I was learning Node.js and also found out that Node.js is best to be used with I/O intensive tasks which confused me a bit. So, after some research I found this statement: "An application that reads and/or writes a large amount of data". So, does it mean that Node.js is best to be used with data, that is, read big data, take necessary data from that and send back to client?
A nodejs application can be architectured just fine to include non-I/O things and is not just suited for big data applications (in fact big data has nothing to do with it at all).
A default, simple implementation of Node.js performs best when your application is not CPU intensive and instead spends most of its time doing I/O (input/output) tasks such as reading/writing to a database, read/writing from files, reading/sending network data and so on. It's not about big data, it's about what does the server spend most of its time doing.
Surprisingly enough (to some) since a web server's primary job is responding to http requests which are usually requests for data, most web servers spend most of their time fetching things, reading and writing things and sending things which are all I/O tasks. In the node.js design, all these I/O tasks happen asynchronously in a non-blocking fashion and they use events to signal when those operations complete. This is where the phrase "event-driven design" comes from when describing node.js. It so happens that this makes node.js very efficient at handling things that involve primarily I/O. This is what a simple implementation of node.js does best. And, it generally does it better than a purely threaded server design that devotes an OS thread to every currently in-flight I/O operation (the original design for many server frameworks).
If you do have CPU intensive things (major calculations, image processing, heavy crypto operations, etc...) and you do them very often or they take very long, then you will be best served if you put those tasks in a Worker Thread or in another process and communicate back and forth between the main process in node.js and this worker to get that CPU-intensive work done. It used to be that node.js didn't have Worker Threads which made this task a little more complicated where you often had to use one or more additional processes (either via clustering or additional dedicated processes) in order to handle this CPU-intensive work, but now you can use Worker Threads which can be a bit more convenient.
For example, I have a server task that requires a very heavy amount of crypto (performing a billion crypto operations). If I put that in the main node.js thread, that essentially blocks the event loop so my server can't process other requests while that heavy duty crypto operation is running which would ruin the responsiveness of my server.
But, I was able to move the crypto work to a worker thread (actually to several worker threads) and then can crunch away on the crypto while my main thread stays nice and lively to handle other, unrelated incoming requests in a timely fashion.
First of all, Big Data has nothing to do with Node.js.
I/O intensive means that the given task often waits for I/O. The best examples for these are file operations, networking.
If the processor has to regularly wait for data to arrive, the task is said to be I/O intensive.
Node.js's asynchronous nature however makes it really good at I/O intensive tasks, as it can keep doing other work while it waits for the data to arrive asynchronously.
For example, if you have 10 clients connected to the server and one of the clients requests for a data or task that is heavy to process, my server should not get stuck or wait until this task is finished as it will cause greater response time to other 9 clients or bad user experience. Rather, server should allow the other 9 clients to request data or task from the server, and when the respective tasks get finished, response should be sent back to clients.
PS: You can study about Event loop in Node.js
What Node.js is great at is serving as the middle layer between clients and data sources, i.e. the inputs and outputs.
The reason Node.js is great at this is in the non-blocking event-driven approach it takes.
For example, when you make a request to a Node.js app that asks for some data from a database, Node.js will request that data and immediately return to other requests without being blocked by the database request.
Once the database sends the data back, Node.js triggers the callback (or resolves the promise) with that data and continues onwards.
There's no race condition between these input and output events because their synchronization is done in a single threaded mechanism called the Event Loop. Only one event gets processed at a time.
We can think of the Event Loop as a single-seat rollercoaster ride in an amusement park that has many lines of people waiting to go on the ride, one by one. When you get to go depends on when you got in a line, how important you are or if a friend saved you a spot but nevertheless only one person at a time will be able to partake.
This non-blocking event-driven approach allows Node.js to very efficiently react to input and output events and process many read/write operations because it's not really doing much processing, the CPU work is quite low. It's just serving as the middle layer between you and the data.
On the other hand, if these events lead to some intense CPU operations, Node.js used to perform quite poorly because the Event Loop can process only one event at a time.
To use the rollercoaster analogy from above, a CPU-intensive task would be as if one person is taking a really long ride while all others have to wait for them to be done.
Newer versions of Node.js did get some tools to allow it do to more than 1 thing at time (parallelism) by using workers. The trick here is that every pool of workers has its own Event Loop which allows applications to move the intense work into a different thread and run it in parallel with the rest of the application. Do note that this will only actually help if you run on a machine with more than 1 core. If your machine has 1 core, no matter what tool you use, you're gonna have a bad time because nothing can actually be done in parallel on a single core machine.
