I think what he’s saying here is you can’t skip the basic math step to arrive at good performance. Staring at profiling results will lead you to a local minima
You do profiling because it's WAY too easy to get obsessed about theoretical problems when a simple measurement will show you the actual problems.
You do the math on the actual problem location, not a method with O(n!) which only gets called with n=3.
You still have to look at the entire call stack when profiling (which means thinking about the overarching algorithm).
The fact remains most projects that do small trivial modular prototypes first will ultimately know which paths are viable before painting themselves into a corner algorithmically.
Best of luck =3
If a problem area is so intuitively obvious, why would you introduce the problem in the first place? In reality, performance optimizations are usually needed where you least expect them. Which means that you can't get there intuitively. Hence, the suggestion of using profiling to help track down where the problem is instead.
Pretty much everyone I know will throw down an O(n^2) algorithm or whatever in their first pass and replace it with something more thought out once they have the time to think deeply about it.
If you're fretting about optimization at every stage of development, you're really doing it wrong. This is precisely the type of early optimization that everyone and their dog will tell you is bad. You should focus first on making your program work and laying out the logic and data flows. Optimization does not matter until the program is almost finished.
Most of the times I've seen this, the faster algorithm is literally just a dictionary with a well-defined key.
I honestly do not understand why that's not the first solution most devs think of and why n^2 seems to dominate.
As an example, I've seen code like this quiet a bit
result = []
for (item: items) {
for (item2: items) {
if (item != item2 && item.foo == item2.foo) {
result.add(item)
}
}
}
result = []
itemFoo = HashSet()
for (item: items) {
if (itemFoo.contains(item.foo))
result.add(item)
else
itemFoo.add(item.foo)
}
A pretty common attitude that I've ran into. People take the "premature optimization" quote to mean "Never think about big oh when writing code".
If n is usually less than 100 then sure maybe it does make sense to do the more brute force approach. However, what's the impact of using the hash table first? If n is usually less than 100, now this method is slower usually, but is that a problem? Well, you should use a profiler to find out if it is or isn't. However, that may be entirely tolerable performance wise.
On the other hand, if your assumption that "n < 100" doesn't hold up and, indeed you start seeing n > 100, or 1000, or 10000. Now all the sudden this method becomes a fault in the application you developed that needs to be rewritten.
What's devious about the "n > 100" problem is when you first write the code, you may have very well been correct that "n < 100". But time and inputs change the way code is executed.
A lot of the code I've fixed has been due to this. I've seen the counter where really searching a list/array was the better option only a handful of times.
To me, premature optimization is using a known algorithmically inferior approach, of the same complexity as the superior approach, because you know that for small n, it is faster without knowing whether or not that will be the case.
Not really. You're just exploring an unknown frontier. Once the frontier is known and you actually understand what you are trying to solve you're still going to put in the right algorithm.
I mean, you might not if you're vibe coding, which I guess is apparently a thing somehow, but I'm assuming we're talking about actual engineering here, not speaking the sordid tales of cowboys. The "premature optimization" line was definitely not directed at those who think code is a feeling.
When the frontier is unknown, whatever algorithm gets something operating as soon as possible is the right one. The given example may be so contrived that the hash map would be what you see as the quickest and dirtiest solution anyway, but with a little more complexity loops do tend to be easier to reason about on a first-pass basis.
> Now all the sudden this method becomes a fault in the application you developed that needs to be rewritten.
In the same way that finding out that a blue button doesn't resonate with users, requiring you to rewrite your application to make the button green. Such is the life of building software. But that's why engineers get paid the big bucks – nobody else is stupid enough to do it.
> A lot of the code I've fixed has been due to this.
You're not really fixing it, you're updating it to reflect the information you gleaned during the exploratory phase. I mean, unless you're coding on vibes or something, but, again, assuming actual engineering is taking place here. Nobody should be coding non-prototypes by the guide of their feelings.
Good code should follow something along these lines:
- A reasonable understanding of what needs to happen. What are the requirements. Including data set sizes. Including how "hot" this code path is.
- A reasonable pick of the algorithm to perform what needs to happen.
Sure. O(N^2) for very small data sets could outperform an O(NlogN) or O(N). The conflation of big oh notation with actual performance is just another form of sloppiness as is picking a bad algorithm. In both cases it reflects a lack of understanding of how computers work and how to do things efficiently.
Let's look at what we're trying to do here. We want to find all values that occur more than once in a vector. Using a hash map means you have to:
- Create this data structure on the fly.
- Hash the values.
- Do the insertions/lookup.
You should consider the size of your dataset, you should consider locality/caches etc.
100 entries squared is 10k though. If this is part of a critical code path, let's say your frontend code does this for every DOM element or every notification it needs to process this can end up the bulk of CPU usage. This sort of stuff is why I've seen applications cause the fans to go on or the browser to crash. I would say bad O(N^2) algorithms for large Ns are pretty common.
I would be surprised if e.g. a C++ implementation of the hash version doesn't win over the double loop for data sets of 100. But maybe that's borderline. For a dataset of 1000 there's no way the double loop is winning.
Other alternatives might be something like an efficient small bloom filter that can be stored in a register, sorting the array. I mean at least cut the iterations in half by not iterating over the entire range in the inner loop!
Then you won't have the trouble with:
if (item != item2 && item.foo == item2.foo) {
The reason I wrote it that way is because the actual common way I find this is in Java where the team wants to use the `stream` api.
IE
items.stream()
.filter((item)->items.stream()
.filter((item2)->item2 != item)
.findAny((item2)->Object.equals(item.foo, item2.foo)).isPresent())
.toList();
Even more commonly, instead of doing the `findAny` I often see the trip up being someone doing a `contains` or other O(n) operations on `List` objects inside iterations of that list.
It's this sort of fluent/functional style of programming that often seems to get tripped up on doing n^2 (or more) algorithms.
Disagree
> faster for small n (which is the common case)
How can you know that? And for smaller n, the choice hardly matters.
> can not run out of memory due to the hash table
If n gets large enough that hash table size becomes a problem, n is large enough that you need the hash table or some other structure that reduces the complexity down from O(n^2).
> compiles to less code
This is just nonsense. Any codebase of significant size already has a hashtable setup and unless you are using a language like C++, the code size of using a hash table anywhere is constant. The cost is already paid.
The actual amount of compiled code from the switch is identical.
> does not need item to be hashable
True.
There would be reasons not to choose option 2, but it should be default until it's been proven to be a problem. And even then, the next step is almost certainly not going to be option 1.
But then this could also be language and application specific. I deal primarily with backend services/Java where memory is plentiful, and allocation is fast. Algorithmic complexity is for my applications very often the better indicator of what good code should be.
Bring in a performance expert and their intuition can quickly identify many performance improvements. The tough part for the business is when and where do you pay for the experience of performant code, and when is good enough to leave alone?
Measuring doesn't mean don't think. Measuring and thinking are two different things. You need to do them both to optimize effectively.