I have a method that runs multiple async methods within it. I have to iterate over a list of devices, and pass the device to this method. I am noticing that this is taking a long time to complete so I am thinking of using Parallel.ForEach so it can run this process against multiple devices at the same time.
Let's say this is my method.
public async Task ProcessDevice(Device device) {
var dev = await _deviceService.LookupDeviceIndbAsNoTracking(device);
var result = await DoSomething(dev);
await DoSomething2(dev);
}
Then DoSomething2 also calls an async method.
public async Task DoSomething2(Device dev) {
foreach(var obj in dev.Objects) {
await DoSomething3(obj);
}
}
The list of devices continuously gets larger over time, so the more this list grows, the longer it takes the program to finish running ProcessDevice() against each device. I would like to process more than one device at a time. So I have been looking into using Parallel.ForEach.
Parallel.ForEach(devices, async device => {
try {
await ProcessDevice(device);
} catch (Exception ex) {
throw ex;
}
})
It appears that the program is finishing before the device is fully processed. I have also tried creating a list of tasks, and then foreach device, add a new task running ProcessDevice to that list and then awaiting Task.WhenAll(listOfTasks);
var listOfTasks = new List<Task>();
foreach(var device in devices) {
var task = Task.Run(async () => await ProcessDevice(device));
listOfTasks.Add(task);
}
await Task.WhenAll(listOfTasks);
But it appears that the task is marked as completed before ProcessDevice() is actually finished running.
Please excuse my ignorance on this issue as I am new to parallel processing and not sure what is going on. What is happening to cause this behavior and is there any documentation that you could provide that could help me better understand what to do?
You can't mix async with Parallel.ForEach. Since your underlying operation is asynchronous, you'd want to use asynchronous concurrency, not parallelism. Asynchronous concurrency is most easily expressed with WhenAll:
var listOfTasks = devices.Select(ProcessDevice).ToList();
await Task.WhenAll(listOfTasks);
In your last example there's a few problems:
var listOfTasks = new List<Task>();
foreach (var device in devices)
{
await Task.Run(async () => await ProcessDevice(device));
}
await Task.WhenAll(listOfTasks);
Doing await Task.Run(async () => await ProcessDevice(device)); means you are not moving to the next iteration of the foreach loop until the previous one is done. Essentially, you're still doing them one at a time.
Additionally, you aren't adding any tasks to listOfTasks so it remains empty and therefore Task.WhenAll(listOfTasks) completes instantly because there's no tasks to await.
Try this:
var listOfTasks = new List<Task>();
foreach (var device in devices)
{
var task = Task.Run(async () => await ProcessDevice(device))
listOfTasks.Add(task);
}
await Task.WhenAll(listOfTasks);
I can explain the problem with Parallel.ForEach. An important thing to understand is that when the await keyword acts on an incomplete Task, it returns. It will return its own incomplete Task if the method signature allows (if it's not void). Then it is up to the caller to use that Task object to wait for the job to finish.
But the second parameter in Parallel.ForEach is an Action<T>, which is a void method, which means no Task can be returned, which means the caller (Parallel.ForEach in this case) has no way to wait until the job has finished.
So in your case, as soon as it hits await ProcessDevice(device), it returns and nothing waits for it to finish so it starts the next iteration. By the time Parallel.ForEach is finished, all it has done is started all the tasks, but not waited for them.
So don't use Parallel.ForEach with asynchronous code.
Stephen's answer is more appropriate. You can also use WSC's answer, but that can be dangerous with larger lists. Creating hundreds or thousands of new threads all at once will not help your performance.
not very sure it this if what you are asking for, but I can give example of how we start async process
private readonly Func<Worker> _worker;
private void StartWorkers(IEnumerable<Props> props){
Parallel.ForEach(props, timestamp => { _worker.Invoke().Consume(timestamp); });
}
Would recommend reading about Parallel.ForEach as it will do some part for you.