In case of Intensive I/O tasks Majority of the time is spent waiting for network, filesystem and perhaps database I/O to complete. Increasing hard disk speed or network connection improves the overall performance.
In its most basic form Node.js is best suited for this type of computing. All I/O in Node.js is non-blocking and it allows other requests to be served while waiting for a particular read or write to complete.
Related
I have an express backend application, which listens for http requests from a web application. This backend application is running on AWS ECS Fargate.
So my question is, does it makes sense use multithreading, worker-threads in Node.js, in this backend application? There are both CPU intensive and non-intensive functions in the endpoints. For instance should I just distribute any incoming request right away ,regardless of the intensity of the call, to other threads so that Main thread is never blocked? Or should I only use multithreading on intensive jobs etc.
Any suggestion, pros and cons on this topic is very much appreciated.
Questions like this ultimately only really get answered by profiling the system under representative load. This is because our guestimates about what takes CPU and how much are just rarely very good - this stuff has to be measured to really know.
That said, there are some general design guidelines you could consider:
If you have specific requests that you believe in advance are likely to be CPU heavy, then you may want to consider putting those into a queue that is served by a pool of threads.
If you have specific requests that you believe in advance are really just doing non-blocking I/O, then those don't need to get complicated at all by threading. The main thread can likely handle those just fine.
If only a relatively small fraction of your requests are CPU heavy, the simplest design may be to use the nodejs clustering module and just let it spread your requests out among CPUs figuring that this by itself (without any other major design changes) will get any CPU-bound requests off the main loop. If a high percentage of your requests are CPU-bound and you want to prioritize the non-CPU requests so they are always quick, then you'd probably be better with the thread pool that CPU-heavy requests are passed off to so the non-CPU heavy requests aren't held up by them.
So my question is, does it makes sense use multithreading, worker-threads in Node.js, in this backend application?
Yes, in some circumstances. But, when to add this extra layer of complication really depends upon the specific metrics of CPU usage in your requests.
For instance should I just distribute any incoming request right away ,regardless of the intensity of the call, to other threads so that Main thread is never blocked? Or should I only use multithreading on intensive jobs etc.
This depends upon how you want to prioritize different requests. If you want requests prioritized in a FIFO order, where each one gets to start in order based on when it arrived, regardless of what type of request, then yes you can just distribute all requests to threads. In fact, it's probably easier to just use the clustering module for this because that's what it does. If, on the other hand, you want non-CPU-heavy requests to always run quick and not have to wait behind CPU-heavy requests, then you may want to push only the CPU-heavy requests into a queue that is processed by a thread pool. This, then allows the main thread to work on the non-CPU-heavy requests immediately regardless of how many CPU-heavy requests are currently crunching away in the thread pool.
I was trying to understand how nodejs can achieve higher concurrency compared to thread-based approaches such as Servlet servers.
I already know that in nodejs "everything runs in parallel except your code", and also there is a backend thread pool in libuv to handle File IO or database calls which are usually the bottlenecks.
So here is my question: if nodejs uses thread pool to handle database calls, how it can service higher concurrent request than Servlet servers such as Tomcat given that Tomcat can also use NIO backed by epoll/kqueue to achieve high concurrency ?
For example, if there's a 100k concurrent request coming in and each requires database operations, if these 100k request are to be serviced concurrently, with nodejs we still end up creating 100k threads which might cause memory exhaustion as Tomcat does. Yes, the 100k threads is just an imagination because (I know) that nodejs has a fixed thread pool and different operations are queued in the event loop, but with Tomcat it handles things in the same way--we also can configure the thread pool size in Tomcat and it also queues request.
Or, am I wrong to say that "nodejs uses backend thread pool in libuv to handle File IO or database calls"? Does nodejs use epoll/kqueue to handle database io without a separate thread?
I was reading this similar question but still didn't get the answer.
if nodejs uses thread pool to handle database calls
That's a wrong assumption. nodejs will typically use networking to talk to a local database running in a different process or on a different host. Networking in node.js does not use threads of any kind - it uses event driven I/O. What the database does for threads is up to the database and independent of node.js since it would be the same no matter which server environment you were using.
node.js does use a thread pool for local disk access, but high scale applications are usually using a database for the crux of their disk access which run in a separate process and have their own I/O optimizations to handle lots of requests. How a given database does it is up to that implementation, but it will not be using a nodejs thread per request.
