myFirstSpark20 object will run in local mode. The previous code block is a typical way to start creating a SparkSession object.

val x = Array(1.0,5.0,8.0,10.0,15.0,21.0,27.0,30.0,38.0,45.0,50.0,64.0) 
val y = Array(5.0,1.0,4.0,11.0,25.0,18.0,33.0,20.0,30.0,43.0,55.0,57.0) 

val xRDD = spark.sparkContext.parallelize(x) 
val yRDD = spark.sparkContext.parallelize(y) 

val zipedRDD = xRDD.zip(yRDD) 
zipedRDD.collect().foreach(println) 

In the console output at runtime (more details on how to run the program in the IntelliJ IDE in the following steps), you will see this:

val xSum = zipedRDD.map(_._1).sum() 
val ySum = zipedRDD.map(_._2).sum() 
val xySum= zipedRDD.map(c => c._1 * c._2).sum() 
val n= zipedRDD.count() 

println("RDD X Sum: " +xSum) 
println("RDD Y Sum: " +ySum) 
println("RDD X*Y Sum: "+xySum) 
println("Total count: "+n) 

Here's the console output:

spark.stop() 

Once the rebuild is complete, there should be a build completed message on the console:

Information: November 18, 2016, 11:46 AM - Compilation completed successfully with 1 warning in 55s 648ms

Process finished with exit code 0

This is also shown in the following screenshot:

In this example, we wrote our first Scala program, myFirstSpark20.scala, and displayed the steps to execute the program in IntelliJ. We placed the code in the path described in the steps for both Windows and Mac.

In the myFirstSpark20 code, we saw a typical way to create a SparkSession object and how to configure it to run in local mode using the master() function. We created two RDDs out of the array objects and used a simple zip() function to create a new RDD.

We also did a simple sum calculation on the RDDs that were created and then displayed the result in the console. Finally, we exited and released the resource by calling spark.stop().

can be downloaded from http://spark.apache.org/downloads.html.

Documentation for Spark 2.0 related to RDD can be found at http://spark.apache.org/docs/latest/programming-guide.html#rdd-operations.

  package spark.ml.cookbook.chapter1

import java.awt.Color 
import org.apache.log4j.{Level, Logger} 
import org.apache.spark.sql.SparkSession 
import org.jfree.chart.plot.{PlotOrientation, XYPlot} 
import org.jfree.chart.{ChartFactory, ChartFrame, JFreeChart} 
import org.jfree.data.xy.{XYSeries, XYSeriesCollection} 
import scala.util.Random 

Logger.getLogger("org").setLevel(Level.ERROR) 

val spark = SparkSession 
  .builder 
  .master("local[*]") 
  .appName("myChart") 
  .config("spark.sql.warehouse.dir", ".") 
  .getOrCreate() 

val data = spark.sparkContext.parallelize(Random.shuffle(1 to 15).zipWithIndex) 

data.foreach(println) 

Here is the console output:

val xy = new XYSeries("") 
data.collect().foreach{ case (y: Int, x: Int) => xy.add(x,y) } 
val dataset = new XYSeriesCollection(xy) 

val chart = ChartFactory.createXYLineChart( 
  "MyChart",  // chart title 
  "x",               // x axis label 
  "y",                   // y axis label 
  dataset,                   // data 
  PlotOrientation.VERTICAL, 
  false,                    // include legend 
  true,                     // tooltips 
  false                     // urls 
)

val plot = chart.getXYPlot() 

configurePlot(plot) 

def configurePlot(plot: XYPlot): Unit = { 
  plot.setBackgroundPaint(Color.WHITE) 
  plot.setDomainGridlinePaint(Color.BLACK) 
  plot.setRangeGridlinePaint(Color.BLACK) 
  plot.setOutlineVisible(false) 
} 

show(chart) 

def show(chart: JFreeChart) { 
  val frame = new ChartFrame("plot", chart) 
  frame.pack() 
  frame.setVisible(true) 
}

spark.stop() 

In this example, we wrote MyChart.scala and saw the steps for executing the program in IntelliJ. We placed code in the path described in the steps for both Windows and Mac.

In the code, we saw a typical way to create the SparkSession object and how to use the master() function. We created an RDD out of an array of random integers in the range of 1 to 15 and zipped it with the Index.

We then used JFreeChart to compose a basic chart that contains a simple x and y axis, and supplied the chart with the dataset we generated from the original RDD in the previous steps.

We set up the schema for the chart and called the show() function in JFreeChart to show a Frame with the x and y axes displayed as a linear graphical chart.

Finally, we exited and released the resource by calling spark.stop().

More about JFreeChart can be found here:

Additional examples about the features and capabilities of JFreeChart can be found at the following website:

http://www.jfree.org/jfreechart/samples.html