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Comment by bunderbunder | Hacker News Reader

I’ve had code review pushback against reorganizing data structures to be more cache-friendly on the grounds that it was “premature optimization” even though I pre-emotively justified the decision with benchmark results indicating that the performance difference was large and business relevant, and addressed a real performance bottleneck that I observed during testing.

This was on a team and at a time where Clean Code was very popular. I somewhat wonder if the problem is that, in the 2010s, many people became convinced that certain aesthetic standards were both objectively, provably correct, and more important than anything else.

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A lot of my poor opinion on developer opinions dates back to a couple projects where Product and Marketing were begging us for substantial performance improvements and the teams pushed back saying this was as fast as you could expect. I got 2x in the first six months on both projects, and delivered 25% per quarter on one for almost 2 years (that maths to around 8x) before I started to scrape the bottom of my barrel.

It’s predominantly a way to shut down thinking, which is why it’s so often misquoted. Knuth’s version is more nuanced. Theirs is not at all.

As a compromise I got really good at doing optimizations that also improved readability. Things like hoisting and better variable naming combined together can be mistaken for just code cleanup.

I also discovered that keyhole optimizations are more palatable. Which is to say, move the slow bit yo a separate function with a single responsibility. Then do the optimization in a separate commit. Then the other devs don’t have to look at your changes and if they turn out to have a bug, we can always revert the change later. They almost never do, but having the option seems to grease the wheels.

Nitpick but 25% improvement over 8 quarters is 1.25^8, almost a runtime in 1/6th of the time.

25% faster is 1.33x work done in the same time. What you want is 0.75^7 (almost 2 years) gives you 13.3% of the original time, which is 1/7.5. Only it was more like 25-30% so I rounded up.

Interesting how people interpret the word faster differently.

To you, 2x faster means 2x the work done in the same time, but 25% faster means 1.33x the work done in the same time.

I see this a lot - in software people interpret 50% faster as a 50% reduction in time taken.

If a car is 50% faster, I hope people would universally interpret that as e.g. 20 to 30 mph. But if I asked people how long would a 1 hour journey take if it were 50% faster, I bet a lot would say 30 mins, because it kind of seems like that's what I mean.

50 percent faster is 2/3 time, but yes, prominent people have made this error and even doubled down on it publicly.

When you're asked to meet a time limit, that's time domain, not rate, and so improvements will be reported in the time domain.

Time domain also works for Little's Law, which is the more prevalent measure these days anyway. If you double the throughput of a server you can halve the cluster size and retain the same level of service. Which isn't 200%, it's 50%.

Performance is for the user. If the user has told you that we have an SLA that requires this operation to complete in 10s, but it currently runs in 30 seconds, and your first set of changes gets it to 15s, what improvement are you going to report to the user?

You're going to report in the time domain. In which case 1/2 off the time is 50%.

One of the benchmarking tools I used to use reported 50k ops/sec -> 200k ops/sec as 75% faster. Which definitely IS wrong but I know how he got there. You see you don't count operations per second, you run the loop ?? thousand times and see how many milliseconds it takes. Then you convert that to ops/sec. If you compare before and after while still in the time domain, that's 75%. But if you report throughput right next to a ratio in the time domain you're gonna have a bad time.

When that tool stopped being maintained, the person who replaced it wisely switched to reporting in multipliers. 4x faster is hard to misinterpret. Though "1.1x slower" is still open to misinterpretation.

My general rule of thumb is that if you see you can 2x it easily, you can actually get somewhere around 10x with effort, since the presence of that easy 2x means nobody really spent any time on performance.

i got over 800 this way. O(n^2) everywhere and n+1 queries

This sounds like being completely performance oblivious, like an extreme opposite of premature optimisation.

My rule of thumb is that as long as it doesn’t really complicate or convolute the code, write with performance in mind.

My expertise is in C# and sometimes this is as simple as using a hash set or dictionary instead of a plain list.

Other times it’s having a decent understanding of how EF (an ORM) actually works (the static typing benefits of them in C# makes ORMs very valuable when used appropriately) and the cost of round trips to any network host (including a DB).

a hashset was used... then ".Where(x=>x == key).Count() == 0"

"Premature optimization" became a battle cry of the Don't Make Me Think crowd.

As I think back to the people who fought me, the same group didn't like when you refactored code either because now they didn't have the code memorized. But I've found in the intervening years that making code discoverable is much more useful than trying to keep it static because coworkers are always going to need to change shit anyway to implement new features even if that feature is neither Correctness nor Performance. What you don't want is for people to hide logic from you. So if you move logic to a function call, that's fine, because it's still in the same neighborhood. But if you hoist it all the way up into a much earlier part of the call tree, you need to announce that in meetings and maybe in the documentation so people know what's what.

It can go to extremes. Like 100x by going from a dynamic to static language then 100x more adjusting the data structures/algorithm…

Once I got very confused when exactly this kind of 10k x speed up I demonstrated for a problem introduced to me as a business bottleneck then got ignored (I wasn’t on this team, though, but it’s still hard to understand the mentality and incentives there).

