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Creating a class that runs tasks sequentially [duplicate]

I know that asynchronous programming has seen a lot of changes over the years. I'm somewhat embarrassed that I let myself get this rusty at just 34 years old, but I'm counting on StackOverflow to bring me up to speed.
What I am trying to do is manage a queue of "work" on a separate thread, but in such a way that only one item is processed at a time. I want to post work on this thread and it doesn't need to pass anything back to the caller. Of course I could simply spin up a new Thread object and have it loop over a shared Queue object, using sleeps, interrupts, wait handles, etc. But I know things have gotten better since then. We have BlockingCollection, Task, async/await, not to mention NuGet packages that probably abstract a lot of that.
I know that "What's the best..." questions are generally frowned upon so I'll rephrase it by saying "What is the currently recommended..." way to accomplish something like this using built-in .NET mechanisms preferably. But if a third party NuGet package simplifies things a bunch, it's just as well.
I considered a TaskScheduler instance with a fixed maximum concurrency of 1, but seems there is probably a much less clunky way to do that by now.
Background
Specifically, what I am trying to do in this case is queue an IP geolocation task during a web request. The same IP might wind up getting queued for geolocation multiple times, but the task will know how to detect that and skip out early if it's already been resolved. But the request handler is just going to throw these () => LocateAddress(context.Request.UserHostAddress) calls into a queue and let the LocateAddress method handle duplicate work detection. The geolocation API I am using doesn't like to be bombarded with requests which is why I want to limit it to a single concurrent task at a time. However, it would be nice if the approach was allowed to easily scale to more concurrent tasks with a simple parameter change.

To create an asynchronous single degree of parallelism queue of work you can simply create a SemaphoreSlim, initialized to one, and then have the enqueing method await on the acquisition of that semaphore before starting the requested work.
public class TaskQueue
{
private SemaphoreSlim semaphore;
public TaskQueue()
{
semaphore = new SemaphoreSlim(1);
}
public async Task<T> Enqueue<T>(Func<Task<T>> taskGenerator)
{
await semaphore.WaitAsync();
try
{
return await taskGenerator();
}
finally
{
semaphore.Release();
}
}
public async Task Enqueue(Func<Task> taskGenerator)
{
await semaphore.WaitAsync();
try
{
await taskGenerator();
}
finally
{
semaphore.Release();
}
}
}
Of course, to have a fixed degree of parallelism other than one simply initialize the semaphore to some other number.

Your best option as I see it is using TPL Dataflow's ActionBlock:
var actionBlock = new ActionBlock<string>(address =>
{
if (!IsDuplicate(address))
{
LocateAddress(address);
}
});
actionBlock.Post(context.Request.UserHostAddress);
TPL Dataflow is robust, thread-safe, async-ready and very configurable actor-based framework (available as a nuget)
Here's a simple example for a more complicated case. Let's assume you want to:
Enable concurrency (limited to the available cores).
Limit the queue size (so you won't run out of memory).
Have both LocateAddress and the queue insertion be async.
Cancel everything after an hour.
var actionBlock = new ActionBlock<string>(async address =>
{
if (!IsDuplicate(address))
{
await LocateAddressAsync(address);
}
}, new ExecutionDataflowBlockOptions
{
BoundedCapacity = 10000,
MaxDegreeOfParallelism = Environment.ProcessorCount,
CancellationToken = new CancellationTokenSource(TimeSpan.FromHours(1)).Token
});
await actionBlock.SendAsync(context.Request.UserHostAddress);

Actually you don't need to run tasks in one thread, you need them to run serially (one after another), and FIFO. TPL doesn't have class for that, but here is my very lightweight, non-blocking implementation with tests. https://github.com/Gentlee/SerialQueue
Also have #Servy implementation there, tests show it is twice slower than mine and it doesn't guarantee FIFO.
Example:
private readonly SerialQueue queue = new SerialQueue();
async Task SomeAsyncMethod()
{
var result = await queue.Enqueue(DoSomething);
}

Use BlockingCollection<Action> to create a producer/consumer pattern with one consumer (only one thing running at a time like you want) and one or many producers.
First define a shared queue somewhere:
BlockingCollection<Action> queue = new BlockingCollection<Action>();
In your consumer Thread or Task you take from it:
//This will block until there's an item available
Action itemToRun = queue.Take()
Then from any number of producers on other threads, simply add to the queue:
queue.Add(() => LocateAddress(context.Request.UserHostAddress));

