So if your working on a physics engine and your optimizing collision detection, you think about the data in -> data out of the problem you are solving as the primary driver of how the code should be written.
You start with defining the data, and build from there.
Different types of applications all have different shapes of data so would have differently shaped optimal code. Eg) a physics engine would use some kind of spatial hash thing which can be optimized differently based on if stuff can be added/removed while it's running. A 3d renderer operates on big buffers of matrices and vertex data. A game is usually composed of some long lived things and a lot of short lived things.
The key message in Mike Acton's talk was:
"If you have different data, you have a different problem."
While ECS systems are not a panacea that solves all problems in a perfect data oriented way, they are generally more malleable than Object Oriented hierarchies. This means it's generally more feasible to write "near optimal" code in an ECS framework than in a mature Object Oriented code base.
But the key message isn't "use X framework", it's "start by defining the data".
And I say this as someone who basically sees programming as data and associated algorithms and always approaches problems by considering state or data first.
At work we are rewriting and reengineering system from scratch and its crazy because the limitations of the old system are now gone we get the most insane feature requests that are even accepted by the team lead et al. This makes such an approach impossible since DoD is exactly the opposite of flexible design in my opinion.
Im curious has anybody really followed this in a big long living commercial project?
An example of failing to follow DoD is the N+1 query problem: a programmer builds an abstraction that operates on individual DB rows, but "where there's one, there's more than one": you will inevitably be running that code in a loop so that you can process multiple rows. If instead the programmer had abstracted over groups of rows, then per-item query overheads suddenly become per-batch overheads.
- Andrew Kelley Practical Data Oriented Design (DoD) - https://youtu.be/IroPQ150F6c?si=F1Z0pLO2W5hbQgpM
- CppCon 2014: Mike Acton "Data-Oriented Design and C++" - https://youtu.be/rX0ItVEVjHc?si=jv4hhTSBh3XH--xQ
- Why You Shouldn’t Forget to Optimize the Data Layout - https://cedardb.com/blog/optimizing_data_layouts/
- Handles are the better pointers - https://floooh.github.io/2018/06/17/handles-vs-pointers.html
- Enum of Arrays - https://tigerbeetle.com/blog/2024-12-19-enum-of-arrays/
- Data oriented design book - https://www.dataorienteddesign.com/dodbook/
- Data-oriented design in practice - Stoyan Nikolov - https://youtu.be/_N5-JjogNXU?si=vhaxYcfE6tl11Sux
- Programming without Pointers - Andrew Kelley - https://www.hytradboi.com/2025/05c72e39-c07e-41bc-ac40-85e83...
- More Speed & Simplicity: Practical Data-Oriented Design in C++ - Vittorio Romeo - CppCon 2025 - https://youtu.be/SzjJfKHygaQ?si=jafavSl2YJWk4vIx
- Rust Handle - https://taintedcoders.com/rust/handles
1. Indexes instead of pointers. This allows you to avoid alignment of 8 bytes in your structure for x86_64.
2. Storing booleans out-of-band. Booleans cause padding all the time.
3. Struct of Arrays. Based on your question I assume you're familiar with it.
4. Store sparse data in hash maps. I remember one time when it allowed to eliminate inheritance.
5. Encoding the data instead of OOP/polymorphism. I haven't got an occasion to use it. The idea is to add extra tags to avoid boolean properties.
People posted a wide variety of specific ideas under my other comment: https://news.ycombinator.com/item?id=49061421
Very often the answer is indeed arrays, but it can easily be something else, depending on the problem. Data driven design is not very complicated, it just means instead of thinking about abstraction you think about the shape the data needs to be in to accommodate the most common transformations you need to do with it.
There are also cases where the optimal data format isn't array oriented because the memory access patterns for the problem in question just require something else.
You also have to think of hot vs cold data, which has nothing to do with arrays.
E.g. if your job is writing game engines or middleware used by AAA games with fancy graphics to run on consumer hardware, getting the most efficient use out of the players' limited memory bandwidth may be very important.
For many (most?) arbitrary commercial software projects in other contexts, performance isn't high priority & memory bandwidth isn't a bottleneck. Performance just has to be 'good enough' & 'good enough' performance may be easily attained by writing typical OO code that uses cache & memory very inefficiently - so in those cases DoD is an engineering trade off that solves a problem that doesn't need to be solved & may create new problems if introduced.
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