Repeating yourself thrice doesn’t turn you into a 3x developer
yrashk.medium.com
yrashk.medium.com
Also ORMs when you have to do anything more complex, specially if they involve aggregations: welcome to my awkward undocumented APIs, you now embark on a journey through hard-to-search-through class definitions and source dives that you'll share with every programmer that will touch your code in the future.
One fun case I witnessed involved a junior developer adding the desired/resulting SQL as a comment to every complicated Rails AREL queries, so that people could know what the query was doing.
Then, after seeing that, one of the tech leads determined that EVERY query should have SQL on top of it, for consistency, even things like User.all had the `SELECT * FROM users` on top.
In hindsight it's funny but it was a terrible team and a terrible software.
Go shove your views up yours, you maniac!
/s, except for way many more ORM lovers than you think.
I understand the sentiment but there is not anything inherently wrong with a stored procedure. If they came out today we'd probably call it edge computing.
If you mean stored procedures are harder to test than something like ORM in Django than that is just a huge misunderstanding of how you properly write stored procedures while also not understanding how hard it is to actually test a lot of ORM logic.
You test your app.
Also, not abusing database and writing code doesnt mean lets get deep into the ORMs madness.
You can do not write logic in db and still write raw sql
Finding data engineers that can actually do it will become difficult in just a bit, as there's a goldrush to take on that role, and lots of people want in with some rudimentary knowledge of SQL and Python.
Anyway, I'm always suspicious of people advocating for stored procedures, because those are version controlled if you're lucky, and I've yet to see them subject to automated testing.
And pgTap as an example of testing them https://pgtap.org/ .
What I'm suspicious is that, having seen untested stored procedures, you haven't bothered to try unit test them. I mean, you can do a lot with BEGIN; set db in good state; call procedure; ROLLBACK; but you have to try.
I haven't seen untested stored procedures in years, because I don't use them, no team I've been in uses them.
Complex prepared statements, that's common, but stored procedures, no.
Migrating from, let's say, Django, to something else requires you to basically rewrite your app from scratch. Migrating from SQLPage to Django requires you to run the standard django `python manage.py inspectdb`, then copy-paste your existing database queries, and your are ready to go.
Unless all product managers get in a room and define the canonical implementation of all web app features i think we are destined to do a lot more plumbing for a long time to come.
Job security for one, but it's hard to say in the abstract which coding style will be better.
But when you are a team of 3 with a startup to launch, for instance, you don't really care about permissions to edit categories and the no-category state. You just want that line of text at the top of the post that says which category it belongs to.
And you want to do it in a way that will allow you to later easily come back to it and start thinking about the "no-category" state and multiple categories for a single post.
Because they're never exactly the same, and you end up with heaps of special cases and handling and it would've been easier to write it three times from the beginning.
And even if they start out as exactly the same, in any non-trivial codebase that won't hold true for long.
Sometimes you want duplication because you believe the different code-copies will continue to diverge and require custom alterations.
Other you believe the copies will remain structurally the same while growing in number, so you hollow them out with reusable helper functions or macros or whatever.
What you say is on-point, and we should have mentioned it in the post.
The way I see it is: at the beginning, everything is repeated three times on the three layers. Then, as time advances, complexity grows, and you start having much more specific requirements that will need one of the layers to differ slightly.
The common approach is to just duplicate everything three times at the beginning to be ready for the moment when something needs to diverge.
What SQLPage [1] is saying, is: when you start, just think about the database. Make it the single source of truth, and iterate quickly to find out what form the data you work on will need to have. You won't get it right the first time, so it's crucial you don't find yourself having to do the work three times for every change. And then, when you need some frontend-specific feature, make just a react component for it and integrate it in the application. Then, as the app grows, you will progressively write a full frontend for it, and an external backend, but you will never have to re-do the work you have done in the beginning. This has allowed me to make some applications that I wouldn't even have thought I would have the courage to start before.