I'm posting a different solution here. To be honest I'm not sure whether this is a good solution.
I'm used to use BlockingCollection to implement a producer/consumer pattern, with a dedicated thread consuming those items. It's fine if there are always data coming in and consumer thread won't sit there and do nothing.
I encountered a scenario that one of the application would like to send emails on a different thread, but total number of emails is not that big.
My initial solution was to have a dedicated consumer thread (created by Task.Run()), but a lot of time it just sits there and does nothing.
Old solution:
private readonly BlockingCollection<EmailData> _Emails =
new BlockingCollection<EmailData>(new ConcurrentQueue<EmailData>());
// producer can add data here
public void Add(EmailData emailData)
{
_Emails.Add(emailData);
}
public void Run()
{
// create a consumer thread
Task.Run(() =>
{
foreach (var emailData in _Emails.GetConsumingEnumerable())
{
SendEmail(emailData);
}
});
}
// sending email implementation
private void SendEmail(EmailData emailData)
{
throw new NotImplementedException();
}
As you can see, if there are not enough emails to be sent (and it is my case), the consumer thread will spend most of them sitting there and do nothing at all.
I changed my implementation to:
// create an empty task
private Task _SendEmailTask = Task.Run(() => {});
// caller will dispatch the email to here
// continuewith will use a thread pool thread (different to
// _SendEmailTask thread) to send this email
private void Add(EmailData emailData)
{
_SendEmailTask = _SendEmailTask.ContinueWith((t) =>
{
SendEmail(emailData);
});
}
// actual implementation
private void SendEmail(EmailData emailData)
{
throw new NotImplementedException();
}
It's no longer a producer/consumer pattern, but it won't have a thread sitting there and does nothing, instead, every time it is to send an email, it will use thread pool thread to do it.

My lib, It can:
Run random in queue list
Multi queue
Run prioritize first
Re-queue
Event all queue completed
Cancel running or cancel wait for running
Dispatch event to UI thread
public interface IQueue
{
bool IsPrioritize { get; }
bool ReQueue { get; }
/// <summary>
/// Dont use async
/// </summary>
/// <returns></returns>
Task DoWork();
bool CheckEquals(IQueue queue);
void Cancel();
}
public delegate void QueueComplete<T>(T queue) where T : IQueue;
public delegate void RunComplete();
public class TaskQueue<T> where T : IQueue
{
readonly List<T> Queues = new List<T>();
readonly List<T> Runnings = new List<T>();
[Browsable(false), DefaultValue((string)null)]
public Dispatcher Dispatcher { get; set; }
public event RunComplete OnRunComplete;
public event QueueComplete<T> OnQueueComplete;
int _MaxRun = 1;
public int MaxRun
{
get { return _MaxRun; }
set
{
bool flag = value > _MaxRun;
_MaxRun = value;
if (flag && Queues.Count != 0) RunNewQueue();
}
}
public int RunningCount
{
get { return Runnings.Count; }
}
public int QueueCount
{
get { return Queues.Count; }
}
public bool RunRandom { get; set; } = false;
//need lock Queues first
void StartQueue(T queue)
{
if (null != queue)
{
Queues.Remove(queue);
lock (Runnings) Runnings.Add(queue);
queue.DoWork().ContinueWith(ContinueTaskResult, queue);
}
}
void RunNewQueue()
{
lock (Queues)//Prioritize
{
foreach (var q in Queues.Where(x => x.IsPrioritize)) StartQueue(q);
}
if (Runnings.Count >= MaxRun) return;//other
else if (Queues.Count == 0)
{
if (Runnings.Count == 0 && OnRunComplete != null)
{
if (Dispatcher != null && !Dispatcher.CheckAccess()) Dispatcher.Invoke(OnRunComplete);
else OnRunComplete.Invoke();//on completed
}
else return;
}
else
{
lock (Queues)
{
T queue;
if (RunRandom) queue = Queues.OrderBy(x => Guid.NewGuid()).FirstOrDefault();
else queue = Queues.FirstOrDefault();
StartQueue(queue);
}
if (Queues.Count > 0 && Runnings.Count < MaxRun) RunNewQueue();
}
}
void ContinueTaskResult(Task Result, object queue_obj) => QueueCompleted((T)queue_obj);
void QueueCompleted(T queue)
{
lock (Runnings) Runnings.Remove(queue);
if (queue.ReQueue) lock (Queues) Queues.Add(queue);
if (OnQueueComplete != null)
{
if (Dispatcher != null && !Dispatcher.CheckAccess()) Dispatcher.Invoke(OnQueueComplete, queue);
else OnQueueComplete.Invoke(queue);
}
RunNewQueue();
}
public void Add(T queue)
{
if (null == queue) throw new ArgumentNullException(nameof(queue));
lock (Queues) Queues.Add(queue);
RunNewQueue();
}
public void Cancel(T queue)
{
if (null == queue) throw new ArgumentNullException(nameof(queue));
lock (Queues) Queues.RemoveAll(o => o.CheckEquals(queue));
lock (Runnings) Runnings.ForEach(o => { if (o.CheckEquals(queue)) o.Cancel(); });
}
public void Reset(T queue)
{
if (null == queue) throw new ArgumentNullException(nameof(queue));
Cancel(queue);
Add(queue);
}
public void ShutDown()
{
MaxRun = 0;
lock (Queues) Queues.Clear();
lock (Runnings) Runnings.ForEach(o => o.Cancel());
}
}

