I'm running an online psychology experiment using the PsiTurk framework. The experiment consists of thousands of trials. In each trial, the user produces some behavioral responses, and the JS frontend sends these responses (along with mouse movement data, reaction times and so on) to the backend using Backbone's model.save() method.
This works fine for clients with fast connections (effectively sending out a .save request once every few seconds), but for clients with a slow connection, the save requests pile up, causing a long queue of requests that takes dozens of seconds to clear. Down the road, this leads to excessive delay when arriving to the final screen (which requires a successful final update).
What is the best approach to this problem? Monitoring and limiting the number of pending requests? aborting pending requests when arriving to the final screen (before the final model.save())? sending out async .save requests (how?)
The solution I found was to keep a counter to monitor unfinished requests (increment when sending out a request, decrement when finish/error events are called), so I can avoid sending out an update if there already a couple of open requests.
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In the app I'm working on, there's a page which makes an excessive amount of requests. A few hundred requests are sent to the server at a time. Some of them are batched to reduce the amount, however it's quite a challenging task to batch all of them. So I am looking for some "cheap trick" to try out first.
But as it currently is, the requests that are deeper in the list end up resolving later than the others and it's because their "stalled" time is increasing. The screenshot displays one of the "latest" requests.
We're using http/3 so it's not because of the TCP connections limit. I feel like it's either because Chrome network thread can't handle so many requests at once and "queues" them or it's because the server can't respond to them quickly enough.
If option 1 is correct, I'm wondering if web worker can help with it. Therefore the question:
Does the web worker spawn another network thread or it's just for calculations and it uses the same process for performing XHR requests as the main thread?
I have an array with a length of one million. Each element is a string. I have a cloud function that takes a string and processes it. What is the fastest way to POST all million strings in my array to the cloud function? I don't care for the response of the cloud function. Ideally, it would POST, not wait for a response, then move on and POST the next one, iterating through the entire list as fast as possible. The issue is apparently with HTTP you cannot not wait for a response. You must wait for the response. Each cloud function takes about 10 seconds to execute. So if I need to wait for each response before moving to the next one, this would take 10 million seconds. Whereas if I could post each and not wait, I could probably run through the entire array in a few seconds.
A lot of this has been covered before in prior questions/answers, but none that I found is a pure duplicate of what you're asking so I'll reference some that have come before and add some explanation. First the ones that have come before:
How to make millions of parallel http requests from nodejs app
How to fire off 1,000,000 requests
Is there a limit to how many promises can or should run concurrently when making requests
In Node js. How many simultaneous requests can I send with the "request" package
What is the limit of sending concurrent ajax requests with node.js?
How to loop many http requests with axios in node.js
Handling large number of outbound HTTP requests
Promise.all consumes all my RAM
Properly batch nested promises in Node
How can I handle a file of 30,000 urls without memory leaks?
First off, you can send a lot of parallel outbound requests. You do not have to wait for a prior response before sending the next one.
Second, you have resource limits on both client and server and ultimately, you will have to explore with testing your local configuration and your target server to find out where those resource limits are and then write your code to stay within those limits. There is no way to reliably send a request and then immediately kill the socket because you don't care about the response. If your socket gets queued by the target server (because you've already overwhelmed it), then killing the socket may drop it from the target server's queue before it gets processed by the target server.
Your local configuration will be limited by how many simultaneous sockets you can have open and how much memory you have (as each outbound request takes some amount of memory to keep track of).
The target server will be limited by its own resources. It may have protections built-in to limit how many posts/sec it can received from one particular source (rate limiting). It may have overall server protections against how many incoming requests at once it can handle. Typically servers protect themselves from overload by configuring things so that once an incoming request queue gets to a certain level, they just immediately hang up on new requests. The idea is to provide some level of protection of service and just deflect new requests when they come in too fast.
If this isn't your target server and there isn't any documentation about what its limits are supposed to be, then you will just have to test how many simutaneous requests you can have "in-flight" at the same time. If they implement rate limiting from a given source, then it's not uncommon that this might be a fairly low number such as 5. If no rate limiting, then you're really just trying to figure out what their http server can handle without causing it to drop connections in defense of service.
Once you figure out (with testing) how many simultaneous requests in flight the target server can comfortably handle, you will have to structure your code to deliver that. Usually, you would take an approach like is show in this mapConcurrent() function where you code things so that only N requests are in flight at the same time where N is a number you figured out experimentally by testing the target server.
