Time complexity of Javascript Array.find() in modern browsers - javascript

Since array.find() iterates over an array, if I handle (potentially) large arrays, I always make sure to have an indexed object like so:
{ [id:string]: Item }
if I need to look up items by id in these arrays.
However, living in a time of V8 (and comparable engine optimisations for Safari and Firefox), I'm wondering if maybe under the hood, a simple array.find() is already optimized for it? Or will optimize for it (create such an indexed object) at runtime as soon as it has to perform this operation once?
Is it true that modern browsers already have some kind of optimization for O(N) type algorithms that could become O(1) with the proper implementation? Or am I thinking too much of what these browsers actually can / will do under the hood?

V8 developer here. The time complexity of Array.prototype.find is O(n) (with n being the array's length), and it's fair to assume that it will remain that way.
Generally speaking, it's often impossible for engines to improve the complexity class of an operation. In case of Array.prototype.find, the predicate function you pass might well care how often it gets called:
[1, 2, 3].find((value, index, object) => {
console.log(`Checking ${value}...`); // Or any other side effect.
return value === 42;
});
In such a case, the engine has no choice but to iterate over the entire array in exactly the right order, because anything else would observably break your program's behavior.
In theory, since JS engines can do dynamic optimizations, they could inspect the predicate function, and if it has no side effects, they could use it to build up some sort of index/cache. Aside from the difficulty of building such a system that works for arbitrary predicates, this technique even when it does work would only speed up repeated searches of the same array with the same function, at the cost of wasting time and memory if this exact same scenario will not occur again. It seems unlikely that an engine can ever make this prediction with sufficient confidence to justify investing this time and memory.
As a rule of thumb: when operating on large data sets, choosing efficient algorithms and data structures is worth it. Typically far more worth it than the micro-optimizations we're seeing so much in SO questions :-)
A highly optimized/optimizing engine may be able to make your O(n) code somewhere between 10% and 10x as fast as it would otherwise be. By switching to an O(log n) or O(1) solution on your end, you can speed it up by orders of magnitude. That's often accomplished by doing something that engines can't possibly do. For example, you can keep your array sorted and then use binary search over it -- that's something an engine can't do for you automatically because obviously it's not allowed to reorder your array's contents without your approval. And as #myf already points out in a comment: if you want to access things by a unique key, then using a Map will probably work better than using an Array.
That said, simple solutions tend to scale better than we intuitively assume; the standard warning against premature optimizations applies here just as everywhere else. Linearly searching through arrays is often just fine, you don't need a (hash) map just because you have more than three items in it. When in doubt, profile your app to find out where the performance bottlenecks are.

Related

Why "Map" manipulation is much slower than "Object" in JavaScript (v8) for integer keys?

