A couple more that don't seem to be represented there. No mention of cause and effect, or the order in which different nodes perceive things happening? Anyway here's three which I think might be more relevant to designing and building software:
* Your system is not a distributed system
Multiple users connect, disconnect, and use your system at the same time, some of the code is running on your servers, some of it's in your partners' servers, some of it's in your storage layer, and some of it's running on your users' computers
* Your DB's ACID transactions are sufficient for distributed thinking
An ACID transaction lets you addUser() to your storage, either succeeding completely or failing completely, with no observable intermediate state. It does not let both your frontend and your storage layer addUser(), same with both your storage and your partner's storage.
* Your DB's transactions are ACID
Your DB vendors cannot build databases that are acceptably fast while running ACID. Therefore isolation is relaxed and transactions can commit through each other. Even if the DB itself was ACID, your ORM and/or programming style is likely breaking ACID independently of the DB configuration.
So many services and daemons are running on your system and most of them believe that they have all the hardware for themselves, while the opposite is true. Designing to capitalize whole hardware while they are other processes which are fighting to do the same never ends well.
OTOH, being a good citizen on a crowded system makes life for everyone better. Both maintenance and performance-wise.
- The computer is plugged to an infinite source of unlimited power
This was big before the mobile era and is true to this day to an extent. Many mainstream languages created in the 1990s (I call them "the children of the 1990s") were designed with this fallacy plus the ones you listed as a basis: JavaScript, Python, Ruby, Java, etc.
Java is basically the "greenest" managed language out there, so not sure putting it into the same list for energy efficiency is warranted. Though of course energy efficiency is fundamentally linked to memory usage, not destructing/collecting dead objects will increase memory usage but increase efficiency.
Reading your link IMHO in today's world I would set a basic rule, if you're touching >20% of a Java codebase you should refactor to Rust. With AI-Native development practices it's worth the SDE time to refactor, replace the underlying subsytem and reduce your fleet by 50% or more.
Unless you are doing something very specific where rust is truly the best choice, I have to disagree.
Rust has overly strict locking (otherwise it couldn't determine safety) that makes certain concurrent algorithms harder to implement, its concurrency model is significantly more complex (for an absolutely good reason, it's a low-level language where the developer should be in control), meanwhile for many applications Java can just have you write ordinary blocking code and they will automagically turn into non-blocking.
For most domains Java has a richer "industry-strength" library ecosystem, and absolutely not even close observability tools. So not really sure what would one win for e.g. a typical backend service doing web and db requests.
JIT gives you almost native performance. AI rewriting tools give you none of the knowledge of running the thing in production. A couple of noticeable mishaps could cost more than halving your fleet saves.
Today even tiny CPUs are really fast. Locally you have to mess up badly to run into trouble. But of course people will do exactly that...
Most real world problems still can be solved with 32-bit software, so the last ~20 years running out of RAM always counted as "using defective hardware". AI workloads now make things interesting again, but it's not that easy to hit the ceiling with real world workload.
Cache is indeed very important. Optimisations like that are gone when you go for distributed computing. Sometimes adding a single nop can do wonders. I wonder how many percent of developers have something in their toolbox to profile for that.
Arguably cache concerns are distributed computing concepts moving closer to the core. Same with concurrency semantics. These were far more exotic concepts when the fallacies were first written.
Very easy to hit the 3GB limit imposed by 32-bit architecture for any non trivial data processing app but luckily 64-bit is firmly established for at least 10 years
There needs to be a distinction - because people are making an honest conflation - between distributed computing and cloud computing. The list in the article applies to both, but the limits and performance variability can apply quicker - and with more effect - in the cloud.
Perfect demonstration of the fallacies in action! If you were used to developing applications on a self contained platform you would think something like “sure, if it fails the user can try again”
On a distributed system the user can only try again if the platform has remained stable, the failure is transient (*) and they have (crucially) have been given the information to retry.
The platform that provides a stable environment for the user to just try again has been built on these principles.
(*) there is one administrator assumes it is within the user’s power to resolve the issue
On the other hand, more fortunes have been made by assuming that physics will catch up (closely enough, anyway) to computational needs, than by assuming that every byte and every cycle and every nanosecond matters.
In 2026 Moore's law has mostly stopped. My computer from 10 years ago still has acceptable performance today. My computer from 15 years ago would struggle a bit but still get the job done. This is nothing like the 90s where you actually could wait two years for all of that year's conceivable performance problems to be solved.
