I see this often in software architecture. Sometimes I like to use the term impedance mismatch. There is a component that someone is suggesting for use in a different variation of its problem space. It fits badly.
Sometimes the issue is access. There are hard limits on the availability of stacks, libraries, frameworks, tools, and services. In some organizations, they need to be vetted and approved first. This can be very slow, but I still think a good idea; using too many technologies is a nightmare.
In some cases, it is knowledge. People tend to gravitate to the things they already know, so they’ll prefer technologies they’ve used in the past, even if it means forcing them into place. That makes little sense if the architect has that preference, but it’s a different development team that does the construction.
Sometimes it is a misunderstanding. The marketing for the component says it will do everything, perfectly, but the reality is that it is far more than a stretch. Hastily cobbled together features to help sales. Still, the people funding the effort got swayed, so now everyone else is forced to jam weak pieces into the wrong place.
The habit I’ve always encouraged is to look carefully and admit that a round hole is, in fact, just a round hole.
I don’t start with the square pegs; that is my last step. Not surprisingly, this can frustrate people, in that there might not be any round pegs available. They don’t exist or can’t be used. For me, though, I still want to know that the hole is round, even if I can’t fill it correctly right now, or in some cases, ever.
But if you can do that, and imagine for any bunch of holes what would fit rather perfectly, first, before getting lost in the messiness, it will really help both simplify the effort and get it as good as possible.
But if you can do that, and imagine for any bunch of holes what would fit rather perfectly, first, before getting lost in the messiness, it will really help both simplify the effort and get it as good as possible.
Otherwise, you risk unintentionally creating a Rube Goldberg machine. For people unfamiliar with those machines, they are works of art that are deliberately overcomplicated. Just a collection of mismatching pieces made to do something interesting. They make great entertainment, but are not something that you’d want to have to rely on.
I’ve seen that too often in enterprise architecture, systems built out of odd, mismatching components sloppily glued together into a giant house of cards. That, paired with excessive brute force for the glue, tends to generate an endless amount of support and bug fixing, while never really working correctly. The system exists, but it is just off by enough that it would be better if it didn’t. It’s a time sink. Now, instead of a solution, it is an ill-placed speed bump.
Often, to avoid that fate, I want to just look at the way the data needs to flow around at the high, rather abstract level.
I’ve seen that too often in enterprise architecture, systems built out of odd, mismatching components sloppily glued together into a giant house of cards. That, paired with excessive brute force for the glue, tends to generate an endless amount of support and bug fixing, while never really working correctly. The system exists, but it is just off by enough that it would be better if it didn’t. It’s a time sink. Now, instead of a solution, it is an ill-placed speed bump.
Often, to avoid that fate, I want to just look at the way the data needs to flow around at the high, rather abstract level.
You need to get the major entities from other sources, persist it all, and then deliver to interfaces, reporting, other systems, etc. If you understand the amount of information, its timeliness, and frequency, you start to get a sense of the minimum pegs you need below it. If you can grok that, then you can start the torturous phase of trying to see what is actually available and whether or not it is close enough to be workable. But if you go the other way and pick the components first, you’ll quickly get lost in gluing together odd parts for no reason.
It’s the same form of thinking that is needed to get good, simple, clean code, too. You have to see it first from a top-down perspective, before trying to build it up from what’s already available. It’s really the only way to keep from getting lost, but also to leverage reuse, encapsulation, etc. You want to know the scope of the problem first, come up with a near-perfect solution, and then map that impossible solution back to things that are possible. It probably sounds a bit crazy to people who can’t see it that way, but it is a perspective that anyone can learn to leverage. A superpower of sorts.
So we can get there with three easy questions.
It’s the same form of thinking that is needed to get good, simple, clean code, too. You have to see it first from a top-down perspective, before trying to build it up from what’s already available. It’s really the only way to keep from getting lost, but also to leverage reuse, encapsulation, etc. You want to know the scope of the problem first, come up with a near-perfect solution, and then map that impossible solution back to things that are possible. It probably sounds a bit crazy to people who can’t see it that way, but it is a perspective that anyone can learn to leverage. A superpower of sorts.
So we can get there with three easy questions.
- What is the ‘full’ scope of the problem?
- What would solve this perfectly?
- What’s available to approximate that perfect solution?
In an enterprise that might be building up a replacement system for tracking some type of inventory or case management. The primary features are pretty well known; the useful secondary ones are findable with a bit of investigation.
Perfection might be a dynamic data store to accommodate wide but slowly changing shallow data. The users need a nice GUI to get at this and keep control. The incoming data is real-time, vibrates occasionally, so a queue would protect it and help with integrity. The system feeds a few others that specialize in other forms of management. It’s always a smallish number of people. It should all run in a managed environment.
This then is the hole that needs to be filled in with whatever technologies are available now, in the future, or can be suitably crafted in a “reasonable” time.
Contrast that with something where the data rarely changes, there are millions of users constantly accessing it, and they are the primary source of the data. It’s a very different hole that likely needs industrial-strength pegs in order to keep it going. It’s not a system running on one or two boxes, but requires a large cluster of machines all cooperating to cope with its huge and variable load. The scale is so large that there is no overlap with that first medium system, so it’s unlikely that they should share any common technologies. It’s more of a star-shaped hole, needs special stuff to fill it.
The converse is also true, in that any of the technologies suitable for the second design would be grossly over-engineered for the first one. You can’t just cherry-pick a few and shove them into place. One is a 2D circle that needs to be painted, the other is a 3D hole that needs to be filled.
In that sense, you learn as much as you can about the full width of the problem, then let your imagination run wild with getting it perfect. With those boundaries in place, you can start picking the fewest number of pieces that come close to filling it. There will be ugliness and rough edges, but you’ve found them early and minimized them, which is the best you can do if you can’t just build it all from the metal to the top.
Perfection might be a dynamic data store to accommodate wide but slowly changing shallow data. The users need a nice GUI to get at this and keep control. The incoming data is real-time, vibrates occasionally, so a queue would protect it and help with integrity. The system feeds a few others that specialize in other forms of management. It’s always a smallish number of people. It should all run in a managed environment.
This then is the hole that needs to be filled in with whatever technologies are available now, in the future, or can be suitably crafted in a “reasonable” time.
Contrast that with something where the data rarely changes, there are millions of users constantly accessing it, and they are the primary source of the data. It’s a very different hole that likely needs industrial-strength pegs in order to keep it going. It’s not a system running on one or two boxes, but requires a large cluster of machines all cooperating to cope with its huge and variable load. The scale is so large that there is no overlap with that first medium system, so it’s unlikely that they should share any common technologies. It’s more of a star-shaped hole, needs special stuff to fill it.
The converse is also true, in that any of the technologies suitable for the second design would be grossly over-engineered for the first one. You can’t just cherry-pick a few and shove them into place. One is a 2D circle that needs to be painted, the other is a 3D hole that needs to be filled.
In that sense, you learn as much as you can about the full width of the problem, then let your imagination run wild with getting it perfect. With those boundaries in place, you can start picking the fewest number of pieces that come close to filling it. There will be ugliness and rough edges, but you’ve found them early and minimized them, which is the best you can do if you can’t just build it all from the metal to the top.
