Alright... I'm getting a bit confused here. The async monad allows you to use let! which will start the computation of the given async method, and suspend the thread, untill the result is available.. thats all fine, I do understand that.
Now what I dont understand is why they made an extension for the WebClient class, thats named AsyncDownloadString - Couldn't you just wrap the normal DownloadString inside an async block? I'm pretty sure, I'm missing an important point here, since I've done some testing that shows DownloadString wrapped inside an async block, still blocks the thread.
There is an important difference between the two:
The DownloadString method is synchronous - the thread that calls the method will be blocked until the whole string is downloaded (i.e. until the entire content is transferred over the internet).
On the other hand, AsyncDownloadString doesn't block the thread for a long time. It asks the operating system to start the download and then releases the thread. When the operating system receives some data, it picks a thread from the thread pool, the thread stores the data to some buffer and is again released. When all data is downloaded, the method will read all data from the buffer and resume the rest of the asynchronous workflow.
In the first case, the thread is blocked during the entire download. In the second case, threads are only busy for very short period of time (when processing received responses, but not when waiting for the server).
Internally, the AsyncDownloadString method is just a wrapper for DownloadStringAsync, so you can also find more information in the MSDN documentation.
The important point to note is that async programming is about doing operations that are not CPU bound i.e those which are IO bound. These IO bound operations are performed on IO threads (using overlapped IO feature of operating system). What this implies is that even if you wrap some factorial function inside a async block and run it inside another async block using let! binding, you won't get any benefit out of it as it will be running on CPU bound thread and the main purpose of doing async programming is to not take up a CPU bound thread when something which is of IO nature, as that CPU bound thread can be used for other purpose in the meantime the IO completes.
If you look at the various IO classes in .NET like File, Socket etc. They all have blocking as well as non blocking read and write operations. The blocking operations will wait for the IO to complete on the CPU thread and hence blocking the CPU thread till IO is done, where as the non blocking operations uses the overlapped IO API calls to perform the operation.
Async have a method to make a async block out of these non blocking APIs of Files, Socket etc. In your case calling DownloadString will block the CPU thread as it uses the blocking API of the underlying class where as AsyncDownloadString uses the non blocking - io overlapped - based API call.
Related
I was reading the documentation of Microsoft specifically the Async programming article and I didn't understand this section while he is explaining the work of the server's threads when using Async code.
because it(The server) uses async and await, each of its threads is freed up when the I/O-bound work starts, rather than when it finishes.
Could anyone help what does it mean by the threads r freed up when the I/O starts??
Here is the article : https://learn.microsoft.com/en-us/dotnet/standard/async-in-depth
When ASP.NET gets an HTTP request, it takes a thread from the thread pool and uses that to execute the handler for that request (e.g., a specific controller action).
For synchronous actions, the thread stays assigned to that HTTP request until the action completes. For asynchronous actions, the await in the action method may cause the thread to return an incomplete task to the ASP.NET runtime. In this case, ASP.NET will free up the thread to handle other requests while the I/O is in flight.
Further reading about the difference between synchronous and asynchronous request handling and how asynchronous work doesn't require a thread at all times.
When your application makes a call to an external resource like Database or HttpClient thread, that initiated connection needs to wait.
Until it gets a response, it waits idly.
In the asynchronous approach, the thread gets released as soon as the app makes an external call.
Here is an article about how it happens:
https://medium.com/#karol.rossa/asynchronous-programming-73b4f1988cc6
And performance comparison between async and sync apporach
https://medium.com/#karol.rossa/asynchronous-performance-1be01a71925d
Here's an analogy for you: have you ever ordered at a restaurant with a large group and had someone not be ready to order when the waiter came to them? Did they bring in a different waiter to wait for him or did the waiter just come back to him after he took other people's orders?
The fact that the waiter is allowed to come back to him later means that he's freed up immediately after calling on him rather than having to wait around until he's ready.
Asynchronous I/O works the same way. When you do a web service call, for example, the slowest part (from the perspective of the client at least) is waiting for the result to come back: most of the delay is introduced by the network (and the other server), during which time the client thread would otherwise have nothing to do but wait. Async allows the client to do other things in the background.
I have the following problem:
I'm trying to call sync closure from async function, but sync closure has to later call another async function.
I cannot make async closure, because they are unstable at the moment:
error[E0658]: async closures are unstable
so I have to do it this way somehow.
I found a couple of questions related to the problem, such as this, but when I tried to implement it, im receiving the following error:
Cannot start a runtime from within a runtime.
This happens because a function (like `block_on`)
attempted to block the current thread while the
thread is being used to drive asynchronous tasks.'
here is playground link which hopefully can show what I'm having problem with.
