I've got no problem with events and callbacks, synchrony/asynchrony, the call stack and the queue.
However, as I understand it, other servers make a new thread for each connection which contain both the blocking request and handler for the response of that request where as in node this handler would be passed to the main thread as a callback. The ability of this kind server to handle multiple requests is therefore limited by it's ability to create and switch between multiple threads.
When Node receives a blocking request it sends it into asynchrony land while it carries on processing the main thread. What happens in asynchrony land, doesn't a thread still need to be created to await the response for that request and then to sent the event to node event loop? If so, why isn't Node as limited by the server's ability to create and switch between threads? If not, what happens to the request?
I think there's some confusion over how the event loop actually works. NodeJS doesn't "receive a blocking request" and "send it into asynchrony land". It's asynchronous to begin with - unless you call a ...Sync() pattern function, EVERY call and EVERY operation is async. Confusingly, once you are inside your CODE, EVERY operation is synchronous.
It's a "cooperative multitasking" approach - all calls to the system are expected to "start the ball rolling" and return immediately, while your own code is suppose to do what it needs to do as quickly as possible and yield control back to the JSVM (by returning from your function).
To understand how this works when you're dealing with network communications, you need to go back in time to before threads really even existed. In the early days, if you had multiple network connections, your single-threaded process would have to put together a list of all the sockets it wanted information on (such as "has data arrived for me to read?"), and ask the OS if that was true by calling select(). This would a yes/no for each socket for each question. This was typically done in a while() loop that ran until the program was terminated. You would ask for a list of sockets with new data, read that data, do something with it, and then go back to sleep, over and over again.
NodeJS is far more sophisticated but this analogy works well for it. It has a main "event loop" that is constantly sleeping until there is work to do, then waking up and doing it.
Everything that you do comes from, or goes into, this channel. If you write data to a network socket, and ask to be notified (called back) when it's done, NodeJS passes your request to the operating system and then goes to sleep. You stop running. Your context is saved - all your local vars are saved. When the OS comes back and says "done!", NodeJS checks its list and sees you wanted to know about this, and calls your function, reloading your context so all your local vars are where you need them.
To be very clear, it is entirely possible that when the data is finished being written to the network, and the OS notification comes back for that, NodeJS is busy with other work! NodeJS won't "create a thread" to handle it - it'll ignore it completely until it gets some free time! It won't be lost... it just won't be handled "yet".
This drives programmers used to threading models nuts - it seems illogical that this constant state of never immediately responding to an incoming event "until it has a chance" could possibly be efficient. But software architectures are often deceiving. Threading models actually have fairly high overhead. CPU core counts aren't infinite - the entire computer as a whole is doing plenty of work all the time. Threads aren't free - just because you make one doesn't mean the CPU itself has time to do anything with it. And the overhead of thread creation and management often means an efficiency loss.
Old-school event-loop models eliminate this overhead. When things go badly like you have an infinite loop in your code, they can behave very badly - often locking up completely. But when things are going well they can actually be a lot faster, and many benchmarks have shown that well-written NodeJS modules can perform as well as or even better than similar modules in other languages.
In summary, the most common confusion in NodeJS is what "async" really means. A good way to think of it is that in threading models, programmers are expected to be "bad"/simplistic (write blocking code and just wait for things to return) and the core VM or OS is expected to be "good"/smart (tolerate this by making threads to handle async work). In NodeJS, programmers are expected to be "good"/sophisticated (write well-structured async code), allowing the JSVM to focus on what it does best and not need as much magic to make things work well. Well-used, NodeJS puts a lot of power in your hands.
Related
This topic was on my mind for a long time.
Let's assume we have a typical web server, one in Node.js and the other in Java(or any other language with threads).
Why would node perform better (handle more IO/network based requests per second) than a java server just because it uses async/await? Isn't it just a syntatic sugar that utilizes the same threads java/c#/c++ use behind the scenes?
There is no reason to expect Node to be faster than a server written in Java. Why do you think it might be?
It seems the other answers here (so far) are explaining the benefits of asynchronous programming in JS compared to single-threaded synchronous operations -- that's obvious, but not the question.
