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.
Related
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.
So we have all heard that v8 uses the thing called hidden classes where when many objects have the same shape, they just store a pointer to the shape struct which stores fixed offsets. I have heard this a million time, and I very much get how this reduces memory usage by A LOT (not having to store a map for each one is amazing) and potentially because of that a bit faster performance.
However I still don't understand how it avoids dynamic lookup. The only thing I have heard is storing a cache between a string (field name) and a fixed offset, and checking it every time, but if there's a cache miss (which is likely to happen) there will still be a dyanmic lookup.
Everyone says that this is almost as fast as C++ field access (which are just a mov instruction usually), however this 1 field access cache isn't even close.
Look at the following function:
function getx(o)
{
return o.x;
}
How will v8 make the access of the x field so fast and avoid dynamic lookup?
(V8 developer here.)
The key is that hidden classes allow caching. So, certainly, a few dynamic lookups will still be required. But thanks to caching, they don't have to be repeated every single time a property access like o.x is executed.
Essentially, the first time your example function getx is called, it'll have to do a full property lookup, and it'll cache both the hidden class that was queried and the result of the lookup. On subsequent calls, it'll simply check the incoming o's hidden class against the cached data, and if it matches, it uses the cached information about how to access the property. Of course, on mismatch, it has to fall back to a dynamic lookup again. (In reality it's a bit more complicated because of additional tradeoffs that are involved, but that's the basic idea.)
So if things go well, a single dynamic lookup is sufficient for an arbitrary number of calls to such a function. In practice, things go well often enough that if you started with a very simple JS engine that didn't do any such tricks, and you added caching of dynamic lookup results, you could expect approximately a 10x performance improvement across the board. Adding an optimizing compiler can then take that idea one step further and embed cached results right into the optimized code; that can easily give another 10x improvement and approach C-like performance: code still needs to contain hidden class checks, but on successful check can read properties with a single instruction. (This is also why JS engines can't just "optimize everything immediately" even if they wanted to: optimization crucially depends on well-populated caches.)
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.
I am writing a code that solves equations for users. The user will be able to enter matrices, vectors and such. Right now, as I solve those equations, I write the solution to a table with each row representing any given variable or equation. The javascript object that was used to hold the equation during calculation is destroyed. I did this on purpose to prevent large arrays of data being stored in memory and potentially slowing down the script. As an array is needed later, it is read into an object from the appropriate table row.
I am wondering how holding memory in the DOM like this compares to holding it in objects in memory. I am more at risk to slow down my code execution by holding these things in memory as objects instead of on the DOM? Also, since these objects will need to be accessed by multiple parts of the code, these would have to be global objects. Right?
In a word, no.
There's one rule that is a constant in web development: the DOM is slow.
Selecting, accessing, and iterating DOM elements and reading and modifying their properties is one of the single slowest things you can do in browser JS. On the other hand, working with native JS objects is very fast. As a rule, I avoid working with the DOM as much as possible.
You make the (incorrect) assumption that low memory usage somehow equates to improved performance. In fact, it is generally1 (and not just in the browser) the case that increased memory usage increases application performance. By the nature of having calculated results cached in memory, you avoid the processor- and time-intensive need to rebuild data at a later time. Of course, you must balance your use of memory with the need for performance.
In your case, I'd definitely keep the equation object in memory. It's probably using a surprisingly small amount of memory. For example, I just created an array of 1,000 integers in Chrome, and the memory profiler told me that it used less than 40 kB – almost all of it object overhead. The values themselves only took 4 kB – 1000 * 4 bytes (32 bits).
When you consider that the routines that read the DOM to regenerate the object probably take tens to hundreds of milliseconds (or much worse, depending on complexity) to run, why wouldn't you just generate the object once and keep it, especially since the cost is so low?
However, as with all performance questions, the real answer is that you have to profile your application to see where the performance bottlenecks really lie. Use Chrome's Developer Tools' Profiles tab to take CPU profiles (which measure processor utilization over time) and heap snapshots (which measure memory usage in an instant) as you use your app.
1 - This really depends on whether your program uses so much memory that pages must be swapped out. On desktop machines where 16 GB of RAM has become commonplace, you have nearly nothing to worry about. However, it is still important to watch memory usage on mobile devices where resources are more constricted.
I am wondering how holding memory in the DOM like this compares to holding it in objects in memory.
Test it and see, I'd expect the DOM to use more memory and be very much slower to access. In any case, unless you are storing an awful lot of stuff the memory implications are likely minimal.
I am more at risk to slow down my code execution by holding these things in memory as objects instead of on the DOM?
The opposite I expect. Accessing the DOM will be very much slower.
Also, since these objects will need to be accessed by multiple parts of the code, these would have to be global objects. Right?
Wrong. You can hold references in a closure, or put all the objects into a single array or object. In any case, global objects aren't bad per se, they are just considered untidy and increase the chance of name collisions.
I have a very large collection of objects in node.js (millions) that I need to keep in memory for caching purposes (they are maintained in several global hash objects). Each hash collections stores about 750k keys.
In order to keep GC to a minimum, I want to find out the best way to store these items. Would it be better to split these items into 100s of thousands of hashes? Should I maybe not use hashes at all? Is there any way to keep them off the heap completely so they never are examined by GC (and if so how would I do so)?
There is no public API to control garbage collection from JavaScript.
But GC has gone a long way over the years. Modern GC implementations will notice that some objects live long and put them into a special "area" which will be collected only rarely.
How this exactly works is completely implementation dependent; every browser does it's own thing and usually, this also often changes when a new browser version is released.
EDIT The layout on memory and the organization is completely irrelevant. Modern GCs are really hard to understand in detail without investing a couple of weeks reading the actual code. So what I explain now is a really simplified picture; the real code will work different (and some GCs will use completely different tricks to achieve the same goal).
Imagine that the GC has a counter for each object in which it counts how often it has seen it in the past. Plus it has several lists where it keeps objects of different age, that is with objects whose counters have passed certain thresholds. So when the counter reaches some limit, the object is moved to the next list.
The first list is visited every time GC runs. The second list is only considered for every Nth GC run.
An alternate implementation might add new objects to the top of the "GC list" and for every GC run, it will only check N elements. So long living objects will move down the list and after a while, they don't be checked every time.
What this means for you is that you don't have to do anything; the GC will figure out that your huge map lives a long time (and the same is true for all objects in the map) and after a while, it will start to ignore this data structure.