I know this thread is old, but it seems all the present solutions are extremely onerous. The simplest way I could find uses the Linq Aggregate function to create a daisy-chained list of tasks.
var arr = new int[] { 1, 2, 3, 4, 5};
var queue = arr.Aggregate(Task.CompletedTask,
(prev, item) => prev.ContinueWith(antecedent => PerformWorkHere(item)));
The idea is to get your data into an IEnumerable (I'm using an int array), and then reduce that enumerable to a chain of tasks, starting with a default, completed, task.

How to limit number of async IO tasks to database?

I have a list of id's and I want to get data for each of those id in parallel from database. My below ExecuteAsync method is called at very high throughput and for each request we have around 500 ids for which I need to extract data.
So I have got below code where I am looping around list of ids and making async calls for each of those id in parallel and it works fine.
private async Task<List<T>> ExecuteAsync<T>(IList<int> ids, IPollyPolicy policy,
Func<CancellationToken, int, Task<T>> mapper) where T : class
{
var tasks = new List<Task<T>>(ids.Count);
// invoking multiple id in parallel to get data for each id from database
for (int i = 0; i < ids.Count; i++)
{
tasks.Add(Execute(policy, ct => mapper(ct, ids[i])));
}
// wait for all id response to come back
var responses = await Task.WhenAll(tasks);
var excludeNull = new List<T>(ids.Count);
for (int i = 0; i < responses.Length; i++)
{
var response = responses[i];
if (response != null)
{
excludeNull.Add(response);
}
}
return excludeNull;
}
private async Task<T> Execute<T>(IPollyPolicy policy,
Func<CancellationToken, Task<T>> requestExecuter) where T : class
{
var response = await policy.Policy.ExecuteAndCaptureAsync(
ct => requestExecuter(ct), CancellationToken.None);
if (response.Outcome == OutcomeType.Failure)
{
if (response.FinalException != null)
{
// log error
throw response.FinalException;
}
}
return response?.Result;
}
Question:
Now as you can see I am looping all ids and making bunch of async calls to database in parallel for each id which can put lot of load on database (depending on how many request is coming). So I want to limit the number of async calls we are making to database. I modified ExecuteAsync to use Semaphore as shown below but it doesn't look like it does what I want it to do:
private async Task<List<T>> ExecuteAsync<T>(IList<int> ids, IPollyPolicy policy,
Func<CancellationToken, int, Task<T>> mapper) where T : class
{
var throttler = new SemaphoreSlim(250);
var tasks = new List<Task<T>>(ids.Count);
// invoking multiple id in parallel to get data for each id from database
for (int i = 0; i < ids.Count; i++)
{
await throttler.WaitAsync().ConfigureAwait(false);
try
{
tasks.Add(Execute(policy, ct => mapper(ct, ids[i])));
}
finally
{
throttler.Release();
}
}
// wait for all id response to come back
var responses = await Task.WhenAll(tasks);
// same excludeNull code check here
return excludeNull;
}
Does Semaphore works on Threads or Tasks? Reading it here looks like Semaphore is for Threads and SemaphoreSlim is for tasks.
Is this correct? If yes then what's the best way to fix this and limit the number of async IO tasks we make to database here.

Task is an abstraction on threads, and doesn’t necessarily create a new thread. Semaphore limits the number of threads that can access that for loop. Execute returns a Task which aren’t threads. If there’s only 1 request, there will be only 1 thread inside that for loop, even if it is asking for 500 ids. The 1 thread sends off all the async IO tasks itself.
Sort of. I would not say that tasks are related to threads at all. There are actually two kinds of tasks: a delegate task (which is kind of an abstraction of a thread), and a promise task (which has nothing to do with threads).
Regarding the SemaphoreSlim, it does limit the concurrency of a block of code (not threads).
I recently started playing with C# so my understanding is not right looks like w.r.t Threads and Tasks.
I recommend reading my async intro and best practices. Follow up with There Is No Thread if you're interested more about how threads aren't really involved.
I modified ExecuteAsync to use Semaphore as shown below but it doesn't look like it does what I want it to do
The current code is only throttling the adding of the tasks to the list, which is only done one at a time anyway. What you want to do is throttle the execution itself:
private async Task<List<T>> ExecuteAsync<T>(IList<int> ids, IPollyPolicy policy, Func<CancellationToken, int, Task<T>> mapper) where T : class
{
var throttler = new SemaphoreSlim(250);
var tasks = new List<Task<T>>(ids.Count);
// invoking multiple id in parallel to get data for each id from database
for (int i = 0; i < ids.Count; i++)
tasks.Add(ThrottledExecute(ids[i]));
// wait for all id response to come back
var responses = await Task.WhenAll(tasks);
// same excludeNull code check here
return excludeNull;
async Task<T> ThrottledExecute(int id)
{
await throttler.WaitAsync().ConfigureAwait(false);
try {
return await Execute(policy, ct => mapper(ct, id)).ConfigureAwait(false);
} finally {
throttler.Release();
}
}
}

