Book Image

Mastering Scala Machine Learning

By : Alex Kozlov
Book Image

Mastering Scala Machine Learning

By: Alex Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
10
Advanced Model Monitoring
Index

Understanding Spark architecture


A parallel execution involves splitting the workload into subtasks that are executed in different threads or on different nodes. Let's see how Spark does this and how it manages execution and communication between the subtasks.

Task scheduling

Spark workload splitting is determined by the number of partitions for Resilient Distributed Dataset (RDD), the basic abstraction in Spark, and the pipeline structure. An RDD represents an immutable, partitioned collection of elements that can be operated on in parallel. While the specifics might depend on the mode in which Spark runs, the following diagram captures the Spark task/resource scheduling:

Figure 03-2. A generic Spark task scheduling diagram. While not shown explicitly in the figure, Spark Context opens an HTTP UI, usually on port 4040 (the concurrent contexts will open 4041, 4042, and so on), which is present during a task execution. Spark Master UI is usually 8080 (although it is changed to 18080 in CDH)...