Relevant pieces of helper code:
mapConcurrent(array, maxConcurrent, fn)
rateLimitMap(array, requestsPerSec, maxInFlight, fn)
runN(fn, limit, cnt, options)
pMap(array, fn, limit)
And, if you want a pre-made library, the async library contains a bunch of control flow helpers like these.
Per the title, is there a maximum number of Get requests?
I need to make a couple hundred get requests to a rest API in order to dynamically load data into webpage, but I find that if I make a Promise.All array and output the promise result in the .then, eventually I get undefined due to request time outs.
Is this due to a limit on the number of connections? Is there a best practice for making large number of simultaneous requests?
Thanks for your insight!
A receiving server has a particular capability for how many simultaneous requests it can handle. It could be a small number or a very large number depending upon a whole bunch of things including the server configuration, the server software architecture, the types of request being sent, etc...
If you're getting timeouts from the server, then you are probably sending enough requests that the server can't process all of them before whatever request timeout is configured (on either client or server) and thus you get a timeout error.
The usual way of handling this on the client is to control how many simultaneous requests you will send at once and then when one finishes, you can send the next and so on. You will have to test to find out what the capabilities are of the receiving server and then you should back off a bit from that to allow other load from other sources some room to execute while your requests are running.
Assuming your requests are not unusually heavy-weight things to do on the server, I would typically test 5 or 10 requests at a time and see how the receiving server handles that.
There's a discussion of a lot of options for controlling this here:
Promise.all consumes all my RAM
Make several requests to an API that can only handle 20 request a minute
Concurrency control is also part of Promise.map() in the Bluebird promise library.
Is there a maximum number of Get requests?
Servers are limited on how many requests they can handle at once for a whole variety of reasons. Every server setup will likely be different and it also depends upon the types of requests you're sending too (and what they have to do). Some servers may be able to handle hundreds of thousands of requests (probably because there's a cluster behind them and they're configured for big load). Smaller configurations may only handle dozens at a time.
Is this due to a limit on the number of connections?
Any receiving server will have a limit on how many incoming connections it will allow to queue. What that is will depend upon many factors and there is no way for you (from the outside) to know exactly what that limit is. Timeout errors usually don't mean you're hitting this limit.
I'm developing a single-page application, which sends multiple AJAX request to the server.
The system works with polling, because some data-request can take about 10-20minutes to calculate.
client asks server for data
server hands out a job-id
client asks server every few seconds for the result
The polling algorithm lowers the polling frequency over time, stopping at intervals of 10seconds.
But when a client sends different data requests in a short time, he ends up with about 10-20 job-ids and starts polling for all of them.
Is it better to simply do it this way and let the browser handle those requests in parallel or should I schedule every request and serialize them all?
Would it bring performance benefits to serialize them?
If each initial request returns a unique id and each page has a unique user id then you can poll on what information for each request.
In the JSON I would return the results for any completed request, and the current status of those that haven't completed, such as whether it has started being processed, and perhaps a percentage of completion, or how many requests are ahead of that request.
This will simplify the work as you won't be making several polling calls, but just one, getting back a complex result to give feedback to the user the status of each request.
I find it useful to give some information on status for long-running queries otherwise the user may think the request was lost.
Some months ago, I faced performance issues due to multiple ajax calls, but I haven't investigated deeper this topic since then : High latencies loading stores in an ExtJS 4.1 MVC application.
Something I am playing with at the moment is a Rest / HTML page that dynamically updates via JSON calls.
Now in the case that I want this to run on as low a bandwidth as possible.
So if the server is shut down, then booted up again I want the updates to continue again in most cases this works some cases parts of the javascript won't work and it times out.
So what is a low overhead solution to detect that the Server has started up again?
(Looking for good ideas or other methods to do this)
thoughts:
So far I have thought of having a status request but this uses bandwidth again to continually run?
Or how to only run this status request once the server had gone down and stop when its up?
You could use the setInterval function to continuously poll the server for updates. Once a request fails you could enter a so called safe-mode by sending only HEAD requests (and as suggested by #sje397 also increase the timeout interval) to reduce bandwidth and once it succeeds you enter again normal mode and continue with GET/POST.
There are also more exotic things like COMET and Web Sockets in HTML 5 that allow the server to push updates to the client.