Related
I'm working on a problem where I have to delete records using a service call. The issue is that I have a for each loop where i have multiple await operations.This is making the operation take lot of time and performance is lacking
foreach(var a in list<long>b)
{
await _serviceresolver().DeleteOperationAsync(id,a)
}
The issue is that I have a for each loop where i have multiple await operations.
This is making the operation take lot of time and performance is lacking
The number one solution is to reduce the number of calls. This is often called "chunky" over "chatty". So if your service supports some kind of bulk-delete operation, then expose it in your service type and then you can just do:
await _serviceresolver().BulkDeleteOperationAsync(id, b);
But if that isn't possible, then you can at least use asynchronous concurrency. This is quite different from parallelism; you don't want to use Parallel or PLINQ.
var service = _serviceresolver();
var tasks = b.Select(a => service.DeleteOperationAsync(id, a)).ToList();
await Task.WhenAll(tasks);
I do not know what code is behind this DeleteOperationAsync, but for sure async/await isn't designed to speed things up. It was designated to "spare" threads (colloquially speaking)
The best would be to change the method to take as a parameter the whole list of ids - instead of taking and sending just one id.
And then to perform this async/await heavy operation only once for all of the ids.
If that is not possible, you could just run it in parallel using TPL (but it is ready the worst-case scenario - really:) )
Parallel.ForEach(listOfIdsToDelete,
async idToDelete => await _serviceresolver().DeleteOperationAsync(id,idToDelete)
);
You're waiting for each async operation to finish right now. If you can fire them all off concurrently, you can just call them without the await, or if you need to know when they finish, you can just fire them all off and then wait for them all to finish by tracking the tasks in a list:
List<Task> tasks = new List<Task>();
foreach (var a in List<long> b)
tasks.Add(_serviceresolveer().DeleteOperationAsync(id, a));
await Task.WhenAll(tasks);
You can use PLINQ (to leverage of all the processors of your machine) and the Task.WhenAll method (to no freeze the calling thread). In code, resulting something like this:
class Program {
static async Task Main(string[] args) {
var list = new List<long> {
4, 3, 2
};
var service = new Service();
var response =
from item in list.AsParallel()
select service.DeleteOperationAsync(item);
await Task.WhenAll(response);
}
}
public class Service {
public async Task DeleteOperationAsync(long value) {
await Task.Delay(2000);
Console.WriteLine($"Finished... {value}");
}
}
I've got an async method, GetExpensiveThing(), which performs some expensive I/O work. This is how I am using it:
// Serial execution
public async Task<List<Thing>> GetThings()
{
var first = await GetExpensiveThing();
var second = await GetExpensiveThing();
return new List<Thing>() { first, second };
}
But since it's an expensive method, I want to execute these calls in in parallel. I would have thought moving the awaits would have solved this:
// Serial execution
public async Task<List<Thing>> GetThings()
{
var first = GetExpensiveThing();
var second = GetExpensiveThing();
return new List<Thing>() { await first, await second };
}
That didn't work, so I wrapped them in some tasks and this works:
// Parallel execution
public async Task<List<Thing>> GetThings()
{
var first = Task.Run(() =>
{
return GetExpensiveThing();
});
var second = Task.Run(() =>
{
return GetExpensiveThing();
});
return new List<Thing>() { first.Result, second.Result };
}
I even tried playing around with awaits and async in and around the tasks, but it got really confusing and I had no luck.
Is there a better to run async methods in parallel, or are tasks a good approach?
Is there a better to run async methods in parallel, or are tasks a good approach?
Yes, the "best" approach is to utilize the Task.WhenAll method. However, your second approach should have ran in parallel. I have created a .NET Fiddle, this should help shed some light. Your second approach should actually be running in parallel. My fiddle proves this!
Consider the following:
public Task<Thing[]> GetThingsAsync()
{
var first = GetExpensiveThingAsync();
var second = GetExpensiveThingAsync();
return Task.WhenAll(first, second);
}
Note
It is preferred to use the "Async" suffix, instead of GetThings and GetExpensiveThing - we should have GetThingsAsync and GetExpensiveThingAsync respectively - source.
Task.WhenAll() has a tendency to become unperformant with large scale/amount of tasks firing simultaneously - without moderation/throttling.