I was trying to understand how nodejs can achieve higher concurrency compared to thread-based approaches such as Servlet servers.
The general concept is that a properly written server app in node.js uses async I/O for all I/O (except perhaps startup code that only runs during server startup). This means that it can have a lot of requests in-flight at the same time with only a single Javascript thread while most of them are waiting on some type of I/O. If you're going to have a lot of requests in-flight at the same time, it can be a lot more efficient for the system to do it the node.js way of a single thread where all the requests are cooperatively switched vs. using OS threads where every thread has OS overhead associated with it and every pre-emptive thread switch has OS and CPU overhead associated with it.
In node-js, there is no pre-emptive switching between the active requests. Only one runs at a time and it runs until it either finishes or hits an asychronous operation and has nothing else to do until that async I/O operation completes. At that point, the JS engine goes back to the event queue and picks out an event (probably for one of the other requests). This type of cooperate switching can be significantly faster and more efficient than OS-level threads. There is sometimes a programming cost in that a node.js developer has to code with async I/O in order to take advantage of this which has a learning curve in order to get proficient at writing good, clean code with proper error handling and has a learning curve for debugging it too.
For example, if there's a 100k concurrent request coming in and each requires database operations, if these 100k request are to be serviced concurrently, with nodejs we still end up creating 100k threads which might cause memory exhaustion as Tomcat does.
No, you will not be creating 100k threads. A node.js database interface layer that interfaces between node.js and the actual database code in another process or on another host may be written entirely in node.js (using TCP networking to talk to the database) and introduce no new threads at all or it may have some native code and use a small number of threads for its own native code operations, but it will likely be a small number of threads and nothing even close to one per request.
Or, am I wrong to say that "nodejs uses backend thread pool in libuv to handle File IO or database calls"? Does nodejs use epoll/kqueue to handle database io without a separate thread?
For file I/O, yes it uses a thread pool in libuv. For database calls, no - While the details depend entirely upon the database implementation, usually there is not a thread per database call. The database is typically in another process and the nodejs interface library for the DB either directly uses nodejs TCP to talk to the database (which uses no threads) or it has its own native code add-on that talks to the database which probably uses a small number of threads for its work, but typically not a thread per request.
I have a simple nodejs webserver running, it:
Accepts requests
Spawns separate thread to perform background processing
Background thread returns results
App responds to client
Using Apache benchmark "ab -r -n 100 -c 10", performing 100 requests with 10 at a time.
Average response time of 5.6 seconds.
My logic for using nodejs is that is typically quite resource efficient, especially when the bulk of the work is being done by another process. Seems like the most lightweight webserver option for this scenario.
The Problem
With 10 concurrent requests my CPU was maxed out, which is no surprise since there is CPU intensive work going on the background.
Scaling horizontally is an easy thing to, although I want to make the most out of each server for obvious reasons.
So how with nodejs, either raw or some framework, how can one keep that under control as to not go overkill on the CPU.
Potential Approach?
Could accepting the request storing it in a db or some persistent storage and having a separate process that uses an async library to process x at a time?
In your potential approach, you're basically describing a queue. You can store incoming messages (jobs) there and have each process get one job at the time, only getting the next one when processing the previous job has finished. You could spawn a number of processes working in parallel, like an amount equal to the number of cores in your system. Spawning more won't help performance, because multiple processes sharing a core will just run slower. Keeping one core free might be preferred to keep the system responsive for administrative tasks.
Many different queues exist. A node-based one using redis for persistence that seems to be well supported is Kue (I have no personal experience using it). I found a tutorial for building an implementation with Kue here. Depending on the software your environment is running in though, another choice might make more sense.
Good luck and have fun!
The following two diagrams are my understanding on how threads work in a event-driven web server (like Node.js + JavaScript) compared to a non-event driven web server (like IIS + C#)
From the diagram is easy to tell that on a traditional web server the number of threads used to perform 3 long running operations is larger than on a event-driven web server (3 vs 1.)
I think I got the "traditional web server" counts correct (3) but I wonder about the event-driven one (1). Here are my questions:
Is it correct to assume that only one thread was used in the event-driven scenario? That can't be correct, something must have been created to handle the I/O tasks. Right?
How did the evented server handled the I/O? Let's say that the I/O was to read from a database. I suspect that the web server had to create a thread to hand off the job of connecting to the database? Right?
If the event-driven web server indeed created threads to handle the I/O where is the gain?
