We Keep Coding

Writing high-performance Javascript code without getting deoptimised

When writing performance-sensitive code in Javascript which operates on large numeric arrays (think a linear algebra package, operating on integers or floating-point numbers), one always wants the the JIT to help out as much as possible. Roughly this means:
We always want our arrays to be packed SMIs (small integers) or packed Doubles, depending on whether we're doing integer or floating-point calculations.
We always want to be passing the same type of thing to functions, so that they don't get labelled "megamorphic" and deoptimised. For instance, we always want to be calling vec.add(x, y) with both x and y being packed SMI arrays, or both packed Double arrays.
We want functions to be inlined as much as possible.
When one strays outside of these cases, a sudden and drastic performance dropoff occurs. This can happen for various innocuous reasons:
You might turn a packed SMI array into a packed Double array via a seemingly innocuous operation, like the equivalent of myArray.map(x => -x). This is actually the "best" bad case, since packed Double arrays are still very fast.
You might turn a packed array into a generic boxed array, for example by mapping the array over a function which (unexpectedly) returned null or undefined. This bad case is fairly easy to avoid.
You might deoptimise a whole function such as vec.add() by passing in too many types of things and turning it megamorphic. This could happen if you want to do "generic programming", where vec.add() is used both in cases where you're not being careful about types (so it sees a lot of types come in) and in cases where you want to eke out maximum performance (it should only ever receive boxed doubles, for instance).
My question is more of a soft question, about how one writes high-performance Javascript code in light of the considerations above, while still keeping the code nice and readable. Some specific sub-questions so that you know what kind of answer I'm aiming for:
Is there a set of guidelines somewhere on how to program while staying in the world of packed SMI arrays (for instance)?
Is possible to do generic high-performance programming in Javascript without using something like a macro system to inline things like vec.add() into callsites?
How does one modularise high-performance code into libaries in light of things like megamorphic call sites and deoptimisations? For instance, if I am happily using Linear Algebra package A at high speed, and then I import a package B that depends on A, but B calls it with other types and deoptimises it, suddenly (without my code changing) my code runs slower.
Are there any good easy to use measurement tools for checking what the Javascript engine is doing internally with types?

