Intro to the Internet of Things

According to a 2014 Forbes article (actually, the autoplay video on the article), 87% of people had never heard the term “the Internet of things.” That has changed rapidly in the last two years (research firm 451 Research pegs 2016 as the year that the Internet of Things goes mainstream). Still, as with many cloud computing concepts, IoT is a vague term.

A Simple Description of the Internet of Things

Consumer-centric devices have emerged over the last forty years, from ATMs to inventory tracking in vending machines. Smart phones were a massive jolt, introducing a new means to connect to and interact with both consumers and physical objects. That networked, digitized environment of physical objects is the Internet of Things. 451 Research had a fantastic term for it: the Internet of things “virtualizes the physical world.”

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Intro to Scalability

Scalability is one of those words that can mean very different things to different people, even in the same context or the same project. It’s not so much nuanced as it is that the definition matters on perspective — scale can be different for different goals.

There will be upcoming posts on data virtualization, in-memory data grids, integration methods — all areas where an understanding of your current and future needs, resourcing, and loads are critical for planning. Going into those concepts, it helps to understand scale — not just “make it bigger,” but how you make it bigger and when and why.

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If All Men Were Angels…

If all men were angels, no government would be necessary.

James Madison made the case that no system is perfect precisely because people aren’t perfect. That was, admittedly, a defense of a political revolutionary moment, but it holds true in software design as well.

Mark Little, vice president of engineering for middleware at Red Hat, has a blog post on this topic this week on jboss.org. The entire post is terrific (and readably brief), but there are a couple of points worth highlighting. His premise starts with the idea of what causes (or devolves) a system into a monolith, and he points to this:

Lack of architect (leadership); the original architect(s) leave the project and those who come in to replace them (if they are replaced) can’t control the developers or perhaps don’t understand the architecture enough to ensure it remains “pure”. Likewise, different developers coming into the project to either add to or replace those there already, can dilute the group knowledge and understanding of the architecture, leading to unforeseen and accidental divergence from the original plan.

What leads to an inflexible, centralized monolith application is, ironically, a lack of central vision. Mark sums it up with a really good point about the risks in microservice architectures:

I believe in and understand the need for distributed systems composed of (micro) services. However, what worries me about some of the current emphasis around microservices is that somehow they will naturally result in a better architecture. That’s simply not the case. If you don’t put in to place the right processes, design reviews, architecture reviews, architects etc. to prevent or forestall a local monolith then you’ve no hope of achieving a good microservices architecture. [emphasis added]

This is such an amazing point, it bears repeating. There is frequently this unspoken, sometimes unrecognized, belief that The Next Good Thing will some how solve all of the issues of the Last Good Thing without requiring special effort. But there are no perfect systems — clear planning, good communication, good team processes are required no matter what architectural pattern you’re using to develop your applications.