Along with the implementation of the map / filter reduce algorithm, the Stream API brings an amazing feature: the possibility to conduct the processing of your data in parallel. Going parallel is as simple as calling a parallel() method, something many developers are tempted to do. In this course, Leveraging Parallel Streams for Fast Data Processing in Java 8, you will learn what is happening under the hood, and how parallelism has been implemented in the Stream API. First, you will see that going parallel may improve the performances of your computations greatly, but may also ruin your application. Next, you will discover how to understand when things are working, which will give you the keys you need to decide when to go parallel and when to stay away from it. Finally, you will analyze the situations where things are not behaving as expected. By the end of this course, you will gain better understanding of parallelism as it is: a complex topic that you can grasp with simple ideas, so that you can use what you will learn immediately.
Author Name: Jose Paumard
Author Description:
Passionate about programming computers for over 20 years, Jose made his debut in assembler and C, C for SIMD parallel machines before adopting Java as an object-oriented language, indispensable to most major modern applications. He brings his expertise and capacity for analysis and synthesis to many projects, mainly in the lower layers, close to the data, but also on complex UI design, or in the browser. PhD in applied maths and computer science, assistant professor in Paris North University for… more

Table of Contents

• Course Overview
  1min
• Introducing Parallelism in the Java Stream API
  34mins
• Getting the Best Performance Gains from Parallel Streams
  21mins
• Analyzing the Fork / Join Implementation of Parallel Streams
  48mins
• Choosing the Right Sources of Data to Efficiently Go Parallel
  27mins