We Keep Coding

Can I pre-allocate memory for a Map/Set with a known number of elements?

In the case of a JS array, it's possible to create one with predefined length. If we pass the length to the constructor, like new Array(itemCount), a JS engine could pre-allocate memory for the array, so it won't be needed to reallocate it while new items are added to the array.
Is it possible to pre-allocate memory for a Map or Set? It doesn't accept the length in the constructor like array does. If you know that a map will contain 10 000 items, it should be much more efficient to allocate the memory once than reallocate it multiple times.
Here is the same question about arrays.
There is discussion in the comments, whether the predefined array size has an impact on the performance or not. I've created a simple test to check it, the result is:
Chrome - filling an array with a predefined size is about 3 times faster
Firefox - there is no much difference, but the array with predefined length is a little bit faster
Edge - filling of a predefined size array is about 1.5 times faster
There is a suggestion to create the entries array and pass it to the Map, so the map can take the length from the array. I've created such test too, the result is:
Chrome - construction of a map which accepts entries array is about 1.5 times slower
Firefox - there is no difference
Edge - passing of entries array to the Map constructor is about 1.5 times faster

There is no way to influence the way the engine allocates memory, so whatever you do, you cannot guarantee that the trick you are using works on every engine or even at another version of the same engine.
There are however typed arrays in JS, they are the only datastructures with a fixed size. With them, you can implement your own hashmap with fixed size.
A very naive implementation is slightly faster² on inserting, not sure how it behaves on reads and others (also it can only store 8bit integers > 0):
function FixedMap(size) {
const keys = new Uint8Array(size),
values = new Uint8Array(size);
return {
get(key) {
let pos = key % size;
while(keys[pos] !== key) {
if(keys[pos] % size !== key % size)
return undefined;
pos = (pos + 1) % size;
}
return values[pos];
},
set(key, value) {
let pos = key % size;
while(key[pos] && key[pos] !== key) pos = (pos + 1) % size;
keys[pos] = value;
values[pos] = value;
}
};
}
console.time("native");
const map = new Map();
for(let i = 0; i < 10000; i++)
map.set(i, Math.floor(Math.random() * 1000));
console.timeEnd("native");
console.time("self");
const fixedMap = FixedMap(10000);
for(let i = 0; i < 10000; i++)
fixedMap.set(i, Math.floor(Math.random() * 1000));
console.timeEnd("self");
² Marketing would say It is up to 20% faster! and I would say It is up to 2ms faster, and I spent more than 10minutes on this ...

No, this is not possible. It's doubtful even that the Array(length) trick still works, engines have become much better at optimising array assignments and might have chosen a different strategy to (pre)determine the size of memory to allocate.
For Maps and Sets, no such trick exists. Your best bet will be creating them by passing an iterable to their constructor, like new Map(array), instead of using the set/add methods. This does at least offer the chance for the engine to take the iterable size as a hint - although filtering out duplicates can still lead to differences.
Disclaimer: I don't know whether any engines implement, or plan to implement, such an optimisation. It might not be worth the effort, especially if the current memory allocation strategy is already good enough to diminish any improvements.

In JS which one makes sense: searching for a value in object collection by foreach vs keeping multiple collections with different keys

