I've a readable NodeJS Stream which I want to use twice. Disclaimer: I'm not very comfortable with streams
Why?
My Service allows uploading of images for users. I want to avoid uploading of the same images.
My workflow is as follows:
upload image per ajax
get hash of image
if hash in database
return url from database
else
pass hash to resize&optimize pipeline
upload image to s3 bucket
get hash of image and write it to database with url
return s3 url
I get the hash of my stream with hashstream and optimize my image with gm.
Hashstream takes a stream, closes it, creates a hash and returns it with a callback.
My question is: What would be the best approach to combine both methods?
There are two ways to solve it:
Buffer the stream
Since you don't know if your stream will be used again, you can simply buffer it up somehow (somehow meaning handling data events, or using some module, for
example accum). As soon as you know what the outcome of the hash function you'd simply write the whole accumulated buffer into the gm stream.
Use stream.pipe twice to "tee"
You probably know the posix command tee, likewise you can push all the data into two places. Here's an example implementation of a tee method in my "scramjet" stream, but I guess for you it'd be quite sufficient to simply pipe twice. Then as soon as you get your hash calculated and run into the first condition I'd simply send an end.
The right choice depends on if you want to conserve memory or CPU. For less memory use two pipes (your optimization process will start, but you'll cancel it before it would output anything). For less CPU and less processes usage I'd go for buffering.
All in all I would consider buffering only if you can easily scale to more incoming images or you know exactly how much load there is and you can handle it. Either way there will be limits and these limit need to be somehow handled, if you can start couple more instances then you should be better of with using more CPU and keeping the memory at a sensible level.
I am quite new to node.js and I want to perform operations(like read,write or store in DB) to large files(typically 5GB ~ 10GB).
What are the possible ways to do it fast and without affecting the main thread(UI).Do I need to implement multithreading?
I think since I/O operations are asynchronous,it will never affect the main thread. And I had tried to read a large file and write the contents to response object of HTTP like this -
var http = require('http'),
fs = require('fs');
fs.readFile('largefile.txt',function(err,data){
if(err) {
throw err;
}
http.createServer(function(request,response){
response.writeHead(200,{
"Content-Type" : "text/plain"
});
response.end(data);
}).listen(8080);
console.log("server started");
});
The size of largefile.txt here is .25GB only, and it has taken almost 5 minutes for this program to run. Now in actual, I want the size to be (as I mentioned earlier) 5~10GB and type of file can be .csv,.xls. How should I do that, please tell the approach with examples(if possible).
Reading from disk to working program memory is very slow. This is a hardware limitation.
If the file is CSV (Comma-separated values separated by newlines), you probably want to read it line by line, or search through for the right line and then read, instead of reading the whole thing into memory and then printing the whole thing out. If you read it line by line at least you're updating something as it's being read.
For a start, you can use fs.read instead of fs.readFile to read the file character by character, looking for a newline character.
But a quick search for "nodejs read file line" shows there are many other ways to approach this with Node.
Edit:
I can't comment yet, but regarding child processes, as jfriend00 and SirDemon said, although NodeJS uses non-blocking IO (reading disk to memory doesn't block code) and it's generally event-oriented/asynchronous in design (execution may swap between sections of code while it's waiting on things) the code is only run single-threaded on a single CPU (code still blocks code). So a child process allows you to make use of another CPU. It was all designed for dynamic servers, so you could have code running and files being read almost all the time, but without the overhead of maintaining a new thread/process for each file read (which servers typically use thread pools for). (I think that's correct?)
I have a question about Node.js streams - specifically how they work conceptually.
There is no lack of documentation on how to use streams. But I've had difficulty finding how streams work at the data level.
My limited understanding of web communication, HTTP, is that full "packages" of data are sent back and forth. Similar to an individual ordering a company's catalogue, a client sends a GET (catalogue) request to the server, and the server responds with the catalogue. The browser doesn't receive a page of the catalogue, but the whole book.
Are node streams perhaps multipart messages?
I like the REST model - especially that it is stateless. Every single interaction between the browser and server is completely self contained and sufficient. Are node streams therefore not RESTful? One developer mentioned the similarity with socket pipes, which keep the connection open. Back to my catalogue ordering example, would this be like an infomercial with the line "But wait! There's more!" instead of the fully contained catalogue?
A large part of streams is the ability for the receiver 'down-stream' to send messages like 'pause' & 'continue' upstream. What do these messages consist of? Are they POST?
Finally, my limited visual understanding of how Node works includes this event loop. Functions can be placed on separate threads from the thread pool, and the event loop carries on. But shouldn't sending a stream of data keep the event loop occupied (i.e. stopped) until the stream is complete? How is it ALSO keeping watch for the 'pause' request from downstream?n Does the event loop place the stream on another thread from the pool and when it encounters a 'pause' request, retrieve the relevant thread and pause it?