I was happily using Map for indexed accessed everywhere in my JavaScript codebase, but I've just stumbled upon this benchmark: https://stackoverflow.com/a/54385459/365104
I've re-created it here as well: https://jsben.ch/HOU3g
What benchmark is doing is basically filling a map with 1M elements, then iterating over them.
I'd expect the results for Map and Object to be on par, but they differ drastically - in favor of Object.
Is this expected behavior? Can it be explained? Is it because of the ordering requirement? Or because map is doing some key hashing? Or just because Map allows any object as key (I'd expect it using pointer address for key then, which does not need any hashing)? What is the difference between the Map and Object indexing algorithms?
This is quite unexpected and discouraging - basically I'll have to revert back to the old-school "object as map" coding style.
Update #1
As suggested in comments, the Object might be optimized to Array (since it is indexed by integer, starting from the zero).
Changing the iteration order from size to 0 - Object is still 2x faster. When using strings as index, Map performs 2x better.
(V8 developer here.)
I'll have to revert back to the old-school "object as map" coding style.
If you do that, you will have fallen victim to a misleading microbenchmark.
In the very special case of using consecutive integers as keys, a plain Object will be faster, yes. Nothing beats a contiguous array in that scenario. So if the "indexed accesses everywhere in your codebase" that you mentioned are indeed using index sets like the integers from 0 to 1M, then using an Object or Array is a good idea. But that's a special case. If the index space is sparse, things will already look different.
In the general case of using arbitrary strings in random order, a Map will perform significantly better than an Object. Even more importantly, the way such object property accesses are handled (in V8, and quite possibly in other engines too) has non-local effects: if one function puts excessive stress on the slow path of the object property lookup handling system, then that will likely slow down some other functions relying on that same slow path for their property accesses.
The fundamental reason is that engines optimize different things for different usage patterns. An engine could implement Objects and Maps pretty much the same under the hood; but that wouldn't be the ideal behavior, because different usage patterns benefit from different internal representations and implementation choices. So engines allow you to provide them with a hint: if you use a Map, the engine will know that you're planning to use the thing as a map (duh!), where random keys will come and go. If you use an Object, then the engine will (at least at first) assume that you want the set of optimizations that work best for your average object, where the set of properties is fairly small and static. If you use an Array (or Object with only integer properties, which is nearly the same thing in JS), then you're making it easy for the engine to give you fast integer-indexed accesses.
Using "x" + i as key is a good suggestion to demonstrate how quickly a microbenchmark can be changed so it appears to produce opposite results. But here's a spoiler: if you do (only) this modification, then a large part of what you're measuring will be number-to-string conversion and string internalization, not Map/Object access performance itself.
Beware of microbenchmarks; they are misleading. You really have to analyze them quite deeply (by profiling, and/or by inspecting generated code, and/or by tracing other engine internals) to be sure that they're measuring what you think they're measuring, and hence are producing results that are telling you what you think they're telling you.
In general, it is strongly recommended to use representative test cases for performance measurements. Ideally, your app itself; or by extracting a realistic part of it into a testcase operating on realistic data. And if you can't measure a difference between two implementation choices with a stress test for your entire production app, then it's not a difference worth worrying about it. With a microbenchmark (i.e. a couple of artificially crafted lines), I can "prove" almost anything that doesn't apply to the general case.

Why is 'delete' slow in javascript?

