Constantly retrieves information from the stream - asynchronous

How Facebook, Google plus or other informations web site, constantly retrieves information from the stream?
I suppose there is an asynchronous recovery , but how he gets constantly? It's like an infinite loop?
Which technology is used ?

There are a few different approaches to displaying updates in near-real time on the web. Here are some of the most common ones:
Short polling
The simplest approach to the problem is to continuously poll the server on a short interval (hence the name). This means that every few seconds, client-side code sends an asynchronous request to the server and displays the result. The downside to this approach is that if updates happen less frequently than the server is queried, the client is doing a lot of work for little payoff. There may also be a slight delay between when the event happens on the server and when the client receives it, based on the polling frequency.
Long polling
The next evolutionary step from short polling is what's known as long polling, where the client-side JavaScript fires off an asynchronous request to the server as soon as the page loads. The server only responds to the request when an update is made, and once the response reaches the client, another request is fired off immediately. The key part of this approach is that the asynchronous request can wait for the server for a long time.
Long polling saves bandwidth and computation time, since the response is only handled when the server has something that changed. It does require more complex server-side logic, but it does allow for near-instant updates on the client side.
This question has a decent sample: How do I implement basic "Long Polling"?
WebSockets
WebSockets are a relatively new technology, and allow for two-way communication in a way that's similar to standard network sockets. The server or client can send messages across the socket that trigger events on the other side of the connection. As nice as this is, browser support isn't as widespread enough to make it a dependable solution.
For the current WebSocket specification, take a look at RFC 6455.

Related

How is non-blocking IO actually works from client's perspective?

So I came across the idea of blocking and non-blocking I/O. But what I understood from the concept and some of the sample implementations is that we implement code on the server side to achieve this nature of the code.
But now my question is, if (for example postman sending HTTP request to the server) the request has to wait for the server to respond, then what's the point of non-blocking I/O? (Please correct me if I am wrong) Or the whole concept is just for the increase of throughput of the server instead of actual async nature w.r.t. to client.
For example, in one of my project what I did was created a post request to create a request in the system for processing which will return the transaction ID, now using this transaction id, I can query the server to know the outcome.
I may sound too naive, but the concept has confused me a lot. I do not understand this concept clearly. Please help.
Thanks
the request has to wait for the server to respond, then what's the point of non-blocking I/O?
There's a confusion. Waiting for a response and (non)blocking i/o are very loosely related. You always have to wait for response. That's why youve made the request to begin with. But the question is: how?
Non-blocking HTTP: "Dear server, here's my request, please process it and send me a response, I'm going to do something else in the meantime, like calculating n-th digit of Pi (I'm a weirdo)".
Blocking HTTP: "Dear server, here's my request, please process it and send me a response, I'm going to patiently wait for it doing nothing".
Or the whole concept is just for the increase of throughput of the server instead of actual async nature w.r.t. to client.
The whole concept is to be able to do other things while waiting for i/o at the same time. And to do that while minimizing the usage of threads which don't scale well.
Asynchronous systems, i.e. systems without "I'm going to wait idly" part tend to perform better at the cost of complexity.
Side note: nonblocking i/o can be used both on the server side and client side. For example almost all JS engines in browsers are built on top of some asynchronous engine. JS is often single-threaded, meaning nonblocking i/o is necessary to achieve any concurrency.
But what I understood from the concept and some of the sample implementations is that we implement code on the server side to achieve this nature of the code.
You implement code in whereever you are doing the non-blocking UI. What a server does has no bearing on whether a client uses blocking or non-blocking UI, and what a client does has no bearing on whether a server uses blocking or non-blocking UI.
if (for example postman sending HTTP request to the server) the request has to wait for the server to respond, then what's the point of non-blocking I/O?
So that you're not wasting resources.
Let's consider first a simple console application that hits the web and then does something with the results. In this case there's very little to gain with non-blocking I/O as the application is just going to be sitting around waiting for something to do anyway.
Now let's consider a simple console application that hits 50 different web resources and collates the responses. Now non-blocking I/O is more useful, because with blocking I/O it would have to either get one resource after the other, or spin up 50 threads. With non-blocking I/O one, a small number of threads is all that is needed to hit 50 resources and respond promptly to each returning a response.
Now let's consider a GUI version of this application that wants to remain responsive to user input, while also running on low-power low-memory devices in which blocked threads are all the more expensive. The advantages of the above are increased.
Finally, consider a web application that is doing I/O both with the client and also as a client to a database, file system and maybe other web applications. It may have multiple requests at the same time, and blocking on either the I/O it does with the client or any of the I/O it does with db, file or other applications would cost a thread, which would put a scalability limit on how many requests it can handle simultaneously. Not blocking on I/O allows threads to be used for other requests while the I/O is pending.