I'm working on some kind of 1:1 chat system, the environment is Node.JS
For each country, there is a country room (lobby), for each socket client there is a js class/object is being created and each object is in a list with their unique user id.
This unique id is preserved even users logged in from different browser tabs etc..
Each object stored in collections like: "connections" (all of them), "operators"(only operators), "{countryISO}_clients" (users) and the reference key is their unique id.
In some circumstances, I need to access these connections by their socket ids.
At this point, I can think of 2 resolutions.
Using a for each loop to find the desired object
Creating another collection, this time instead of unique id use socket id (or something else.)
Which one makes sense? Because in JS since this collection will be a reference list instead of a copy, it feels like it makes sense (and beautiful looking) but I can't be sure. Which one is expensive in memory/performance terms?
I can't make thorough tests since I don't know how to create dummy (simultaneous) socket connections.
Expected connected socket client count: 300 - 1000 (depends on the time of the day)
e.g. user:
"qk32d2k":{
"uid":"qk32d2k",
"name":"Josh",
"socket":"{socket.io's socket reference}",
"role":"user",
"rooms":["room1"],
"socketids":["sid1"]
"country":"us",
...
info:() => { return gatherSomeData(); },
update:(obj) => { return updateSomeData(obj); },
send:(data)=>{ /*send data to this user*/ }
}
e.g. Countries collection:
{
us:{
"qk32d2k":{"object above."}
"l33t71":{"another user object."}
},
ca:{
"asd231":{"other user object."}
}
}

Pick Simple Design First that Optimizes for Most Common Access
There is no ideal answer here in the absolute. CPUs are wicked fast these days, so if I were you I'd start out with one simple mechanism of storing the sockets that you can access both ways you want, even if one way is kind of a brute force search. Pick the data structure that optimizes the access mechanism that you expect to be either most common or most sensitive to performance.
So, if you are going to be looking up by userID the most, then I'd probably store the sockets in a Map object with the userID as the key. That will give you fast, optimized access to get the socket for a given userID.
For finding a socket by some other property of the socket, you will just iterate the Map item by item until you find the desired match on some other socket property. I'd probably use a for/of loop because it's both fast and easy to bail out of the loop when you've found your match (something you can't do on a Map or Array object with .forEach()). You can obviously make yourself a little utility function or method that will do the brute force lookup for you and that will allow you to modify the implementation later without changing much calling code.
Measure and Add Further Optimization Later (if data shows you need to)
Then, once you get up to scale (or simulated scale in pre-production test), you take a look at the performance of your system. If you have loads of room to spare, you're done - no need to look further. If you have some operations that are slower than desired or higher than desired CPU usage, then you profile your system and find out where the time is going. It's most likely that your performance bottlenecks will be elsewhere in your system and you can then concentrate on those aspects of the system. If, in your profiling, you find that the linear lookup to find the desired socket is causing some of your slow-down, then you can make a second parallel lookup Map with the socketID as the key in order to optimize that type of lookup.
But, I would not recommend doing this until you've actually shown that it is an issue. Premature optimization before you have actual metrics that prove it's worth optimizing something just add complexity to a program without any proof that it is required or even anywhere close to a meaningful bottleneck in your system. Our intuition about what the bottlenecks are is often way, way off. For that reasons, I tend to pick an intelligent first design that is relatively simple to implement, maintain and use and then, only when we have real usage data by which we can measure actual performance metrics would I spend more time optimizing it or tweaking it or making it more complicated in order to make it faster.
Encapsulate the Implementation in Class
If you encapsulate all operations here in a class:
Adding a socket to the data structure.
Removing a socket from the data structure.
Looking up by userID
Looking up by socketID
Any other access to the data structure
Then, all calling code will access this data structure via the class and you can tweak the implementation some time in the future (to optimize based on data) without having to modify any of the calling code. This type of encapsulation can be very useful if you suspect future modifications or change of modifications to the way the data is stored or accessed.
If You're Still Worried, Design a Quick Bench Measurement
I created a quick snippet that tests how long a brute force lookup is in a 1000 element Map object (when you want to find it by something other than what the key is) and compared it to an indexed lookup.
On my computer, a brute force lookup (non-indexed lookup) takes about 0.002549 ms per lookup (that's an average time when doing 1,000,000 lookups. For comparison an indexed lookup on the same Map takes about 0.000017 ms. So you save about 0.002532 ms per lookup. So, this is fractions of a millisecond.
function addCommas(str) {
var parts = (str + "").split("."),
main = parts[0],
len = main.length,
output = "",
i = len - 1;
while(i >= 0) {
output = main.charAt(i) + output;
if ((len - i) % 3 === 0 && i > 0) {
output = "," + output;
}
--i;
}
// put decimal part back
if (parts.length > 1) {
output += "." + parts[1];
}
return output;
}
let m = new Map();
// populate the Map with objects that have a property that
// you have to do a brute force lookup on
function rand(min, max) {
return Math.floor((Math.random() * (max - min)) + min)
}
// keep all randoms here just so we can randomly get one
// to try to find (wouldn't normally do this)
// just for testing purposes
let allRandoms = [];
for (let i = 0; i < 1000; i++) {
let r = rand(1, 1000000);
m.set(i, {id: r});
allRandoms.push(r);
}
// create a set of test lookups
// we do this ahead of time so it's not part of the timed
// section so we're only timing the actual brute force lookup
let numRuns = 1000000;
let lookupTests = [];
for (let i = 0; i < numRuns; i++) {
lookupTests.push(allRandoms[rand(0, allRandoms.length)]);
}
let indexTests = [];
for (let i = 0; i < numRuns; i++) {
indexTests.push(rand(0, allRandoms.length));
}
// function to brute force search the map to find one of the random items
function findObj(targetVal) {
for (let [key, val] of m) {
if (val.id === targetVal) {
return val;
}
}
return null;
}
let startTime = Date.now();
for (let i = 0; i < lookupTests.length; i++) {
// get an id from the allRandoms to search for
let found = findObj(lookupTests[i]);
if (!found) {
console.log("!!didn't find brute force target")
}
}
let delta = Date.now() - startTime;
//console.log(`Total run time for ${addCommas(numRuns)} lookups: ${delta} ms`);
//console.log(`Avg run time per lookup: ${delta/numRuns} ms`);
// Now, see how fast the same number of indexed lookups are
let startTime2 = Date.now();
for (let i = 0; i < indexTests.length; i++) {
let found = m.get(indexTests[i]);
if (!found) {
console.log("!!didn't find indexed target")
}
}
let delta2 = Date.now() - startTime2;
//console.log(`Total run time for ${addCommas(numRuns)} lookups: ${delta2} ms`);
//console.log(`Avg run time per lookup: ${delta2/numRuns} ms`);
let results = `
Total run time for ${addCommas(numRuns)} brute force lookups: ${delta} ms<br>
Avg run time per brute force lookup: ${delta/numRuns} ms<br>
<hr>
Total run time for ${addCommas(numRuns)} indexed lookups: ${delta2} ms<br>
Avg run time per indexed lookup: ${delta2/numRuns} ms<br>
<hr>
Net savings of an indexed lookup is ${(delta - delta2)/numRuns} ms per lookup
`;
document.body.innerHTML = results;