People from those communities say it's a relief building this way, though they're building simple proofs of concept still and it's not clear to me how well the approach holds up in fully fleshed out products. But it does seem to make a lot of sense in situations where a lot of the work involves keeping a bunch of devices in sync with each other.
If you're writing code where the front or back end data objects will break if you add a new db field, you're doing something wrong.
Something I have noticed about Fowler-esque / Uncle Bob-esque codebases is that usually only the guys who wrote it understand how it works. Which is either a blessing or a curse depending on whether you wrote the thing yourself or somebody else did it. And it also seems to defy the point of "making it easy to swap implementations by writing a ton of interfaces".
Is this entire article basically forgetting that as an option?
Autogeneration is a thing.
Yes it does suck. You know what sucks worse? Zero separation of concerns and the tar pit you get from it.
I don't want to write tests for the same functionality over and over. Repeated functionality should be extracted, tested in isolation and then used in composition with other tested code.
This is how you write correct code without stress or worry. People that take "just write the code" as dogma have produced some of the most untestable, bug-ridden code I've ever encountered.
What did they end up with? The few hundred lines of code expressing the BI queries shrank in half, but behind the simplicity was close to a thousand lines of dense, inscrutable magic. It was a net increase in LOC, but the value of the magic was supposed to compound as they added more queries. What happened was, the original programmer moved on, and every attempt to add more queries failed, until I joined and it was my turn to be sacrificed to the monster. (I did manage to figure it out. The key was realizing that the whole thing was stupid, from conception to execution — the other engineers had put the original programmer on a pedestal, and they were trying to make the code make sense, which it didn't.)
After making the query generator work for a few queries, I had established the credibility to say that we shouldn't use it anymore, and we should just write out all the boilerplate instead. Suddenly adding and modifying queries became something that anybody could do.
It isn't just custom code that ends up this way. I'm currently working on a project that uses SQLAlchemy, and as the glutton for punishment I am, I'm the person who cleans up all our SQLAlchemy difficulties. I virtually always have the documentation open in a tab, and I have the source code checked out to the version we use. If we just wrote raw SQL and wrote our own row mappers, we'd have twice as much database code, but we'd understand it, and anybody could write and debug it. Instead, half the team treats it as witchcraft, and I feel like I've invested more time learning SQLAlchemy in the last year than I ever spent learning SQL.
This is not to say I'm against abstraction, just that it can be done so poorly that it's counterproductive. You always have to compare -- are we better off with this, or without it? Saying that something reduces boilerplate or reduces repetition isn't the end of the conversation, even if it's true. You have to ask what the cost is.
Write the raw SQL and then generate the boilerplate from that.
This has very few surprises because it’s a bottom up approach. And even better: you can do the exact same thing by hand.
There’s tools/libs that help with that like hugsql (Clojure) or sqlc (Go and other languages).
Doing it top down (ORM etc.) is what can cause so many problems outside of the happy path and trivial cases. These tools basically need to reinvent SQL and map it into a procedural language.
Just use SQL!
OP is saying don't write a "magic thingcombobulator factory" that "simplifies X endpoints with Y and Z similar behavior". This might be an earnest attempt to try to speed development, but it all collapses under its own weight at scale. The maintainers after you will be left holding the bag and have immense difficulty refactoring, adding a new set of requirements, migrating to a new data model, or moving to an entirely new service.
Clever abstraction kills.
I've dealt with undoing insane balls of twine left by unthoughtful devs, mostly in magic method dispatch, included behavioral overrides, and monkey patching (some of these behaviors are a hallmark in Ruby land).
One person once exposed the entire database as a "safe" SQL-like query parameter DSL. No more endpoints to write - just use the thing.
There are so many problems with this. For example, when millions of transactions per day on mobile clients or via third party integrators bake these assumptions in, you can't easily migrate them away. You have to keep serving the same data assumptions, even while you're gutting and changing everything under the hood. You have to understand the callers, the data flows, the read and write paths. For complex spider webs of business critical logic, it can take several people entire quarters to even years to unwind the mess.
