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Comment by godelski | Hacker News Reader

I think the problem with the quote is that everyone forgets the line that comes after it.

  We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.

  vvvvvvvvvv
  Yet we should not pass up our opportunities in that critical 3%. A good programmer will not be lulled into complacency by such reasoning, he will be wise to look carefully at the critical code; but only after that code has been identified.
  ^^^^^^^^^^

This makes it clear, in context, that Knuth defines "Premature Optimization" as "optimizing before you profile your code"

@OP, I think you should lead with this. I think it gets lost by the time you actually reference it. If I can suggest, place the second paragraph after

  > People always use this quote wrong, and to get a feeling for that we just have to look at the original paper, and the context in which it was written.

The optimization part gets lost in the middle and this, I think, could help provide a better hook to those who aren't going to read the whole thing. Which I think how you wrote works good for that but the point (IMO) will be missed by more inattentive readers. The post is good also, so this is just a minor critique because I want to see it do better.

https://dl.acm.org/doi/10.1145/356635.356640 (alt) https://sci-hub.se/10.1145/356635.356640

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Amdahl’s Law is the single best thing I learned in 4 years of university. It sounds obvious when spelled out but it blew my mind.

No amount of parallelization will make your program faster than the slowest non-parallelizable path. You can be as clever as you want and it won’t matter squat unless you fix the bottleneck.

This extends to all types of optimization and even teamwork. Just make the slowest part faster. Really.

https://en.wikipedia.org/wiki/Amdahl%27s_law

  > It sounds obvious when spelled out but it blew my mind.

I think there's a weird thing that happens with stuff like this. Cliches are a good example, and I'll propose an alternative definition to them.

  A cliche is a phrase that's so obvious everyone innately knows or understands it; yet, it is so obvious no one internalizes it, forcing the phrase to be used ad nauseam

At least, it works for a subset of cliches. Like "road to hell," "read between the lines," Goodheart's Law, and I think even Amdahl's Law fits (though certainly not others. e.g. some are bastardized, like Premature Optimization or "blood is thicker than water"). Essentially they are "easier said than done," so require system 2 thinking to resolve but we act like system 1 will catch them.

Like Amdahl's Law, I think many of these take a surprising amount of work to prove despite the result sounding so obvious. The big question is if it was obvious a priori or only post hoc. We often confuse the two, getting us into trouble. I don't think the genius of the statement hits unless you really dig down into proving it and trying to make your measurements in a nontrivially complex parallel program. I think that's true about a lot of things we take for granted

another commonly misinterpreted one is the `shouting fire in a crowded theatre` quote.

In it's original context it means the opposite of how people use it today.

Well, sort of. The specific point Justice Oliver Wendell Holmes was making is pretty much the same as how it’s used today: some speech is so obviously harmful that it can’t be protected by freedom of speech. The problem is that Holmes was using it as an example of what even “the most stringent protection of free speech” would leave unprotected, and he went on to interpret the First Amendment rather less stringently, ruling that it didn’t protect anti-draft flyers.

> faster than the slowest non-parallelizable path

Rather, than the slowest non-parallelized path. Ultimately you may reach maximum speed on that path but the assumptions that we are close to it often turn out to be poorly considered, or considered before 8 other engineers added bug fixes and features to that code.

From a performance standpoint you need to challenge all of those assumptions. Re-ask all of those questions. Why is this part single threaded? Does the whole thing need to be single threaded? What about in the middle here? Can we rearrange this work? Maybe by adding an intermediate state?

Interestingly, you didn't learn the full lesson:

When optimizing, always consider the cost of doing the optimization vs. it's impact.

In a project where you are looking a 45/30/25 type split. The 45 may actually be well optimized, so the real gains may be in the 30 or 25.

The key is to understand the impact you CAN have, and what the business value of that impact is. :)

The other rule I've learned is: There is always a slowest path.

> The key is to understand the impact you CAN have, and what the business value of that impact is. :)

Like I tell everyone in system design interviews: AWS will rent you a machine with 32TB of RAM. Are you still sure about all this extra complexity?

... to the tune of about 10 cents a second, yeah. If you can hire a dev team to optimize one of those out, you plausibly come out ahead (well, vs face value).

How do you come out ahead hiring an entire team to save $6/hour?

