(This probably duplicates the question ASP.NET MVC4 Async controller - Why to use?, but about webapi, and I do not agree with answers in there)
Suppose I have a long running SQL request. Its data should be than serialized to JSON and sent to browser (as a response for xhr request). Sample code:
public class DataController : ApiController
{
public Task<Data> Get()
{
return LoadDataAsync(); // Load data asynchronously?
}
}
What actually happens when I do $.getJson('api/data', ...) (see this poster http://www.asp.net/posters/web-api/ASP.NET-Web-API-Poster.pdf):
[IIS] Request is accepted by IIS.
[IIS] IIS waits for one thread [THREAD] from the managed pool (http://msdn.microsoft.com/en-us/library/0ka9477y(v=vs.110).aspx) and starts work in it.
[THREAD] Webapi Creates new DataController object in that thread, and other classes.
[THREAD] Uses task-parallel lib to start a sql-query in [THREAD2]
[THREAD] goes back to managed pool, ready for other processing
[THREAD2] works with sql driver, reads data as it ready and invokes [THREAD3] to reply for xhr request
[THREAD3] sends response.
Please, feel free to correct me, if there's something wrong.
In the question above, they say, the point and profit is, that [THREAD2] is not from The Managed Pool, however MSDN article (link above) says that
By default, parallel library types like Task and Task<TResult> use thread pool threads to run tasks.
So I make a conclusion, that all THREE THREADS are from managed pool.
Furthermore, if I used synchronous method, I would still keep my server responsive, using only one thread (from the precious thread pool).
So, what's the actual point of swapping from 1 thread to 3 threads? Why not just maximize threads in thread pool?
Are there any clearly useful ways of using async controllers?
I think the key misunderstanding is around how async tasks work. I have an async intro on my blog that may help.
In particular, a Task returned by an async method does not run any code. Rather, it is just a convenient way to notify callers of the result of that method. The MSDN docs you quoted only apply to tasks that actually run code, e.g., Task.Run.
BTW, the poster you referenced has nothing to do with threads. Here's what happens in an async database request (slightly simplified):
Request is accepted by IIS and passed to ASP.NET.
ASP.NET takes one of its thread pool threads and assigns it to that request.
WebApi creates DataController etc.
The controller action starts an asynchronous SQL query.
The request thread returns to the thread pool. There are now no threads processing the request.
When the result arrives from the SQL server, a thread pool thread reads the response.
That thread pool thread notifies the request that it is ready to continue processing.
Since ASP.NET knows that no other threads are handling that request, it just assigns that same thread the request so it can finish it off directly.
If you want some proof-of-concept code, I have an old Gist that artificially restricts the ASP.NET thread pool to the number of cores (which is its minimum setting) and then does N+1 synchronous and asynchronous requests. That code just does a delay for a second instead of contacting a SQL server, but the general principle is the same.
The profit of asynchronous actions is that while the controller is waiting for the sql query to finish no threads are allocated for this request, while if you used a synchronous method a thread would be locked up in the execution of this method from the start to end of that method. While SQL server is doing its job the thread isn't doing anything but waiting.
If you use asynchronous methods this same thread can respond to other requests while SQL server is doing its thing.
I believe your steps are wrong at step 4, I don't think it will create a new thread to do the SQL query. At 6 there isn't created a new thread, it is just one of the available threads that will be used to continue from where the first thread left off. The thread at 6 could be the same as started the async operation.
The point of async is not to make an application multi threaded but rather to let a single threaded application carry on with something different instead of waiting for a response from an external call that is executing on a different thread or process.
Consider a desk top application that shows stock prices from different exchanges. The application needs to make a couple of REST / http calls to get some data from each of the remote stock exchange servers.
A single threaded application would make the first call, wait doing nothing until it got the first set of prices, update it's window, then make a call to the next external stock price server, again wait doing nothing until it got the prices, update it's window... etc..
We could go all multi threaded kick off the requests in parallel and update the screen in parallel, but since most of the time is spent waiting for a response from the remote server this seems overkill.
It might be better for the thread to:
Make a request for the first server but instead of waiting for the answer leave a marker, a place to come back to when the prices arrive and move on to issuing the second request, again leaving a marker of a place to come back to...etc.
When all the requests have been issued the application execution thread can go on to dealing with user input or what ever is required.
Now when a response from one of the servers is received the thread can be directed to continue from the marker it laid down previously and update the window.
All of the above could have been coded long hand, single threaded, but was so horrendous going multi threaded was often easier. Now the process of leaving the marker and coming back is done by the compiler when we write async/await. All single threaded.