ASP.Net MVC Delayed requests arriving long after client browser closed

I think I know what is happening here, but would appreciate a confirmation and/or reading material that can turn that "think" into just "know", actual questions at the end of post in Tl,DR section:
Scenario:
I am in the middle of testing my MVC application for a case where one of the internal components is stalling (timeouts on connections to our database).
On one of my web pages there is a Jquery datatable which queries for an update via ajax every half a second - my current task is to display correct error if that data requests times out. So to test, I made a stored procedure that asks DB server to wait 3 seconds before responding, which is longer than the configured timeout settings - so this guarantees a time out exception for me to trap.
I am testing in Chrome browser, one client. Application is being debugged in VS2013 IIS Express
Problem:
Did not expect the following symptoms to show up when my purposeful slow down is activated:
1) After launching the page with the rigged datatable, application slowed down in handling of all requests from the client browser - there are 3 other components that send ajax update requests parallel to the one I purposefully broke, and this same slow down also applied to any actions I made in the web application that would generate a request (like navigating to other pages). The browser's debugger showed the requests were being sent on time, but the corresponding break points on the server side were getting hit much later (delays of over 10 seconds to even a several minutes)
2) My server kept processing requests even after I close the tab with the application. I closed the browser, I made sure that the chrome.exe process is terminated, but breakpoints on various Controller actions were still getting hit for 20 minutes afterward - mostly on the actions that were "triggered" by automatically looping ajax requests from several pages I was trying to visit during my tests. Also breakpoints were hit on main pages I was trying to navigate to. On second test I used RawCap monitor the loopback interface to make sure that there was nothing actually making requests still running in the background.
Theory I would like confirmed or denied with an alternate explanation:
So the above scenario was making looped requests at a frequency that the server couldn't handle - the client datatable loop was sending them every .5 seconds, and each one would take at least 3 seconds to generate the timeout. And obviously somewhere in IIS express there has to be a limit of how many concurrent requests it is able to handle...
What was a surprise for me was that I sort of assumed that if that limit (which I also assumed to exist) was reached, then requests would be denied - instead it appears they were queued for an absolutely useless amount of time to be processed later - I mean, under what scenario would it be useful to process a queued web request half an hour later?
So my questions so far are these:
Tl,DR questions:
Does IIS Express (that comes with Visual Studio 2013) have a concurrent connection limit?
If yes :
{
Is this limit configurable somewhere, and if yes, where?
How does IIS express handle situations where that limit is reached - is that handling also configurable somewhere? ( i mean like queueing vs. immediate error like server is busy)
}
If no:
{
How does the server handle scenarios when requests are coming faster than they can be processed and can that handling be configured anywhere?
}
Here - http://www.iis.net/learn/install/installing-iis-7/iis-features-and-vista-editions
I found that IIS7 at least allowed unlimited number of silmulatneous connections, but how does that actually work if the server is just not fast enough to process all requests? Can a limit be configured anywhere, as well as handling of that limit being reached?
Would appreciate any links to online reading material on the above.
First, here's a brief web server 101. Production-class web servers are multithreaded, and roughly one thread = one request. You'll typically see some sort of setting for your web server called its "max requests", and this, again, roughly corresponds to how many threads it can spawn. Each thread has overhead in terms of CPU and RAM, so there's a very real upward limit to how many a web server can spawn given the resources the machine it's running on has.
When a web server reaches this limit, it does not start denying requests, but rather queues requests to handled once threads free up. For example, if a web server has a max requests of 1000 (typical) and it suddenly gets bombarded with 1500 requests. The first 1000 will be handled immediately and the further 500 will be queued until some of the initial requests have been responded to, freeing up threads and allowing some of the queued requests to be processed.