Slow delete of object properties in JS in V8

Just to train myself a bit of Typescript I wrote a simple ES6 Map+Set-like implementation based on plain JS Object. It works only for primitive keys, so no buckets, no hash-codes, etc. The problem I encountered is implementing delete method. Using plain delete is just unacceptably slow. For large maps it's about 300-400x slower than ES6 Map delete. I noticed the huge performance degradation if size of the object is large. On Node JS 7.9.0 (and Chrome 57 for example) if object has 50855 properties delete performance is the same as ES6 Map. But for 50856 properties the ES6 Map is faster on 2 orders of magnitude. Here is the simple code to reproduce:
// for node 6: 76300
// for node 7: 50855
const N0 = 50855;
function fast() {
const N = N0
const o = {}
for ( let i = 0; i < N; i++ ) {
o[i] = i
}
const t1 = Date.now()
for ( let i = 0; i < N; i++ ) {
delete o[i]
}
const t2 = Date.now()
console.log( N / (t2 - t1) + ' KOP/S' )
}
function slow() {
const N = N0 + 1 // adding just 1
const o = {}
for ( let i = 0; i < N; i++ ) {
o[i] = i
}
const t1 = Date.now()
for ( let i = 0; i < N; i++ ) {
delete o[i]
}
const t2 = Date.now()
console.log( N / (t2 - t1) + ' KOP/S' )
}
fast()
slow()
I guess I could instead of delete properties just set them to undefined or some guard object, but this will mess the code, because hasOwnProperty will not work correctly, for...in loops will need additional check and so on. Are there more nice solutions?
P.S. I'm using node 7.9.0 on OSX Sierra
Edited
Thanks for comments guys, I fixed OP/S => KOP/S. I think I asked rather badly specified question, so I changed the title. After some investigation I found out that for example in Firefox there is no such problems -- deleting cost grows linearly. So it's problem of super smart V8. And I think it's just a bug:(