There are two key points here:
1) Multi threaded does still happen! The processing of our request for stock prices happens on a different thread (on a different machine). If we were doing db access the same would be true. In examples where the wait is for a timer, the timer runs on a different thread. Our application though is single threaded, the execution point just jumps around (in a controlled manner) while external threads execute
2) We lose the benefit of async execution as soon as the application requires an async operation to complete. Consider an application showing the price of coffee from two exchanges, the application could initiate the requests and update it's windows asynchronously on a single thread, but now if the application also calculated the difference in price between two exchanges it would have to wait for the async calls to complete. This is forced on us because an async method (such as one we might write to call an exchange for a stock price) does not return the stock price but a Task, which can be thought of as a way to get back to the marker that was laid down so the function can complete and return the stock price.
This means that every function that calls an async function needs to be async or to wait for the "other thread/process/machine" call at the bottom of the call stack to complete, and if we are waiting for the bottom call to complete well why bother with async at all?
When writing a web api, IIS or other host is the desktop application, we write our controller methods async so that the host can execute other methods on our thread to service other requests while our code is waiting for a response from work on a different thread/process/machine.
I my opinion the following describes a clear advantage of async controllers over synchronous ones.
A web application using synchronous methods to service high latency
calls where the thread pool grows to the .NET 4.5 default maximum of
5, 000 threads would consume approximately 5 GB more memory than an
application able the service the same requests using asynchronous
methods and only 50 threads. When you’re doing asynchronous work,
you’re not always using a thread. For example, when you make an
asynchronous web service request, ASP.NET will not be using any
threads between the async method call and the await. Using the thread
pool to service requests with high latency can lead to a large memory
footprint and poor utilization of the server hardware.
from Using Asynchronous Methods in ASP.NET MVC 4
Related
We started a new project with Quarkus and Mutiny, and created a bunch of endpoints with Quarkus #Funq, everything has been working fine so far. Now we want to process something very time-consuming in one of the endpoints, and what we are expecting is, once user clicks a button to send the http request from frontend and hits this specific endpoint, we are going to return 202 Accepted immediately, leaving the time-consuming operation processing in another thread from backend, then send notification email accordingly to user once it completes.
I understand this can be done with #Async or CompletableFuture, but now we want to do this with Mutiny. Based on how I read Mutiny documentation here https://smallrye.io/smallrye-mutiny/guides/imperative-to-reactive, runSubscriptionOn will avoid blocking the caller thread by running the time-consuming method on another thread, and my testing showed the time-consuming codes did get executed on a different thread. However, the http request does not return immediately, it is still pending until the time-consuming method finishes executing (as I observe in the browser's developer tool). Did I misunderstand how runSubscriptionOn works? How do I implement this feature with Mutiny?
My #Funq endpoint looks like this
#Inject
MyService myService;
#Funq("api/report")
public Uni<String> sendReport(MyRequest request) {
ExecutorService executor = Executors.newFixedThreadPool(10, r -> new Thread(r, "CUSTOM_THREAD"));
return Uni.createFrom()
.item(() -> myService.timeConsumingMethod(request))
.runSubscriptionOn(executor);
}
Edit: I found the solution using Uni based on #Ladicek's answer. After digging deeper into Quarkus and Uni I have a follow-up question:
Currently most of our blocking methods are not returning Uni on Service level, instead we create Uni object from what they return (i.e. object or list), and return the Uni on Controller level in their endpoints like this
return Uni.createFrom().item(() -> myService.myIOBlockingMethod(request)).
As #Ladicek explained, I do not have to use .runSubscriptionOn explicitly as the IO blocking method will automatically run on a worker thread (as my method on Service level does not return Uni). Is there any downside for this? My understanding is, this will lead to longer response time because it has to jump between the I/O thread and worker thread, am I correct?
What is the best practice for this? Should I always return Uni for those blocking methods on Service level so that they can run on the I/O threads as well? If so I guess I will always need to call .runSubscriptionOn to run it on a different worker thread so that the I/O thread is not blocked, correct?
By returning a Uni, you're basically saying that the response is complete when the Uni completes. What you want is to run the action on a thread pool and return a complete response (Uni or not, that doesn't matter).
By the way, you're creating an extra thread pool in the method, for each request, and don't shut it down. That's wrong. You want to create one thread pool for all requests (e.g. in a #PostConstruct method) and ideally also shut it down when the application ends (in a #PreDestroy method).
Consider: you have a collection of user ids and want to load the details of each user represented by their id from an API. You want to bag up all of those users into some kind of collection and send it back to the calling code. And you want to use LINQ.