I just stumbled upon this jsperf result: http://jsperf.com/delet-is-slow
It shows that using delete is slow in javascript but I am not sure I get why. What is the javascript engine doing behind the scene to make things slow?
I think the question is not why delete is slow... The speed of a simple delete operation is not worth measuring...
The JS perf link that you show does the following:
Create two arrays of 6 elements each.
Delete at one of the indexes of one array.
Iterate through all the indexes of each array.
The script shows that iterating through an array o which delete was applied is slower than iterating though a normal array.
You should ask yourself, why delete makes an array slow?
The engine internally stores array elements in contiguous memory space, and access them using an numeric indexer.
That's what they call a fast access array.
If you delete one of the elements in this ordered and contiguous index, you force the array to mutate into dictionary mode... thus, what before was the exact location of the item in the array (the indexer) becomes the key in the dictionary under which the array has to search for the element.
So iterating becomes slow, because don't move into the next space in memory anymore, but you perform over and over again a hash search.
You'll get a lot of answers here about micro-optimisation but delete really does sometimes have supreme problems where it becomes incredibly slow in certain scenarios that people must be aware of in JS. These are to my knowledge edge cases and may or may not apply to you.
I recommend to profile and benchmark in different browsers to detect these anomalies.
I honestly don't know the reasons as I tend to workaround it this but I would guess combinations of quirks in the GC (it is might be getting invoked too often), brutal rehashing, optimisations for other cases and weird object structure/bad time complexity.
The cases usually involve moderate to large numbers of keys, for example:
Delete from objects with many keys:
(function() {
var o={},s,i,c=console;
s=new Date();for(i=0;i<1000000;i+=10)o[i]=i;c.log('Set: '+(new Date()-s));
s=new Date();for(i=0;i<50000;i+=10)delete(o[i]);c.log('Delete: '+(new Date()-s));})();
Chrome:
Set: 21
Delete: 2084
Firefox:
Set: 74
Delete: 2
I have encountered a few variations of this and they are not always easy to reproduce. A signature is that it usually seems to degrade exponentially. In one case in Firefox delete inside a for in loop would degrade to around 3-6 operations a second where as deleting when iterating Object.keys would be fine.
I personally tend to think that these cases can be considered bugs. You get massive asymptotic and disproportionate performance degradation that you can work around in ways that shouldn't change the time or space complexity or that might even normally make performance moderately worse. This means that when considered as a declarative language JS gets the implementation/optimisations wrong. Map does not have the same problem with delete so far that I have seen.
Reasons:
To be sure, you would have to look into the source code or run some profiling.
delete in various scenarios can change speed arbitrarily based on how engines are written and this can change from version to version.
JavaScript objects tend to not be used with large amounts of properties and delete is called relatively infrequently in every day usage. They're also used as part of the language heavily (they're actually just associative arrays). Virtually everything relies on an implementation of an object. IF you create a function, that makes an object, if you put in a numeric literal it's actually an object.
It's quite possible for it to be slow purely because it hasn't been well optimised (either neglect or other priorities) or due to mistakes in implementation.
There are some common possible causes aside from optimisation deficit and mistakes.
Garbage Collection:
A poorly implemented delete function may inadvertently trigger garbage collection excessively.
Garbage collection has to iterate everything in memory to find out of there are any orphans, traversing variables and references as a graph.
The need to detect circular references can make GC especially expensive. GC without circular reference detection can be done using reference counters and a simple check. Circular reference checking requires traversing the reference graph or another structure for managing references and in either case that's a lot slower than using reference counters.
In normal implementations, rather than freeing and rearranging memory every time something is deleted, things are usually marked as possible to delete with GC deferred to perform in bulk or batches intermittently.
Mistakes can potentially lead to the entire GC being triggered too frequently. That may involve someone having put an instruction to run the GC every delete or a mistake in its statistical heuristics.
Resizing:
It's quite possible for large objects as well the memory remapping to shrink them might not be well optimised. When you have dynamically sized structures internally in the engine it can be expensive to resize them. Engines will also have varying levels of their own memory management on top of that of that provides by the operating system which will significantly complicate things.
Where an engine manages its own memory efficiently, even a delete that deletes an object efficiently without the need for a full GC run (has no circular references) this can trigger the need to rearrange memory internally to fill the gap.
That may involve reallocating data of the new size, then copying everything from the old memory to the new memory before freeing the old memory. It can also require updating all of the pointers to the old memory in some cases (IE, where a pointer pointer [all roads lead to Rome] wasn't used).
Rehashing:
It may also rehash (needs as the object is an associative array) on deletes. Often people only rehash on demand, when there are hash collisions but some engines may also rehash on deletes.
Rehashing on deletes prevents a problem where you can add 10 items to an object, then add a million objects, then remove those million objects and the object left with the 10 items will both take up more memory and be slower.
A hash with ten items needs ten slots with an optimum hash, though it'll actually require 16 slots. That times the size of a pointer will be 16 * 8 bytes or 128bytes. When you add the million items then it needs a million slots, 20 bit keys or 8 megabytes. If you delete the million keys without rehashing it then the object you have with ten items is taking up 8 megabyte when it only needs 128 bytes. That makes it important to rehash on item removal.
The problem with this is that when you add you know if you need to rehash or not because there will be a collision. When deleting, you don't know if you need to rehash or not. It's easy to make a performance mistake with that and rehash every time.
There are a number of strategies for reasonable downhashing intervals although without making things complicated it can be easy to make a mistake. Simple approaches tend to work moderately well (IE, time since last rehash, size of key versus minimum size, history of collision pairs, tombstone keys and delete in bulk as part of GC, etc) on average but can also easily get stuck in corner cases. Some engines might switch to a different hash implementation for large objects such as nested where as others might try to implement one for all.
Rehashing tends to work the same as resizing for simply implementations, make an entirely new one then insert the old one into it. However for rehashing a lot more needs to be done beforehand.
No Bulk:
It doesn't let you delete a bunch of things at once from a hash. It's usually much more efficient to delete items from a hash in bulk (most operations on the same thing work better in bulk) but the delete keyword only allows you to delete one by one. That makes it slow by design for cases with multiple deletes on the same object.
Due to this, only a handful of implementation of delete would be comparable to creating a new object and inserting the items you want to keep for speed (though this doesn't explain why with some engines delete is slow in its own right for a single call).
V8:
Slow delete of object properties in JS in V8
Apparently this was caused due to switching out implementations and a problem similar to but different to the downsizing problem seen with hashes (downsizing to flat array / runthrough implementation rather than hash size). At least I was close.
Downsizing is a fairly common problem in programming which results in what is somewhat akin to a game of Jenga.