(V8 developer here.) Yes, this is a known issue. The underlying problem is that objects should switch their elements backing store from a flat array to a dictionary when they become too sparse, and the way this has historically been implemented was for every delete operation to check if enough elements were still present for that transition not to happen yet. The bigger the array, the more time this check took. Under certain conditions (recently created objects below a certain size), the check was skipped -- the resulting impressive speedup is what you're observing in the fast() case.
I've taken this opportunity to fix the (frankly quite silly) behavior of the regular/slow path. It should be sufficient to check every now and then, not on every single delete. The fix will be in V8 6.0, which should be picked up by Node in a few months (I believe Node 8 is supposed to get it at some point).
That said, using delete causes various forms and magnitudes of slowdown in many situations, because it tends to make things more complicated, forcing the engine (any engine) to perform more checks and/or fall off various fast paths. It is generally recommended to avoid using delete whenever possible. Since you have ES6 maps/sets, use them! :-)

To answer the question "why adding 1 to N slows the delete operation".
My guess: the slowness comes from the way the memory is allocated for your Object.
Try changing your code to this:
(() => {
const N = 50855
const o = {}
for ( let i = 0; i < N; i++ ) {
o[i] = i
}
// Show the heap memory allocated
console.log(process.memoryUsage().heapTotal);
const t1 = Date.now()
for ( let i = 0; i < N; i++ ) {
delete o[i]
}
const t2 = Date.now()
console.log( N / (t2 - t1) + ' OP/S' )
})();
Now, when you run with N = 50855 the memory allocated is: "8306688 bytes" (8.3MB)
When you run with N = 50856 the memory allocated is: "8929280 bytes" (8.9MB).
So you got a 600kb increase in the size of the memory allocated, only by adding one more key to your Object.
Now, I say I "guess" that this is where the slowness come from, but I think it makes sense for the delete function to be slower as the size of your Object increases.
If you try with N = 70855 you would still have same 8.9MB used. This is because usually memory allocators allocate memory in fixed "batches" while increasing the size of an Array/Object in order to reduce the number of memory allocations they do.
Now, same thing might happen with delete and the GC. The memory you delete has to be picked up by the GC, and if the Object size is larger the GC will be slower. Also the memory might be released if the number of keys goes under a specific number.
(You should read about memory allocation for dynamic arrays if you want to know more; there was a cool article on what increase rate you should use for memory allocation, I can't find it atm :( )
PS: delete is not "extremely slow", you just compute the op/s wrong. The time passed is in milliseconds, not in seconds so you have to multiply by 1000.

Why are these Javascript for loops significantly slower on Firefox then Chrome / Safari?