Your colleague has probably in mind the Semaphore class, which is indeed a thread-centric throttler, with no asynchronous capabilities.
Limits the number of threads that can access a resource or pool of resources concurrently.
The SemaphoreSlim class is a lightweight alternative to Semaphore, which includes the asynchronous method WaitAsync, that makes all the difference in the world. The WaitAsync doesn't block a thread, it blocks an asynchronous workflow. Asynchronous workflows are cheap (usually less than 1000 bytes each). You can have millions of them "running" concurrently at any given moment. This is not the case with threads, because of the 1 MB of memory that each thread reserves for its stack.
As for the ExecuteAsync method, here is how you could refactor it by using the LINQ methods Select, Where, ToArray and ToList:
Update: The Polly library supports capturing and continuing on the current synchronization context, so I added a bool executeOnCurrentContext
argument to the API. I also renamed the asynchronous Execute method to ExecuteAsync, to be in par with the guidelines.
private async Task<List<T>> ExecuteAsync<T>(IList<int> ids, IPollyPolicy policy,
Func<CancellationToken, int, Task<T>> mapper,
int concurrencyLevel = 1, bool executeOnCurrentContext = false) where T : class
{
var throttler = new SemaphoreSlim(concurrencyLevel);
Task<T>[] tasks = ids.Select(async id =>
{
await throttler.WaitAsync().ConfigureAwait(executeOnCurrentContext);
try
{
return await ExecuteAsync(policy, ct => mapper(ct, id),
executeOnCurrentContext).ConfigureAwait(false);
}
finally
{
throttler.Release();
}
}).ToArray();
T[] results = await Task.WhenAll(tasks).ConfigureAwait(false);
return results.Where(r => r != null).ToList();
}
private async Task<T> ExecuteAsync<T>(IPollyPolicy policy,
Func<CancellationToken, Task<T>> function,
bool executeOnCurrentContext = false) where T : class
{
var response = await policy.Policy.ExecuteAndCaptureAsync(
ct => executeOnCurrentContext ? function(ct) : Task.Run(() => function(ct)),
CancellationToken.None, continueOnCapturedContext: executeOnCurrentContext)
.ConfigureAwait(executeOnCurrentContext);
if (response.Outcome == OutcomeType.Failure)
{
if (response.FinalException != null)
{
ExceptionDispatchInfo.Throw(response.FinalException);
}
}
return response?.Result;
}

You are throttling the rate at which you add tasks to the list. You are not throttling the rate at which tasks are executed. To do that, you'd probably have to implement your semaphore calls inside the Execute method itself.
If you can't modify Execute, another way to do it is to poll for completed tasks, sort of like this:
for (int i = 0; i < ids.Count; i++)
{
var pendingCount = tasks.Count( t => !t.IsCompleted );
while (pendingCount >= 500) await Task.Yield();
tasks.Add(Execute(policy, ct => mapper(ct, ids[i])));
}
await Task.WhenAll( tasks );

Actually the TPL is capable to control the task execution and limit the concurrency. You can test how many parallel tasks is suitable for your use-case. No need to think about threads, TPL will manage everything fine for you.
To use limited concurrency see this answer, credits to #panagiotis-kanavos
.Net TPL: Limited Concurrency Level Task scheduler with task priority?
The example code is (even using different priorities, you can strip that):
QueuedTaskScheduler qts = new QueuedTaskScheduler(TaskScheduler.Default,4);
TaskScheduler pri0 = qts.ActivateNewQueue(priority: 0);
TaskScheduler pri1 = qts.ActivateNewQueue(priority: 1);
Task.Factory.StartNew(()=>{ },
CancellationToken.None,
TaskCreationOptions.None,
pri0);
Just throw all your tasks to the queue and with Task.WhenAll you can wait till everything is done.