A related topic area here is async, which in the context of a web application, allows threads to be returned to the "pool" when they're in a wait-state. For example, if you were talking to an API, there's a period of waiting, usually due to network latency, between sending the request and getting a response from the API. If you handled this asynchronously, then during that period, the thread could be returned to the pool to handle other requests (like those 500 queued up requests from the previous example). When the API finally responded, a thread would be returned to finish processing the request. Async allows the server to handle resources more efficiently by using threads that otherwise would be idle to handle new requests.
Then, there's the concept of client-server. In protocols like HTTP, the client makes a request and the server responds to that request. However, there's no persistent connection between the two. (This is somewhat untrue as of HTTP 1.1. Connections between the client and server are sometimes persisted, but this is only to allow faster future requests/responses, as the time it takes to initiate the connection is not a factor. However, there's no real persistent communication about the status of the client/server still in this scenario). The main point here is that if a client, like a web browser, sends a request to the server, and then the client is closed (such as closing the tab in the browser), that fact is not communicated to the server. All the server knows is that it received a request and must respond, and respond it will, even though there's technically nothing on the other end to receive it, any more. In other words, just because the browser tab has been closed, doesn't mean that the server will just stop processing the request and move on.
Then there's timeouts. Both clients and servers will have some timeout value they'll abide by. The distributed nature of the Internet (enabled by protocols like TCP/IP and HTTP), means that nodes in the network are assumed to be transient. There's no persistent connection (aside from the same note above) and network interruptions could occur between the client making a request and the server responding to the request. If the client/server did not plan for this, they could simply sit there forever waiting. However, these timeouts are can vary widely. A server will usually timeout in responding to a request within 30 seconds (though it could potentially be set indefinitely). Clients like web browsers tend to be a bit more forgiving, having timeouts of 2 minutes or longer in some cases. When the server hits its timeout, the request will be aborted. Depending on why the timeout occurred the client may receive various error responses. When the client times out, however, there's usually no notification to the server. That means that if the server's timeout is higher than the client's, the server will continue trying to respond, even though the client has already moved on. Closing a browser tab could be considered an immediate client timeout, but again, the server is none the wiser and keeps trying to do its job.
So, what all this boils down is this. First, when doing long-polling (which is what you're doing by submitting an AJAX request repeatedly per some interval of time), you need to build in a cancellation scheme. For example, if the last 5 requests have timed out, you should stop polling at least for some period of time. Even better would be to have the response of one AJAX request initiate the next. So, instead of using something like setInterval, you could use setTimeout and have the AJAX callback initiate it. That way, the requests only continue if the chain is unbroken. If one AJAX request fails, the polling stops immediately. However, in that scenario, you may need some fallback to re-initiate the request chain after some period of time. This prevents bombarding your already failing server endlessly with new requests. Also, there should always be some upward limit of the time polling should continue. If the user leaves the tab open for days, not using it, should you really keep polling the server for all that time?
On the server-side, you can use async with cancellation tokens. This does two things: 1) it gives your server a little more breathing room to handle more requests and 2) it provides a way to unwind the request if some portion of it should time out. More information about that can be found at: http://www.asp.net/mvc/overview/performance/using-asynchronous-methods-in-aspnet-mvc-4#CancelToken