The key point everyone agrees on is that certain operations are inherently slow (e.g.: waiting for network requests, waiting for disk/database access), and it's efficient to let the CPU do something else while such operations are in flight. Using several threads in your application is one well-established way to do that; but of course that's only possible in languages that give you threads. Many traditional server implementations (in Java, C, C++, ...) use one thread per request (or, equivalently, a thread pool to distribute incoming requests over). These threads can block waiting for, say, the database -- that's okay, the operating system will put the waiting thread to sleep and let the CPU work on another thread (handling another request) in the meantime. The end result is fairly similar to what you get with Node.
JavaScript, of course, doesn't make threads available to the programmer. But instead, it has this concept of scheduling requests with the JavaScript engine and providing a callback to be invoked upon completion of the request. That's how the overall system behaves similarly to a traditional threaded programming language: user code can say "do this stuff, then schedule a database access, and when the result is available, continue with this [callback] code here", and while waiting for the database request, the CPU gets to execute some other code. What you want to avoid is the CPU sitting around idly waiting while there is other work waiting for the CPU to have time for it, and both approaches (Java threads and JavaScript callbacks) accomplish that.
Finally, async/await (just like Promises) are indeed just syntactic sugar that make it easier to write callback-based code. Code using async/await isn't any faster than old-style code using callbacks directly, just prettier and less error-prone. It also isn't any faster than a (well-written) Java-based server.
Node.js is popular because it's convenient to use the same language for the client and server parts of an app. From a performance point of view, it's not better than traditional alternatives, it's just also not worse (or at least not much; in practice how efficiently you design your app matters more than whether you implement it in Java or JavaScript). Don't fall for the hype :-)
Asynchrony(asyn/await) is essential for activities that are potentially blocking, such as when your application accesses the web. Access to a web resource sometimes is slow or delayed. If such an activity is blocked within a synchronous process, the entire application must wait.
In an asynchronous process (thread), the application can continue with other work that doesn't depend on the web resource until the potentially blocking task finishes.
https://learn.microsoft.com/en-us/previous-versions/visualstudio/visual-studio-2012/hh191443(v=vs.110)?redirectedfrom=MSDN#threads Though you should understand the differences between threads and async/await programming.
In regards to Asynchronous programming, Usability is the key. For instance, one request can be split up into smaller tasks i.e. fetching into internal results, reading, writing, establishing connections etc... thus, half the time gets wasted waiting on dependent tasks. Asynchronous models use this time to handle other incoming requests keeping a callback function, registering in a queue saving the state and becomes available for another request. Thus, they can handle more requests.
Understand more on handling requests: https://www.youtube.com/watch?v=cCOL7MC4Pl0
I am building a small two-player gaming app. It is very important to send data from one player to another in real-time and for that sockets look promising.
A few places I read that javascript doesn't support multithreading. Then what can be possible solution for both side communication as two threads will be needed to manage C1->C2 and C2->C1 communication in parallel.
My high-level architecture looks like
How can three threads can be managed by javascript in a webpage? One for C1 to C2 message transfer, second for C2 to C1 message transfer and third for user interface?
A JavaScript program runs on a single thread of execution using a "run to completion" semantic.
Operations that would normally block in other languages are non-blocking, and simply handed off to the host (in this case, the browser), with your program notified of progress asynchronously via events.
When the host raises an event to be consumed by your program (eg. an inbound message), it puts a notification of that event in a queue as a "job". When that job reaches the front of the queue, and as soon as the call stack is empty (ie. the current script being run has run to completion), the JavaScript runtime dequeues the job and invokes the continuation function associated with it (ie. the part of your program configured to handle the event).
Your game will be sending messages over the network (eg via WebSocket). Your program will simply hand each message to the browser. This process is not computationally expensive or time consuming. The browser is multithreaded and will handle the low-level and time consuming networking concerns for you.
JavaScript is an event-based language. If you wish to be notified of future events related to a message you sent, then you can supply a callback (or use a promise) to be invoked by the runtime in the future at the appropriate time, rather than simply waiting for it. In this way the time available on the main thread of execution is used efficiently.
Your game loop will probably use requestAnimationFrame. That gives you about 16 milliseconds of computation per frame. Computation of game state might take a few milliseconds. Handling scheduled and time-based events might take another few milliseconds. Finally rendering needs some time too. In effect your program cooperatively multi-tasks on a single thread of execution.