That's $6/minute, $360/hour. Granted, if you're using it at a scale approaching FTE costs, you probably want at least some reserved capacity and you're likely not paying face value, but then the comparison was never going to be rigorous anyway.

Oh, I’m an idiot. Sorry, ignore me.

That's not even the main problem with the comparison. Suppose it was actually $6/hour. Even that's >$50k/year, indefinitely.

How long does it take your team once to make that resource consumption go away permanently? An hour? Even a month? You'd still be ahead.

Working for a smaller digital agency, while using .NET Framework, and then moving to .NET Core (now just .NET) this savings was felt. Using a performance profiler and slightly complicating the code a bit (at least in terms of the documentation for the CMS we use), I was able to get the code in less than the minimum recommended requirements for the base CMS software, without having to alter the source of the CMS. I did this with the Umbraco CMS, but also started using it in 2013 when the documentation was lacking, so reading the source code was a requirement to getting as much as possible out of the software.

Hopefully if you’re doing something that actually needs that much RAM you’re also making 6-figures per hour in revenue.

I’ve heard that some companies spend hundreds of millions per year on cloud hosting and it’s still worth it. I can’t even imagine that level of scale.

PS: The context in which I bring this up is a design exercise that would deal with maybe 5gb of data per month. People really over-complicate it :)

I didn't get that impression from their response. I mean I could be wrong, but in context of "use a profiler" I don't think anything you said runs counter. I think it adds additional information, and it's worth stating explicitly, but given my read yours comes off as unnecessarily hostile. I think we're all on the same page, so let's make sure we're on the same side because we have the same common enemy: those who use Knuth's quote to justify the slowest piece of Lovecraftian spaghetti and duct tape imaginable

It is hostile because I've seen people mess up optimization too many times.

Sometimes leaving the spaghetti alone IS correct. Sometimes it isn't.

But most awful spaghetti happens because what it was asked to do was awful, IMHO.

Nearly all the awful spaghetti code I've seen started out as good code, but as requirements changed in unexpected ways fitting the new features of version 5.0 while keeping all the previous version features working resulted in a mess that nobody knows how to clean up without the expensive big rewrite.

This has been my experience as well. A real "the road to hell is paved with good intentions" situation. No one was intentionally acting malicious nor trying to make bad code, but the ultimate truth is that software "rots". We write for static environments, but the environment isn't static.

I'd expect even rather junior programmers to recognize that the more time you spend working on a project the more new and unexpected things you find. New features, better ways to implement things, whatever. And ultimately, we all look back at code we wrote a year ago and are going to say "what idiot wrote this garbage?" I mean, if you don't, it is probably a sign that you aren't improving. It's impossible to write perfect code, and even if it was, what would be "perfect" is a moving target. So either way, you should be writing better code today when compared to yesterday.

  > without the expensive big rewrite.

While it isn't possible to completely put this off, I find there's a helpful tactic to reduce spaghetti-creep. "Maintenance is cheaper than repair." People say "don't fix what isn't broken" but I think that's wrong. You should "fix" things before they are broken. Because frankly, it is cheaper to replace your visibly degrading water pipe than it is to wait for it to burst. You not only have to pay for the new pipe, but all the damage it caused when it broke. Maintenance increases longevity and helps you monitor so you can replace things before they break. Doesn't matter if you're plumbing water pipes or plumbing spaghetti, the tactic is the same.

There is more to it than that.

1. Decide if optimization is even necessary.

2. Then optimize the slowest path

There is also the "death by a thousand cuts" kind of slowness that accrues over time and it doesn't really matter where you start peeling the onion and the part you started is rarely the best.

While Amdahl’s Law is very important, its practical effects are very frequently overestimated, at least as frequently as Knuth is misquoted.

Simple problems, e.g. solving a system of equations, will usually include some non-negligible sequential part, which, according to Amdahl’s Law will limit the amount of speed-up provided by hardware parallelism.

On the other hand, complex problems, e.g. designing an integrated circuit, can usually be decomposed in a very great number of simpler subproblems that have weaker dependencies between them, than between the parts of a subproblem, so that by distributing the execution of the simple subproblems over parallel hardware that executes sequentially each subproblem you can obtain much greater acceleration factors than when attempting to parallelize the execution of each simple subproblem.

With clever decomposition of a complex problem and with good execution planning for its subtasks, it is much easier to approach the performance of an embarrassingly parallel problem, than when trying to find parallel versions of simple algorithms, whose performance is frequently limited to low values by Amdahl’s Law.