If you are doing a lot of tasks in a list and wanting to await the final outcome, then I propose using a partition with a limit on the degree of parallelism.
I have modified Stephen Toub's blog elegant approach to modern LINQ:
public static Task ParallelForEachAsync<T>(this IEnumerable<T> source, Func<T, Task> funcBody, int maxDoP = 4)
{
async Task AwaitPartition(IEnumerator<T> partition)
{
using (partition)
{
while (partition.MoveNext())
{
await Task.Yield(); // prevents a sync/hot thread hangup
await funcBody(partition.Current);
}
}
}
return Task.WhenAll(
Partitioner
.Create(source)
.GetPartitions(maxDoP)
.AsParallel()
.Select(p => AwaitPartition(p)));
}
How it works is simple, take an IEnumerable - dissect it into evenish partitions and the fire a function/method against each element, in each partition, at the same time. No more than one element in each partition at anyone time, but n Tasks in n partitions.
Extension Usage:
await myList.ParallelForEachAsync(myFunc, Environment.ProcessorCount);
Edit:
I now keep some overloads in a repository on Github if you need more options. It's in a NuGet too for NetStandard.
Edit 2: Thanks to comments from Theodor below, I was able to mitigate poorly written Async Tasks from blocking parallelism by using await Task.Yield();.
You can your the Task.WhenAll, which returns when all depending tasks are done
Check this question here for reference
If GetExpensiveThing is properly asynchronous (meaning it doesn't do any IO or CPU work synchronously), your second solution of invoking both methods and then awaiting the results should've worked. You could've also used Task.WhenAll.
However, if it isn't, you may get better results by posting each task to the thread-pool and using the Task.WhenAll combinator, e.g.:
public Task<IList<Thing>> GetThings() =>
Task.WhenAll(Task.Run(() => GetExpensiveThing()), Task.Run(() => GetExpensiveThing()));
(Note I changed the return type to IList to avoid awaits altogether.)
You should avoid using the Result property. It causes the caller thread to block and wait for the task to complete, unlike await or Task.WhenAll which use continuations.
My code is continuing to execute before all tasks have been completed.
I've had a look at other people with a similar problem but can't see anything obvious!
static Task MoveAccountAsync(MoverParams moverParams)
{
return Task.Run(() =>
{
Console.WriteLine("Moving {0}", moverParams.Account.Name);
moverParams.Account.Mover.RefreshRoom();
moverParams.Account.Mover.PathfindTo(moverParams.Room);
});
}
static async void MoveAccountsAsync(List<Account> accounts, int room)
{
List<Task> theTasks = new List<Task>();
foreach (Account account in accounts)
{
// Create a new task and add it to the task list
theTasks.Add(MoveAccountAsync(new MoverParams(account, room)));
}
await Task.WhenAll(theTasks);
Console.WriteLine("Finished moving.");
}
Then simply calling it from static main:
MoveAccountsAsync(theAccounts, room);
Help much appreciated!
Cheers,
Dave
async void methods are highly discouraged and often times (e.g. here) sign of an issue.
Because you're not awaiting your method call (and you can't await it because it returns void) caller will not wait for all the work to finish before moving on to the next statement.
Change your method to return Task and await it to fix the problem. If you're calling into MoveAccountsAsync from synchronous context (e.g. Main method) use Wait to wait on the results. But be aware that in certain conditions (e.g. if run as part of ASP.NET application) that might cause deadlocks.
I have an enumeration of items (RunData.Demand), each representing some work involving calling an API over HTTP. It works great if I just foreach through it all and call the API during each iteration. However, each iteration takes a second or two so I'd like to run 2-3 threads and divide up the work between them. Here's what I'm doing:
ThreadPool.SetMaxThreads(2, 5); // Trying to limit the amount of threads
var tasks = RunData.Demand
.Select(service => Task.Run(async delegate
{
var availabilityResponse = await client.QueryAvailability(service);
// Do some other stuff, not really important
}));
await Task.WhenAll(tasks);
The client.QueryAvailability call basically calls an API using the HttpClient class:
public async Task<QueryAvailabilityResponse> QueryAvailability(QueryAvailabilityMultidayRequest request)
{
var response = await client.PostAsJsonAsync("api/queryavailabilitymultiday", request);
if (response.IsSuccessStatusCode)
{
return await response.Content.ReadAsAsync<QueryAvailabilityResponse>();
}
throw new HttpException((int) response.StatusCode, response.ReasonPhrase);
}
This works great for a while, but eventually things start timing out. If I set the HttpClient Timeout to an hour, then I start getting weird internal server errors.