V8 developer here. Given the amount of interest in this question, and the lack of other answers, I can give this a shot; I'm afraid it won't be the answer you were hoping for though.
Is there a set of guidelines somewhere on how to program while staying in the world of packed SMI arrays (for instance)?
Short answer: it's right here: const guidelines = ["keep your integers small enough"].
Longer answer: giving a comprehensive set of guidelines is difficult for various reasons. In general, our opinion is that JavaScript developers should write code that makes sense to them and their use case, and JavaScript engine developers should figure out how to run that code fast on their engines. On the flip side, there are obviously some limitations to that ideal, in the sense that some coding patterns will always have higher performance costs than others, regardless of engine implementation choices and optimization efforts.
When we talk about performance advice, we try to keep that in mind, and carefully estimate what recommendations have a high likelihood of remaining valid across many engines and many years, and also are reasonably idiomatic/non-intrusive.
Getting back to the example at hand: using Smis internally is supposed to be an implementation detail that user code doesn't need to know about. It'll make some cases more efficient, and shouldn't hurt in other cases. Not all engines use Smis (for example, AFAIK Firefox/Spidermonkey historically hasn't; I've heard that for some cases they do use Smis these days; but I don't know any details and can't speak with any authority on the matter). In V8, the size of Smis is an internal detail, and has actually been changing over time and over versions. On 32-bit platforms, which used to be the majority use case, Smis have always been 31-bit signed integers; on 64-bit platforms they used to be 32-bit signed integers, which recently seemed like the most common case, until in Chrome 80 we shipped "pointer compression" for 64-bit architectures, which required lowering Smi size to the 31 bits known from 32-bit platforms. If you happened to have based an implementation on the assumption that Smis are typically 32 bits, you'd get unfortunate situations like this.
Thankfully, as you noted, double arrays are still very fast. For numerics-heavy code, it probably makes sense to assume/target double arrays. Given the prevalence of doubles in JavaScript, it is reasonable to assume that all engines have good support for doubles and double arrays.
Is possible to do generic high-performance programming in Javascript without using something like a macro system to inline things like vec.add() into callsites?
"generic" is generally at odds with "high-performance". This is unrelated to JavaScript, or to specific engine implementations.
"Generic" code means that decisions have to be made at runtime. Every time you execute a function, code has to run to determine, say, "is x an integer? If so, take that code path. Is x a string? Then jump over here. Is it an object? Does it have .valueOf? No? Then maybe .toString()? Maybe on its prototype chain? Call that, and restart from the beginning with its result". "High-performance" optimized code is essentially built on the idea to drop all these dynamic checks; that's only possible when the engine/compiler has some way to infer types ahead of time: if it can prove (or assume with high enough probability) that x is always going to be an integer, then it only needs to generate code for that case (guarded by a type check if unproven assumptions were involved).
Inlining is orthogonal to all this. A "generic" function can still get inlined. In some cases, the compiler might be able to propagate type information into the inlined function to reduce polymorphism there.
(For comparison: C++, being a statically compiled language, has templates to solve a related problem. In short, they let the programmer explicitly instruct the compiler to create specialized copies of functions (or entire classes), parameterized on given types. That's a nice solution for some cases, but not without its own set of drawbacks, for example long compile times and large binaries. JavaScript, of course, has no such thing as templates. You could use eval to build a system that's somewhat similar, but then you'd run into similar drawbacks: you'd have to do the equivalent of the C++ compiler's work at runtime, and you'd have to worry about the sheer amount of code you're generating.)
How does one modularise high-performance code into libaries in light of things like megamorphic call sites and deoptimisations? For instance, if I am happily using Linear Algebra package A at high speed, and then I import a package B that depends on A, but B calls it with other types and deoptimises it, suddenly (without my code changing) my code runs slower.
Yes, that's a general problem with JavaScript. V8 used to implement certain builtins (things like Array.sort) in JavaScript internally, and this problem (which we call "type feedback pollution") was one of the primary reasons why we have entirely moved away from that technique.
That said, for numerical code, there aren't all that many types (only Smis and doubles), and as you noted they should have similar performance in practice, so while type feedback pollution is indeed a theoretical concern, and in some cases can have significant impact, it's also fairly likely that in linear algebra scenarios you won't see a measurable difference.
Also, inside the engine there are many more situations than "one type == fast" and "more than one type == slow". If a given operation has seen both Smis and doubles, that's totally fine. Loading elements from two kinds of arrays is fine too. We use the term "megamorphic" for the situation when a load has seen so many different types that it's given up on tracking them individually and instead uses a more generic mechanism that scales better to large numbers of types -- a function containing such loads can still get optimized. A "deoptimization" is the very specific act of having to throw away optimized code for a function because a new type is seen that hasn't been seen previously, and that the optimized code therefore isn't equipped to handle. But even that is fine: just go back to unoptimized code to collect more type feedback, and optimize again later. If this happens a couple of times, then it's nothing to worry about; it only becomes a problem in pathologically bad cases.
So the summary of all that is: don't worry about it. Just write reasonable code, let the engine deal with it. And by "reasonable", I mean: what makes sense for your use case, is readable, maintainable, uses efficient algorithms, doesn't contain bugs like reading beyond the length of arrays. Ideally, that's all there is to it, and you don't need to do anything else. If it makes you feel better to do something, and/or if you're actually observing performance issues, I can offer two ideas:
Using TypeScript can help. Big fat warning: TypeScript's types are aimed at developer productivity, not execution performance (and as it turns out, those two perspectives have very different requirements from a type system). That said, there is some overlap: e.g. if you consistently annotate things as number, then the TS compiler will warn you if you accidentally put null into an array or function that's supposed to only contain/operate on numbers. Of course, discipline is still required: a single number_func(random_object as number) escape hatch can silently undermine everything, because the correctness of the type annotations is not enforced anywhere.
Using TypedArrays can also help. They have a little more overhead (memory consumption and allocation speed) per array compared to regular JavaScript arrays (so if you need many small arrays, then regular arrays are probably more efficient), and they're less flexible because they can't grow or shrink after allocation, but they do provide the guarantee that all elements have exactly one type.
Are there any good easy to use measurement tools for checking what the Javascript engine is doing internally with types?
No, and that's intentional. As explained above, we don't want you to specifically tailor your code to whatever patterns V8 can optimize particularly well today, and we don't believe that you really want to do that either. That set of things can change in either direction: if there's a pattern you'd love to use, we might optimize for that in a future version (we have previously toyed with the idea of storing unboxed 32-bit integers as array elements... but work on that hasn't started yet, so no promises); and sometimes if there's a pattern we used to optimize for in the past, we might decide to drop that if it gets in the way of other, more important/impactful optimizations. Also, things like inlining heuristics are notoriously difficult to get right, so making the right inlining decision at the right time is an area of ongoing research and corresponding changes to engine/compiler behavior; which makes this another case where it would be unfortunate for everyone (you and us) if you spent a lot of time tweaking your code until some set of current browser versions does approximately the inlining decisions you think (or know?) are best, only to come back half a year later to realize that then-current browsers have changed their heuristics.
You can, of course, always measure performance of your application as a whole -- that's what ultimately matters, not what choices specifically the engine made internally. Beware of microbenchmarks, for they are misleading: if you only extract two lines of code and benchmark those, then chances are that the scenario will be sufficiently different (e.g., different type feedback) that the engine will make very different decisions.