A possible explanation for my confusion could be that on both scenarios, traditional and event-driven, three separate threads were indeed created to handle the I/O (not shown in the pictures) but the difference is really on the number of threads on the web server per-se, not on the I/O threads. Is that accurate?
Node may use threads for IO. The JS code runs in a single thread, but all the IO requests are running in parallel threads. If you want some JS code to run in parallel threads, use thread-a-gogo or some other packages out there which mitigate that behaviour.
Same as 1., threads are created by Node for IO operations.
You don't have to handle threading, unless you want to. Easier to develop. At least that's my point of view.
A node application can be coded to run like another web server. Typically, JS code runs in a single thread, but there are ways to make it behave differently.
Personally, I recommend threads-a-gogo (the package name isn't that revealing, but it is easy to use) if you want to experiment with threads. It's faster.
Node also supports multiple processes, you may run a completely separate process if you also want to try that out.
The best way to picture NodeJS is like a furious squirrel (i.e. your thread) running in a wheel with an infinite number of pigeons (your I/O) available to pass messages around.
I/O in node is "free". Your squirrel works to set up the connection and send the pigeon off, then can go on to do other things while the pigeon retrieves the data, only dealing with the data when the pigeon returns.
If you write bad code, you can end up having the squirrel waiting for each pigeon.
So always write non-blocking i/o code.
If you can encourage your Pigeons to promise to come back ;)
Promises and generators are probably the best approach you can take to this.
HOWEVER you can always use Node cluster to establish a master squirrel that will procreate child squirrels based on the number of CPUs the master squirrel can find to dole out the work.
Hope this helps and note the complete lack of a car analogy.
I am trying to learn Node.js and some of points that I understand:
Node.js does'nt create a seperate process for each request, instead it is just one process which processes all requests.
It is asynchronous which means you can attach a callback to a long-lasting process and continue your rest of the work without waiting for it to finish.
What I really don't understand is author's point in Understanding node.js - "Everything runs in parallel except your code". I have understood the analogy and the code that explains it but still I don't get it what is the distinction between "Everything" and "code". I have more often heard this about node.js.
Also, people pat node.js for its efficiency since memory overhead for one concurrent connection may be as low as 8KB but what about CPU load. Does node.js make it way less as compared to PHP+Apache?
Node.js uses a single thread any time it is running the JavaScript in your application. Tasks that are asynchronous (network, filesystem, etc.) are all handled on separate threads automatically for you. This means that you get much of the usefulness of a multithreaded application without having to worry about all of the trouble that comes with locking resources and what not.
Node is not a tool for every job. It is ideal for applications that are IO bound. For example, if your application required a ton of work to process templates and what not, Node probably isn't for you. If instead you're just shuffling data around, Node can be very effective.
The reason Node is often quoted as being faster than servers like Apache is that it doesn't create a thread and all of the resources with it to handling requests. In Apache, most of the time, that thread handling requests is waiting on network or filesystem data. While it does this, it is wasting resources. With Node, only one thread processes those requests (in your application). Again, this is great for some things, but if you have a lot of processing to do, Node would not be effective as it can really only handle a single request at a time in these situations.
This video does a pretty good job of explaining: http://www.youtube.com/watch?v=F6k8lTrAE2g&feature=youtube_gdata
Everything runs in parallel except your code.
It means if you do
while(true){}
anywhere in your code the entire node application will stop. While the code you write executes, nothing else does. Requests will not be handled, responses won't be returned, nothing. You have to be extremely careful to not hog the cpu in node.
but what about CPU load?
That completely depends on the nature of your application and the load. If your app is busy, it'll use more cpu.
Imagine a busy intersection with a traffic cop in the middle. When the cop is doing his job properly, hundreds of cars can pass through the intersection in a very fast and efficient way.
If the cop starts receiving and answering SMS messages on his cell while doing traffic, then things might go out of hand really quickly.
The traffic cop is your node.js app, and the time he spends doing SMS is what the author refers to as "your code".
In other words: node.js performance will shine the more you use it as a traffic cop. The more you start using it to do things other than pulling and pushing data (i.e.: sorting a list of numbers, rendering an html template, etc.), the more your capacity to accept and process new connections quickly will suffer.
"Everything" refers to everything else besides your code. For example, the stuff that handles HTTP. Another way to say the same thing is "your code doesn't wait for node.js to do stuff, like send data over TCP, because that's done asynchronously."
To answer your second question, I don't know which has less CPU load, I'm guessing they're similar. Node.js' touted advantage is the CPU is better utilized due to the aforementioned asynchronicity.