That's like saying money is only spent on sw/hw systems which rely on ever-growing compute capacity.
Reality: embedded systems are a thing. And there's (lots of!) money in that business too. There's maaaany applications where some (fixed) amount of compute does the job, and the simplest/cheapest device that does it wins out.
This article reiterates a lot of the Wikipedia stuff, while contradicting the main extant source which is Deutsch himself (https://se-radio.net/2021/07/episode-470-l-peter-deutsch-on-...). Nobody really knows who wrote the first four fallacies. They were just floating around it is Deutsch who pinned them down and it was Gosling’s endorsement that made them into the shibboleth that they are.
This is highly relevant to the recent craze over microservices, which has settled down now (after un-neccasarily complicating systems at multiple companies).
Micoserices or Monolith. It’s like being caught between the devil and the deep blue see. It’s a pity domain sockets never took off but I guess TCP/IP is the only truly cross platform IPC mechanism …
I don’t think either that or domain sockets are quite as ubiquitous as TCP sockets though.
The issue I see with domain sockets is that although they may be supported for example by spring, you can’t rely on a consistent cross platform experience which is perhaps (anachronistically?) a core ethic of the Java community.
I would favour domain sockets as to make a component go from being embedded to networked would require a small but significant implementation step.
But established best practice unfortunately disagrees with me.
A couple more that don't seem to be represented there. No mention of cause and effect, or the order in which different nodes perceive things happening? Anyway here's three which I think might be more relevant to designing and building software:
* Your system is not a distributed system
Multiple users connect, disconnect, and use your system at the same time, some of the code is running on your servers, some of it's in your partners' servers, some of it's in your storage layer, and some of it's running on your users' computers
* Your DB's ACID transactions are sufficient for distributed thinking
An ACID transaction lets you addUser() to your storage, either succeeding completely or failing completely, with no observable intermediate state. It does not let both your frontend and your storage layer addUser(), same with both your storage and your partner's storage.
* Your DB's transactions are ACID
Your DB vendors cannot build databases that are acceptably fast while running ACID. Therefore isolation is relaxed and transactions can commit through each other. Even if the DB itself was ACID, your ORM and/or programming style is likely breaking ACID independently of the DB configuration.
Another one from my experience:
* Hardware is cheap.
So many services and daemons are running on your system and most of them believe that they have all the hardware for themselves, while the opposite is true. Designing to capitalize whole hardware while they are other processes which are fighting to do the same never ends well.
OTOH, being a good citizen on a crowded system makes life for everyone better. Both maintenance and performance-wise.
* You will have logs
Always gets me
> No mention of cause and effect, or the order in which different nodes perceive things happening?
8. The network is homogeneous
Often misconstrued as a recapitulation of “there is one administrator”
A homogenous system, such as a single node Java application, for instance usually provides very clear semantics for this.
Also, the four fallacies of local computing:
- The CPU is infinitely fast.
- RAM is infinite.
- CPU caches don't exist.
- Cache lines don't exist.
- The computer is plugged to an infinite source of unlimited power
This was big before the mobile era and is true to this day to an extent. Many mainstream languages created in the 1990s (I call them "the children of the 1990s") were designed with this fallacy plus the ones you listed as a basis: JavaScript, Python, Ruby, Java, etc.
Java is basically the "greenest" managed language out there, so not sure putting it into the same list for energy efficiency is warranted. Though of course energy efficiency is fundamentally linked to memory usage, not destructing/collecting dead objects will increase memory usage but increase efficiency.
https://www.sciencedirect.com/science/article/pii/S016764232...
Reading your link IMHO in today's world I would set a basic rule, if you're touching >20% of a Java codebase you should refactor to Rust. With AI-Native development practices it's worth the SDE time to refactor, replace the underlying subsytem and reduce your fleet by 50% or more.
Unless you are doing something very specific where rust is truly the best choice, I have to disagree.
Rust has overly strict locking (otherwise it couldn't determine safety) that makes certain concurrent algorithms harder to implement, its concurrency model is significantly more complex (for an absolutely good reason, it's a low-level language where the developer should be in control), meanwhile for many applications Java can just have you write ordinary blocking code and they will automagically turn into non-blocking.