What is the complexity of JSON.parse() in JavaScript?

The title says it all. I'm going to be parsing a very large JSON string and was curious what the complexity of this built in method was.
I would hope that it's θ(n) where n is the number of characters in the string since it can determine whether there is a syntax error or not.
I tried searching but couldn't come up with anything.

JSON is very simple grammar that does not require even lookaheads. As soon as GC is not involved then it is purely O(n).

I do not know of the implementations in browsers, but your assumption is correct to a certain point. If the JSON includes mainly strings, it will be straight forward and very linear. If you have many floating points, it will take a bit of time to convert the numbers, but again quite linear (numbers with more digits take slightly longer, but in comparison to a long string... very similar).
Since in most cases arrays and objects are declared as maps, the memory allocation grows as required and will generally be linear. Many (if not most) implementations will make use of Java as a backend. This means garbage collection and thus a quite impossible way to know for sure how much time will be required to transform all the data as it will very much depend on things such as the size of the memory model used on the target computer and how often the garbage collection runs. However, it should generally just grow as items are added to the map and it will thus mostly look like it is linear as well. I would not expect an implementation to make use of a realloc() which would mean copying data and thus being slower and slower as an array/object grows bigger and bigger.

Was curious a little more so to add more info I believe this is the "high-level" implementation of JSON.parse. I tried finding if Chromium has their own source for it and not sure if this is it? This is going off of the source from Github.
Things to note:
worst case is probably the scenario of handling objects which requires O(N) time where N is the number of characters.
in the case a reviver function is passed, it has to rewalk the entire object after it's created but that only happens once so it's fairly negligible. Also depends what the reviver function is doing and you will have to account for it's own time complexity.

Are promises and closures consuming all of my memory?

I am currently working on a project that is using node.js as a controlling system to do some relatively large scale machine learning with images. I am running out of memory pretty quickly while trying to do this even though I am trying to optimize the usage as much as possible and my data should not take up an excessive amount of space. My code relies heavily on promises and anonymous functions to manage the pipeline and I am wondering if that's why I'm seeing crazy high usage on my test case.
Just for context I am using the mnist dataset for my testing which can be found here. The training set consists of 60,000 20x20 images. From these I am extracting overfeat features, a description of this can be found here. What this boils down to is a 4,096 element array for each image, so 60,000 of them. I am caching all the image and feature data in redis.
A quick computation tells me that the full feature set here should be 4096 * 8 * 60000 = 1966080000 bytes or appx 1.82GB of memory assuming that each element of the array is a 64bit javascript number. The images themselves should only take up a very small amount of space and I am not storing them in memory. However when I run my code I am seeing more like 8-10GB of memory used after extraction/loading. When trying to do more operations on this data (like print it all out to a JSON file so I can make sure the extraction worked right) I quickly consume the 16GB of available memory on my computer, crashing the node script.
So my general question is: why am I seeing such high memory usage? Is it because of my heavy use of promises/closures? Can I refactor my code to use less memory and allow more variables to be garbage collected?
The code is available here for review. Be aware that it is a little rough as far as organization goes.

Your code uses the "promise" library which to be fair is very memory hoggy and was not really built for raw performance. If you switch to Bluebird promises you can get considerably more items in RAM as it will drastically reduce your memory usage.
Here are benchmark results for doxbee-sequential:
results for 10000 parallel executions, 1 ms per I/O op
file time(ms) memory(MB)
promises-bluebird.js 280 26.64
promises-then-promise.js 1775 134.73
And under bench parallel (--p 25):
file time(ms) memory(MB)
promises-bluebird.js 483 63.32
promises-then-promise.js 2553 338.36
You can see the full benchmark here.