Amdahl’s Law frequently prevents you from reducing the execution time of some task from 1 minute to 1 second, but it normally does not prevent you from reducing the execution time of some task from 1 year to 1 week, because a task so complex to have required weeks, months or years before parallelization normally contains a great enough number of weakly-coupled subproblems.

I find Amdahl's Law very useful for parallelizable work as well.

Or at least, I find it trivially scalable to parallel case. Often it helped for me in modeling how interconnect would be a possibly limiting element for a task even for embarassingly parallel tasks.

Before optimizing, I always balance the time I'll need to code the optimization and the time I (or the users of my code) will effectively gain once the optimization is there (that is, real-life time, not CPU time).

If I need three weeks to optimize a code that will run for 2 hours per month, it's not worth it.

But by not optimizing, you don't grow your profiling/optimizing skills and you miss out on a reduction in how long optimizing takes you for future work. Therefore many more codes will not meet the threshold and your skills may not grow for a long time.

You couldn't know, but my job is 50% about optimizing computational workloads. But many times, when questionning my users, it happens that they want an optimisation for some code that will run 2 or 3 times. So eventhough they'll have to wait a week for the computation to run, it'll take me just as much time to make the optimisation run :-)

But if code happens to be used 10 times a week and takes about a day or two to run, it's a no brainer: spending a month optimizing for 10% speed increase is worth it !

The one question that needs to be asked is would the users run it more often if it didn't take so long? There is nothing a computer can do that a room full of clerks in 1800 couldn't do, but the runtime would be so slow (or the cost in clerks so high) that nobody dared ask those questions.

Exercise for the reader, given an unlimited budget to hire 1800's clerks how many FFS could you achieve running doom. (obviously the number is too low to make the game playable)

A former boss: an optimization made at a non-bottleneck is not an optimization.

it's more nuanced:

you're still releasing resources - so you might not become faster overall but you can compute more in the same time later if necessity arises (althougth that might be somewhat premature but can be good for library code - so it becomes more applicable in different environments)

and there are some rare but tricky scenarios like:

hardware is mobile phone: app seem to be bottlenecked on arithmetics according to the profiler, so it feels obvious to start optimization there

in reality what happens - hardware has limit on power, so it can't give full power to CPU, GPU and memory all at the same time

since app uses too much memory - it has to redirect power there, also memory emits heat, so CPU becomes throttled

By optimizing memory, everything runs colder -> CPU gets more power, can run sustained higher frequencies - app becomes faster

And if the app becoming faster doesn't mean anything because the app is waiting for user input the whole time, it was a lot of work for naught.

Perhaps restated: If the optimization cannot be felt (ie, impact on the product experience), it is not an optimization worth pursuing.

> And if the app becoming faster doesn't mean anything because the app is waiting for user input the whole time, it was a lot of work for naught.

Oh, that might still be good for battery life (or power consumption in general).

This one is more dangerous, as there may be backend resources in use that could be optimized, which could drop costs drastically. This may not change anything for your users, but is definitely worth exploring when it comes to getting the most out of your investment.

Just remember that there can be hundreds of bottlenecks. Your second slowest matters too. And sometimes there are different requirements - the UI needs to respond within 1/10th second (sometimes much faster, sometimes much slower). Users can often live with your main calculation taking 10x longer than the optimized version so long as there is fast feedback that you are working. Eventually they will want the calculations faster too, but taking a minuter off of those is less valuable than a few ms off of your UI feedback.

Translation: I'm "The Boss" so it's not a bottleneck unless I say it is.

It's not true, though. Speedups are speedups even if there are still slow parts.

His boss is essentially making the same argument as Knuth: Spend your time optimizing what benefits the most from optimization. Or in other words, prioritize, don't optimize blindly.

It's a single line phrase, I wouldn't interpret it too literally. Usually you got to be pretty liberal when interpreting those easy to remember lines. It's a lot harder to remember the literally multiple books we could fill when bringing up exceptions.

It is exactly this "lulled into complacency" that I rail against when most people cite that line. Far too many people are trying to shut down down dialog on improving code (not just performance) and they're not above Appeal to Authority in order to deflect.

"Curiosity killed the cat, but satisfaction brought it back." Is practically on the same level.