For long-running, computationally expensive tasks you can use the Worker API to create new threads of execution with which you can communicate in a controlled way, but you will probably not need this here.
There is plenty of information online already about this topic. Search for "how the event loop works".
Relevant questions here, here, here, here, and here.
So I was looking at this module, and I cannot understand why it uses callbacks.
I thought that memory caching is supposed to be fast and that is also the purpose someone would use caching, because it's fast... like instant.
Adding callbacks implies that you need to wait for something.
But how much you need to wait actually? If the result gets back to you very fast, aren't you slowing things down by wrapping everything in callbacks + promises on top (because as a user of this module you are forced to promisify those callbacks) ?
By design, javascript is asynchronous for most of its external calls (http, 3rd parties libraries, ...).
As mention here
Javascript is a single threaded language. This means it has one call stack and one memory heap. As expected, it executes code in order and must finish executing a piece code before moving onto the next. It's synchronous, but at times that can be harmful. For example, if a function takes a while to execute or has to wait on something, it freezes everything up in the meanwhile.
Having synchronous function will block the thread and the execution of the script. To avoid any blocking (due to networking, file access, etc...), it is recommended to get these information asynchronously.
Most of the time, the redis caching will take a few ms. However, this is preventing a possible network lag and will keep your application up and running with a tiny amount of connectivity errors for your customers.
TLDR: reading from memory is fast, reading from network is slower and shouldn't block the process
You're right. Reading from memory cache is very fast. It's as fast as accessing any variable (micro or nano seconds), so there is no good reason to implement this as a callback/promise as it will be significantly slower. But this is only true if you're reading from the nodejs process memory.
The problem with using redis from node, is that the memory cache is stored on another machine (redis server) or at least another process. So the even if redis reads the data very quickly, it still has to go through the network to return to your node server, which isn't always guaranteed to be fast (usually few milliseconds at least). For example, if you're using a redis server which is not physically close to your nodejs server, or you have too many network requests, ... the request can take longer to reach redis and return back to your server. Now imagine if this was blocking by default, it would prevent your server from doing anything else until the request is complete. Which will result in a very poor performance as your server is sitting idle waiting for the network trip. That's the reason why any I/O (disk, network, ..) operation in nodejs should be async by default.
Alex, you remarked with "I thought that memory caching is supposed to be fast and that is also the purpose someone would use caching, because it's fast... like instant." And you're near being wholly right.
Now, what does Redis actually mean?
It means REmote DIctionary Server.
~ FAQ - Redis
Yes, a dictionary usually performs in O(1) time. However, do note that the perception of the said performance is effective from the facade of procedures running inside the process holding the dictionary. Therefore, access to the memory owned by the Redis process from another process, is a channel of operations that is not O(1).
So, because Redis is a REmote DIctionary Server asynchronous APIs are required to access its service.
As it has already been answered here, your redis instance could be on your machine (and accessing redis RAM storage is nearly as fast as accessing a regular javascript variable) but it could also be an another machine/cloud/cluster/you name it. And in that case, network latency could be problematic, that's why the promises/callbacks syntax.
If you are 100% confident that your Redis instance would always lay on the same machine your code is, that having some blocking asynchronous calls to it is fine, you could just use the ES6 await syntax to write it as blocking synchronous events and avoid the callbacks or the promises :)
But I'm not sure it is worth it, in term of coding habit and scalability. But every project is different and that could suits you.
IN NODEJS:
IF we can only run one function at the same time if node is not using multiple threads. How can this work when a lot of requests arrive at the web server at the same time?
Can to clear the panorama about thread and process??
Theoretically if a large number of requests happened in the same second - or if each request has to do something that takes a while, like hard math - your server could get bogged down. Either by not responding to people at the "end" of the line (late by milliseconds), or never finishing all of the things Node needs to do to serve those requests at the "front" of the line.
In general the strategy Node takes is that if you're going to perform a long operation - like querying the database - the execution of the program should not sit around waiting, but should "call back" to some other function when the database query is eventually done.
I talk more about this in another SO answer. You could Google "node.js is cancer" for other examples of just what you are talking about.
But the prevalence of this strategy is one of the major differences between Node and other languages/frameworks: that's just how Javascript deals.
