Thursday, September 28, 2017

Learning to Code

I know folks who struggle with the core concepts of writing software.

Some of them are IT professionals. With jobs. They can't really code. It seems like they don't understand it.

Maybe a gentler introduction to programming will help?

I have my doubts. The folks who seem to struggle the hardest are really fighting against their own assumptions. They seem to make stuff up and then seek confirmation in everything they do. The idea of a falsifiable experiment seems to be utterly unknown to them. Also, because they're driven by their assumptions, the idea of exhaustively enumerating alternatives isn't something they do well, either.

For example, if you try to explain python's use of " or ' for string literals -- a syntax not used by a language like SQL -- they will argue that Python is "wrong" based on their knowledge of SQL. Somehow they wind up with a laser-like focus on mapping Python to SQL. They'll argue that apostrophe's are standard, and they'll always use those. Problem solved, right?

Or is it problem ignored? Or problem refused?

And. Why the laser-like focus on mapping among programming languages? It seems that they're missing the core concept of abstract semantics mapped to specific syntax.

Tuesday, September 26, 2017

Learning About Data Science.

I work with data scientists. I am not a scientist.

This kind of thing on scikit learn is helpful for understanding what they're trying to do and how I can help.

Tuesday, September 19, 2017

Three Unsolvable Problems in Computing

The three unsolvable problems in computing:

  • Naming
  • Distributed Cache Coherence
  • Off-By-One Errors

Let's talk about naming.

The project team decided to call the server component "FlaskAPI".

Seriously.

It serves information about two kinds of resources: images and running instances of images. (Yes, it's a kind of kubernetes/dockyard lite that gives us a lot of control over servers with multiple containers.)

The feature set is growing rapidly. The legacy name needs to change. As we move forward, we'll be adding more microservices. Unless they have a name that reflects the resource(s) being managed, this is rapidly going to become utterly untenable.

Indeed, the name chosen may already be untenable: the name doesn't reflect the resource, it reflects an implementation choice that is true of all the microservices. (It's a wonder they didn't call it "PythonFlaskAPI".)

See https://blogs.mulesoft.com/dev/api-dev/best-practices-for-building-apis/ for some general guidelines on API design.

These guidelines don't seem to address naming in any depth. There are a few blog posts on this, but there seem to be two extremes.

  • Details/details/details. Long paths: class-of-service/service/version-of-service/resources/resource-id kind of paths. Yes. I get it. The initial portion of the path can then route the request for us. But it requires a front-end request broker or orchestration layer to farm out the work. I'm not enamored of the version information in the path because the path isn't an ontology of the entities; it becomes something more and reveals implementation details. The orchestration is pushed down the client. Yuck.
  • Resources/resource. I kind of like this. The versioning information can be in the Content-Type header: application/json+vnd.yournamehere.vx+json.  I like this because the paths don't change. Only the vx in the header. But how does the client select the latest version of the service if it doesn't go in the path? Ugh. Problem not solved.
I'm not a fan of an orchestration layer. But there's this: https://medium.com/capital-one-developers/microservices-when-to-react-vs-orchestrate-c6b18308a14c  tl;dr: Orchestration is essentially unavoidable.

There are articles on choreography. https://specify.io/concepts/microservices the idea is that an event queue is used to choreograph among microservices. This flips orchestration around a little bit by having a more peer-to-peer relationship among services. It replaces complex orchestration with a message queue, reducing the complexity of the code.

On the one hand, orchestration is simple. The orchestrator uses the resource class and content-type version information to find the right server. It's not a lot of code.

On the other hand, orchestration is overhead. Each request passes through two services to get something done. The pace of change is slow. HATEOAS suggests that a "configuration" or "service discovery" service (with etags to support caching and warning of out-of-date cache) might be a better choice. Clients can make a configuration request, and if cache is still valid, it can then make the real working request.

The client-side overhead is a burden that is -- perhaps -- a bad idea. It has the potential to make  the clients very complex. It can work if we're going to provide a sophisticated client library. It can't work if we're expecting developers to make RESTful API requests to get useful results. Who wants to make the extra meta-request all the time?

Tuesday, September 12, 2017

The No Code Approach to Software and Why It Might Be Bad

Start here: https://www.forbes.com/sites/jasonbloomberg/2017/07/20/the-low-codeno-code-movement-more-disruptive-than-you-realize/#98cfc4a722a3

I'm not impressed. I have been not impressed for 40 years and many previous incarnations of this idea of replacing code with UX.

Of course, I'm biased. I create code. Tools that remove the need to create code reflect a threat.

Not really, but my comments can be seen that way.

Here's why no code is bad.

Software Captures Knowledge

If we're going to represent knowledge in the form of software, then, we need to have some transparency so that we can see the entire stack of abstractions. Yes, it's turtles all the way down, but some of those abstractions are important, and other abstractions can be taken as "well known" and "details don't matter."

The C libraries that support the CPython implementation, for example, is where the turtles cease to matter (for many people.) Many of us have built a degree of trust and don't need to know how the libraries are implemented or how the hardware works, or what a transistor is, or what electricity is, or why electrons even have a mass or how mass is imparted by the Higgs boson.

A clever UI that removes (or reduces) code makes the abstractions opaque. We can't see past the UI. The software is no longer capturing useful knowledge. Instead, the software is some kind of interpreter, working on a data structure that represents the state of the UI buttons.

Instead of software describing the problem and the problem's state changes, the software is describing a user experience and those state changes.

I need the data structure, the current values as selected by the user, and the software to understand the captured knowledge. 

Perhaps the depiction of the UI will help. 

Perhaps it won't. 

In general, a picture of the UI is useless. It can't answer the question "Why click that?" We can't (and aren't expected) to provide essay answers on a UI. We're expected to click and move on.

If we are forced to provide a essay answers, then the UI could come closer to capturing knowledge. Imagine having a "Reason:" text box next to every clickable button.

We all know what the essay answers will look like. They'll look like bad comments in code. And bad commit comments in Git. And bad documentation.

Some Option: ☑️ Reason: Required
Other Option: ☐ Reason: Not sure if its needed

The problem with fancy UI's and low-code/no-code software is low-information/no-information software. Maintenance becomes difficult, perhaps impossible, because it's difficult understand what's going on.

Tuesday, September 5, 2017

Seven Performance Tips

Packt (@PacktPub)
Want to improve your #Python performance? We've got 7 great tips for you: bit.ly/28YiGeE via @ggzes #CodingTips pic.twitter.com/cGhoGyTSS9

I have one thing to add: Learn to use the profiler and timeit. They will eliminate and hand-wringing over what might be better or worse. The policy is this: Code, Measure, and Choose.