If you're careful to exclude creeping featurism and architectural astronautics from the definition of 'optimization', then very few people I've seen be warned off of digging into that sort of work actually needed to be reined in. YAGNI covers a lot of those situations, and generally with fewer false positives. Still false positives though. In large part because people disagree on what "The last responsible moment" in part because our estimates are always off by 2x, so by the time we agree to work on things we've waited about twice as long as we should have and now it's all half assed. Irresponsible.

I'm with you, and been on a bit of a rampage about it lately. Honestly, just too much broken shit, though I'm not sure what the last straw was.

A big thing I rail against is the meaning of an engineer and that our job is about making the best product, not making the most profitable product (most times I bring this up someone will act like there's no difference between these. That itself is concerning). The contention between us engineers and the business people is what creates balance, but I'm afraid we've turned into yesmen instead. Woz needs Jobs, but Jobs also needs Woz (probably more than the other way around). The "magic" happens at the intersection of different expertise.

There's just a lot of weird but subtle ways these things express themselves. Like how a question like "but what about x problem" is interpreted as "no" instead of "yes, but". Or like how people quote Knuth and use it as a thought terminating cliche. In ML we see it with "scale is all you need."

In effect, by choosing to do things the easy way we are choosing to do things the hard way. Which this really confuses me, because for so long in CS the internalization was to "be lazy." Not in the way that you put off doing the dishes now but in the way that you recognize that doing the dishes now is easier than doing them tomorrow when you 1) have more dishes 2) the dishes you left out are harder to clean as the food hardens on the plate. What happened to that "efficient lazy" mindset and how did we turn into "typical lazy"?[0]

[0] (I'm pretty sure I need to add this) https://en.wikipedia.org/wiki/Rhetorical_question

One of the aphorisms I operate by is that when the order of magnitude of a problem changes, the appropriate solution to that problem may also need to change.

Here we are sitting at four to seven orders of magnitude separated from Knuth, depending on whether you mean number of devs or number of machines or size of problems tackled.

Size of machines is pretty amazing. The PDP-10s and 360/67s Knuth was talking about in 01974 were about 1 MIPS (with 32-bit or 36-bit operations) and could scale as high as 16 mebibytes of RAM. Today you can put 6 tebibytes in a 384-core two-socket AMD server that can do in excess of 10 trillion 32-bit operations per second: 6 orders of magnitude more RAM, 7 orders of magnitude more arithmetic.

But that's really understating the case, because those were large shared mainframes. Today's equivalent would be not a 2-socket rackmount server, or even a whole rack, but quite plausibly a whole data center, three more orders of magnitude. 9 orders of magnitude more RAM, 10 orders of magnitude more arithmetic.

Probably also worth mentioning that the absolute number of computers has also increased a lot; every USB-C power brick contains a multi-MIPS computer.

I agree that the number of devs has increased by only about four or five orders of magnitude. In 01974 I'd guess there might have been 50,000 programmers; my grandparents took programming classes around that time involving batch submission of punched cards, and that was a reasonably common thing at US universities. Today, Roblox has 380 million monthly active users; what percentage of them write their own games in it? And the popular programming environments Microsoft Excel and Google Sheets have over a billion users each.

> It is exactly this "lulled into complacency" that I rail against when most people cite that line. Far too many people are trying to shut down down dialog on improving code (not just performance) and they're not above Appeal to Authority in order to deflect.

Your comment reads like a strawman argument. No one is arguing against "improving code". What are you talking about? It reads like you are misrepresenting any comment that goes against your ideas, no matter how misguided they are, by framing your ideas as obvious improvements that can only be conceivably criticized by anyone who is against good code and in favor of bad code.

It's a rehash of the old tired software developer cliche of "I cannot do wrong vs everyone around me cannot do right".

Ironically, you are the type of people Knuth's quote defends software from: those who fail to understand that using claims of "optimization" as a cheat code to push through unjustifiable changes are not improving software, and are actually making it worse.

> "Curiosity killed the cat, but satisfaction brought it back." Is practically on the same level.

This is the same strawman. It's perfectly fine to be curious. No one wants to take the magic out of you. But your sense of wonder is not a cheat code that grants you the right to push nonsense into production code. Engineers propose changes based on sound reasoning. If the engineers in your team reject a change it's unlikely you're surrounded by incompetent fools who are muffling your brilliant sense of wonder.

> If you're careful to exclude creeping featurism and architectural astronautics from the definition of 'optimization', (...)