Simple endpoint logic is best. Your data model should be well thought out, and the CRUD code serves as a well-defined, super literate, super maintainable means to manipulate it.
Simplicity of design is important from the simplest Django endpoints all the way up to the most battle-hardened active/active 500k transaction per second endpoints.
My conclusion is that the real “star” developers will, most of the time, write code that’s so simple, it looks like anyone could have written it. They ship a project on time, with good performance and availability, and then they move on. Anyone can come in and maintain it because the code is so obvious.
Reading function pointer dispatch code, disguised as whatever it is called when not C, can be hopeless.
Too much "just write the code" ends up creating huge unmaintainable monstrosities.
When you have a lot of time in front of you and a large team, it's okay to just put two junior developers at work for two weeks, and get a big CRUD REST api in the end.
But when you are trying to iterate quickly with a small team, exposing your database is not as stupid an idea as it sounds. And that's why things like Firebase, Hasura, Apollo, Postgraphile, etc. are so popular.
The post is not trying to convince people to build custom DSLs just for querying their database (sorry you had to work with that). It is saying that there are things that exist today, that dramatically reduce the complexity of full stack applications. And that whether or not we like it, this is probably the direction the industry is taking.
The best way to handle cases that will be almost the same but may diverge over time is to create a functional mini DSL that describes the domain behavior, and create a template implementation that can be used if desired. Then everything is using a common language, and a non-template implementation indicates the presence of non-standard logic.
I mean yeah, I'm a big fan of DSLs. The problem occurs when someone writes the DSL, doesn't document it and leaves. Then it becomes super, super painful to maintain and extend.
Basically I'm coming round to the conclusion that (assuming reasonably competent colleagues), the least experienced person should be able to maintain and extend the code if it's to have any hope of remaining useful over time.
And good tests, for gods sake test the crap out of anything complicated with well-chosen names so that people can read the tests and understand how the code should be used.
f(base data) .validate() .domainAction1() .domainAction2() .log() .describe() .sendHtmlResponse()
Development heavily slows down under unpredictability. Maintainance is slower partially because knowledge loss hightens unpredictability. One-off half-documented pseudo-frameworks create much higher knowledge loss in maintenance, and are a much worse time eater than simple code, even if tripled.
Complexity is what a developer experiences at a particular point in time when trying to achieve a particular goal. It doesn’t necessarily relate to the overall size or functionality of the system. People often use the word “complex” to describe large systems with sophisticated features, but if such a system is easy to work on, then, for the purposes of this book, it is not complex. Of course, almost all large and sophisticated software systems are in fact hard to work on, so they also meet my definition of complexity, but this need not necessarily be the case. It is also possible for a small and unsophisticated system to be quite complex.
I'm not sure if you had a look at what SQLPage really does. It is not a framework in the same sense as Django, Rails, or Laravel. It doesn't have a large set of functions you need to interact with.
It lets you write the database queries you would have written anyway to get data out of your database, and just renders that as a nice frontend. All the components you can use for rendering are heavily documented with many examples on https://sql.ophir.dev/documentation.sql
What I meant: consider any random big software development. It might be mind-numbingly boring, very technically repetitive, you might have devs who never did any maintenance, or devs being expensive got the command to start building something anything while the business has yet to start delivering something resembling requirements.
In this kind of case, programmers tend to start building abstractions based on their imagined needs, with an We-will-add-the-business-stuff-later attitude. The results are generally some kind of architecture astronaut horror. Abstraction will be very high, weird features and handling of useless corner cases will abound. In-code documentation, logging, debugging features will be absent. Higher level documentation was either not written or lost long ago. That's your average one-off half-documented pseudo-framework.
I've seen plenty of these (and committed a few crimes of my own). From the top of my head, some of the worst:
* A full-blown 3000 lines templating library, for rendering exactly 1 report that was basically a for loop dumping an sql query to a html file.