I've known about a student, long ago in the '90s, who got to speed up a batch codebase in some bigcorp, as a summer job. The original did basically everything wrong, up to bubblesort. So student diligently cleaned up the mess and got a speedup from multiple hours to a few minutes.

Then he could not get an interview anywhere. After some backchannel communication it turned out he had to remove that experience from his CV, as all employers assumed he was just lying.

To be fair, I would have assumed that too. Occam's razor: in a sea of lying candidates, the next one is probably lying too.

Long long ago I filed a bug against TogetherJ, where I determined empirically that they had a bug with monorepo/separate compilation units that resulted in n^5 time complexity for loading multiple project files at once. For the last data point I started loading it at 5pm one day and then it finished at 11 am the following morning. They gave us a workaround that got it down to around 20 minutes, the patch made it a little bit better than that but that was still a long time.

I'm sure I've found factorial complexity code, at least in the design phase, but that's the worst one I've encountered in the wild.

That’s just pattern matching. Most people don’t do this level of prep so they assume you didn’t either. There’s usually no ill intent, and people are maybe a little embarrassed when you point it out, but if you are tactful it’s fine.

Something like this happened to me on a very high budget java project. I said I didn't want to make high memory pressure code that performed badly, and the senior guy said the optimization was premature. It didn't matter in the end because they didn't adopt our solution. (They stuck with mainframes, and I don't blame them.)

> objectively

I see what you did there

All types of architecture have advantages and disadvantages. Clean code might not be very cache-friendly, but it does a pretty good job of making sure you don't have to hunt in too many places to update things and It also makes it pretty simple to split things up so that you can write unit test for most everything.

This is not the right choice for everything, just as using Python is not the right choice for everything. The question to ask is: what are we trying to do in the first place? Then take it from there.

You did that by making sure the speed-up was business relevant. I'm mostly putting this out to force people to think about whether that also applies to their specific thing.

"Clean" code for modelling links between objects would be to store pointers within them. But that means dangling pointers when deleting objects, so you need to either update manually everywhere (easy to forget somewhere) or use shared/weak pointers (extra bookkeeping and allocations).

"Unclean" code for this is putting objects in a table, giving objects a generation (that increments whenever you create/delete one) and storing pairs of (index, generation) numbers everywhere you need a link. Link is live only if the generations matches and since only odd generations are live objects, default construction means no link. Lifted straight from 2000s video game tricks and with modern C++ it's basically all encapsulated away.

I've made that refactoring in a codebase, not because of speed but because of maintenance and memory fragmentation concerns. Sometimes, the clean solution really isn't the right one and it's really sad that compsci only ever teaches classical data structures, like we're still coding in the 1970s on computers with no caches that don't suffer from pointer chasing.

Processors I mostly write firmware for have little cache at all.

I think cache friendly code also tends to be reason about friendly and debugger friendly. None of that is remotely premature optimization.

> I think cache friendly code also tends to be reason about friendly and debugger friendly.

Not sure what examples you have in mind, but the very first things I think of when I hear cache friendly code are cache-blocking and cache-oblivious algorithms, which are quite the opposite of that. See also B-trees vs. binary search trees, etc.

I usually start with simpler things like paying attention to locality of reference and adjusting data so that it takes up less space in memory.

Even CPUs like the ARM cores have caches, usually 16K, occasionally 32K, and even larger L2 caches (512K).

Well, MCUs tend to have a buffer for execute in place from flash, but often they just have SRAM as main memory which can be accessed with a fixed latency and there is not much point in having a cache.

Before AI, nobody has been responsible for more psychic damage against software developers than Bob Martin.

think you mean "psychological damage". Can you give some examples of what you mean?

I’m not who you were replying to, but there was a thread here a couple of years ago in which many people chimed in with examples. I added my two cents here, but the whole discussion is much more worth reading than my gripes: https://news.ycombinator.com/item?id=43166836

Psychic is a damage type in D&D (and probably other RPGs too), so that is most likely a reference.

From what I understand, it is mostly the very rigid and prescriptive attitude which the average reader gets from his book, which, if you listen to him talk, was not his intent. He even published a new 2025 edition of “Clean Code” to try and fix this, by making the text less self-assured. So while he may or may not be responsible, he did not intend it.

I liked the book, so did my colleagues, and we even had a meetup to discuss the book on how to improve the legacy codebase we had inherited.

The exact same can be about agile, and all the arguments against that are the same.

The same applies there, except he hasn’t made a second book.

Absolute nonsense, people claiming that on here are usually hiding their deep political biases and as liberal lefties dont like it when someone in their space calls a spade a spade, especially someone with a lot more reach and respect. Slightly different playbook than calling everything you disagree with racist. Yawn spells are spent. This is how they attack Bob now because they realize being a leftie is ridiculous and this “im a centralist” crap carries no weight, so they hide that and try to attack him on his contributions to software engineering.

There's not enough context for me (or other commenters) to really evaluate this, but every change has risk, and some projects aren't in a position to be accepting that risk.

A "real performance bottleneck that I observed during testing" is a little bit unclear whether it occurred outside of test conditions, or whether speeding up that particular bit improves anything anyone cares about.

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