Something like this:
var userTasks = userIds.Select(userId => GetUserDetailsAsync(userId));
var users = await Task.WhenAll(tasks); // users is User[]
This was fine for my app when I had relatively few users. But, there came a point where it didn't scale. When it got to the point of thousands of users, this resulted in thousands of HTTP requests being fired concurrently and bad things started to happen. Not only did we realise we were launching a denial of service attack on the API we were consuming as did this, we were also bringing our own application to the point of collapse through thread starvation.
Not a proud day.
Once we realised that the cause of our woes was a Task.WhenAll / Select combo, we were able to move away from that pattern. But my question is this:
What is going wrong here?
As I read around on the topic, this scenario seems well described by #6 on Mark Heath's list of Async antipatterns: "Excessive parallelization":
Now, this does "work", but what if there were 10,000 orders? We've flooded the thread pool with thousands of tasks, potentially preventing other useful work from completing. If ProcessOrderAsync makes downstream calls to another service like a database or a microservice, we'll potentially overload that with too high a volume of calls.
Is this actually the reason? I ask as my understanding of async / await becomes less clear the more I read about the topic. It's very clear from many pieces that "threads are not tasks". Which is cool, but my code appears to be exhausting the number of threads that ASP.NET Core can handle.
So is that what it is? Is my Task.WhenAll and Select combo exhausting the thread pool or similar? Or is there another explanation for this that I'm not aware of?
Update:
I turned this question into a blog post with a little more detail / waffle. You can find it here: https://blog.johnnyreilly.com/2020/06/taskwhenall-select-is-footgun.html
N+1 Problem
Putting threads, tasks, async, parallelism to one side, what you describe is an N+1 problem, which is something to avoid for exactly what happened to you. It's all well and good when N (your user count) is small, but it grinds to a halt as the users grow.
You may want to find a different solution. Do you have to do this operation for all users? If so, then maybe switch to a background process and fan-out for each user.
Back to the footgun (I had to look that up BTW 🙂).
Tasks are a promise, similar to JavaScript. In .NET they may complete on a separate thread - usually a thread from the thread pool.
In .NET Core, they usually do complete on a separate thread if not complete and the point of awaiting, for an HTTP request that is almost certain to be the case.
You may have exhausted the thread pool, but since you're making HTTP requests, I suspect you've exhausted the number of concurrent outbound HTTP requests instead. "The default connection limit is 10 for ASP.NET hosted applications and 2 for all others." See the documentation here.
Is there a way to achieve some parallelism and not take exhaust a resource (threads or http connections)? - Yes.
Here's a pattern I often implement for just this reason, using Batch() from morelinq.
IEnumerable<User> users = Enumerable.Empty<User>();
IEnumerable<IEnumerable<string>> batches = userIds.Batch(10);
foreach (IEnumerable<string> batch in batches)
{
Task<User> batchTasks = batch.Select(userId => GetUserDetailsAsync(userId));
User[] batchUsers = await Task.WhenAll(batchTasks);
users = users.Concat(batchUsers);
}
You still get ten asynchronous HTTP requests to GetUserDetailsAsync(), and you don't exhaust threads or concurrent HTTP requests (or at least max out with the 10).
Now if this is a heavily used operation or the server with GetUserDetailsAsync() is heavily used elsewhere in the app, you may hit the same limits when your system is under load, so this batching is not always a good idea. YMMV.
You already have an excellent answer here, but just to chime in:
There's no problem with creating thousands of tasks. They're not threads.
The core problem is that you're hitting the API way too much. So the best solutions are going to change how you call that API:
Do you really need user details for thousands of users, all at once? If this is for a dashboard display, then change your API to enforce paging; if this is for a batch process, then see if you can access the data directly from the batch process.
Use a batch route for that API if it supports one.
Use caching if possible.
Finally, if none of the above are possible, look into throttling the API calls.
The standard pattern for asynchronous throttling is to use SemaphoreSlim, which looks like this:
using var throttler = new SemaphoreSlim(10);
var userTasks = userIds.Select(async userId =>
{
await throttler.WaitAsync();
try { await GetUserDetailsAsync(userId); }
finally { throttler.Release(); }
});
var users = await Task.WhenAll(tasks); // users is User[]
Again, this kind of throttling is best only if you can't make the design changes to avoid thousands of API calls in the first place.
While there is no thread waiting for async operation if the async operation is pure, there is a thread for continuation, so assuming that your GetUserDetailsAsync will await for some IO-bound operation the continuation (parsing output, returning result ...) will need to run on some thread so your Task.Result which was created by GetUserDetailsAsync can be set, so every one of them will wait for a thread from thread pool to finish.
According to my research, I found out that in thread per request model, every request that comes spawns a new thread. Let's say I had 100 requests, I'd be having 100 threads running at once. Coming to event looped model (similar to spring webflux), we have a main thread that listens to the requests and delegates tasks to other threads.
