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May. 19th, 2014
Ничего нового, но хорошо сформулировано
May. 19th, 2014 02:51 pmTechnically, all languages are synchronous, even Javascript. However, Javascript works a lot better in an asynchronous design because it was designed to be single threaded.
Basically there are two types of programs:
CPU bound- the only way to make it go faster is to get more CPU time
IO bound- spends a lot of time waiting for data, so a faster processor won't matter
Video games, number crunchers and compilers are CPU bound, whereas web servers and GUIs are generally IO bound. Javascript is relatively slow (because of how complex it is), so it wouldn't be able to compete in a CPU bound scenario (trust me, I've written my fair share of CPU-bound Javascript).
Instead of coding in terms of classes and objects, Javascript lends itself to coding in terms of simple functions that can be strung together. This works very well in asynchronous design, because algorithms can be written to process data incrementally as it comes in. IO (especially network IO) is very slow, so there's quite a bit of time between packets of data.
Example
Let's suppose you have 1000 live connections, each delivering a packet every millisecond, and processing each packet takes 1 microsecond (very reasonable). Let's also assume each connection sends 5 packets.
In a single-threaded, synchronous application, each connection will be handled in series. The total time taken is (5*1 + 5*.001) * 1000 milliseconds, or ~5005 milliseconds.
In a single-threaded, asynchronous application, each connection will be handled in parallel. Since every packet takes 1 millisecond, and processing each packet takes .001 milliseconds, we can process every connection's packet between packets, so our formula becomes: 1000*.001 + 5*1 milliseconds, or ~6 milliseconds.
The traditional solution to this problem was to create more threads. This solved the IO problem, but then when the number of connections rose, so did the memory usage (threads cost lots of memory) and CPU usage (multiplexing 100 threads onto 1 core is harder than 1 thread on 1 core).
However, there are downsides. If your web application happens to also need to do some heavy number crunching, you're SOL because while you're crunching numbers, connections need to wait. Threading solves this because the OS can swap out your CPU-intensive task when data is ready for a thread waiting on IO. Also, node.js is bound to a single core, so you can't take advantage of your multi-core processor unless you spin up multiple instances and proxy requests.
http://stackoverflow.com/questions/17607280/why-is-node-js-asynchronous
Basically there are two types of programs:
CPU bound- the only way to make it go faster is to get more CPU time
IO bound- spends a lot of time waiting for data, so a faster processor won't matter
Video games, number crunchers and compilers are CPU bound, whereas web servers and GUIs are generally IO bound. Javascript is relatively slow (because of how complex it is), so it wouldn't be able to compete in a CPU bound scenario (trust me, I've written my fair share of CPU-bound Javascript).
Instead of coding in terms of classes and objects, Javascript lends itself to coding in terms of simple functions that can be strung together. This works very well in asynchronous design, because algorithms can be written to process data incrementally as it comes in. IO (especially network IO) is very slow, so there's quite a bit of time between packets of data.
Example
Let's suppose you have 1000 live connections, each delivering a packet every millisecond, and processing each packet takes 1 microsecond (very reasonable). Let's also assume each connection sends 5 packets.
In a single-threaded, synchronous application, each connection will be handled in series. The total time taken is (5*1 + 5*.001) * 1000 milliseconds, or ~5005 milliseconds.
In a single-threaded, asynchronous application, each connection will be handled in parallel. Since every packet takes 1 millisecond, and processing each packet takes .001 milliseconds, we can process every connection's packet between packets, so our formula becomes: 1000*.001 + 5*1 milliseconds, or ~6 milliseconds.
The traditional solution to this problem was to create more threads. This solved the IO problem, but then when the number of connections rose, so did the memory usage (threads cost lots of memory) and CPU usage (multiplexing 100 threads onto 1 core is harder than 1 thread on 1 core).
However, there are downsides. If your web application happens to also need to do some heavy number crunching, you're SOL because while you're crunching numbers, connections need to wait. Threading solves this because the OS can swap out your CPU-intensive task when data is ready for a thread waiting on IO. Also, node.js is bound to a single core, so you can't take advantage of your multi-core processor unless you spin up multiple instances and proxy requests.
http://stackoverflow.com/questions/17607280/why-is-node-js-asynchronous