I've read the node.js docs, completed the nodeschool tutorials, built a heroku app, purchased TWO books (real, self contained, books, kinda like the catalogues spoken before and likely not like node streams), asked several "node" instructors at code bootcamps - all speak about how to use streams but none speak about what's actually happening below.
Perhaps you have come across a good resource explaining how these work? Perhaps a good anthropomorphic analogy for a non CS mind?
The first thing to note is: node.js streams are not limited to HTTP requests. HTTP requests / Network resources are just one example of a stream in node.js.
Streams are useful for everything that can be processed in small chunks. They allow you to process potentially huge resources in smaller chunks that fit into your RAM more easily.
Say you have a file (several gigabytes in size) and want to convert all lowercase into uppercase characters and write the result to another file. The naive approach would read the whole file using fs.readFile (error handling omitted for brevity):
fs.readFile('my_huge_file', function (err, data) {
var convertedData = data.toString().toUpperCase();
fs.writeFile('my_converted_file', convertedData);
});
Unfortunately this approch will easily overwhelm your RAM as the whole file has to be stored before processing it. You would also waste precious time waiting for the file to be read. Wouldn't it make sense to process the file in smaller chunks? You could start processing as soon as you get the first bytes while waiting for the hard disk to provide the remaining data:
var readStream = fs.createReadStream('my_huge_file');
var writeStream = fs.createWriteStream('my_converted_file');
readStream.on('data', function (chunk) {
var convertedChunk = chunk.toString().toUpperCase();
writeStream.write(convertedChunk);
});
readStream.on('end', function () {
writeStream.end();
});
This approach is much better:
You will only deal with small parts of data that will easily fit into your RAM.
You start processing once the first byte arrived and don't waste time doing nothing, but waiting.
Once you open the stream node.js will open the file and start reading from it. Once the operating system passes some bytes to the thread that's reading the file it will be passed along to your application.
Coming back to the HTTP streams:
The first issue is valid here as well. It is possible that an attacker sends you large amounts of data to overwhelm your RAM and take down (DoS) your service.
However the second issue is even more important in this case:
The network may be very slow (think smartphones) and it may take a long time until everything is sent by the client. By using a stream you can start processing the request and cut response times.
On pausing the HTTP stream: This is not done at the HTTP level, but way lower. If you pause the stream node.js will simply stop reading from the underlying TCP socket.
What is happening then is up to the kernel. It may still buffer the incoming data, so it's ready for you once you finished your current work. It may also inform the sender at the TCP level that it should pause sending data. Applications don't need to deal with that. That is none of their business. In fact the sender application probably does not even realize that you are no longer actively reading!
So it's basically about being provided data as soon as it is available, but without overwhelming your resources. The underlying hard work is done either by the operating system (e.g. net, fs, http) or by the author of the stream you are using (e.g. zlib which is a Transform stream and usually bolted onto fs or net).
The below chart seems to be a pretty accurate 10.000 feet overview / diagram for the the node streams class.
It represents streams3, contributed by Chris Dickinson.
So first of all, what are streams?
Well, with streams we can process meaning read and write data piece by piece without completing the whole read or write operation. Therefore we don't have to keep all the data in memory to do these operations.
For example, when we read a file using streams, we read part of the data, do something with it, then free our memory, and repeat this until the entire file has been processed. Or think of YouTube or Netflix, which are both called streaming companies because they stream video using the same principle.
So instead of waiting until the entire video file loads, the processing is done piece by piece or in chunks so that you can start watching even before the entire file has been downloaded. So the principle here is not just about Node.JS. But universal to computer science in general.
So as you can see, this makes streams the perfect candidate for handing large volumes of data like for example, video or also data that we're receiving piece by piece from an external source. Also, streaming makes the data processing more efficient in terms of memory because there is no need to keep all the data in memory and also in terms of time because we can start processing the data as it arrives, rather than waiting until everything arrives.
How they are implemented in Node.JS:
So in Node, there are four fundamental types of streams:
readable streams, writable streams, duplex streams, and transform streams. But the readable and writeable ones are the most important ones, readable streams are the ones from which we can read and we can consume data. Streams are everywhere in the core Node modules, for example, the data that comes in when an http server gets a request is actually a readable stream. So all the data that is sent with the request comes in piece by piece and not in one large piece. Also, another example from the file system is that we can read a file piece by piece by using a read screen from the FS module, which can actually be quite useful for large text files.