I was messing around with the benchmark site jfprefs and created my own benchmark at http://jsperf.com/prefix-or-postfix-increment/9.
The benchmarks are variations of Javascript for loops, using prefix and postfix incrementors and the Crockford jslint style of not using an in place incrementor.
for (var index = 0, len = data.length; index < len; ++index) {
data[index] = data[index] * 2;
}
for (var index = 0, len = data.length; index < len; index++) {
data[index] = data[index] * 2;
}
for (var index = 0, len = data.length; index < len; index += 1) {
data[index] = data[index] * 2;
}
After getting the numbers from a couple of runs of the benchmark, I noticed that Firefox is doing about 15 operations per second on average and Chrome is doing around 300.
I thought JaegerMonkey and v8 were fairly comparable in terms of speed? Are my benchmarks flawed somehow, is Firefox doing some kind of throttling here or is the gap really that large between the performance of the Javascript interpreters?
UPDATE: Thanks to jfriend00, I've concluded the difference in performance is not entirely due to the loop iteration, as seen in this version of the test case. As you can see Firefox is slower, but not as much of a gap as we see in the initial test case.
So why is the statement,
data[index] = data[index] * 2;
So much slower on Firefox?

Arrays are tricky in JavaScript. The way you create them, how you fill them (and with what values) can all affect their performance.
There are two basic implementations that engines use. The simplest, most obvious one is a contiguous block of memory (just like a C array, with some metadata, like the length). It's the fastest way, and ideally the implementation you want in most cases.
The problem is, arrays in JavaScript can grow very large just by assigning to an arbitrary index, leaving "holes". For example, if you have a small array:
var array = [1,2,3];
and you assign a value to a large index:
array[1000000] = 4;
you'll end up with an array like this:
[1, 2, 3, undefined, undefined, undefined, ..., undefined, 4]
To save memory, most runtimes will convert array into a "sparse" array. Basically, a hash table, just like regular JS objects. Once that happens, reading or writing to an index goes from simple pointer arithmetic to a much more complicated algorithm, possibly with dynamic memory allocation.
Of course, different runtimes use different heuristics to decide when to convert from one implementation to another, so in some cases, optimizing for Chrome, for example, can hurt performance in Firefox.
In your case, my best guess is that filling the array backwards is causing Firefox to use a sparse array, making it slower.

I hate to give you such a simple answer, but pretty simply: instruction branching: http://igoro.com/archive/fast-and-slow-if-statements-branch-prediction-in-modern-processors/
From what I get from the benchmark, there's something under the hood in these engines that is giving the instruction prediction features of the processor hell.

What is the performance of Objects/Arrays in JavaScript? (specifically for Google V8)

Performance associated with Arrays and Objects in JavaScript (especially Google V8) would be very interesting to document. I find no comprehensive article on this topic anywhere on the Internet.
I understand that some Objects use classes as their underlying data structure. If there are a lot of properties, it is sometimes treated as a hash table?
I also understand that Arrays are sometimes treated like C++ Arrays (i.e. fast random indexing, slow deletion and resizing). And, other times, they are treated more like Objects (fast indexing, fast insertion/removal, more memory). And, maybe sometimes they are stored as linked lists (i.e. slow random indexing, fast removal/insertion at the beginning/end)
What is the precise performance of Array/Object retrievals and manipulations in JavaScript? (specifically for Google V8)
More specifically, what it the performance impact of:
Adding a property to an Object
Removing a property from an Object
Indexing a property in an Object
Adding an item to an Array
Removing an item from an Array
Indexing an item in an Array
Calling Array.pop()
Calling Array.push()
Calling Array.shift()
Calling Array.unshift()
Calling Array.slice()
Any articles or links for more details would be appreciated, as well. :)
EDIT: I am really wondering how JavaScript arrays and objects work under the hood. Also, in what context does the V8 engine "know" to "switch-over" to another data structure?
For example, suppose I create an array with...
var arr = [];
arr[10000000] = 20;
arr.push(21);
What's really going on here?
Or... what about this...???
var arr = [];
//Add lots of items
for(var i = 0; i < 1000000; i++)
arr[i] = Math.random();
//Now I use it like a queue...
for(var i = 0; i < arr.length; i++)
{
var item = arr[i].shift();
//Do something with item...
}
For conventional arrays, the performance would be terrible; whereas, if a LinkedList was used... not so bad.

