O(n) and O(2n) are equivalent. So run speed is not good argument. IMHO

Scientifically sure, practically though, it matters when working on large data sets and you're already exhausting the snappiness that you would have liked.

Correct, however if this "exact runtime bound" is what you are going for, then please use the correct notation. T(N) = 2N would be legitimate, as would \Theta(N) = 2N. Saying that O(F(N)) = 2N is misleading because the definition of the big-o notation explicitly discards all constant factors and constant annends.

Thanks, I will use Theta(N) next time. There are a bunch of complexity notations, omega, theta, o, etc. I need to brush up on them [1]

[1] http://www.youtube.com/watch?v=6Ol2JbwoJp0

It's still worth noting, as others have, that there's a huge assumption baked in to your estimate: that all operations are created equal when it comes to execution time.

If we're going to talk "practically" here :) things like branch prediction will ensure that nothing like 2x the execution time will be spent on just the double conditional.

Meanwhile, what you do inside that loop becomes much more important to the time estimate than the conditional (.length is a fast single property lookup, whereas just accessing a value in the array (which is presumably the purpose of this method) will often trigger a bounds check on length, then an offset into a looked-up offset into memory, etc).

So it's not really worth doing for speed. All that said, I think yours reads better anyway.

Not to mention the anti pattern of doing this in the first place.

When manipulating collections - you almost always want .map .filter .reduce .some .all or a variation of thereof and not a plain old for loop for this sort of thing, doing this a million or a billion times a second theoretically shouldn't matter. (explained in : http://stackoverflow.com/a/17253577/1348195)

Also, checking for instanceof Array is a JS anti pattern to begin with and makes code a lot less generic.

I prefer for loops because they have easier to read and debug exception stack traces. And typing for(i=;...) is calming for me.

I agree that the Array methods are extremely useful other than their ugly performance-sacrificing function scope. In this case though, when you need alternate behavior for the first index, it is much clearer to read a standard for loop with some precursor code behind it, than to read a forEach loop with an if-else.

Really? You find the above loop more readable than:

    if(array.length > 0) doFunction(array[0]);
    array.slice(1).forEach(doOtherFunction);

You're kind of cheating because you're using slice which will create a copy of the Array, and that's not cheap if the array is large.

People use arrays for things like queues and stacks all the time in JavaScript - they're _much_ slower than hand rolled collection (eg. http://jsperf.com/deque-vs-array-2 ) - why not micro optimize that as well?

If you have a very large array of course optimization can/should be considered but that's simply not the average case.

I actually like the slice code for it's ease of readability. Functional methods are extremely nice and I hope they get faster and faster with the optimization of JS JITs.

I never heard of Dequeue [1], that is mighty cool. I agree that premature optimization is not good - but when it can be done cleanly without hurt to the readability of the code, I'd say to keep it in ones' repertoire, to effectively write more performant code, more often, on average.

[1] http://code.stephenmorley.org/javascript/queues/

I see your point, but it's a bit of a stretch to call a style preference an anti-pattern. You even caveat it yourself in your stackoverflow response: "it's also a lot more readable (at least to me)".

Agreed on the hideousness of that 'instanceof Array' check.

Can you elaborate on what's wrong with the final solution from the article? I don't see anything wrong there and I wouldn't expect your snippet to be faster.

Speed isn't the only issue here. In fact, the execution of the looping construct itself is almost meaningless compared to what you're actually doing in it. (Explained here http://stackoverflow.com/a/17253577/1348195 )

JSPerf is a horrible place to benchmark these things, see http://mrale.ph/blog/2012/12/15/microbenchmarks-fairy-tale.h... and http://mrale.ph/blog/2013/08/14/hidden-classes-vs-jsperf.htm... by Vyacheslav Egorov (worked on v8)

For example:

    if(array.length > 0) doFunction(array[0]);
    array.slice(1).forEach(doOtherFunction);

Is far more readable and maintainable than that mess with the useless indexes and does the same thing.