Well, another important thing to note is that streams are actually instances of the EventEmitter class. Meaning that all streams can emit and listen to named events. In the case of readable streams, they can emit, and we can listen to many different events. But the most important two are the data and the end events. The data event is emitted when there is a new piece of data to consume, and the end event is emitted as soon as there is no more data to consume. And of course, we can then react to these events accordingly.
Finally, besides events, we also have important functions that we can use on streams. And in the case of readable streams, the most important ones are the pipe and the read functions. The super important pipe function, which basically allows us to plug streams together, passing data from one stream to another without having to worry much about events at all.
Next up, writeable streams are the ones to which we can write data. So basically, the opposite of readable streams. A great example is the http response that we can send back to the client and which is actually a writeable stream. So a stream that we can write data into. So when we want to send data, we have to write it somewhere, right? And that somewhere is a writeable stream, and that makes perfect sense, right?
For example, if we wanted to send a big video file to a client, we would just like Netflix or YouTube do. Now about events, the most important ones are the drain and the finish events. And the most important functions are the write and end functions.
About duplex streams. They're simply streams that are both readable and writeable at the same time. These are a bit less common. But anyway, a good example would be a web socket from the net module. And a web socket is basically just a communication channel between client and server that works in both directions and stays open once the connection has been established.
Finally, transform streams are duplex streams, so streams that are both readable and writeable, which at the same time can modify or transform the data as it is read or written. A good example of this one is the zlib core module to compress data which actually uses a transform stream.
*** Node implemented these http requests and responses as streams, and we can then consume, we can use them using the events and functions that are available for each type of stream. We could of course also implement our own streams and then consume them using these same events and functions.
Now let's try some example:
const fs = require('fs');
const server = require('http').createServer();
server.on('request', (req, res) =>{
fs.readFile('./txt/long_file.txt', (err, data)=>{
if(err) console.log(err);
res.end(data);
});
});
server.listen('8000','127.0.01', ()=>{
console.log(this);
});
Suppose long_file.txt file contain 1000000K lines and each line contain more thean 100 words, so this is a hug file with a big chunk of data, now in the above example problem is by using readFile() function node will load entire file into memory, because only after loading the whole file into memory node can transfar the data as a responce object.
When the file is big, and also when there are a ton of request hitting your server, by means of time node process will very quickly run out of resources and your app will quit working, everything will crash.
Let's try to find a solution by using stream:
const fs = require('fs');
const server = require('http').createServer();
server.on('request', (req, res) =>{
const readable = fs.createReadStream('./txt/long_file.txt');
readable.on('data', chunk=>{
res.write(chunk);
});
readable.on('end',()=>{
res.end();
})
readable.on('error', err=>{
console.log('err');
res.statusCode=500;
res.end('File not found');
});
});
server.listen('8000','127.0.01', ()=>{
console.log(this);
});
Well in the above example with the stream, we are effectively streaming the file, we are reading one piece of the file, and as soon as that's available, we send it right to the client, using the write method of the respond stream. Then when the next pice is available then that piece will be sent, and all the way until the entire file is read and streamed to the client.
So the stream is basically finished reading the data from the file, the end event will be emitted to signals that no more data will be written to this writable stream.
With the above practice, we solved previous problem, but still, there is a huge problem remain with the above example which is called backpressure.
The problem is that our readable stream, the one that we are using to read files from the disk, is much much faster than actually sending the result with the response writable stream over the network. And this will overwhelm the response stream, which cannot handle all this incoming data so fast and this problem is called backpressure.
The solution is using the pipe operator, it will handle the speed of data coming in and speed of data going out.
const fs = require('fs');
const server = require('http').createServer();
server.on('request', (req, res) =>{
const readable = fs.createReadStream('./txt/long_file.txt');
readable.pipe(res);
});
server.listen('8000','127.0.01', ()=>{
console.log(this);
});
I think you are overthinking how all this works and I like it.
What streams are good for
Streams are good for two things:
when an operation is slow and it can give you partials results as it gets them. For example read a file, it is slow because HDDs are slow and it can give you parts of the file as it reads it. With streams you can use these parts of the file and start to process them right away.
they are also good to connect programs together (read functions). Just as in the command line you can pipe different programs together to produce the desired output. Example: cat file | grep word.
How they work under the hood...
Most of these operations that take time to process and can give you partial results as it gets them are not done by Node.js they are done by the V8 JS Engine and it only hands those results to JS for you to work with them.
To understand your http example you need to understand how http works
There are different encodings a web page can be send as. In the beginning there was only one way. Where a whole page was sent when it was requested. Now it has more efficient encodings to do this. One of them is chunked where parts of the web page are sent until the whole page is sent. This is good because a web page can be processed as it is received. Imagine a web browser. It can start to render websites before the download is complete.