Javascript forcing array to behave like array

Javascript arrays used to be just objects with some special syntax, but JIT engines can optimize the array to behave like a true array with O(1) access rather than O(log n) access. I am currently writing a cpu intensive loop where I need to a certain I get O(1) access rather than O(log n). But the problem is that I may need to do array [5231] = obj1 right after its created but the array will eventually be filled. I am worried that such behaviour may trick the JIT to thinking that I am using it as a sparse array and thus I wouldnt get O(1) access time I require. My question is that is there ways I can tell or at least hint the javascript engine that I want a true array?
In particular, do I need to initialize the array with values first? Like fill all of it with reference to a dummy object (my array will only contain references to objects of same prototype or undefined). Would just set the array.length = 6000 be enough?
EDIT: based on http://jsperf.com/sparse-array-v-true-array, it seems filling the array beforehand is a good idea.
I realized I have made a big derp on my profiling test because it is an animation program with varying frame rate and my profiling was focused on detecting where the cpu intensive parts are. It was not suitable in comparing between the two methods as one method would simply run slower but the relative cpu time between different function calls would be the same.
Anyhow, I wrote a test on jspref: http://jsperf.com/sparse-array-v-true-array and (unless I made an error , which so, please do correct me!) A randomly accessed array without filling it first runs twice slower in Chrome and Firefox compared to filled version. So it seems that it is a good idea to fill it in first if you are writing things like a spatial hashmap.

JavaScript Objects as Hashes? Is the complexity greater than O(1)?

For some algorithm I was writing recently I thought that a hash would be excellent. I thought that I could probably just use the member variables in an object as key value pairs. I am not sure if this is optimal since I don't really know what is going on behind the scenes. I also suppose that V8 does it differently than other environments. I do however imagine that looking up member variables would be pretty quick (hopefully)?
That all said, I am wondering if the run time complexity of writing, reading, creating and removing member variables in JavaScript objects are all O(1). If there are differences in environment (v8 vs others) what are they?
Yes they are hashes. The implementation is different across browsers. Despite many articles that would claim that objects are not hashes they very much behave like hashes, and therefore could be used as such.
I had to prove this by running performance tests:
The way to read these tests is if there is no performance difference in ops/sec when the size of object grows then that means objects are hashes. The defining characteristic of a hash is that the complexity of each operation is O(1) regardless of it being faster or slower in comparison to other operations.
Tests:
http://jsperf.com/objectsashashes/2 (100 keys)
http://jsperf.com/objectsashashes/3 (100k keys)
http://jsperf.com/objectsashashes/ (1 million keys)
http://jsperf.com/objects-as-hashes-300-mil (10m keys)
Note: Each browser is faster/slower at different operations. This seems to change between releases and year to year.
JavaScript objects are hashes. I cannot imagine any sane implementation that would not provide constant-time CRUD operations on object properties.
Are you seeing specific performance issues with this approach?
Objects in JavaScript are an example of a Hash Table because object data is represented as keys & values.

Do modern JavaScript JITers need array-length caching in loops?