I'm using tokio as stated in the title.
As the error message states, Tokio doesn't allow you to have nested runtimes.
There's a section in the documentation for Tokio's Mutex which states the following (link):
[It] is ok and often preferred to use the ordinary Mutex from the standard library in asynchronous code. [...] the feature that the async mutex offers over the blocking mutex is that it is possible to keep the mutex locked across an .await point, which is rarely necessary for data.
Additionally, from Tokio's mini-Redis example:
A Tokio mutex is mostly intended to be used when locks need to be held
across .await yield points. All other cases are usually best
served by a std mutex. If the critical section does not include any
async operations but is long (CPU intensive or performing blocking
operations), then the entire operation, including waiting for the mutex,
is considered a "blocking" operation and tokio::task::spawn_blocking
should be used.
If the Mutex is the only async thing you need in your synchronous call, it's most likely better to make it a blocking Mutex. In that case, you can lock it from async code by first calling try_lock(), and, if that fails, attempting to lock it in a blocking context via spawn_blocking.
I'm writing web services in C++/CLI (not my choice) using Microsoft's Web API. A lot of functions in Web API are async, but because I'm using C++/CLI, I don't get the async/await support of C# or VB. So the fallback position is to use ContinueWith() to schedule a continuation delegate for reading the async task's result safely.
However, because C++/CLI also doesn't support inline anonymous delegates or managed lambdas, every delegate continuation must be written as a separate function somewhere. That quickly turns into spaghetti with the number of async functions in Web API.
So, to avoid the deadlock issues of Task<T>::Result, I've been trying this:
[HttpGet, Route( "get/some/dto" )]
Task< SomeDTO ^ > ^ MyActionMethod()
{
return Task::Run( gcnew Func< SomeDTO ^ >( this, &MyController::MyActionMethod2 ) );
}
SomeDTO ^ MyActionMethod2()
{
// execute code and use any task->Result calls I need without deadlocking
}
Okay, so I know this isn't great, but how bad is it? I don't yet understand enough of the guts of Web API or ASP.NET to comprehend the performance or scaling ramifications this will have.
Also, what other consequences may this have that aren't necessarily related to performance? For example, exceptions get wrapped in an extra AggregateException, which represents additional complexity and work for handling exceptions.
Your memory usage will increase with your application's parallelism. For every concurrent call to MyActionMethod you will need a separate thread with its own stack. That will cost you about 1 MB of RAM for each concurrent call. If MyActionMethod runs long enough so that 10000 instances run at once, you're looking at 10 GB of RAM. There is also CPU overhead in setting up each thread.
If concurrency is low, dropping async support won't be a problem. In that case, don't bother with Task::Run. Just change MyActionMethod to return SomeDTO^ (no Task wrapper).
Another potential concern is that lose easy use of cancellation tokens. However, for Web API it's usually fine to just let an exception propagate back to Web API, which ends up cancelling the synchronous call anyway.
Finally, if you were planning on performing any operation within your action method in parallel, you'll still need to use ContinueWith to accomplish that. Going non-async by default means you'll always perform one operation at a time. Fortunately, it's often just fine to do so.
Okay, so I know this isn't great, but how bad is it?
It's difficult to answer this without load-testing your specific scenario. But you can walk through the known semantics (taken largely from my blog).
First, when a request comes in, ASP.NET executes your handler on a thread pool thread within that request context. Your request handler calls Task.Run, which takes another thread from the thread pool and executes the actual request logic on it. The handler then returns the task returned from Task.Run; this releases the original request thread back to the thread pool.
Then, the Task.Run delegate will block on any asynchronous parts. So, this pattern has the scaling disadvantages of a regular synchronous handler, plus an extra thread context switch. Also, it uses a thread from the ASP.NET thread pool, which is not necessarily a bad thing, but in some scenarios it may throw off the ASP.NET thread pool heuristics.
Also, what other consequences may this have that aren't necessarily related to performance? For example, exceptions get wrapped in an extra AggregateException, which represents additional complexity and work for handling exceptions.
Yes, the exceptions from any .Result or Wait() calls will be wrapped in AggregateException. You may be able to avoid this by calling .GetAwaiter().GetResult() instead.
Another important consideration is that the code executing within the Task.Run is executing without a request context. So, ambient data like HttpContext.Current, current culture, thread principal, etc. are not going to be set correctly. You'll have to capture any important data before calling Task.Run and pass it down manually.
How do all the languages implements asynchronous callbacks?