Your .pause and .continue questions
First, Node.js streams only work within the same Node.js program. Node.js streams can't interact with a stream in another server or even program.
That means that in the example below, Node.js can't talk to the webserver. It can't tell it to pause or resume.
Node.js <-> Network <-> Webserver
What really happens is that Node.js asks for a webpage and it starts to download it and there is no way to stop that download. Just dropping the socket.
So, what really happens when you make in Node.js .pause or .continue?
It starts to buffer the request until you are ready to start to consume it again. But the download never stopped.
Event Loop
I have a whole answer prepared to explain how the Event Loop works but I think it is better for you to watch this talk.
How should I stream the output from one program to an undefined number of programs in such a fashion that the data isn't buffered anywhere and that the application where the stream originates from doesn't block even if there's nothing reading the stream, but the programs reading the stream do block if there's no output from the first-mentioned program?
I've been trying to Google around for a while now, but all I find is methods where the program does block if nothing is reading the stream.
How should I stream the output from one program to an undefined number of programs in such a fashion that the data isn't buffered anywhere and that the application where the stream originates from doesn't block even if there's nothing reading the stream
Your requirements as stated can not possibly be satisfied without some form of a buffer.
Most straightforward option is to write the output to the file and let consumers read that file.
Another option is to have a ring-buffer in a form of a memory mapped file. As the capacity of a ring-buffer is normally fixed there needs to be a policy for dealing with slow consumers. Options are: block the producer; terminate the slow consumer; let the slow consumer somehow recover when it missed data.
Many years ago I wrote something like what you describe for an audio stream processing app (http://hewgill.com/nwr/). It's on github as splitter.cpp and has a small man page.
The splitter program currently does not support dynamically changing the set of output programs. The output programs are fixed when the command is started.
Without knowing exactly what sort of data you are talking about (how large is the data, what format is it, etc, etc) it is hard to come up with a concrete answer. Let's say for example you want a "ticker-tape" application that sends out information for share purchases on the stock exchange, you could quite easily have a server that accepts a socket from each application, starts a thread and sends the relevant data as it appears from the recoder at the stock market. I'm not aware of any "multiplexer" that exists today (but Greg's one may be a starting point). If you use (for example) XML to package the data, you could send the second half of a packet, and the client code would detect that it's not complete, so throws it away.
If, on the other hand, you are sending out high detail live update weather maps for the whole country, the data is probably large enough that you don't want to wait for a full new one to arrive, so you need some sort of lock'n'load protocol that sets the current updated map, and then sends that one out until (say) 1 minute later you have a new one. Again, it's not that complex to write some code to do this, but it's quite a different set of code to the "ticker tape" solution above, because the packet of data is larger, and getting "half a packet" is quite wasteful and completely useless.
If you are streaming live video from the 2016 Olympics in Brazil, then you probably want a further diffferent solution, as timing is everything with video, and you need the client to buffer, pick up key-frames, throw away "stale" frames, etc, etc, and the server will have to be different.
I'm returning A LOT (500k+) documents from a MongoDB collection in Node.js. It's not for display on a website, but rather for data some number crunching. If I grab ALL of those documents, the system freezes. Is there a better way to grab it all?
I'm thinking pagination might work?
Edit: This is already outside the main node.js server event loop, so "the system freezes" does not mean "incoming requests are not being processed"
After learning more about your situation, I have some ideas:
Do as much as you can in a Map/Reduce function in Mongo - perhaps if you throw less data at Node that might be the solution.
Perhaps this much data is eating all your memory on your system. Your "freeze" could be V8 stopping the system to do a garbage collection (see this SO question). You could Use V8 flag --trace-gc to log GCs & prove this hypothesis. (thanks to another SO answer about V8 and Garbage collection
Pagination, like you suggested may help. Perhaps even splitting up your data even further into worker queues (create one worker task with references to records 1-10, another with references to records 11-20, etc). Depending on your calculation
Perhaps pre-processing your data - ie: somehow returning much smaller data for each record. Or not using an ORM for this particular calculation, if you're using one now. Making sure each record has only the data you need in it means less data to transfer and less memory your app needs.
I would put your big fetch+process task on a worker queue, background process, or forking mechanism (there are a lot of different options here).
That way you do your calculations outside of your main event loop and keep that free to process other requests. While you should be doing your Mongo lookup in a callback, the calculations themselves may take up time, thus "freezing" node - you're not giving it a break to process other requests.
Since you don't need them all at the same time (that's what I've deduced from you asking about pagination), perhaps it's better to separate those 500k stuff into smaller chunks to be processed at the nextTick?
You could also use something like Kue to queue the chunks and process them later (thus not everything in the same time).