Queuing asynchronous task in C#

I have few methods that report some data to Data base. We want to invoke all calls to Data service asynchronously. These calls to data service are all over and so we want to make sure that these DS calls are executed one after another in order at any given time. Initially, i was using async await on each of these methods and each of the calls were executed asynchronously but we found out if they are out of sequence then there are room for errors.
So, i thought we should queue all these asynchronous tasks and send them in a separate thread but i want to know what options we have? I came across 'SemaphoreSlim' . Will this be appropriate in my use case?
Or what other options will suit my use case? Please, guide me.
So, what i have in my code currently
public static SemaphoreSlim mutex = new SemaphoreSlim(1);
//first DS call
public async Task SendModuleDataToDSAsync(Module parameters)
{
var tasks1 = new List<Task>();
var tasks2 = new List<Task>();
//await mutex.WaitAsync(); **//is this correct way to use SemaphoreSlim ?**
foreach (var setting in Module.param)
{
Task job1 = SaveModule(setting);
tasks1.Add(job1);
Task job2= SaveModule(GetAdvancedData(setting));
tasks2.Add(job2);
}
await Task.WhenAll(tasks1);
await Task.WhenAll(tasks2);
//mutex.Release(); // **is this correct?**
}
private async Task SaveModule(Module setting)
{
await Task.Run(() =>
{
// Invokes Calls to DS
...
});
}
//somewhere down the main thread, invoking second call to DS
//Second DS Call
private async Task SendInstrumentSettingsToDS(<param1>, <param2>)
{
//await mutex.WaitAsync();// **is this correct?**
await Task.Run(() =>
{
//TrackInstrumentInfoToDS
//mutex.Release();// **is this correct?**
});
if(param2)
{
await Task.Run(() =>
{
//TrackParam2InstrumentInfoToDS
});
}
}

Initially, i was using async await on each of these methods and each of the calls were executed asynchronously but we found out if they are out of sequence then there are room for errors.
So, i thought we should queue all these asynchronous tasks and send them in a separate thread but i want to know what options we have? I came across 'SemaphoreSlim' .
SemaphoreSlim does restrict asynchronous code to running one at a time, and is a valid form of mutual exclusion. However, since "out of sequence" calls can cause errors, then SemaphoreSlim is not an appropriate solution since it does not guarantee FIFO.
In a more general sense, no synchronization primitive guarantees FIFO because that can cause problems due to side effects like lock convoys. On the other hand, it is natural for data structures to be strictly FIFO.
So, you'll need to use your own FIFO queue, rather than having an implicit execution queue. Channels is a nice, performant, async-compatible queue, but since you're on an older version of C#/.NET, BlockingCollection<T> would work:
public sealed class ExecutionQueue
{
private readonly BlockingCollection<Func<Task>> _queue = new BlockingCollection<Func<Task>>();
public ExecutionQueue() => Completion = Task.Run(() => ProcessQueueAsync());
public Task Completion { get; }
public void Complete() => _queue.CompleteAdding();
private async Task ProcessQueueAsync()
{
foreach (var value in _queue.GetConsumingEnumerable())
await value();
}
}
The only tricky part with this setup is how to queue work. From the perspective of the code queueing the work, they want to know when the lambda is executed, not when the lambda is queued. From the perspective of the queue method (which I'm calling Run), the method needs to complete its returned task only after the lambda is executed. So, you can write the queue method something like this:
public Task Run(Func<Task> lambda)
{
var tcs = new TaskCompletionSource<object>();
_queue.Add(async () =>
{
// Execute the lambda and propagate the results to the Task returned from Run
try
{
await lambda();
tcs.TrySetResult(null);
}
catch (OperationCanceledException ex)
{
tcs.TrySetCanceled(ex.CancellationToken);
}
catch (Exception ex)
{
tcs.TrySetException(ex);
}
});
return tcs.Task;
}
This queueing method isn't as perfect as it could be. If a task completes with more than one exception (this is normal for parallel code), only the first one is retained (this is normal for async code). There's also an edge case around OperationCanceledException handling. But this code is good enough for most cases.
Now you can use it like this:
public static ExecutionQueue _queue = new ExecutionQueue();
public async Task SendModuleDataToDSAsync(Module parameters)
{
var tasks1 = new List<Task>();
var tasks2 = new List<Task>();
foreach (var setting in Module.param)
{
Task job1 = _queue.Run(() => SaveModule(setting));
tasks1.Add(job1);
Task job2 = _queue.Run(() => SaveModule(GetAdvancedData(setting)));
tasks2.Add(job2);
}
await Task.WhenAll(tasks1);
await Task.WhenAll(tasks2);
}