Your comment has a strong theme of accusing anything not aligned with your personal taste as extremely bad and everything aligned with your personal taste as unquestionably good that can only possibly be opposed if there's an almost conspiratorial persecution. The changes you like are "improving code" whereas the ones proposed by third parties you don't like suddenly receive blanket accusations such as "creeping featurism and architectural astronautics".

Perhaps the problems you experience, and create, have nothing to do with optimization? Food for thought.

  > No one is arguing against "improving code". What are you talking about?

This is actually a frequent occurrence. But honestly it usually doesn't come with that exact same phrasing. It usually comes with "let's spend our time on this other thing that isn't important but is new therefore important". It's true, sometimes you need to triage, but there's a clear bias that once something "works" (no matter how poorly) there is much less incentive to make it work not poorly.

Some food for thought: your comment entirely relies upon wisdom of the crowds. Which I agree is usually a good bet to go with. But there are conditions where it fails, even at scale. It's actually fairly easy to upend. All you need to do is remove independence of actors. Which, you can probably guess is fairly common in our field. The more homogeneous we become the less reliable wisdom of the crowds is.

Plus, people have different experiences and different environments. What has been true is your experience need not be true in mine or any others.

I'll give you an example of irrationality here. I was working at a big tech company and as part of breaking down my project I was playing around with another part of the code. I more then doubled the prediction accuracy on customer data and made the code run faster and use less memory. Objectively better. It even happened "for free" because the tasks involved were part of my original job, though the goal was not explicitly. What happened? Last I know, the PR is still open. Boss never pushed for it because they were more invested in their other work that was yet to be completed but had more buzzwords. That work was only a bit more accurate than mine but 100x shower and required 10x more memory. I even told them what I did would work for the other one too, yet it never happened.

I've seen many instances like this. Sometimes people just don't want things to be better unless it's better in a very specific kind of way (and not necessarily via performance). Sometimes it's just politics.

On the other hand, I've seen very good teams where there's the exact opposite experience. It's a very common thread that those teams openly discuss and explain why a seemingly good idea is actually bad. Frequently, they'll let you have a go at it too, because it's a no lose situation. If you're right, we all win. If you're wrong there's an important lesson about the code that's leaned because there's hidden complexity that makes the seemingly good idea bad and it's often hard to explain. But you end up with a lot more knowledge about the code, which helps you in the long run

> This is actually a frequent occurrence. But honestly it usually doesn't come with that exact same phrasing. It usually comes with "let's spend our time on this other thing that isn't important but is new therefore important". It's true, sometimes you need to triage, but there's a clear bias that once something "works" (no matter how poorly) there is much less incentive to make it work not poorly.

Exactly. “It technically functions and therefore doesn’t need attention” has become an industry norm. There’s a massive bias towards piling on more features over making older ones good.

  > “It technically functions and therefore doesn’t need attention” has become an industry norm.

Most commonly seen with the aphorism "don't fix what isn't broken." But I really hate that aphorism. There's some truth to it, but it is commonly used as a thought terminating cliche. If you see a rusty pipe you should sure as hell fix it. Sure, it isn't "broken" in the sense that it has burst and is leaking water everywhere, but it sure is a ticking time bomb. And boy, is it a hell of a lot cheaper to fix a rusty pipe that isn't leaking than to fix a burst pipe and also pay for all the water damage.

The aphorism misses that "maintenance" is much cheaper than "repair". The aphorism misses that you can get major cost savings by doing thing differently. Sure, it costs more in the moment, but are we trying to run a business paycheck to paycheck or are we trying to create something sustainable. I sure hope your business is thinking more than just 3 months out. "More expensive now" is far too common of an excuse, enough that I see it used to justify not implementing things that would have a ROI within 6 months! Which not doing anything with under a year ROI is absolutely batshit insane (unless you're a startup living on the edge or something, but I see this in big tech companies and I hope we understand that's the context here).

I became the SME for batch processing on this project. The whole project was a read heavy workflow that should have had all decisions made at write time but was so far in the weeds when I got there that I couldn’t pull it out. And everyone I convinced of this fact decided to work somewhere else instead of stay and help fix it.

But there were parts we could sort out at build or deploy time, and I was able to fix a number of those.