* A C10K database connection manager built on top of apache commons pooling (which while a good library was not fit for this purpose at all), hyperoptimized for TCP port open/close speed, for an application making at most a few connections per minute.
* A cache manager for files, deciding when to remove a file based on either AI or linear regression, with a web UI for configuring this decision and all the zillion config parameters and strategies, but the time to generate the cached data was shorter than the time to read it from disk and the files easily lived for months.
* A java message building code that did everything humanly possible to only allocate a big buffer once at the beginning because 'GC is too slow', but the coder forgot how joining strings together created temporaries that were of course cleaned up by the GC.
Needless to say, the people maintaining these beast cursed the devs who implemented them, and tended to rip them out on sight if possible, or pay the very heavy maintenance cost.
I wish I could publish examples from my current codebase, because that's exactly what happened. Difficult and verbose abstractions, with sometimes 50 classes being involved in displaying a simple table (one class per column display, one class per filterable column), and that's just the "R" part of CRUD.
And there are 6 or 7 different teams working on it, and each one uses different methodologies to do their work. In some cases it's abstractions on top of GraphQL.
Everyone involved had the best intentions possible, but the end result doesn't reflect it.
I used to believe that, but I don't think it holds true any more.
The trick is to write code with automated tests and comprehensive documentation.
If you do this, you can leave projects in a state where you can pick them up in the future as if you weren't the original author.
The problem arises when there is a change in what we want the code to do. Changing a feature that is implemented over three codebases in three different languages is definitely much more work than updating something that was written in SQLPage, for instance.
I'll add it to the Datasette 1.0 milestone so it definitely gets my attention before shipping that release.
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LoC itself is a bad proxy for complexity, but I think taking the log of the number of LoC tells you enough to build some expectations. A codebase where log LoC is ~6 (so in the neighborhood of ~1M LoC) is different enough from one where log LOC is ~3 (so ~1,000 LoC) that you have an idea of what you're getting into if someone asks you to make a change to either one of those.
> that when the business rules change
Yes, when things change complexity has a cost. The inverse is also true however, if nothing changes, it has no cost. If class A, B, and C do almost the same thing, then nobody cares because the computer will gladly execute almost the same thing in different locations in memory. The modern computer built today is essentially perfect. It will execute the same thing every time, it will not suddenly require changes because there was some degradation in an adder, and no cogs need changing. All the maintenance is stuff we make up because we want it to do something it never did before.
Things always change. Software does not perform in a vacuum. It's subject to the inexorable progression of hardware decay and business knowledge loss, at the very least.
The lower the log LoC of their Visual Basic app, the easier it should be to replace and rewrite atop a modern tech stack.
If it ain't broke... you point out. It's old and creaky, and everyone's just afraid of the thing. There's no real backup (working on that!), there's no accessibility to it from the Internet - looking up info on that computer via a smartphone or tablet would be a boon to the company. It's absolutely load bearing, but it's like a bridge that's too small for the city that's grown around it.
The world moves forwards around software that's sat in place, so the software wants to move as well. We're not "making up" maintenance stuff just for the hell of it. Unless you work on the same chair and desk you used when you were 5. I don't fit in mine, and they were lost to a move anyway.
Once a code base is a certain size, explicit but bigger can be a boon. Magic dynamic dispatch systems and other tools that simplify plumbing make onboarding and routine, drive-by maintenance way harder IME.
I find that once you understand systems that have a dash of "magic", though, it is easier to add features and stuff. Single points of maintenance and all that.
It's a continuum, with each side having different benefits.
I did debug apps where UI and DB access lived in a single code space (VB/Delphi style). This was pretty hard to debug and logic was so tightly coupled with the UI code that it was nearly impossible to write tests for it.
The benefits: you write code faster, automatically uniform and the result is “dumb” and less abstract AKA easy to debug and modify. Tedium/boilerplate is gone, you focus in the overall model.