I created a test suite, precisely to explore these issues (and more) (archived copy).
And in that sense, you can see the performance issues in this 50+ test case tester (it will take a long time).
Also as its name suggest, it explores the usage of using the native linked list nature of the DOM structure.
(Currently down, rebuilt in progress) More details on my blog regarding this.
The summary is as followed
V8 Array is Fast, VERY FAST
Array push / pop / shift is ~approx 20x+ faster than any object equivalent.
Surprisingly Array.shift() is fast ~approx 6x slower than an array pop, but is ~approx 100x faster than an object attribute deletion.
Amusingly, Array.push( data ); is faster than Array[nextIndex] = data by almost 20 (dynamic array) to 10 (fixed array) times over.
Array.unshift(data) is slower as expected, and is ~approx 5x slower than a new property adding.
Nulling the value array[index] = null is faster than deleting it delete array[index] (undefined) in an array by ~approx 4x++ faster.
Surprisingly Nulling a value in an object is obj[attr] = null ~approx 2x slower than just deleting the attribute delete obj[attr]
Unsurprisingly, mid array Array.splice(index,0,data) is slow, very slow.
Surprisingly, Array.splice(index,1,data) has been optimized (no length change) and is 100x faster than just splice Array.splice(index,0,data)
unsurprisingly, the divLinkedList is inferior to an array on all sectors, except dll.splice(index,1) removal (Where it broke the test system).
BIGGEST SURPRISE of it all [as jjrv pointed out], V8 array writes are slightly faster than V8 reads =O
Note: These metrics applies only to large array/objects which v8 does not "entirely optimise out". There can be very isolated optimised performance cases for array/object size less then an arbitrary size (24?). More details can be seen extensively across several google IO videos.
Note 2: These wonderful performance results are not shared across browsers, especially
*cough* IE. Also the test is huge, hence I yet to fully analyze and evaluate the results : please edit it in =)
Updated Note (dec 2012): Google representatives have videos on youtubes describing the inner workings of chrome itself (like when it switches from a linkedlist array to a fixed array, etc), and how to optimize them. See GDC 2012: From Console to Chrome for more.

At a basic level that stays within the realms of JavaScript, properties on objects are much more complex entities. You can create properties with setters/getters, with differing enumerability, writability, and configurability. An item in an array isn't able to be customized in this way: it either exists or it doesn't. At the underlying engine level this allows for a lot more optimization in terms of organizing the memory that represents the structure.
In terms of identifying an array from an object (dictionary), JS engines have always made explicit lines between the two. That's why there's a multitude of articles on methods of trying to make a semi-fake Array-like object that behaves like one but allows other functionality. The reason this separation even exists is because the JS engines themselves store the two differently.
Properties can be stored on an array object but this simply demonstrates how JavaScript insists on making everything an object. The indexed values in an array are stored differently from any properties you decide to set on the array object that represents the underlying array data.
Whenever you're using a legit array object and using one of the standard methods of manipulating that array you're going to be hitting the underlying array data. In V8 specifically, these are essentially the same as a C++ array so those rules will apply. If for some reason you're working with an array that the engine isn't able to determine with confidence is an array, then you're on much shakier ground. With recent versions of V8 there's more room to work though. For example, it's possible to create a class that has Array.prototype as its prototype and still gain efficient access to the various native array manipulation methods. But this is a recent change.
Specific links to recent changes to array manipulation may come in handy here:
http://code.google.com/p/v8/source/detail?r=10024
http://code.google.com/p/v8/source/detail?r=9849
http://code.google.com/p/v8/source/detail?r=9747
As a bit of extra, here's Array Pop and Array Push directly from V8's source, both implemented in JS itself:
function ArrayPop() {
if (IS_NULL_OR_UNDEFINED(this) && !IS_UNDETECTABLE(this)) {
throw MakeTypeError("called_on_null_or_undefined",
["Array.prototype.pop"]);
}
var n = TO_UINT32(this.length);
if (n == 0) {
this.length = n;
return;
}
n--;
var value = this[n];
this.length = n;
delete this[n];
return value;
}
function ArrayPush() {
if (IS_NULL_OR_UNDEFINED(this) && !IS_UNDETECTABLE(this)) {
throw MakeTypeError("called_on_null_or_undefined",
["Array.prototype.push"]);
}
var n = TO_UINT32(this.length);
var m = %_ArgumentsLength();
for (var i = 0; i < m; i++) {
this[i+n] = %_Arguments(i);
}
this.length = n + m;
return this.length;
}

