Build Stuff'16 Lithuania

As the microservice train thunders into town, it is bringing with it an old enemy - the Remote Procedure Call. Libraries like Thrift are being used for client-server communication and no one seems to be looking back to the past to understand why we ran screaming from RPC last time. Drawing on bitter experience of DCOM, .NET remoting and Web Services, and his very British desire for a decent cup of tea, in this talk Ian Cooper will explain the anti-patterns of RPC and look at the alternatives which will ensure your system stays stable and he gets a decent brew.

Kotlin is a fairly easy language to grasp given its similarity with other mainstream ones such as Java, C# and JavaScript. However, Kotlin provides a few characteristics which makes it possible to write nice DSL’s. But the question is, do we always need full-blown domain specific languages in our applications? Are we really going to write all our business rules in a specific language?

Not necessarily, but that doesn’t mean we should discard DSL’s. In fact, DSL’s are really powerful when they are small and focused. In this talk we’re going to show a few DSL’s that we can create to deal with different aspects of our application, whether it’s business dealing with tax rules or infrastructure and working with transactions, and see how with very little effort we can create more concise, maintainable and readable code.

As you know, naming stuff is one of the two hard things in Computer Science. And we fail at it every time we create another Manager or Service, or when we follow "framework conventions". Those of us living in OOP world desperately need better abstractions.  And Functional Programming is no panacea since we still need DDD practices to guide our design. Regardless of your background, this talk is designed (pun intended) to help you improve your names and naming habits. No need to reinvent the wheel - we will steal ideas from Eric Evans, Uncle Bob, Kent Beck and other experts.

Have you ever wondered where your favorite feature came from? Was it influenced by a feature in another language? How are the different programming languages even related? I spent a couple months researching the history of some programming languages, and wanted to share that with you. In this talk, I cover the history of the ML family from approximately the dawn of time, eventually focusing on F# specifically.

Want to simplify state management, improve scalability and reduce the code base? Rewrite your system in F# and use the actor model (Akka.NET). The functional language discourages use of mutable state and actors contribute to efficient communication, routing and scalability. And what about the code base? The code metrics speak for themselves. This talk is about real-world project and summarizes experience writing a robust and performant message based system for distributing media files to the cloud.

When we talk about scaling, we usually mean up (bigger machine) or out (more machines). However, there is another alternative, which is changing our workload in a way that makes it inherently more parallelizable and then taking advantage of specialized hardware that's very good at handling that sort of thing. Most of us have exactly this type of hardware just sitting in our computers, doing very little most of the time. I'm talking, of course, about the GPU. General-purpose computing on the GPU (GPGPU) is no longer the domain of pure academic research. It is being used in real-world applications such as image processing and face recognition, cryptography, big data analysis, and Bitcoin mining. In this session we will examine the available GPGPU frameworks, learn how to integrate C++ AMP and OpenCL into regular .NET and Java applications, how to debug mixed .NET and GPGPU code in Visual Studio, and how to use the Aparapi framework to seamlessly mix regular CPU and GPGPU code in Java. We’ll even look at using GPGPU from server and client-side JavaScript with WebCL. We will discuss the kinds of tasks that can benefit from graphics card processing, how best to parallelize workloads, and what some of the performance trade-offs are.