Optimizing based on microbenchmarks and not checking for deopts and doing profiling is worthless.

While in general I indeed think that people should profile code before they optimize it, I also think that they should avoid unnecessarily pessimising it by writing code that is not exactly idiomatic and is known to have an overhead.

In this particular case I am not sure I would prefer slicing+forEach to writing a old-style for-loop on any relatively hot path. Of course either profiling or thorough knowledge of the code should be applied before making this decision to determine whether this code path is hot or not, what's the average size of the array and so on. The main motivation for this decision would be the fact that slice produces the copy of the array. This is both hindrance in terms of performance (as VMs right now would not elide copy operation) and in terms of readability (e.g. when I see such code I would have to ask myself: does doOtherFunction require fresh copy or not, does it use only the first argument or all of what forEach passes into it and so on?).

So you can see the choice for me is pretty complicated.

It's not worthless when you are writing an app that happens to run in the browser.

I agree 1000-fold that the Array manipulation techniques are extremely easy to read. Keep in mind though, you are creating a new copy of the [1-n]array through the slicing, and the overhead of the function call with forEach will matter on large Arrays. Really depends what you are building. Don't call performance optimization worthless.

"Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%." - Knuth

Micro benchmarks are not how we optimize - we profile.

Who says the function call actually happens? If the function is short enough, they are a very good candidates for the JIT to inline. If they aren't short, the overhead of calling the function shouldn't matter much anymore.

Checking the index in every iteration of a loop will effectively double the amount of operations performed in every iteration of the loop for this scenario. That is, O(2n) versus O(n)

Not really, things like branch prediction and JIT optimizations will kick in pretty fast. This is why these benchmarks on jsperf suck - the code gets a different 'fresh' version every time and jits (like crankshaft in v8 ) don't get to work.

Effectively, you're checking 'the performance of interperted JavaScript' most the the time.

I bet my bottom dollar that branch prediction is not going to magic away that index dependent if statement in a language like JS, even with Crankshaft in full effect.

Here, it's within 20% of the performance. Definitely not n vs 2n http://jsperf.com/oh-look-branches

The actual percentage depends on the CPU. On mobile the cost would likely be higher (e.g. on my phone it's around 33%), on old CPU can be even higher, but people don't usually care about them these days.

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But the code is actually doing a whole bunch of things, let's say 20, with each loop. So it's O(21n) vs. O(20n).

2n implies looping twice or something, not a single extra operation.

(and 20 is probably a gross underestimate if we're comparing console.log to a mere ==)

I made an assumption, you're making an assumption too. I'd agree that in any case where it would actually matters, you'd be doing a small amount of ops in the if statement. The complexity change will depend on what you were doing in the loop as compared to the speed of a single if statement. If it were a large amount of ops, then yes, it'd be a more minor difference.

My point is that "O(2n)" is an extremely specific and misleading way to characterize 'extra conditional per iteration'. It doesn't matter what the actual percentage difference is.

I agree, now what is the correct notation so we all can learn? :-)

It's completely pointless to use such a notation here in lieu of description or benchmarks, but if you insist then use something like O(kn) vs. O((k+1)n)

I've expected the JS interpreter to optimize that out. Turns out I was wrong and the version from the article is even 40% slower for a loop that iterates over 5000 elements and does a trivial amount of work:

http://jsperf.com/for-loop-vs-for-in-loop/18

You're not giving the JIT a chance to warm up at all. Also, you're degrading into 'slow mode' because your array has different types. Basically, it looks like you're doing a lot of bad things to the JIT on purpose :P

Both the different types and the delete (array with holes) are _very_ harmful for performance in v8.

The arrays with holes are called Sparse Arrays, I googled for 'sparse array v8 performance' and didn't find much, but I've previously seen a Google Presentation on a slide-sharing site that did mention how poor sparse arrays perform when compared to normal contiguous arrays.

That's not how big O notation works. You need to eliminate constant multipliers because you can't make any assumptions about the amount of work each iteration involves. It certainly doesn't correlate directly to machine instructions.