How to throttle SignalR clients on the server side

I am using a PersistentConnection for publishing large amounts of data (many small packages) to the connected clients.
It is basically a one way direction of data (since each client will call endpoints on other servers to set up various subscriptions, so they will not push any data back to the server via the SignalR connection).
Is there any way to detect that the client cannot keep up with the messages sent to it?
The example could be a mobile client on a poor connection (e.g. in a roaming situation, the speed may vary a lot). If we are sending 100 messages per second, but the client can only handle 10, we will eventually lose the messages (due to the message buffer on the server side).
I was looking for a server side event, similar to what has been done on the (SignalR) client, e.g.
protected override Task OnConnectionSlow(IRequest request, string connectionId) {}
but that is not part of the framework (for good reasons, I assume).
I have considered using the approach (suggested elsewhere on Stackoverflow), to let the client tell the server (e.g. every 10-30 seconds) how many messages it has received, and if that number differentiates a lot from the number of messages sent to the client, it is likely that the client cannot keep up.
The event would be used to tell the distributed backend that the client cannot keep up, and then turn down the data generation rate.
There's no way to to this right now other than coding something custom. We have discussed this in the past as a potential feature but it isn't anywhere the roadmap right now. It's also not clear what "slow" means as it's up to the application to decide. There'd probably be some kind of bandwidth/time/message based setting that would make this hypothetical event trigger.
If you want to hook in at a really low level, you could use owin middleware to replace the client's underlying stream with one that you owned so that you'd see all of the data going over the write (you'd have to do the same for websockets though and that might be non trivial).
Once you have that, you could write some time based logic that determined if the flush was taking too long and kill the client that way.
That's very fuzzy but it's basically a brain dump of how a feature like this could work.

Server -> Many Clients: Simultaneous Events

Not sure what category this question falls into; perhaps general networking / design / algorithms.
For a project I am looking at having one server with multiple connected clients. After some time, when all clients have connected, the server should send a message to each client instructing them to take some action. I need to guarantee that each client will execute this action at exactly the same time. Theoretically, how can this be done? What are the practical complications I will come up against? My target platform is mobile.
One solution I can think of;
The server actively and continuously keep track of the round-trip latency for each client. Provided this latency doesn't change too fast over time, the server should be able to compensate for each client's lag and send messages to each such that they all start execution at roughly the same time. Is there a better way?
One not-really related question: Client side and server side events not firing simultaneously
It can easily be done.
You don't care about latency nor you need the same machine time at clients.
The key here is to create a precise appointment.
Since clients communicate to the server, and not vice versa (you didn't say anything about it though). I can give you the following solution:
When a client connects to the server, it should send their local time.
When the server thinks it's time for the event to be set. It should send an appointment event to each client, with their local time in it. Server can calculate this.
Then, each client knows when exactly they need to do something by setting a timer till the time for their appointment comes.
In theory yes you can but not in real life.
At least you should add some a validity time-slot. All actions should be in that predefined time-slot in order that action to be valid.
So basically "same moment" = "a predefined time slot".
A predefined time-slot can be any value that is close to same moment or real-time.

Connecting http request/response model with asynchronous queue

What's a good way to connect the synchronous http request/response model with an asynchronous queue based model?
When the user's HTTP request comes it generates a work request that goes onto a queue (beanstalkd in this case). One of the workers picks up the request, does the work, and prepares a response.
The queue model is not request/response - there are only requests, not responses. So the question is, how best do we get the response back into the world of HTTP and back to the user?
Ideas:
Beanstalkd supports light weight topics or queues (they call them tubes). We could create a tube for each request, have the worker create a message on that tube, and have the http process sit and wait on the tube for the response. Don't particularly like this one since it has apache processes sitting around taking memory.
Have the http client poll for the response. The user's initial HTTP request kicks off the job on the queue and returns immediately. The client (the user's browser) polls periodically for a response. On the backend the worker puts its response into memcached, and we connect nginx to memcached so the polling is light weight.
Use Comet. Similar to the second option, but with fancier http communication to avoid polling.
I'm leaning towards 2 since it's easy and well know (I haven't used comet yet). I'm guessing there's probably also a much better obvious model I haven't thought of. What do you think?
Here's how to implement request-response efficiently on JMS which might be helpful (though Java/JMS centric). The general idea is to create a temporary queue per client/thread then use correlationIDs to correlate requests to replies etc.
Polling is the simple solution; comet is the more efficient solution. You've got it nailed :)
I personally love comet (although I'm biased, since I helped write WebSync), it nicely lets your clients subscribe to a channel and get the message when your server process is ready. Works like a champ.
I'm looking to implement a Beanstalkd and memcached system to run a number of processes following a request - in this case, looking up information when a user logs in (the number of messages a user has waiting for example). The info is stored in Memcached and then read back on the next page load.
Without knowing a little more about what tasks you are doing though, it's not so easy to say what needs to be done, or how. Option #2 is however the simplest, and that may be all you need - depending on what you are pushing back into the workers.

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