Have I reached the limits of the size of objects JavaScript in my browser can handle?

I'm embedding a large array in <script> tags in my HTML, like this (nothing surprising):
<script>
var largeArray = [/* lots of stuff in here */];
</script>
In this particular example, the array has 210,000 elements. That's well below the theoretical maximum of 231 - by 4 orders of magnitude. Here's the fun part: if I save JS source for the array to a file, that file is >44 megabytes (46,573,399 bytes, to be exact).
If you want to see for yourself, you can download it from GitHub. (All the data in there is canned, so much of it is repeated. This will not be the case in production.)
Now, I'm really not concerned about serving that much data. My server gzips its responses, so it really doesn't take all that long to get the data over the wire. However, there is a really nasty tendency for the page, once loaded, to crash the browser. I'm not testing at all in IE (this is an internal tool). My primary targets are Chrome 8 and Firefox 3.6.
In Firefox, I can see a reasonably useful error in the console:
Error: script stack space quota is exhausted
In Chrome, I simply get the sad-tab page:
Cut to the chase, already
Is this really too much data for our modern, "high-performance" browsers to handle?
Is there anything I can do* to gracefully handle this much data?
Incidentally, I was able to get this to work (read: not crash the tab) on-and-off in Chrome. I really thought that Chrome, at least, was made of tougher stuff, but apparently I was wrong...
Edit 1
#Crayon: I wasn't looking to justify why I'd like to dump this much data into the browser at once. Short version: either I solve this one (admittedly not-that-easy) problem, or I have to solve a whole slew of other problems. I'm opting for the simpler approach for now.
#various: right now, I'm not especially looking for ways to actually reduce the number of elements in the array. I know I could implement Ajax paging or what-have-you, but that introduces its own set of problems for me in other regards.
#Phrogz: each element looks something like this:
{dateTime:new Date(1296176400000),
terminalId:'terminal999',
'General___BuildVersion':'10.05a_V110119_Beta',
'SSM___ExtId':26680,
'MD_CDMA_NETLOADER_NO_BCAST___Valid':'false',
'MD_CDMA_NETLOADER_NO_BCAST___PngAttempt':0}
#Will: but I have a computer with a 4-core processor, 6 gigabytes of RAM, over half a terabyte of disk space ...and I'm not even asking for the browser to do this quickly - I'm just asking for it to work at all! ☹
Edit 2
Mission accomplished!
With the spot-on suggestions from Juan as well as Guffa, I was able to get this to work! It would appear that the problem was just in parsing the source code, not actually working with it in memory.
To summarize the comment quagmire on Juan's answer: I had to split up my big array into a series of smaller ones, and then Array#concat() them, but that wasn't enough. I also had to put them into separate var statements. Like this:
var arr0 = [...];
var arr1 = [...];
var arr2 = [...];
/* ... */
var bigArray = arr0.concat(arr1, arr2, ...);
To everyone who contributed to solving this: thank you. The first round is on me!
*other than the obvious: sending less data to the browser

Here's what I would try: you said it's a 44MB file. That surely takes more than 44MB of memory, I'm guessing this takes much over 44MB of RAM, maybe half a gig. Could you just cut down the data until the browser doesn't crash and see how much memory the browser uses?
Even apps that run only on the server would be well served to not read a 44MB file and keep it in memory. Having said all that, I believe the browser should be able to handle it, so let me run some tests.
(Using Windows 7, 4GB of memory)
First Test
I cut the array in half and there were no problems, uses 80MB, no crash
Second Test
I split the array into two separate arrays, but still contains all the data, uses 160Mb, no crash
Third Test
Since Firefox said it ran out of stack, the problem is probably that it can't parse the array at once. I created two separate arrays, arr1, arr2 then did arr3 = arr1.concat(arr2); It ran fine and uses only slightly more memory, around 165MB.
Fourth Test I am creating 7 of those arrays (22MB each) and concatting them to test browser limits. It takes about 10 seconds for the page to finish loading. Memory goes up to 1.3GB, then it goes back down to 500MB. So yeah chrome can handle it. It just can't parse it all at once because it uses some kind of recursion as can be noticed by the console's error message.
Answer Create separate arrays (less than 20MB each) and then concat them. Each array should be on its own var statement, instead of doing multiple declarations with a single var.
I would still consider fetching only the necessary part, it may make the browser sluggish. however, if it's an internal task, this should be fine.
Last point: You're not at maximum memory levels, just max parsing levels.