Here's a compact solution that has the least amount of moving parts but still guarantees FIFO ordering (unlike some of the suggested SemaphoreSlim solutions). There are two overloads for Enqueue so you can enqueue tasks with and without return values.
using System;
using System.Threading;
using System.Threading.Tasks;
public class TaskQueue
{
private Task _previousTask = Task.CompletedTask;
public Task Enqueue(Func<Task> asyncAction)
{
return Enqueue(async () => {
await asyncAction().ConfigureAwait(false);
return true;
});
}
public async Task<T> Enqueue<T>(Func<Task<T>> asyncFunction)
{
var tcs = new TaskCompletionSource(TaskCreationOptions.RunContinuationsAsynchronously);
// get predecessor and wait until it's done. Also atomically swap in our own completion task.
await Interlocked.Exchange(ref _previousTask, tcs.Task).ConfigureAwait(false);
try
{
return await asyncFunction().ConfigureAwait(false);
}
finally
{
tcs.SetResult();
}
}
}

Please keep in mind that your first solution queueing all tasks to lists doesn't ensure that the tasks are executed one after another. They're all running in parallel because they're not awaited until the next tasks is startet.
So yes you've to use a SemapohoreSlim to use async locking and await. A simple implementation might be:
private readonly SemaphoreSlim _syncRoot = new SemaphoreSlim(1);
public async Task SendModuleDataToDSAsync(Module parameters)
{
await this._syncRoot.WaitAsync();
try
{
foreach (var setting in Module.param)
{
await SaveModule(setting);
await SaveModule(GetAdvancedData(setting));
}
}
finally
{
this._syncRoot.Release();
}
}
If you can use Nito.AsyncEx the code can be simplified to:
public async Task SendModuleDataToDSAsync(Module parameters)
{
using var lockHandle = await this._syncRoot.LockAsync();
foreach (var setting in Module.param)
{
await SaveModule(setting);
await SaveModule(GetAdvancedData(setting));
}
}

One option is to queue operations that will create tasks instead of queuing already running tasks as the code in the question does.
PseudoCode without locking:
Queue<Func<Task>> tasksQueue = new Queue<Func<Task>>();
async Task RunAllTasks()
{
while (tasksQueue.Count > 0)
{
var taskCreator = tasksQueue.Dequeu(); // get creator
var task = taskCreator(); // staring one task at a time here
await task; // wait till task completes
}
}
// note that declaring createSaveModuleTask does not
// start SaveModule task - it will only happen after this func is invoked
// inside RunAllTasks
Func<Task> createSaveModuleTask = () => SaveModule(setting);
tasksQueue.Add(createSaveModuleTask);
tasksQueue.Add(() => SaveModule(GetAdvancedData(setting)));
// no DB operations started at this point
// this will start tasks from the queue one by one.
await RunAllTasks();
Using ConcurrentQueue would be likely be right thing in actual code. You also would need to know total number of expected operations to stop when all are started and awaited one after another.

Building on your comment under Alexeis answer, your approch with the SemaphoreSlim is correct.
Assumeing that the methods SendInstrumentSettingsToDS and SendModuleDataToDSAsync are members of the same class. You simplay need a instance variable for a SemaphoreSlim and then at the start of each methode that needs synchornization call await lock.WaitAsync() and call lock.Release() in the finally block.
public async Task SendModuleDataToDSAsync(Module parameters)
{
await lock.WaitAsync();
try
{
...
}
finally
{
lock.Release();
}
}
private async Task SendInstrumentSettingsToDS(<param1>, <param2>)
{
await lock.WaitAsync();
try
{
...
}
finally
{
lock.Release();
}
}
and it is importend that the call to lock.Release() is in the finally-block, so that if an exception is thrown somewhere in the code of the try-block the semaphore is released.

Manage Program Flow with Tasks or Events

My program executes other programs remotely using a service that does not implement change or complete notification. To determine if the program is complete there is a background thread that runs an infinite loop (until the cancellation token is passed). When a job is submitted it is added to a list of active jobs in the status queue and the background thread checks the status of each job in the list and fires an event with a job as arguments when it completes and another event when the queue is empty.
While this solution does work, I wonder if this is the best possible solution for this problem. It seems to me that tasks are ideally suited for this situation because they are what I would use if I wasn't relying on the server to dispatch the job.
Is there an advantage to using tasks instead of events in this case?

