Software Transactional Memory for the Cloud at µCon London 2017

I have just returned from µCon London 2017: The Microservices Conference. I was down there talking about our Software Transactional Memory implementation which our regular readers may recall was first introduced by Mark back in 2013. In particular I wanted to show how it can be used with the actor model features of Vert.x and the nice scaling features of OpenShift.

I have put the presentation slides in the narayana git repo so feel free to go and take a look to see what you missed out on. You can also access the source code I used for the practical demonstration of the technology from the same repository. The abstract for the talk gives a good overview of the topics discussed:

"Vert.x is the leading JVM-based stack for developing asynchronous, event-driven applications. Traditional ACID transactions, especially distributed transactions, are typically difficult to use in such an environment due to their blocking nature. However, the transactional actor model, which pre-dates Java, has been successful in a number of areas over the years. In this talk you will learn how they have been integrating this model using Narayana transactions, Software Transactional Memory and Vert.x. Michael will go through an example and show how Vert.x developers can now utilise volatile, persistent and nested transactions in their applications, as well as what this might mean for enterprise deployments."

And the demo that this abstract is referring to is pretty trivial but it does serve to draw out some of the impressive benefits of combining actors with STM. Here's how I introduced the technology:

1. Firstly I showed how to write a simple Vert.x flight booking application that maintains an integer count of the number of bookings.
2. Run the application using multiple Vert.x verticle instances.
3. Demonstrate concurrency issues using parallel workloads.
4. Fix the concurrency issue by showing how to add volatile STM support to the application.

I put the without STM and the with STM versions of the demo code in the same repository as the presentation slides.

I then showed how to deploy the application to the cloud using a single-node OpenShift cluster running on my laptop using Red Hat's minishift solution. For this I used "persistent" STM which allows state to be shared between JVMs and this gave me the opportunity to show some of the elastic scaling features of STM, Vert.x and OpenShift.