The Value of Flexibility
Posted by bsstahl on 2019-02-14 and Filed Under: development
Have you ever experienced that feeling you get when you need to extend an existing system and there is an extension point that is exactly what you need to build on?
For example, suppose I get a request to extend a system so that an additional action is taken whenever a new user signs-up. The system already has an event message that is published whenever a new user signs-up that contains all of the information I need for the new functionality. All I have to do is subscribe a new microservice to this event message, and have that service take the new action whenever it receives a message. Boom! Done.
Now think about the converse. The many situations we’ve all experienced where there is no extension point. Or maybe there is an extension mechanism in place but it isn’t quite right; perhaps an event that doesn’t fire on exactly the situation you need, or doesn’t contain the data you require for your use case and you have to build an entirely new data support mechanism to get access to the bits you need.
The cost to “go live” is only a small percentage of the lifetime total cost of ownership. – Andy Kyte for Gartner Research, 30 March 2010
There are some conflicting principles at work here, but for me, these situations expose the critical importance of flexibility and extensibility in our application architectures. After all, maintenance and extension are the two greatest costs in a typical application’s life-cycle. I don’t want to build things that I don’t yet need because the likelihood is that I will never need them (see YAGNI). However, I don’t want to preclude myself from building things in the future by making decisions that cripple flexibility. I certainly don’t want to have to do a full system redesign ever time I get a new requirement.
For me, this leads to a principle that I like to follow:
I value Flexibility over Optimization
As with the principles described in the Agile Manifesto that this is modeled after, this does not eliminate the item on the right in favor of the item on the left, it merely states that the item on the left is valued more highly. This makes a ton of sense to me in this case because it is much easier to scale an application by adding instances, especially in these heady days of cloud computing, than it is to modify and extend it. I cannot add a feature by adding another instance of a service, but I can certainly overcome a minor or even moderate inefficiency by doing so. Of course, there is a cost to that as well, but typically that cost is far lower, especially in the short term, than the cost of maintenance and extension.
So, how does this manifest (see what I did there?) in practical terms?
For me, it means that I allow seams in my applications that I may not have a functional use for just yet. I may not build anything on those seams, but they exist and are available for use as needed. These include:
- Separating the tiers of my applications for loose-coupling using the Strategy and Repository patterns
- Publishing events in event-driven systems whenever it makes sense, regardless of the number of subscriptions to that event when it is created
- Including all significant data in event messages rather than just keys
There are, of course, dangers here as well. It can be easy to fire events whenever we would generally issue a logging message. Events should be limited to those in the problem domain (Domain Events), not application events. We can also reach a level of absurdity with the weight of each message. As with all things, a balance needs to be struck. In determining that balance, I value Flexibility over Optimization whenever it is reasonable and possible to do so.
Do you feel differently? If so, let me know on Twitter @bsstahl.
A Requirement for AI Systems
Posted by bsstahl on 2017-05-24 and Filed Under: development
I've written and spoken before about the importance of using the Strategy Pattern to create maintainable and testable systems. Strategies are even more important, almost to the level of necessity, when building AI systems.
The Strategy Pattern is to algorithms what the Repository Pattern is to data stores, a useful and well-known abstraction for loose-coupling.
— Barry Stahl (@bsstahl) January 6, 2017
The Strategy Pattern is an abstraction tool used to maintain loose-coupling between an application and the algorithm(s) that it uses to do its job. Since the algorithms used in AI systems have many different ways they could be implemented, it is important to abstract the implementation from the system that uses it. I tend to work with systems that use combinatorial optimization methods to solve their problems, but there are many ways for AIs to make decisions. Machine Learning is one of the hottest methods right now but AI systems can also depend on tried-and-true object-oriented logic. The ability to swap algorithms without changing the underlying system allows us the flexibility to try multiple methods before settling on a specific implementation, or even to switch-out implementations as scenarios or situations change.
When I give conference talks on building AI Systems using optimization methods, I always encourage the attendees to create a "naïve" solution first, before spending a lot of effort to build complicated logic. This allows the developer to understand the problem better than he or she did before doing any implementation. Creating this initial solution has another advantage though, it allows us to define the Strategy interface, giving us a better picture of what our application truly needs. Then, when we set-out to build a production-worthy engine, we do so with the knowledge of exactly what we need to produce.
There is also another component of many AIs that can benefit from the use of the Strategy pattern, and that is the determination of user intent. Many implementations of AI will include a user interaction, perhaps through a text-based interface as in a chatbot or a voice interface such as a personal assistant. Each cloud provider has their own set of services designed to determine the intent of the user based on the text or voice input. Each of these implementations has its own strengths and weaknesses. It is beneficial to be able to swap those mechanisms out at will, along with the ability to implement a "naïve" user intent solution during development, and the ability to mock user intent for testing. The strategy pattern is the right tool for this job as well.
As more and more of our applications depend heavily on algorithms, we will need to make a concerted effort to abstract those algorithms away from our applications to maintain loose-coupling and all of the benefits that loose-coupling provides. This is why I consider the Strategy Pattern to be a necessity when developing Artificial Intelligence solutions.