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How to Make Technology Choices

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How to Make Technology Choices

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I get emails looking for help with technology choice. Essentially: "I've got this idea for game-changing software idea, what technology should I use?" These questions have disturbing expectations. There's a Gordian Knot of dependencies that's sometimes baffling.

Sometimes, the questions are about choosing a "tech stack" or an "architecture". Sometimes it's the "framework" or the "platform".

All the questions, however, are very similar. They amount to either this

"What's the one, perfect and final technology choice we need to make?"
or this

"We're considering [X, Y and Z] can you validate this choice?"
Notice that the emphasis is on making  One  Perfect  Final  Decision.

An incidental part of this question is the context: this varies widely:
  • There might be a pretty good software idea.
  • Sometimes there's a list of user stories or use cases. Other times, there's a blatant refusal to consider human users, and a bizarre focus on technologies.
  • Less often, there's some sense of the business model; i.e., who will pay for this. Simply saying "advertisers" is a hint that there's no business model. Lack of a business model is a hint that technology choices are premature.
I'm not asked handle questions on business models; I'm not a venture capitalist; I'm just a tech consultant. But I expect that a business model is in place. Technology choices support a business; not the other way around. If there's no income, then there's no point in making technology choices, is there?
Unreasonable Expectations
What's disturbing are the expectations. We'll start with one expectation that is disturbing and then look at another.
The expectation of  finality is the most disturbing: the expectation that someone can make  One  Perfect  Final  Decision.
No technology choice is ever final. Today's greatest ever state-of-the-art, kick-ass-and-take-names SDK may evaporate in a cloud of lawsuits tomorrow. Today's tech giant may collapse. Today's platform of choice may be tomorrows weird anachronism.

Worse, a super-popular framework or platform may (after deeper examination) be totally brain-dead regarding some specific API or standard. Details matter, and details emerge slowly. A vendor (or open source community) may claim that it's (for example) RESTful, but you won't know until you try it.
Principle 0. Software Development is Knowledge Capture. You do not already know everything about the business, the technology or problem being solved. If you already know everything, it means you learned everything based on already having working software. 
Principle 1. Change happens. A fixed technology stack is a mistake. A fixed set of interface specifications is less of a mistake than a fixed set of technology choices. Software development involves learning, and while the learning is going on, the marketplace is changing. Note that learning is a two-way street, also. You learn about the users, the users learn about your technology. The problem you're trying to solve can morph as the users learn.
Principle  2. Change happens quickly.  As you learn about the marketplace, the problem, the technology and the business model, you'll be changing your software. Agility matters more than perfection. The most adaptable solution wins.
This next rule is harsh. But it's important.
Principle  3. If you have nothing to demonstrate, you have nothing. A good idea without a demo is difficult, almost impossible to work with. Without a demo, it's all just hand-waving. You must encode your knowledge in working software  before you can make a technology choice.

Yes. It's circular. Sorry. You can't make a software technology choice until you have demo software that shows the problem areas. You can't create the demo without making a (potentially inappropriate) technology choice.
Demo To Product
When I ask about the existence of any demo software, I get into trouble because some folks don't want to even start building a demo until they have the  One  Perfect  Final  Decision firmly in hand.
This leads to a second unreasonable expectation.
The expectation of  continuous evolution from demo to product is also disturbing: the expectation that even one line of code from the initial demo will become part of the final product.
Getting from idea to product will involve many changes. The user stories, the technology choices, the business model, every aspect is a candidate for a disruptive change. Success comes from making these changes. The first developer to abandon a bad idea is the furthest ahead. The most adaptable solution wins.
Cutting the Gordian Knot: Making Choices
Making a final, perfect technology choice for building the initial demo is not even helpful.

So don't.