About asynchronous programming patterns
Is there an advantage to using tasks instead of events in this case?
I think Tasks may make this particular code clearer. You're implementing asynchronous operation — submit job and wait it to complete. There are different patterns for that kind of operations, they're called Asynchronous Programming Patterns.
Tasks or Task-based Asynchronous Pattern (TAP) is the most recent asynchronous programming pattern. The others patterns are Asynchronous Programming Model (APM) and Event-based Asynchronous Pattern (EAP). Currently you're using EAP and it is about events. APM is the pattern where you have BeginOperation and EndOperation methods.
See this page about Asynchronous Programming Patterns. It says that EAP and APM is no longer recommended for new development and TAP is the recommended pattern.
I agree with these recommendations, I already forgot when I used events or Begin/End methods in my code. It doesn't mean that TAP should be used everywhere, EAP or APM may fit better in some situations but in your particular situation TAP may be the best choice.
How it could be implemented with TAP
We need a Task for each job which we will await. So the top-level code will be something like this:
static void Main(string[] args)
{
MainAsync(args).Wait();
}
static async Task MainAsync(string[] args)
{
// creating Enumerable of jobs
// ...
IEnumerable<Task> tasks = jobs.Select(job => client.SubmitJob(job));
await Task.WhenAll(tasks);
}
client.SubmitJob returns Task. It could be implemented with TaskCompletionSource:
var tcs = new TaskCompletionSource<object>();
var jobInfo = new JobInfo {Job = job, TaskCompletionSource = tcs};
activeJobs.Add(jobInfo);
return tcs.Task;
TaskCompletionSource for Task is just like CancellationTokenSource for CancellationToken. We set Task to finish state with taskCompletionSource.SetResult() call. So we keep track of all active jobs with assigned TaskCompletionSource and the background thread calls SetResult:
// ... in the backgroung thread
// ... when the job is completed
jobInfo.TaskCompletionSource.SetResult(null);
It would be the same code as for events, you just need to keep the list of TaskCompletionSource for each job and call SetResult instead of trigger the event.
You can read the details about TAP such as exception and timeout handling here or download a great document about TAP here.
Further adventures
In the document you can find interesting method called Interleaved which you may want to use. It allows processing an IEnumerable<Task> in the order of completion — which tasks completes first will be returned first.
You can return Task<Job> from SumbitJob method and call SetResult(job) in the background thread. Then you can do this:
IEnumerable<Task<Job>> tasks = jobs.Select(job => client.SubmitJob(job));
IEnumerable<Task<Job>> interleaved = Interleaved(tasks);
foreach (var task in interleaved)
{
Job job = await task;
Console.WriteLine($"Job {job.Title} is completed");
}
The jobs will be reported as they completes.

This is a sample solution following the selected answer
public class MainViewModel
{
private static void Main()
{
Task.Run(() => JobMonitor.Start());
MainAsync().Wait();
}
public async Task MainAsync()
{
var test = new string[2];
var jobs = test.Select(x => randomTask());
var tasks = jobs.Select(x => x.TCS.Task);
await Task.WhenAll(tasks);
}
public Job randomTask()
{
var job = new Job();
job.Submit();
job.TCS.Task.ContinueWith(task => WelcomeTitle += "\n" + DateTime.Now, TaskScheduler.FromCurrentSynchronizationContext());
return job;
}
}
public class Job
{
public TaskCompletionSource<object> TCS = new TaskCompletionSource<object>();
public readonly DateTime Expires;
public bool IsExpired
{
get
{
var ret = Expires < DateTime.Now;
return ret;
}
}
public Job()
{
Random rnd = new Random();
System.Threading.Thread.Sleep(20);
var num = rnd.Next(1, 20);
Expires = DateTime.Now.AddSeconds(num);
}
internal void Submit()
{
JobMonitor.SubmitJob(this);
}
}
class JobMonitor
{
public static List<Job> activeJobs = new List<Job>();
private static object _lock = new object();
public static void SubmitJob(Job job)
{
lock(_lock)
{
activeJobs.Add(job);
}
}
public static void Start()
{
while (true)
{
lock (_lock)
{
var expired = activeJobs.Where(job => job.IsExpired).ToList();
foreach (var job in expired)
{
job.TCS.SetResult(null);
activeJobs.Remove(job);
}
}
System.Threading.Thread.Sleep(1000);
}
}
}

Continue with a method after completing producer-consumer

I have a producer-consumer application in WPF. After I click a button.
private async void Start_Click(object sender, RoutedEventArgs e)
{
try
{
// set up data
var producer = Producer();
var consumer = Consumer();
await Task.WhenAll(producer, consumer);
// need log the results in Summary method
Summary();
}
}
The summary method is a void one; I assume it is proper.
private void Summary(){}
async Task Producer(){ await something }
async Task Consumer(){ await something }
EDIT:
My question is in Summary() method I have to use the calculated values from the tasks, however the Consumer task is a long running process. The program run Summary quickly even not getting the updated values. It use the initial values.
My thought:
await Task.WhenAll(producer, consumer);
Summary();
EDIT2: 11:08 AM 11/05/2014
private void Summary()
{
myFail = 100 - mySuccess;
_dataContext.MyFail = myFail; // update window upon property changed
async Task Consumer()
{
try
{
Dictionary<string, string> dict = new Dictionary<string, string>();
var executionDataflowBlockOptions = new ExecutionDataflowBlockOptions
{
MaxDegreeOfParallelism = 5,
CancellationToken = cToken
};
var c = new ActionBlock<T>(
t=>
{
if (cToken.IsCancellationRequested)
return;
dict = Do(t, cToken);
if(dict["Success"] == "Success")
mySuccess++;
The current problem is mySuccess is always the initial value in Summary method.

