Systems that defy detailed understanding
blog.nelhage.com
Systems that defy detailed understanding
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Re: Systems that defy detailed understanding
#2Great article.
Recalls Gall's Law[1]. "A complex system that works is invariably found to have evolved from a simple system that worked."
Also, TFA invites a question: if handed a big ball of mud, is it riskier to start from scratch and go for something more triumphant, or try to evolve the mud gradually?
I favor the former, but am quite often wrong.
Re: Systems that defy detailed understanding
#3Big balls of mud result from a process that resembles reinforcement learning, in that modifications are made with a goal in mind and with testing to weed out changes that are not satisfactory, but without any correct, detailed theory about how the changes will achieve the goal without breaking anything.
Re: Systems that defy detailed understanding
#4Great article. Recalls Gall's Law[1]. "A complex system that works is invariably found to have evolved from a simple system that worked." Also, TFA invites a question: if handed a big ball of mud, is it riskier to start from scratch and go for something more triumphant, or try to evolve the mud gradually? I favor the former, but am quite often wrong. [1] https://en.m.wikiquote.org/wiki/John_Gall
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Re: Systems that defy detailed understanding
#5Big balls of mud result from a process that resembles reinforcement learning, in that modifications are made with a goal in mind and with testing to weed out changes that are not satisfactory, but without any correct, detailed theory about how the changes will achieve the goal without breaking anything.
So, lack of a larger test suite that can detect ripple effects across the overall system, and not just a component?
Or are test suites just a nice fantasy for a real distributed system?
Re: Systems that defy detailed understanding
#6"Introduction to Cybernetics" W. Ross Ashby
http://pespmc1.vub.ac.be/ASHBBOOK.html
> ... still the only real textbook on cybernetics (and, one might add, system theory). It explains the basic principles with concrete examples, elementary mathematics and exercises for the reader. It does not require any mathematics beyond the basic high school level. Although simple, the book formulates principles at a high level of abstraction.
Re: Systems that defy detailed understanding
#7Great article. Recalls Gall's Law[1]. "A complex system that works is invariably found to have evolved from a simple system that worked." Also, TFA invites a question: if handed a big ball of mud, is it riskier to start from scratch and go for something more triumphant, or try to evolve the mud gradually? I favor the former, but am quite often wrong. [1] https://en.m.wikiquote.org/wiki/John_Gall
One of my favorites:
There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies and the other way is to make it so complicated that there are no obvious deficiencies. — C.A.R. Hoare, The 1980 ACM Turing Award Lecture
Re: Systems that defy detailed understanding
#8Big balls of mud result from a process that resembles reinforcement learning, in that modifications are made with a goal in mind and with testing to weed out changes that are not satisfactory, but without any correct, detailed theory about how the changes will achieve the goal without breaking anything.
Sounds like all of Agile, really. One can characterize Agile as a ball-of-mud maintenance process that scales desirably with the amount of mud.
Re: Systems that defy detailed understanding
#9> If you run an even-moderately-sophisticated web application and install client-side error reporting for Javascript errors, it’s a well-known phenomenon that you will receive a deluge of weird and incomprehensible errors from your application, many of which appear to you to be utterly nonsensical or impossible.
...
> These failures are, individually, mostly comprehensible! You can figure out which browser the report comes from, triage which extensions might be implicated, understand the interactions and identify the failure and a specific workaround. Much of the time.
> However, doing that work is, in most cases, just a colossal waste of effort; you’ll often see any individual error once or twice, and by the time you track it down and understand it, you’ll see three new ones from users in different weird predicaments. The ecosystem is just too heterogenous and fast-changing for deep understanding of individual issues to be worth it as a primary strategy.
Sadly far too accurate.
Re: Systems that defy detailed understanding
#10Big balls of mud result from a process that resembles reinforcement learning, in that modifications are made with a goal in mind and with testing to weed out changes that are not satisfactory, but without any correct, detailed theory about how the changes will achieve the goal without breaking anything.
So, lack of a larger test suite that can detect ripple effects across the overall system, and not just a component? Or are test suites just a nice fantasy for a real distributed system?
In the situation I am thinking of, the tests that select successful modifications are, almost by definition, integration tests, because with a big ball of mud, you don't know what the proper specification for the components are, and they don't have clear interfaces.
By 'tests' I am including live failures, which are also a feature of mudballs.
A distributed system is always much more difficult to test than a functionally-equivalent localized version. That's not, of course, a reason to give up on testing, but one must be realistic about how much faith one can put in it to make up for an inadequate use of abstraction and separation of concerns.