Twitter Not Rocket Science, but Still a Work in Progress

While it may not be rocket science, the Twitter team has been making a concerted effort to effect better communication with their community at large. Recently they were set-upon by a barrage of technical and related questions and the resulting answers are actually somewhat interesting. "Before we share our answers, it's important to note one very big piece of information: We are currently taking a new approach to the way Twitter functions technically with the help of a recently enhanced staff of amazing systems engineers formerly of Google, IBM, and other high-profile technology companies added to our core team. Our answers below refer to how Twitter has worked historically--we know it is not correct and we're changing that."

Comments

Obligatory xkcd

[Krishnoid]

Grammar can be fun!

Re:Big Brother(s)

[Anonymous Coward]

Hiring folks who used to work at IBM or Google is not the same thing as "large companies control[ling] how Twitter works." Some day, you'll have a job and you'll understand that. [Sorry to be an asshole about this, but your comment just shouts "teenage kid who's never had a serious job."] People with experience with large-scale applications may already know solutions to some of the problems Twitter is seeing. Those solutions aren't always in the text books; and if they were trivial and obvious, then such applications would be much more common.

Re:Big Brother(s)

[Otter]

To argue against myself on your behalf though, having someone from a larger corporation, can be benificial aswell, because they may have been fired, or quit because the larger corporation wasn't listening to them, or was doing something in a way that went against their ideology, thereby possibly preventing said smaller company from becomming similar to the larger one.

Like the AC said, I think you're wildly exaggerating how ideological workplaces are, particularly from the point of view of a server monkey.

Re:It's the algorithm, stupid

[QuoteMstr]

Thank you for your polite reply, but I feel like I haven't adequately communicated my point.

The language you choose may affect your ability to scale when you take its concurrency model (or lack thereof in some cases) in to account. For instance, I can have a O(1) algorithm, using a hashmap, but that doesn't mean that I'll be able to have the runtime performance of constant time.

If your whole web application is bottlenecked by one hashmap, you're going to run into scalability problems as soon as you need more than one machine anyway. On the other hand, If the performance of the web application as a whole does not depend on the hashmap, then your argument is irrelevant to the scalability of the application as a whole.

I concede that a more efficient runtime environment might make better use of the same hardware, supporting, say, 70 clients instead of 50 per machine. But that's not the kind of scalability I'm talking about. Even a platform that achieved only one client per machine that scaled linearly would be better than one that handled 70 clients per machine, except that you were limited to one machine.

And yes, on one machine, a bad choice of data structure can affect scalability. But the blame for that rests on the data structure itself, not the language in which it is implemented. As an associative array, a Python hash table (dict) will scale far better than a C linked list. Why? Because one's a hash table and one is a linked list!

Which data structures are available in which language might factor into the choice of language, but it's only a convenience: you can always create your own data structure implementations.

Granted, there are ways around this, but you can't just throw hardware at the problem and pretend it doesn't exist.

Creating a scalable application means being able to throw hardware at the problem.

Let's assume you've gotten your application to scale beyond one machine anyway. That's a prerequisite for this section.

Now, if the machines don't communicate and users don't care, you automatically win O(N) scalability.

If your machines must communicate, they do so over some kind of network. The way this communication is achieved determines the scalability of the application. While some environments might have more intuitive network facilities than others (think Erlang), ultimately one can use any approach to networking with any language.

Again, we're reduced to choice of data structures and algorithms, not language, as the marker of scalability.

The choice of language does not dictate the data structure the designer of the application uses, and so the language is not a serious barrier to scalability. I concede it may be more difficult to implement efficient protocols in some languages than in others, but we're dealing with turing-complete languages here, aren't we?

I should note that languages typically thought of as "slower" are often more expressive. It often takes less effort to write efficient algorithms in expressive languages.

(Returning to our previous example, since writing a hash table is more complex than writing a naive linked list in C, a C programmer is more likely to use a linked list at the expense of scalability. In Python, using a hash table is as simple as writing {}, so an equally-skilled programmer is more likely to use the more efficient data structure, resulting in better performance in a "slower" language.)

The bottom line is that if communication between nodes is required, complexity must be > O(N). And if complexity is greater than O(N), then as N increase without bound, the communication overhead approaches infinity anyway. The key is to make that growth as slow as possible.

The tools and techniques used to slow that growth --- thinking about the problem, designing efficient algorithms --- are features of the human mind, and not any particular language.

Saying that one language is better at scaling than another is like arguing that one human language is better for building cars than another!