Mon, 2 Mar




Participants should have experience programming in Java. They should be familiar with working with collections and using generics.
During the workshop we run through hands on exercises in class in order to support teaching material. In order to do these exercises you need to bring your own laptop with you with Java 8 installed, which can be downloaded here.
If you are using an IDE then please make sure that you have upgraded to a version which includes support for Java 8 in order to have a smooth experience. This means:
  • Luna or later of Eclipse
  • Version 12 or later of Intellij, preferably version 13.
  • 7.4 or later for netbeans, preferably 8.
Please make sure that you have also Maven installed to set up the project.

Tutorial: Java 8 Lambda Expressions & Streams [Sold out]

This tutorial is sold out.

Java 8 is the largest update to Java in its history. Some of the best ideas from functional programming are migrating their way into Java 8. This intense one day workshop is a hands-on lab session comprising slides, quizzes, live coding and unit test exercises which covers important Java 8 topics.

Why take this tutorial?

Java 8 encourages a different style of programming that: - lets you cope for requirement changes (less engineering efforts!) - lets you take advantages of multi-core architecture easily (faster code!) - lets you process large collections with SQL-like operations (do more in less time!) - lets you write more concise code (better readability & maintainability!)

At the end of the workshop you will be able to use the most important Java 8 features to write easier to read, more flexible code that scales to multicore.

Topics covered include:

Lambdas & Streams

  • Behaviour Parameterisation
  • What is a lambda?
  • Method references
  • Collection Processing
  • Stream operations and patterns
  • Stream Optimisation

Collectors & Data Parallelism

  • Grouping and partitioning
  • Collection Operations
  • Arithmetic collectors
  • Advanced Queries
  • What is Data Parallelism?
  • Parallelising your streams
  • Decomposition performance