What I started doing was setting a Stopwatch within the QueryAvailability method to see what was going on.
What's happening is all 1200 items in RunData.Demand are being created at once and all 1200 await client.PostAsJsonAsync methods are being called. It appears it then uses the 2 threads to slowly check back on the tasks, so towards the end I have tasks that have been waiting for 9 or 10 minutes.
Here's the behavior I would like:
I'd like to create the 1,200 tasks, then run them 3-4 at a time as threads become available. I do not want to queue up 1,200 HTTP calls immediately.
Is there a good way to go about doing this?
As I always recommend.. what you need is TPL Dataflow (to install: Install-Package System.Threading.Tasks.Dataflow).
You create an ActionBlock with an action to perform on each item. Set MaxDegreeOfParallelism for throttling. Start posting into it and await its completion:
var block = new ActionBlock<QueryAvailabilityMultidayRequest>(async service =>
{
var availabilityResponse = await client.QueryAvailability(service);
// ...
},
new ExecutionDataflowBlockOptions { MaxDegreeOfParallelism = 4 });
foreach (var service in RunData.Demand)
{
block.Post(service);
}
block.Complete();
await block.Completion;
Old question, but I would like to propose an alternative lightweight solution using the SemaphoreSlim class. Just reference System.Threading.
SemaphoreSlim sem = new SemaphoreSlim(4,4);
foreach (var service in RunData.Demand)
{
await sem.WaitAsync();
Task t = Task.Run(async () =>
{
var availabilityResponse = await client.QueryAvailability(serviceCopy));
// do your other stuff here with the result of QueryAvailability
}
t.ContinueWith(sem.Release());
}
The semaphore acts as a locking mechanism. You can only enter the semaphore by calling Wait (WaitAsync) which subtracts one from the count. Calling release adds one to the count.
You're using async HTTP calls, so limiting the number of threads will not help (nor will ParallelOptions.MaxDegreeOfParallelism in Parallel.ForEach as one of the answers suggests). Even a single thread can initiate all requests and process the results as they arrive.
One way to solve it is to use TPL Dataflow.
Another nice solution is to divide the source IEnumerable into partitions and process items in each partition sequentially as described in this blog post:
public static Task ForEachAsync<T>(this IEnumerable<T> source, int dop, Func<T, Task> body)
{
return Task.WhenAll(
from partition in Partitioner.Create(source).GetPartitions(dop)
select Task.Run(async delegate
{
using (partition)
while (partition.MoveNext())
await body(partition.Current);
}));
}
While the Dataflow library is great, I think it's a bit heavy when not using block composition. I would tend to use something like the extension method below.
Also, unlike the Partitioner method, this runs the async methods on the calling context - the caveat being that if your code is not truly async, or takes a 'fast path', then it will effectively run synchronously since no threads are explicitly created.
public static async Task RunParallelAsync<T>(this IEnumerable<T> items, Func<T, Task> asyncAction, int maxParallel)
{
var tasks = new List<Task>();
foreach (var item in items)
{
tasks.Add(asyncAction(item));
if (tasks.Count < maxParallel)
continue;
var notCompleted = tasks.Where(t => !t.IsCompleted).ToList();
if (notCompleted.Count >= maxParallel)
await Task.WhenAny(notCompleted);
}
await Task.WhenAll(tasks);
}
So here's the situation: I need to make a call to a web site that starts a search. This search continues for an unknown amount of time, and the only way I know if the search has finished is by periodically querying the website to see if there's a "Download Data" link somewhere on it (it uses some strange ajax call on a javascript timer to check the backend and update the page, I think).