You can use ContinueWith method to execute Summary after both producer and consumer have finished:
Task.WhenAll(producer, consumer)
.ContinueWith(continuation => Summary());
EDIT 1
It seems that you are abusing or using wrong the Producer/Consumer pattern.
The producer is supposed to produce the values and shovel them into one end of a communication pipe. On the other end of the pipe, the consumer consumes the values as they become available. In other words, the consumer waits for the producer to produce some value and to put the value in the pipe and for the value to arrive at the end of the pipe.
Usually this involves some sort of signaling mechanism where the producer signals (awakes) the consumer whenever a value has been created.
In your case, you don't have the signaling mechanism and I strongly suspect that your producer is generating only one value. If the later is the case you can just return a value from the "producer".
If however, your producer is creating more than one values, you can use the BlockingCollection<T> class to send values from producer to consumer.
In your Producer class, get a reference to the pipe and put data into it:
public class Producer
{
private BlockingCollection<Data> _pipe;
public void Start()
{
while(!done)
{
var value = ProduceValue();
_pipe.Add(value);
}
// Signal the consumer that we're finished
_pipe.CompleteAdding();
}
}
In the Consumer class wait for the values to arrive and process each one:
public class Consumer
{
private BlockingCollection<Data> _pipe;
public void Start()
{
foreach(var value in _pipe.GetConsumingEnumerable())
{
// GetConsumingEnumerable will block until a value arrives and
// will exit when producer calls CompleteAdding()
Process(value);
}
}
}
Having the above in place you can use ContinueWith or await on the WhenAll method to run the Summary.
EDIT 2
As promised in the comments I have analyzed the code you've posted on MSDN Forum. There are several problems in the code.
First of all and the simplest one to fix is that you're not incrementing the counter in a thread-safe manner. An increment (value++) is not an atomic operation so you should be careful when incrementing shared fields. An easy way to do this is:
Interlocked.Increment(ref evenNumber);
Now, the actual problems in your code:
As I mentioned earlier, the consumer does not know when the producer has finished producing the values. So, after the producer exits the for block it should signal that it has finished. The consumer waits for the finish signal of the producer; otherwise it will wait forever for the next value but there won't be one.
You are linking the BufferBlock with the consumer code which starts to execute but you're not waiting for the consumer block to finish - you're only waiting 0.5 of a second and exit the consumer method leaving the worker threads of the consumer block to do their work in vain.
As a consequence of the above, your Report method executes before the processing is finished outputting the value of the evenNumber counter at the moment when the method executes not when all processing is finished.
Below is the edited code with some comments:
class Program
{
public static BufferBlock<int> m_Queue = new BufferBlock<int>(new DataflowBlockOptions { BoundedCapacity = 1000 });
private static int evenNumber;
static void Main(string[] args)
{
var producer = Producer();
var consumer = Consumer();
Task.WhenAll(producer, consumer).Wait();
Report();
}
static void Report()
{
Console.WriteLine("There are {0} even numbers", evenNumber);
Console.Read();
}
static async Task Producer()
{
for (int i = 0; i < 500; i++)
{
// Send a value to the consumer and wait for the value to be processed
await m_Queue.SendAsync(i);
}
// Signal the consumer that there will be no more values
m_Queue.Complete();
}
static async Task Consumer()
{
var executionDataflowBlockOptions = new ExecutionDataflowBlockOptions
{
MaxDegreeOfParallelism = 4
};
var consumerBlock = new ActionBlock<int>(x =>
{
int j = DoWork(x);
if (j % 2 == 0)
// Increment the counter in a thread-safe way
Interlocked.Increment(ref evenNumber);
}, executionDataflowBlockOptions);
// Link the buffer to the consumer
using (m_Queue.LinkTo(consumerBlock, new DataflowLinkOptions { PropagateCompletion = true }))
{
// Wait for the consumer to finish.
// This method will exit after all the data from the buffer was processed.
await consumerBlock.Completion;
}
}
static int DoWork(int x)
{
Thread.Sleep(100);
return x;
}
}