I'd like to complement existing answers with an investigation to the question of how implementations behave regarding growing arrays: If they implement them the "usual" way, one would see many quick pushes with rare, interspersed slow pushes at which point the implementation copies the internal representation of the array from one buffer to a larger one.
You can see this effect very nicely, this is from Chrome:
16: 4ms
40: 8ms 2.5
76: 20ms 1.9
130: 31ms 1.7105263157894737
211: 14ms 1.623076923076923
332: 55ms 1.5734597156398105
514: 44ms 1.5481927710843373
787: 61ms 1.5311284046692606
1196: 138ms 1.5196950444726811
1810: 139ms 1.5133779264214047
2731: 299ms 1.5088397790055248
4112: 341ms 1.5056755767118273
6184: 681ms 1.5038910505836576
9292: 1324ms 1.5025873221216042
Even though each push is profiled, the output contains only those that take time above a certain threshold. For each test I customized the threshold to exclude all the pushes that appear to be representing the fast pushes.
So the first number represents which element has been inserted (the first line is for the 17th element), the second is how long it took (for many arrays the benchmark is done for in parallel), and the last value is the division of the first number by that of the one in the former line.
All lines that have less than 2ms execution time are excluded for Chrome.
You can see that Chrome increases array size in powers of 1.5, plus some offset to account for small arrays.
For Firefox, it's a power of two:
126: 284ms
254: 65ms 2.015873015873016
510: 28ms 2.0078740157480315
1022: 58ms 2.003921568627451
2046: 89ms 2.0019569471624266
4094: 191ms 2.0009775171065494
8190: 364ms 2.0004885197850513
I had to put the threshold up quite a bit in Firefox, that's why we start at #126.
With IE, we get a mix:
256: 11ms 256
512: 26ms 2
1024: 77ms 2
1708: 113ms 1.66796875
2848: 154ms 1.6674473067915691
4748: 423ms 1.6671348314606742
7916: 944ms 1.6672283066554338
It's a power of two at first and then it moves to powers of five thirds.
So all common implementations use the "normal" way for arrays (instead of going crazy with ropes, for example).
Here's the benchmark code and here's the fiddle it's in.
var arrayCount = 10000;
var dynamicArrays = [];
for(var j=0;j<arrayCount;j++)
dynamicArrays[j] = [];
var lastLongI = 1;
for(var i=0;i<10000;i++)
{
var before = Date.now();
for(var j=0;j<arrayCount;j++)
dynamicArrays[j][i] = i;
var span = Date.now() - before;
if (span > 10)
{
console.log(i + ": " + span + "ms" + " " + (i / lastLongI));
lastLongI = i;
}
}

While running under node.js 0.10 (built on v8) I was seeing CPU usage that seemed excessive for the workload. I traced one performance problem to a function that was checking for the existence of a string in an array. So I ran some tests.
loaded 90,822 hosts
loading config took 0.087 seconds (array)
loading config took 0.152 seconds (object)
Loading 91k entries into an array (with validate & push) is faster than setting obj[key]=value.
In the next test, I looked up every hostname in the list one time (91k iterations, to average the lookup time):
searching config took 87.56 seconds (array)
searching config took 0.21 seconds (object)
The application here is Haraka (a SMTP server) and it loads the host_list once at startup (and after changes) and subsequently performs this lookup millions of times during operation. Switching to an object was a huge performance win.