Cut the Gordian Knot by building something. Build early. Build often. 
What's essential is to build something which (a) works, (b) has automated tests, and (c) can be evolved as the user stories evolve and improve. As you learn, you'll encode your evolving knowledge into evolving software. This is what software development really is: learning and encoding.
The initial demo may have to be discarded because better technology is located. Usually, however, the initial demo must be discarded based on experience in the marketplace, experience with the users, or experience solving the user's problems. It's more often these "other" non-technology lessons learned that trash the initial demo.
It's impossible to make a "future proof" technology choice. The future technology alternatives are difficult to know in advance. We distinguish between future and past by the lack of certainty in the future. As experience is gained, the initial round of user stories will get rewritten or possibly even discarded. A technology choice based on obsolete user stories is a liability, not an asset.
Some folks beg for something that will be "scalable" or "responsive" or "efficient" without having any actual scaling or performance problem that needs to be solved.
Using appropriate data structures and algorithms leads to inherently high-performance software. Beyond this vague platitude nothing much can be said.
Until there's a demo that has a specific scalability issue or performance bottleneck. Once a problem has been uncovered, then there's something to solve, and technology choices begin to matter. Most of the time, this will be a data structure or algorithm choice. Less often, this will be a larger architectural choice regarding parallelism or persistence.

Hand Wringing

"But what if," the professional hand-wringer asks, "What if my user stories are perfect, my demo is perfect, but I've made some sub-optimal technology choice and I'm forced to rework  everything for purely technical reasons that -- in hindsight -- I could have foreseen?"

The answers are (A) Are you an absolute genius of flawless user story creation? (B) Is your code so bad that the rewrite is more than just a refactoring? (C) When did you plan to fix you code so it could be refactored? (D) Did you really think you were never going to be forced to make a core technology change?

"But what if," the hand-wringer asks, "What if I can't afford to write the whole thing twice."

The answers are (A) Is your business plan so fragile that a rewrite invalidates everything? (B) What do you think "user support" entails? (C) What will you do when users ask for new features?

If this is about "time-to-market" and you have to rush to be early or first or something, then technology choice doesn't matter, does it? Time to market matters. So build something that works and get it to the market first. 

"But what if," the hand-wringer asks, "I choose a lousy platform initially?"

The answers are (A) Nothing is really wrong, it's just somewhat more costly or somewhat more complex. (B) So do others. (C) They rewrite, also.

"But what if I don't have skills in the best technology choice? What if I master a lousy technology to build the demo and release 1 and now I have to learn a whole new technology for release 2?"

The answers are (A) Did you really think that any technology would last forever? (B) Why can't you learn something new?
Basic Rules
The essential rules are these.

Build Early. Build Often.
The first step in making technology choices, then, is to pick a technology that you can actually make work, and build a demo.
Once you have a demo, recruit some potential or actual users.
Learn your lessons from these users: solve their problems: be sure your software is testable: troubleshoot your software as it is applied by  real users to their  realproblems.

Plan to rebuild your demo to satisfy your user's demands. You will be learning from your users.
In order to maximize the learning, you're going to need to log carefully. The default logging in something like Apache is useless; log scraping is useless. You'll need detailed, carefully planned, application-specific logging to capture enough information that you really know what's really going on.
Once you have working software with real users, you're going to switch into support mode. You'll be using your application-specific logging to figure out what they're doing. 
[War Story. For testability purposes, I added a special logger for a particularly gnarly and visible calculation of actuarial risk. The logger dumped  everything in a giant JSON document. To simplify debugging, I wrote a little app that loaded the JSON document and produced a ReStructured Text document so that I could read it and understand it. When requested, I could trivially pump the RST through docutils to create PDF's and send them to customer actuaries who questioned a result. This PDF-of-the-details became a user story for a link that would show supporting details to an actuarial user.]
Once you have working software, and a base of users, you can consider more refined technology choices. Now the question of PHP vs. Python vs. Java might become material.

[Hint. The right answer was RESTful web services with Python and mod_wsgi all along. Now you know.]
When the product is evolving from release 1 to release 2, you may have to reconsider your choice of database, web server, protocols, API's, etc. It turns out you're always going to be making technology choices. There will never be a final decision. Until no one wants your software.
If you are really, really lucky, you may get big enough to have scalability issues. Having a scalability issue is something we all dream about. Until you actually have a specific scalability issue, don't try to "pre-solve" a potential problem you don't yet have. If your software is even moderately well design, adding architectural layers to increase parallelism is not as painful as supporting obscure edge cases in user stories.

When you're still circulating your ideas prior to writing a demo, all technology choices are equally good. And equally bad. It's more important to get started than it is to make some impossibly  Perfect  Final  Decision. Hence the advice to build early and build often.

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Published at DZone with permission of Steven Lott, DZone MVB. See the original article here.

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