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Of all the productivity tools available to UI/UX developers today, few are as ubiquitous and effective as the humble checklist.
In "The Checklist Manifesto", Atul Gawande explains that simple checklists can transform the way we work. In his book, Gawande was trying to find a way that doctors, surgeons, and nurses could deliver better, more consistent outcomes when working in an operating room - a literal life-or-death situation.
Ever since applications started moving data records, we’ve needed ways to annotate those records with formatting instructions. Many of these record notation formats are familiar to developers. For example, according to the IETF, JSON “defines a small set of formatting rules for the portable representation of structured data.” In practical terms, JSON makes it possible to describe value pairs, arrays, or a series of values as a human-readable document.
Similarly, the prolific XML markup language makes it possible to encode data into a format that is both human and machine-readable. Without formatting instructions provided by JSON and XML, machines would lack the context necessary to express and analyze documents. However, what happens when data cannot be expressed as a document?
Even the most simple chat user interfaces bely a world of architectural complexity. Features like authentication, user presence, chat rooms, user counts, message encryption, and countless others represent a significant undertaking. However, with the right tools, building an enterprise-scale chat application is not only possible, it can be done relatively quickly.
This post is a tutorial for building a basic chat application using the open source Swim platform. The app we’ll be referencing was built by Scott Clarke, a UI developer at Swim, and the source code is available on GitHub here. Because this chat application is intended to demonstrate Swim development patterns, as opposed to being a usable product, we have not included features like authentication or compressive user state tracking. While we do include user presence in the chat app, we took the simplest approach possible and just display a user’s local IP address. This app may be simple, but the same patterns we demonstrate here can be used to build a massively scalable version, and can easily be integrated with authentication services or other third-party software.
Five years ago when I started tracking media buzz around stateful architectures, I’d see a few articles every month about running stateful containers. That's about when Caitie McCaffrey first shared this awesome presentation about building scalable stateful architectures. Since then, the dominant software paradigm has become functional application design. The actor model and other object-oriented paradigms are still in use, but database-centric RESTful architectures are standard means of building web applications today.
However, the tides are beginning to shift. Due to innovations like the blockchain, growing demand for real-time applications, the digitization of OT assets, and the proliferation of cheap compute resources at the network edge; there’s renewed interest in decentralized application architectures. As such, there’s also been increased focus on stateful applications. For example, at least five Apache Foundation projects (Beam, Flink, Spark, Samza, and TomEE) are touting statefulness as a benefit today. Modern applications communicate across multiple application silos and must span real-world machines, devices, and distributed data centers around the world. Stateful application architectures provide a way to abstract away the logistical effort of state management, thereby reducing development and management effort necessary to operate massive-scale distributed applications.
For the rest of this post, I want to disprove the notion that building scalable, stateful applications is a task too complex for everyday Java developers. In order to illustrate how easily a stateful application can be setup, we’ll walk through a tutorial for building a simple to-do list using the Swim platform. You can find all the source code for the to-do list tutorial here on GitHub.