Now, in practice, several things actually happen.
First, any production Node app really should be running with Cluster or some kind of solution that provides load balancing. Because you'd be having multiple processes of your app working, your solution can do more than one thing at once.
Secondly, in general Node.js stays up really well, because the idea of not waiting around for everything. It keeps your server busy, instead of cooling it's jets waiting for something to be done.
Thirdly, yes you do have to be careful about what you do in the server. If something's going to take too long (modiying all the records in the database), probably wise to do it in the background via some kind of worker queue system: "Hey, I need to update this person's username in all of (these) records in the database" probably should happen by yet another Node.js process being the worker.
Node.js server is works on event based models where callback functions are supported. But I am not able to understand how is it better than traditional thread based servers where threads wait for system IO. In case of thread based model, when a thread needs to wait for IO, it gets preempted so doesn't consume CPU cycles hence doesn't contribute to wait time.
How Node.js improves wait time?
when a thread needs to wait for IO, it gets preempted
Actually, it's not preempted. Preemption is something completely different. What happens is that the thread is blocked.
For an event based model something similar happens. Event based interpreters are basically state machines. Only, the state machine is abstracted away and is not visible to the user. When something is waiting for an event it passes the control back to the interpreter. When the interpreter has nothing else to process it blocks itself waiting for I/O. Only, unlike traditional threading code the interpreter waits for multiple I/O.
What's happening at the C level is that the interpreter is using something like select(), poll(), epoll() and friends (depends on the OS and library installed) to do the blocking and waiting for I/O.
Now, why does a select()/poll() based mechanism generally perform better? Actually, 'generally' here depends on what you mean. A select() based server executes all code in a single process/thread. The biggest performance gain from this is that it avoids context switching - every time the OS transfers control over from one thread to another it has to save all the relevant registers, memory map, stack pointers, FPU context etc. so that the other thread can resume execution where it left off. The overhead of doing this can be quite significant.
In fact, there is a historical example of how extreme the overhead can be. Back in the early 2000s someone started benchmarking web servers. To the surprise of everyone, tclhttpd outperformed Apache for serving static files. Now, tcl is not only an interpreted language, but back in 2000 it was a very slow interpreted language because it didn't have a seperate compilation phase (it sort of does now). Tcl scripts are interpreted directly in string form making it around 400x slower than C. Apache is obviously written in C so what's making tclhttpd faster?
It turned out that tclhttpd is event based running only on a single thread while Apache was multithreaded. The overhead of constant thread switching turned out to give tclhttpd enough advantage to perform better than Apache.
Of course, there is always a compromise. A single threaded server like tclhttpd or node.js cannot take advantage of multiple CPUs. Back in the early 2000s multiple CPUs were uncommon. These days they are almost default. Not to mention that most CPUs are also hyperthreaded (hyperthreading adds hardware to the CPU to make context switching cheap).
The best servers these days have learned from history and are a combination of both. Apache2, and Nginx use therad pools: they are multithreaded but each thread serves more than a single connection. This is a hybrid of the two approaches but is more complex to manage.
Read the following article for a more in-depth discussion on this topic: The C10K problem
Threads are relatively heavy-weight objects that have a resource footprint extending all the way into the kernel. When you park a thread in a blocking syscall or on a mutex or condition variable, you are tying up all those resources but doing nothing. Now the OS has to find more resources so your program can create another thread... Then you idle them too. It doesn't take long before the OS is struggling to scavenge more resources for your program to waste.
CPU time is just one small part of he bigger picture. :-)
Simply put:
In a threaded server, no matter how many threads you have, you can always have that many threads waiting for IO.
In node, no matter how many IO operations are pending, you always have your event loop ready to do the next thing.
When having a lot of threads you are going to have a lot of context switching which is going to be expensive. You want have this overhead when using node.js's Event loop
Context Switch
A context switch is the
computing process of storing and
restoring state (context) of a CPU so
that execution can be resumed from the
same point at a later time.
Event loop
In computer science, the event loop,
message dispatcher, message loop or
message pump is a programming
construct that waits for and
dispatches events or messages in a
program.
I think you are full of myths regarding to threads and cost of context switching.
Discover yourself the truth.