Yes, it's too much to ask of a browser.
That amount of data would be managable if it already was data, but it isn't data yet. Consider that the browser has to parse that huge block of source code while checking that the syntax adds up for it all. Once parsed into valid code, the code has to run to produce the actual array.
So, all of the data will exist in (at least) two or three versions at once, each with a certain amount of overhead. As the array literal is a single statement, each step will have to include all of the data.
Dividing the data into several smaller arrays would possibly make it easier on the browser.

Do you really need all the data? can't you stream just the data currently needed using AJAX? Similar to Google Maps - you can't fit all the map data into browser's memory, they display just the part you are currently seeing.
Remember that 40 megs of hard data can be inflated to much more in browser's internal representation. For example the JS interpreter may use hashtable to implement the array, which would add additional memory overhead. Also, I expect that the browsers stores both source code and the JS memory, that alone doubles the amount of data.
JS is designed to provide client-side UI interaction, not handle loads of data.
EDIT:
Btw, do you really think users will like downloading 40 megabytes worth of code? There are still many users with less than broadband internet access. And execution of the script will be suspended until all the data is downloaded.
EDIT2:
I had a look at the data. That array will definitely be represented as hashtable. Also many of the items are objects, which will require reference tracking...that is additional memory.
I guess the performance would be better if it was simple vector of primitive data.
EDIT3: The data could certainly be simplified. The bulk of it are repeating strings, which could be encoded
in some way as integers or something. Also, my Opera is having trouble just displaying the text, let alone interpreting it.
EDIT4: Forget the DateTime objects! Use unix era timestamps or strings, but not objects!
EDIT5: Your processor doesn't matter because JS is single-threaded. And your RAM doesn't matter either, most browsers are 32bit, so they can't use much of that memory.
EDIT6: Try changing the array indices to sequential integers (0, 1, 2, 3...). That might make the browser use more efficient array data structure. You can use constants to access the array items efficiently. This is going to cut down the array size by huge chunk.

Try retrieving the data with Ajax as an JSON page. I don't know the exact size but I've been able to pull large amounts of data into Google Chrome that way.

Use lazy loading. Have pointers to the data and get it when the user asks.
This technique is used in various places to manage millions of records of data.
[Edit]
I found what I was looking for. Virtual scrolling in the jqgrid. That's 500k records being lazy loaded.

I would try having it as one big string with separator between each "item" then use split, something like:
var largeString = "item1,item2,.......";
var largeArray = largeString.split(",");
Hopefully string won't exhaust the stack so fast.
Edit: in order to test it I created dummy array with 200,000 simple items (each item one number) and Chrome loaded it within an instant. 2,000,000 items? Couple of seconds but no crash. 6,000,000 items array (50 MB file) made Chrome load for about 10 seconds but still, no crash in either ways.
So this leads me to believe the problem is not with the array itself but rather it's contents.. optimize the contents to simple items then parse them "on the fly" and it should work.

String concatenation vs string buffers in Javascript

I was reading this book - Professional Javascript for Web Developers where the author mentions string concatenation is an expensive operation compared to using an array to store strings and then using the join method to create the final string. Curious, I did a couple test here to see how much time it would save and this is what I got -
http://jsbin.com/ivako
Somehow, the Firefox usually produces somewhat similar times to both ways, but in IE, string concatenation is much much faster. So, can this idea now be considered outdated (browsers probably have improved since?

Even if it were true and the join() was faster than concatenation it wouldn't matter. We are talking about tiny amounts of miliseconds here which are completely negligible.
I would always prefer well structured and easy to read code over microscopic performance boost and I think that using concatenation looks better and is easier to read.
Just my two cents.