So here's the trick: I have hundreds of items I need to search for, one at a time. So I have some code that looks a little bit like this:
var items = getItems();
Parallel.ForEach(items, item =>
{
startSearch(item);
var finished = isSearchFinished(item);
while(finished == false)
{
finished = isSearchFinished(item); //<--- How do I delay this action 30 Secs?
}
downloadData(item);
}
Now, obviously this isn't the real code, because there could be things that cause isSearchFinished to always be false.
Obvious infinite loop danger aside, how would I correctly keep isSearchFinished() from calling over and over and over, but instead call every, say, 30 seconds or 1 minute?
I know Thread.Sleep() isn't the right solution, and I think the solution might be accomplished by using Threading.Timer() but I'm not very familiar with it, and there are so many threading options that I'm just not sure which to use.
It's quite easy to implement with tasks and async/await, as noted by #KevinS in the comments:
async Task<ItemData> ProcessItemAsync(Item item)
{
while (true)
{
if (await isSearchFinishedAsync(item))
break;
await Task.Delay(30 * 1000);
}
return await downloadDataAsync(item);
}
// ...
var items = getItems();
var tasks = items.Select(i => ProcessItemAsync(i)).ToArray();
await Task.WhenAll(tasks);
var data = tasks.Select(t = > t.Result);
This way, you don't block ThreadPool threads in vain for what is mostly a bunch of I/O-bound network operations. If you're not familiar with async/await, the async-await tag wiki might be a good place to start.
I assume you can convert your synchronous methods isSearchFinished and downloadData to asynchronous versions using something like HttpClient for non-blocking HTTP request and returning a Task<>. If you are unable to do so, you still can simply wrap them with Task.Run, as await Task.Run(() => isSearchFinished(item)) and await Task.Run(() => downloadData(item)). Normally this is not recommended, but as you have hundreds of items, it sill would give you a much better level of concurrency than with Parallel.ForEach in this case, because you won't be blocking pool threads for 30s, thanks to asynchronous Task.Delay.
You can also write a generic function using TaskCompletionSource and Threading.Timer to return a Task that becomes complete once a specified retry function succeeds.
public static Task RetryAsync(Func<bool> retryFunc, TimeSpan retryInterval)
{
return RetryAsync(retryFunc, retryInterval, CancellationToken.None);
}
public static Task RetryAsync(Func<bool> retryFunc, TimeSpan retryInterval, CancellationToken cancellationToken)
{
var tcs = new TaskCompletionSource<object>();
cancellationToken.Register(() => tcs.TrySetCanceled());
var timer = new Timer((state) =>
{
var taskCompletionSource = (TaskCompletionSource<object>) state;
try
{
if (retryFunc())
{
taskCompletionSource.TrySetResult(null);
}
}
catch (Exception ex)
{
taskCompletionSource.TrySetException(ex);
}
}, tcs, TimeSpan.FromMilliseconds(0), retryInterval);
// Once the task is complete, dispose of the timer so it doesn't keep firing. Also captures the timer
// in a closure so it does not get disposed.
tcs.Task.ContinueWith(t => timer.Dispose(),
CancellationToken.None,
TaskContinuationOptions.ExecuteSynchronously,
TaskScheduler.Default);
return tcs.Task;
}
You can then use RetryAsync like this:
var searchTasks = new List<Task>();
searchTasks.AddRange(items.Select(
downloadItem => RetryAsync( () => isSearchFinished(downloadItem), TimeSpan.FromSeconds(2)) // retry timout
.ContinueWith(t => downloadData(downloadItem),
CancellationToken.None,
TaskContinuationOptions.OnlyOnRanToCompletion,
TaskScheduler.Default)));
await Task.WhenAll(searchTasks.ToArray());
The ContinueWith part specifies what you do once the task has completed successfully. In this case it will run your downloadData method on a thread pool thread because we specified TaskScheduler.Default and the continuation will only execute if the task ran to completion, i.e. it was not canceled and no exception was thrown.