For most domains Java has a richer "industry-strength" library ecosystem, and absolutely not even close observability tools. So not really sure what would one win for e.g. a typical backend service doing web and db requests.
JIT gives you almost native performance. AI rewriting tools give you none of the knowledge of running the thing in production. A couple of noticeable mishaps could cost more than halving your fleet saves.
better than native, sometimes, due to the ability to profile and do profile guided recompilation at runtime
You hate Java so much you think AI code is better? You're not even getting memory safety from the deal, because Java already has it.
Indeed, the Java mobile platform had power consciousness baked in 25 or so years ago.
Was big before the AMD athlon. First commodity GHz processor was also the first to make obscene power demands.
Today even tiny CPUs are really fast. Locally you have to mess up badly to run into trouble. But of course people will do exactly that...
Most real world problems still can be solved with 32-bit software, so the last ~20 years running out of RAM always counted as "using defective hardware". AI workloads now make things interesting again, but it's not that easy to hit the ceiling with real world workload.
Cache is indeed very important. Optimisations like that are gone when you go for distributed computing. Sometimes adding a single nop can do wonders. I wonder how many percent of developers have something in their toolbox to profile for that.
Arguably cache concerns are distributed computing concepts moving closer to the core. Same with concurrency semantics. These were far more exotic concepts when the fallacies were first written.
Very easy to hit the 3GB limit imposed by 32-bit architecture for any non trivial data processing app but luckily 64-bit is firmly established for at least 10 years
Disk never gets filled up.
There needs to be a distinction - because people are making an honest conflation - between distributed computing and cloud computing. The list in the article applies to both, but the limits and performance variability can apply quicker - and with more effect - in the cloud.
In this instance latency must’ve been 10 years, per my memory this paper came out in 1994
According to Wikipedia it was first shown to Scott McNeally, but according to Deutsch himself it was more like 92…
Do people actually believe these dot points or are they just out of scope for most applications to tackle beyond letting the user try again?
Perfect demonstration of the fallacies in action! If you were used to developing applications on a self contained platform you would think something like “sure, if it fails the user can try again”
On a distributed system the user can only try again if the platform has remained stable, the failure is transient (*) and they have (crucially) have been given the information to retry.
The platform that provides a stable environment for the user to just try again has been built on these principles.
(*) there is one administrator assumes it is within the user’s power to resolve the issue
On the one hand, the list isn't wrong.
On the other hand, more fortunes have been made by assuming that physics will catch up (closely enough, anyway) to computational needs, than by assuming that every byte and every cycle and every nanosecond matters.
In 2026 Moore's law has mostly stopped. My computer from 10 years ago still has acceptable performance today. My computer from 15 years ago would struggle a bit but still get the job done. This is nothing like the 90s where you actually could wait two years for all of that year's conceivable performance problems to be solved.
That's like saying money is only spent on sw/hw systems which rely on ever-growing compute capacity.
Reality: embedded systems are a thing. And there's (lots of!) money in that business too. There's maaaany applications where some (fixed) amount of compute does the job, and the simplest/cheapest device that does it wins out.
Making money and being highly available are different goals.
Stock markets and commercial Telecomms beg to differ
Is every business a stock market and commercial Telecomm?
> Making money and being highly available are different goals.
These are large, highly profitable vertical markets.
The above remark is demonstrably foolish and ignorant.
This article reiterates a lot of the Wikipedia stuff, while contradicting the main extant source which is Deutsch himself (https://se-radio.net/2021/07/episode-470-l-peter-deutsch-on-...). Nobody really knows who wrote the first four fallacies. They were just floating around it is Deutsch who pinned them down and it was Gosling’s endorsement that made them into the shibboleth that they are.
This is highly relevant to the recent craze over microservices, which has settled down now (after un-neccasarily complicating systems at multiple companies).
Micoserices or Monolith. It’s like being caught between the devil and the deep blue see. It’s a pity domain sockets never took off but I guess TCP/IP is the only truly cross platform IPC mechanism …
Aren't Windows's named pipes very similar?
I believe so.
I don’t think either that or domain sockets are quite as ubiquitous as TCP sockets though.
The issue I see with domain sockets is that although they may be supported for example by spring, you can’t rely on a consistent cross platform experience which is perhaps (anachronistically?) a core ethic of the Java community.
I would favour domain sockets as to make a component go from being embedded to networked would require a small but significant implementation step.
But established best practice unfortunately disagrees with me.