One guy was tasked with a project I got turned into an epic: build fallback error pages at deploy time and push them into CDN. I told him not to build it the way he was, copy the one I had been working on. I got busy and he left, and we discovered that they hadn’t been updating when a customer changed their contact info and noticed months later that we still had the old info.

The build trigger had no downstream dependencies and there was no alert being sent for failure. The job was timing out for reasons similar to the ones I’d fixed. I tried to bandaid it still errored out an hour into what looked like at least a 100-120 minute workload.

I discovered the main problem was that our customers could have multiple domain names and he was frobbing through three or four service calls trying to find the canonical one, and making one call per customer to see if they were still customers (we had an endpoint that would paginate all active customers, so ~400x more efficient and rising due to turnover).

The main table I had been using had one column I didn’t use, which had a url in it. I confirmed with our SME that this was in fact the canonical domain name, I just had to add the field to the QL and do a `new URL(url).hostname`. At the end of the day I ended up extracting about half the code from my tool, deleting over half of his code and replacing it with calls into mine (do it like I did it, but literally).

Four and a half minutes, and no more intermittent failures, because my version was built on back pressure to throttle requests under high server load, and total outbound requests around 1/8 of the original.

This just kinda made me think, are there less war stories being shared on HN? It sure feels like it. But what got me thinking is how sharing these types of war stories is an effective way to teach and share with one another. Maybe it's just my bubble, but I feel like I hear them infrequent. Anyways, this was a good one. Thanks for sharing.

A lot of people are still thinking of Knuth's comment as being about just finding the slow path or function and making it faster. What Knuth has talked about, however, and why any senior engineer who cares about performance has either been taught or discovered, is that most real optimization is in the semantics of the system and - frankly - not optimizing things but finding ways not to do them at all.

Tuning the JSON parser is not nearly as effective as replacing it with something less stupid, which is, in turn, not nearly as effective as finding ways to not do the RPC at all.

Most really performant systems of a certain age are also full of layer violations for this reason. If you care about performance (as in you are paid for it), you learn these realities pretty quickly. You also focus on retaining optionality for future optimizations by not designing the semantics of your system in a way that locks you permanently into low performance, which is unfortunately very common.

Well said.

Another example of this is using the right algorithm for the job. I see a lot of code like let's say you have two lists and you need to go through the first one and find the corresponding element by id in the second. The naive implementation uses linear search of the second list, making the algorithm O(n^2). This will stall and take hours, days or longer to run when the lists get large, say into the tens to hundreds of thousands of elements.

If both lists come from your own database then you should have used a foreign key constraint and had the database join them for you. If you can't do that, let's say one or both lists come from a third-party api, you can create a dictionary(hashmap) of the second list to allow O(1) lookup rather than O(n) which brings your full algorithm's complexity to O(n). Now the you can have millions of elements and it'll still run in milliseconds or seconds. And if this is something you need to do very often then consider storing the second list in your database so you can use a join instead. Avoid doing the work, as you say.

These are the most common mistakes I see people make. That and situations where you make one request, wait for a response, then use that to make another request, wait for response and so on.

You don't need a profiler to avoid this type of problem, you just need to understand what you're doing at a fundamental level. Just use a dictionary by default, it doesn't matter if it might be slightly slower for very small inputs - it'll be fast enough and it'll scale.

Related, people not understanding that the latency alone from a DB call (especially when the DB has network-based storage, which is most of the cloud offerings) is substantial. I’ve found so many cases where people are unknowingly doing serialized queries because the ORM obscured everything, and they didn’t realize they could get everything in a single query with joins.

Yeah, I have that problem right now. A colleague and I recently took over development of an application that two others have started. It's not even in prod yet but everything is slow. Loading a page can take up to a minute.

The reason for this is they have a frontend where they do stuff like

let a = await fetchA(); let b = await fetchB(a.id); let c = await fetchC(b.id); // ...

This on its own is obviously slow. But then you look at the backend code and it turns out the backend code for A, B and C also do this same kind of thing. Even worse, it's fetching way more data than is actually needed so even a single request is quite slow simply because there's a lot of data. And worse yet, it's fetching the data in a shape that is completely wrong for the application, so the frontend has thousands of lines of code that reshapes the data before it can be used - making things even slower, and making our jobs difficult because it's really hard to wrap your head around everything that's happening.