For example in C++, one need to have a "monitor thread" to start a std::async. If it is started in main thread, it has to wait for the callback.
std::thread t{[]{std::async(callback_function).get();}}.detach();
v.s.
std::async(callback_function).get(); //Main thread will have to wait
What about asynchronous callbacks in JavaScript? In JS callbacks are massively used... How does V8 implement them? Does V8 create a lot of threads to listen on them and execute callback when it gets message? Or does it use one thread to listen on all the callbacks and keep refreshing?
For example,
setInterval(function(){},1000);
setInterval(function(){},2000);
Does V8 create 2 threads and monitor each callback state, or it has a pool thing to monitor all the callbacks?
V8 does not implement asynchronous functions with callbacks (including setInterval). Engine simply provides a way to execute JavaScript code.
As a V8 embedder you can create setInterval JavaScript function linked to your native C++ function that does what you want. For example, create thread or schedule some job. At this point it is your responsibility to call provided callback when it is necessary. Only one thread at a time can use V8 engine (V8 isolate instance) to execute code. This means synchronization is required if a callback needs to be called from another thread. V8 provides locking mechanism is you need this.
Another more common approach to solve this problem is to create a queue of functions for V8 to execute and use infinite queue processing loop to execute code on one thread. This is basically an event loop. This way you don't need to use execution lock, but instead use another thread to push callback function to a queue.
So it depends on a browser/Node.js/other embedder how they implement it.
TL;DR: To implement asynchronous callback is basically to allow the control flow to proceed without blocking for the callback. Before the callback function is finally called, the control flow is free to execute anything that has no dependence on the callback's result, e.g., the caller can proceed as if the callback function has returned, or the caller may yield its control to other functions.
Since the question is for general implementation rather than a specific language, my answer tries to be as general as to cover the implementation commonalities.
Different languages have different implementations for asynchronous callbacks, but the principles are the same. The key is to decouple the control flow from the code executed. They correspond to the execution context (like a thread of control with a runtime stack) and the executed task. Traditionally the execution context and the executed task are usually 1:1 associated. With asynchronous callbacks, they are decoupled.
1. The principles
To decouple the control flow from the code, it is helpful to think of every asynchronous callback as a conditional task. When the code registers an asynchronous callback, it virtually installs the task's condition in the system. The callback function is then invoked when the condition is satisfied. To support this, a condition monitoring mechanism and a task scheduler are needed, so that,
The programmer does not need to track the callback's condition;
Before the condition is satisfied, the program may proceed to execute other code that does not depend on the callback's result, without blocking on the condition;
Once the condition is satisfied, the callback is guaranteed to execute. The programmer does not need to schedule its execution;
After the callback is executed, its result is accessible to the caller.
2. Implementation for Portability
For example, if your code needs to process the data from a network connection, you do not need to write the code checking the connection state. You only registers a callback that will be invoked once the data is available for processing. The dirty work of connection checking is left to the language implementation, which is known to be tricky especially when we talk about scalability and portability.
The language implementation may employ asynchronous io, nonblocking io or a thread pool or whatever techniques to check the network state for you, and once the data is ready, the callback function is then scheduled to execute. Here the control flow of your code looks like directly going from the callback registration to the callback execution, because the language hides the intermediate steps. This is the portability story.
3. Implementation for Scalability
To hide the dirty work is only part of the whole story. The other part is that, your code itself does not need to block waiting for the task condition. It does not make sense to wait for one connection's data when you have lots of network connections simultaneously and some of them may already have data ready. The control flow of your code can simply register the callback, and then moves on with other tasks (e.g., the callbacks whose conditions have been satisfied), knowing that the registered callbacks will be executed anyway when their data are available.
If to satisfy the callback's condition does not involve much of the CPU (e.g., waiting for a timer, or waiting for the data from network), and the callback function itself is light-weighted, then single CPU (or single thread) is able to process lots of callbacks concurrently, such as incoming network requests processing. Here the control flow may look like jumping from one callback to another. This is the scalability story.
4. Implementation for Parallelism
Sometimes, the callbacks are not pending for non-blocking IO condition, but for blocking operations such as page fault; or the callbacks do not rely on any condition, but are pure computation logics. In this case, asynchronous callback does not save you the CPU waiting time (because there is no idle waiting). But since asynchronous callback implies that the callback function can be executed in parallel with the caller or other callbacks (subject to certain data sharing and synchronization constraints), the language implementation can dispatch the callback tasks to different threads, achieving the benefits of parallelism, if the platform has more than one hardware thread context. It still improves scalability.
5. Implementation for Productivity
The productivity with asynchronous callback may not be very positive when the code need to deal with chained callbacks, i.e., when callbacks register other callbacks in recursive way, known as callback hell. There are ways to rescue.
The semantics of an asynchronous callback can be explored so as to substitute the hopeless nested callbacks with other language constructs. Basically there can be two different views of callbacks:
From data flow point of view: asynchronous callback = event + task.