The costs: you think more up front, you have to see the result first (hand written). It’s easy to see common patterns too early.
With some patience, caution and experience some of the costs can be mitigated.
The articles example would imply in our use case:
1) add one key to the schema (which is database independent), which will generate encoders, decoders, apis (to work with the data structure, not in network-sense) automatically
2) add the key in the views you want to add it (when updating/reading or more complex network apis)
3) specify how the key is retrieved/saved in the use cases (controller-like)
4) use the key in the frontend.
It took me longer to write this post from mobile than it would've taken me do the first 3 steps.
I’m on my first project that resembles your description, and I _really_ like it (so far).
Auto-documentation is also a big plus, imo. Our “truth schema” also outputs OpenAPI specs, markdown docs, etc with zero added effort (past writing inline comments). Love it.
Notice that we use a custom typescript compiler (tsplus), we make use of some quite advanced typescript, and we add codegeneration via eslint on top of it.
Took me 3 months here before it started making sense, but then it started clicking.
A large code base that was auto-generated and then subtly modified in some places is hard to refactor, and if you need to change the signature of a function that is used thousands of time across the generated code, you are in for a long ride.
I routinely generate 80% of the code needed to implement a typical business application.
So much time & frustration expended simply to avoid typing out the magic database commands... And the constant ego trips attempting to outperform 30+ year old query planner codebases on 7-way+ joins by using baby's first ORM.
> the tar pit
If you find yourself stuck in one of these, I strongly recommend giving this a shot: https://curtclifton.net/papers/MoseleyMarks06a.pdf
"but it won't scale"
We are in the era of hyperscale SQL engines. Database engines that are spread out across multiple servers, racks and buildings. Engines so vast & complex the compute & storage responsibilities have to be separated into different stacks. But, they (the good ones) still work just like the old school approach from an application perspective. The workload necessary to actually saturate one of these databases would be incredible. I some days wonder if Twitter could be rewritten on top of one without much suffering.
And, if you aren't trying to go big and bold or spend a bunch of money, there's always SQLite. It also supports basically all the same damn things. It can run entirely in memory. It has FTS indexing. Your CTE-enabled queries will work just fine on it. If you find SQLite doesn't scale with you, swapping to a different engine really isn't that big of a deal either. You will have some dialect conflicts but it's generally very workable, especially if you use some thin layer like Dapper between your code and the actual connection instances.
Ultimately they tried to do more than asked which then caused problems because maintenance is now harder, and some types were removed while others were “enriched”, and much like uranium, became more dangerous to wield.
Other than that I completely agree. Devs get hang-up on trivial syntax topics waaaay too often, when the actual time-killer lies in reasoning and performing test-cycles.
I view abstraction as the single best way to make each other permanently more productive.
Don't we have ChatGPT/Copilot to do it for us now?
One one winner with splitting tech on horizontal boundaries is that changing a feature is a largely high cohesion change. All the code bung updated in that commit are related to one another, and despite the fact that there are "many" places that the code needs updates, at least they all relate to one another.
There was some effort in the java community to meet the problem half way with something called point cuts. This allowed some level of contracts which you could "insert behaviour into all instances of X" which had some success, but I haven't see it in the wild for a while, so I'm not entirely sure it survived.
There's no need for a separate API layer or painstaking synchronization with a standalone frontend. Features that took days of work across all three tiers now take just hours in a single unified backend context.
* Update your BlogPostModel, to add a ManyToMany(CategoryModel) field
* Update your .html template with a for loop over blog_post.categories.
* `django-admin makemigrations && django-admin migrate`
Your life can be so simple. All you need to do is to reject Javascript.
Regarding solo projects, I agree that simpler stacks like BaaS or other innovations can be sufficient. However, fast-scaling companies often require the unparalleled flexibility and customizations offered by an in-house 3-tier model. This tailored approach ensures they can effectively meet the evolving demands of their growing operations.