Now let's say we have 100 requests on event looped model. Here, the main thread will be free to listen but it will also have threads which will be waiting for response from DB or network, just like thread per request model. How does it make event looped model more scalable.
The key difference between Tomcat with Servlet API < 3.1 and servers as Netty powered with a Spring WebFlux is the way which IO and requests are processed : blocking or non-blocking.
Spring WebFlux favors the second approach :
Part of the answer is the need for a non-blocking web stack to handle
concurrency with a small number of threads and scale with fewer
hardware resources.
So to sum that, by using the Spring WebFlux API, much less threads will be created for as many as client http requests because a thread is not dedicated to a single client http request in this model.
The no-blocking approach means that : whatever the time to process a request, a thread that handles that will not block the application and keep the thread waited for a long time but will process another request during this time.
Take that example : your Rest or Mvc controller receives a request and the essential of the task to perform is requesting the database. With a blocking approach you create one thread by http request. With a no blocking approach, the thread delegates to the database, may serve other requests and that thread or another of the pool will go on the processing when the interaction with the database is finished.
Suppose an ASP.NET WebAPI request arrives at a controller method.
Suppose the request represents an 'event' which needs processed. The event has multiple operations associated with it that should be performed in parallel. For example, each operation may need to call out to a particular REST endpoint on other servers, which are I/O bound operations that should get started as soon as possible and should not wait for one to return before starting the next one.
What is the most correct/performant way to implement this pattern?
I've read that using Task.Run is a bad idea, because it just grabs additional ThreadPool threads, leaving the main request thread idle/blocked. While that makes sense if I was running a single task, I'm not sure that advice applies in this case.
For example, if the event has 4 operations that needed completed (each having possibly multiple I/O bound calls of their own), I would call Task.Run in a loop 4 times to initialize each operation, then wait on the resulting tasks with Task.WaitAll.
Q1: Would the main request thread be returned to the ThreadPool for use by another request while waiting for Task.WaitAll to return, or would it just hog the main thread leaving it idle until Task.WaitAll completes?
Q2: If it hogs the main thread, could that be resolved by marking the controller method with the async keyword, and using an await Task.WhenAll call instead? I'd imaging that this would return the main thread to the pool while waiting, allowing it to be used for other requests or event operations.
Q3: Since Task.Run queues up a work item that could be blocked on an I/O bound call, would performance improve if the operations were all implemented with async and used await calls on Task-based asynchronous I/O methods?
Regarding the whole approach of using Task.Run for the event's operations, the goal is just get all of the operation's I/O bound calls started as soon as possible. I suppose if (as in Q3) all operations were async methods, I could just get them all started on the main request thread in a loop, but I'm not sure that would be better than starting them with separate Task.Run calls. Maybe there's a completely different approach that I'm unaware of.
So I'm using Play as my MVC framework. My web application simply does calls to a Postgres database to feed data to the views. I'm currently using AJAX in the view to get the data. I figured there are times when the database will lag a bit in sending the data so I would use AJAX to allow other elements in the view to load.
Now my question is, since I'm already using AJAX in the view, should I be using Promises in the controllers? Would it make a difference if I used Promises? I haven't had enough experience to figure out how having asynchronous actions both in the view and in the controller can affect my web application. My intuition says that the AJAX action is enough for a web application with about 100 - 150 hits a day.
What are your insights on this?
The point of non-blocking I/O and Promises is not about speed or latency, but about better use of resources. By default, Play gives you one thread per CPU core. This works very well when all of the work you're doing on those threads is extremely fast - that is, you avoid expensive computations and use only non-blocking I/O. However, all JDBC calls are synchronous, so they will block the few available threads for a long time, and if you have enough traffic, any new requests will have to queue up, increasing load time for your pages.
Therefore, whether you should use Promises - or, more accurately, a separate thread pool - for DB calls has nothing to do with whether you make those DB calls on the initial page load or via AJAX calls. It's simply about how much traffic you expect, how many threads you have, and how long you'll be using each thread. For most applications, the best practice is to run DB calls on a separate thread pool so they don't block the main worker threads.
For example, configure a thread pool (ie, ExecutionContext) in application.conf as follows:
akka {
actor {
db-context {
fork-join-executor {
parallelism-factor = 20.0
parallelism-max = 200
}
}
}
}
Then use that thread pool in your DB code:
def dbLookup(someId: Int): Future[SomeDbValue] = {
val dbExecutionContext = Akka.system.dispatchers.lookup("db-context")
Future {
// DB code to fetch SomeDbValue
}(dbExecutionContext)
}
See Play Framework Thread Pools for full instructions and Play Framework: async I/O without the thread pool and callback hell for more background info.