On my system (IE 8 in Windows 7) the times of StringBuilder in that test very from about 70-100% in range -- that is, it is not stable -- although the mean is about 95% of that of the normal appending.
While it's easy now just to say "premature optimization" (and I suspect that in almost every case it is) there are things worth considering:
The problem with repeated string concatenation comes repeated memory allocations and repeated data copies (advanced string data-types can reduce/eliminate much of this, but let's keep assuming a simplistic model for now). From this lets raise some questions:
What memory allocation is used? In the naive case each str+=x requires str.length+x.length new memory to be allocated. The standard C malloc, for instance, is a rather poor memory allocator. JS implementations have undergone changes over the years including, among other things, better memory subsystems. Of course these changes don't stop there and touch really all aspects of modern JS code. Because now ancient implementations may have been incredibly slow in certain tasks does not necessarily imply that the same issues still exist, or to the same extents.
As with above the implementation of Array.join is very important. If it does NOT pre-allocate memory for the final string before building it then it only saves on data-copy costs -- how many GB/s is main memory these days? 10,000 x 50 is hardly pushing a limit. A smart Array.join operation with a POOR MEMORY ALLOCATOR would be expected to perform a good bit better simple because the amount of re-allocations is reduced. This difference would be expected to be minimized as allocation cost decreases.
The micro-benchmark code may be flawed depending on if the JS engine creates a new object per each UNIQUE string literal or not. (This would bias it towards the Array.join method but needs to be considered in general).
The benchmark is indeed a micro benchmark :)
Increase the growing size should have an impact of performance based on any or all (and then some) above conditions. It is generally easy to show extreme cases favoring some method or another -- the expected use case is generally of more importance.
Although, quite honestly, for any form of sane string building, I would just use normal string concatenation until such a time it was determined to be a bottleneck, if ever.
I would re-read the above statement from the book and see if there perhaps other implicit considerations the author was indeed meaning to invoke such as "for very large strings" or "insane amounts of string operations" or "in JScript/IE6", etc... If not, then such a statement is about as useful as "Insert sort is O(n*n)" [the realized costs depend upon the state of the data and the size of n of course].
And the disclaimer: the speed of the code depends upon the browser, operating system, the underlying hardware, moon gravitational forces and, of course, how your computer feels about you.

In principle the book is right. Joining an array should be much faster than repeatedly concatenating to the same string. As a simple algorithm on immutable strings it is demonstrably faster.
The trick is: JavaScript authors, being largely non-expert dabblers, have written a load of code out there in the wild that uses concatenating, and relatively little ‘good’ code that using methods like array-join. The upshot is that browser authors can get a better improvement in speed on the average web page by catering for and optimising the ‘bad’, more common option of concatenation.
So that's what happened. The newer browser versions have some fairly hairy optimisation stuff that detects when you're doing a load of concatenations, and hacks it about so that internally it is working more like an array-join, at more or less the same speed.

I actually have some experience in this area, since my primary product is a big, IE-only webapp that does a LOT of string concatenation in order to build up XML docs to send to the server. For example, in the worst case a page might have 5-10 iframes, each with a few hundred text boxes that each have 5-10 expando properties.
For something like our save function, we iterate through every tab (iframe) and every entity on that tab, pull out all the expando properties on each entity and stuff them all into a giant XML document.
When profiling and improving our save method, we found that using string concatention in IE7 was a lot slower than using the array of strings method. Some other points of interest were that accessing DOM object expando properties is really slow, so we put them all into javascript arrays instead. Finally, generating the javascript arrays themselves is actually best done on the server, then you write then onto the page as a literal control to be exectued when the page loads.

As we know, not all browsers are created equal. Because of this, performance in different areas is guaranteed to differ from browser to browser.
That aside, I noticed the same results as you did; however, after removing the unnecessary buffer class, and just using an array directly and a 10000 character string, the results were even tighter/consistent (in FF 3.0.12): http://jsbin.com/ehalu/
Unless you're doing a great deal of string concatenation, I would say that this type of optimization is a micro-optimization. Your time might be better spent limiting DOM reflows and queries (generally the use of document.getElementbyById/getElementByTagName), implementing caching of AJAX results (where applicable), and exploiting event bubbling (there's a link somewhere, I just can't find it now).

Okay, regarding this here is a related module:
http://www.openjsan.org/doc/s/sh/shogo4405/String/Buffer/0.0.1/lib/String/Buffer.html
This is an effective means of creating String buffers, by using
var buffer = new String.Buffer();
buffer.append("foo", "bar");
This is the fastest sort of implementation of String buffers I know of. First of all if you are implementing String Buffers, don't use push because that is a built-in method and it is slow, for one push iterates over the entire arguments array, rather then just adding one element.
It all really depends upon the implementation of the join method, some implementations of the join method are really slow and some are relatively large.