I find the practice of caching an array's length property inside a for loop quite distasteful. As in,
for (var i = 0, l = myArray.length; i < l; ++i) {
// ...
}
In my eyes at least, this hurts readability a lot compared with the straightforward
for (var i = 0; i < myArray.length; ++i) {
// ...
}
(not to mention that it leaks another variable into the surrounding function due to the nature of lexical scope and hoisting.)
I'd like to be able to tell anyone who does this "don't bother; modern JS JITers optimize that trick away." Obviously it's not a trivial optimization, since you could e.g. modify the array while it is being iterated over, but I would think given all the crazy stuff I've heard about JITers and their runtime analysis tricks, they'd have gotten to this by now.
Anyone have evidence one way or another?
And yes, I too wish it would suffice to say "that's a micro-optimization; don't do that until you profile." But not everyone listens to that kind of reason, especially when it becomes a habit to cache the length and they just end up doing so automatically, almost as a style choice.
It depends on a few things:
Whether you've proven your code is spending significant time looping
Whether the slowest browser you're fully supporting benefits from array length caching
Whether you or the people who work on your code find the array length caching hard to read
It seems from the benchmarks I've seen (for example, here and here) that performance in IE < 9 (which will generally be the slowest browsers you have to deal with) benefits from caching the array length, so it may be worth doing. For what it's worth, I have a long-standing habit of caching the array length and as a result find it easy to read. There are also other loop optimizations that can have an effect, such as counting down rather than up.
Here's a relevant discussion about this from the JSMentors mailing list: http://groups.google.com/group/jsmentors/browse_thread/thread/526c1ddeccfe90f0
My tests show that all major newer browsers cache the length property of arrays. You don't need to cache it yourself unless you're concerned about IE6 or 7, I don't remember exactly. However, I have been using another style of iteration since those days since it gives me another benefit which I'll describe in the following example:
var arr = ["Hello", "there", "sup"];
for (var i=0, str; str = arr[i]; i++) {
// I already have the item being iterated in the loop as 'str'
alert(str);
}
You must realize that this iteration style stops if the array is allowed to contain 'falsy' values, so this style cannot be used in that case.
First of all, how is this harder to do or less legible?
var i = someArray.length;
while(i--){
//doStuff to someArray[i]
}
This is not some weird cryptic micro-optimization. It's just a basic work avoidance principle. Not using the '.' or '[]' operators more than necessary should be as obvious as not recalculating pi more than once (assuming you didn't know we already have that in the Math object).
[rantish elements yoinked]
If someArray is entirely internal to a function it's fair game for JIT optimization of its length property which is really like a getter that actually counts up the elements of the array every time you access it. A JIT could see that it was entirely locally scoped and skip the actual counting behavior.
But this involves a fair amount of complexity. Every time you do anything that mutates that Array you have to treat length like a static property and tell your array altering methods (the native code side of them I mean) to set the property manually whereas normally length just counts the items up every time it's referenced. That means every time a new array-altering method is added you have to update the JIT to branch behavior for length references of a locally scoped array.
I could see Chrome doing this eventually but I don't think it is yet based on some really informal tests. I'm not sure IE will ever have this level of performance fine-tuning as a priority. As for the other browsers, you could make a strong argument for the maintenance issue of having to branch behavior for every new array method being more trouble than its worth. At the very least, it would not get top priority.
Ultimately, accessing the length property every loop cycle isn't going to cost you a ton even in the old browsers for a typical JS loop. But I would advise getting in the habit of caching any property lookup being done more than once because with getter properties you can never be sure how much work is being done, which browsers optimize in what ways or what kind of performance costs you could hit down the road when somebody decides to move someArray outside of the function which could lead to the call object checking in a dozen places before finding what it's looking for every time you do that property access.
Caching property lookups and method returns is easy, cleans your code up, and ultimately makes it more flexible and performance-robust in the face of modification. Even if one or two JITs did make it unnecessary in circumstances involving a number of 'ifs', you couldn't be certain they always would or that your code would continue to make it possible to do so.
So yes, apologies for the anti-let-the-compiler-handle-it rant but I don't see why you would ever want to not cache your properties. It's easy. It's clean. It guarantees better performance regardless of browser or movement of the object having its property's examined to an outer scope.
But it really does piss me off that Word docs load as slowly now as they did back in 1995 and that people continue to write horrendously slow-performing java websites even though Java's VM supposedly beats all non-compiled contenders for performance. I think this notion that you can let the compiler sort out the performance details and that "modern computers are SO fast" has a lot to do with that. We should always be mindful of work-avoidance, when the work is easy to avoid and doesn't threaten legibility/maintainability, IMO. Doing it differently has never helped me (or I suspect anybody) write the code faster in the long term.

Categories