And there's no technical reason for it, it doesn't have to be like this. We're working on refactoring it at the moment, we have a deadline approaching quite fast so we don't really want to spend time refactoring but the system they've created is complete ass and we eventually decided we have to spend the time to refactor now. It'll be much more difficult to fix the database schema and such when we're in prod and need to worry about losing data.

We had one of those, except it was two loops and a python "in" on a list, so actually O(n^3). Changing the "in" to be on a set brought it from around two hours to around five minutes. And the best part is related to the topic at hand: This was a small postprocessing function, and other more experienced developers had tried to speed up the overall process but assumed the slowness was earlier in the process instead of in this function, and after failing they assumed it couldn't be sped up further. They never bothered to check which part of the process was actually slow.

Nice work! The example I used was based on a report I looked at once. The two lists were 50k and 80k elements respectively, the report took nearly 30 minutes to generate. Changing two lines of code to make a dictionary instead of a list and then use the dictionary instead of linear search brought it from 30 minutes to 5. Could probably have optimized it even more but it wasn't my task so I just left it at that. Nice improvement for a few minutes of work. I've seen the same mistake and similar ones many times, though usually the inputs aren't large enough to have that much of an impact. Still usually worth fixing though.

It's kind of astounding how few people seem to understand these things. We all (me and my colleagues who are generally university educated) learned about complexity analysis, algorithms and data structures, but it seems like quite few actually understood the material.

You can almost simplify it to simply observing that PREMATURE optimization is not the same as OPTIMIZATION.

Most people I see who get offended are reacting to the claim that optimization is never useful. But it's pretty easy to knock that claim over.

I don't deny that plenty of people use adjectives very sloppily, and that much writing is improved by just ignoring them, but Knuth is not one of them.

Preprofiling optimisation is stab in the dark.

Isn't that more clear and at least as concise as the original one, also using some idiomatic metaphor while avoiding the reference to mythical dark forces.

By definition anything premature is going to be bad. It's not any kind of an insight.

A pet peeve of mine is how common it is for people to ignore qualifying words. Like you can say "most people do 'x'" and you can guarantee if you post that online people will come out of the woodwork saying "I don't do 'x'". Sure, the most claims can often be inaccurate, but those types of responses aren't helpful and aren't meaningfully responsive even if true. "Most" is just a very different word from "all". It implies a distribution but idky we often think things are discrete or binary.

I also see ignoring qualifiers as a frequent cause for online arguments. They're critical words when communicating and dropping them can dramatically change what's being communicated. And of course people are fighting because they end up talking about very different things and don't even realize it.

While I agree with you, that it's common for people to use these words sloppily, I think it is best to default to not ignore them. IME, in the worst case, it aids in communication as you get clarification.

The operative word was always "premature". As with everything, whether or not a particular optimization is worthwhile depends on its ROI, which in the case of software depends on how often your code is going to be run. If you are building a prototype that's only going to run once, then the only optimizations that are worthwhile are the ones that speed things up by times comparable to the time it take to write them. On the other hand, if you're writing the inner loop of an LLM training algorithm, a 1% speedup can be worth millions of dollars, so it's probably worth it even if it takes months of effort.

> This makes it clear, in context, that Knuth defines "Premature Optimization" as "optimizing before you profile your code"

As with that Google testing talk ("We said all your tests are bad, but that's not entirely true because most of you don't have any tests"), the reality is that most people don't have any profiling.

If you don't have any profiling then in fact every optimisation is premature.

You have joined a noble line of Knuth Quote Expanders, https://hn.algolia.com/?dateRange=all&page=3&prefix=true&que...

They are all worth reading.

Twice in the last 2 months. I'm not sure if that's a good thing or a bad thing hahaha

Yes but the second half of the second half is "only after that code has been identified." So the advice is still 'don't waste your time until you profile.'

I'm appalled by the number of developers that don't know that profilers exist.

BTW, my junior developers don't know what a debugger is.

too late, all the slackers got promoted and now are demanding to keep pushing features no one is asking about.

Same is true for lots of things. Classic example is we are so leetcode obsessed because all the people that were leetcode obsessed got hired and promoted. We've embraced p-hacking, forgetting Goodhart's Law (and the original intention of leetcode style interviewing. It's just the traditional engineering interview, where you get to see how the interviewee problem solves. It is less about the answer and more about the thought process. It's easy to educate people on answers, but it is hard to teach someone how to think in a different framework... (how much money do we waste through this and by doing so many rounds of interviewing?))

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