To register a callback essentially generates an event that will emit
when the task condition is satisfied. In this view, the chained
callbacks are just events whose processing triggers other event
emission. It can be naturally implemented in event-driven
programming, where the task execution is driven by events. Promise
and Observable may also be regarded as event-driven concept. When
multiple events are ready concurrently, their associated tasks can
be executed concurrently as well.
From control flow point of view: to register a callback yields the
control to other code, and the callback execution just resumes the
control flow once its condition is satisfied. In this view, chained
asynchronous callbacks are just resumable functions. Multiple
callbacks can be written as one after another in traditional
"synchronous" way, with yield operation in between (or await). It
actually becomes coroutine.
I haven't discussed the implementation of data passing between the asynchronous callback and its caller, but that is usually not difficult if using shared memory where caller and callback can share data. Actually Golang's channel can also be considered in line of yield/await but with its focus on data passing.
The callbacks that are passed to browser APIs, like setTimeout, are pushed into the same browser queue when the API has done its job.
The engine can check this queue when the stack is empty and push the next callback into the JS stack for execution.
You don’t have to monitor the progress of the API calls, you asked it to do a job and it will put your callback in the queue when it’s done.
Do I need to do anything to make all requests asynchronous or are they automatically handled that way?
I ran some tests and it appears that each request comes in on its own thread, but I figure better to ask as I might have tested wrong.
Update: (I have a bad habit of not explaining fully - sorry) Here's my concern. A client browser makes a REST request to my server of http://data.domain/com/employee_database/?query=state:Colorado. That comes in to the appropriate method in the controller. That method queries the database and returns an object which is then turned into a JSON structure and returned to the calling app.
Now let's say 10,000 clients all make a similar query to the same server. So I have 10,000 requests coming in at once. Will my controller method be called simultaneously in 10,000 distinct threads? Or must the first request return before the second request is called?
I'm not asking about the code in my handler method having asynchronous components. For my case the request becomes a single SQL query so the code has nothing that can be handled asynchronously. And until I get the requested data, I can't return from the method.
No REST is not async by default. the request are handled synchronously. However, your web server (IIS) has a number of max threads setting which can work at the same time, and it maintains a queue of the request received. So, the request goes in the queue and if a thread is available it gets executed else, the request waits in the IIS queue till a thread is available
I think you should be using async IO/operations such as database calls in your case. Yes in Web Api, every request has its own thread, but threads can run out if there are many consecutive requests. Also threads use memory so if your api gets hit by too many request it may put pressure on your system.
The benefit of using async over sync is that you use your system resources wisely. Instead of blocking the thread while it is waiting for the database call to complete in sync implementation, the async will free the thread to handle more requests or assign it what ever process needs a thread. Once IO (database) call completes, another thread will take it from there and continue with the implementation. Async will also make your api run faster if your IO operations take longer to complete.
To be honest, your question is not very clear. If you are making an HTTP GET using HttpClient, say the GetAsync method, request is fired and you can do whatever you want in your thread until the time you get the response back. So, this request is asynchronous. If you are asking about the server side, which handles this request (assuming it is ASP.NET Web API), then asynchronous or not is up to how you implemented your web API. If your action method, does three things, say 1, 2, and 3 one after the other synchronously in blocking mode, the same thread is going to the service the request. On the other hand, say #2 above is a call to a web service and it is an HTTP call. Now, if you use HttpClient and you make an asynchronous call, you can get into a situation where one request is serviced by more than one thread. For that to happen, you should have made the HTTP call from your action method asynchronously and used async keyword. In that case, when you call await inside the action method, your action method execution returns and the thread servicing your request is free to service some other request and ultimately when the response is available, the same or some other thread will continue from where it was left off previously. Long boring answer, perhaps but difficult to explain just through words by typing, I guess. Hope you get some clarity.
UPDATE:
Your action method will execute in parallel in 10,000 threads (ideally). Why I'm saying ideally is because a CLR thread pool having 10,000 threads is not typical and probably impractical as well. There are physical limits as well as limits imposed by the framework as well but I guess the answer to your question is that the requests will be serviced in parallel. The correct term here will be 'parallel' but not 'async'.
Whether it is sync or async is your choice. You choose by the way to write your action. If you return a Task, and also use async IO under the hood, it is async. In other cases it is synchronous.
Don't feel tempted to slap async on your action and use Task.Run. That is async-over-sync (a known anti-pattern). It must be truly async all the way down to the OS kernel.
No framework can make sync IO automatically async, so it cannot happen under the hood. Async IO is callback-based which is a severe change in programming model.
This does not answer what you should do of course. That would be a new question.