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Semaphore slim to handle throttling per time period

I have a requirement from a client, to call their API, however, due to the throttling limit, we can only make 100 API calls in a minute. I am using SemaphoreSlim to handle that, Here is my code.
async Task<List<IRestResponse>> GetAllResponses(List<string> locationApiCalls)
{
var semaphoreSlim = new SemaphoreSlim(initialCount: 100, maxCount: 100);
var failedResponses = new ConcurrentBag<IReadOnlyCollection<IRestResponse>>();
var passedResponses = new ConcurrentBag<IReadOnlyCollection<IRestResponse>>();
var tasks = locationApiCalls.Select(async locationApiCall =>
{
await semaphoreSlim.WaitAsync();
try
{
var response = await RestApi.GetResponseAsync(locationApi);
if (response.IsSuccessful)
{
passedResponses.Add((IReadOnlyCollection<IRestResponse>)response);
}
else
{
failedResponses.Add((IReadOnlyCollection<IRestResponse>)response);
}
}
finally
{
semaphoreSlim.Release();
}
});
await Task.WhenAll(tasks);
var passedResponsesList = passedResponses.SelectMany(x => x).ToList();
}
However this line
var passedResponsesList = passedResponses.SelectMany(x => x).ToList();
never gets executed and I see Lots of failedResponses as well, I guess I have to add Task.Delay (for 1 minute) somewhere in the code as well.

You need to keep track of the time when each of the previous 100 requests was executed. In the sample implementation below, the ConcurrentQueue<TimeSpan> records the relative completion time of each of these previous 100 requests. By dequeuing the first (and hence earliest) time from this queue, you can check how much time has passed since 100 requests ago. If it's been less than a minute, then the next request needs to wait for the remainder of the minute before it can be executed.
async Task<List<IRestResponse>> GetAllResponses(List<string> locationApiCalls)
{
var semaphoreSlim = new SemaphoreSlim(initialCount: 100, maxCount: 100);
var total = 0;
var stopwatch = Stopwatch.StartNew();
var completionTimes = new ConcurrentQueue<TimeSpan>();
var failedResponses = new ConcurrentBag<IReadOnlyCollection<IRestResponse>>();
var passedResponses = new ConcurrentBag<IReadOnlyCollection<IRestResponse>>();
var tasks = locationApiCalls.Select(async locationApiCall =>
{
await semaphoreSlim.WaitAsync();
if (Interlocked.Increment(ref total) > 100)
{
completionTimes.TryDequeue(out var earliest);
var elapsed = stopwatch.Elapsed - earliest;
var delay = TimeSpan.FromSeconds(60) - elapsed;
if (delay > TimeSpan.Zero)
await Task.Delay(delay);
}
try
{
var response = await RestApi.GetResponseAsync(locationApi);
if (response.IsSuccessful)
{
passedResponses.Add((IReadOnlyCollection<IRestResponse>)response);
}
else
{
failedResponses.Add((IReadOnlyCollection<IRestResponse>)response);
}
}
finally
{
completionTimes.Enqueue(stopwatch.Elapsed);
semaphoreSlim.Release();
}
});
await Task.WhenAll(tasks);
var passedResponsesList = passedResponses.SelectMany(x => x).ToList();
}
If you're calling this method from the UI thread of a WinForms or WPF application, remember to add ConfigureAwait(false) to its await statements.

Async Producer / Consumer with throttled duration and batched consumption

I am trying to build a service that provides a queue for many asynchronous clients to make requests and await a response. I need to be able to throttle the queue processing by X requests per Y duration. For example: 50 web requests per second. It is for a 3rd party REST Service where I can only issue X requests per second.
Found many SO questions, it is lead me down the path of using TPL Dataflow, I've used a TranformBlock to provide my custom throttling and then X number of ActionBlocks to complete the tasks in parallel. The implementation of the Action seems a bit clunky, so wondering if there is a better way for me to pass Tasks into the pipeline that notify the callers once completed.
I'm wondering if there is there a better or more optimal/simpler way to do what I want? Is there any glaring issues with my implementation? I know it is missing cancellation and exception handing and I'll be doing this next, but your comments are most welcomed.
I've Extended Stephen Cleary's example for my Dataflow pipeline and used
svick's concept of a time throttled TransformBlock. I am wondering if what I've built could be easily achieved with a pure SemaphoreSlim design, its the time based throttling with max operations that I think will complicate things.
Here is the latest implementation. FIFO queue async queue where I can pass in custom actions.
public class ThrottledProducerConsumer<T>
{
private class TimerState<T1>
{
public SemaphoreSlim Sem;
public T1 Value;
}
private BufferBlock<T> _queue;
private IPropagatorBlock<T, T> _throttleBlock;
private List<Task> _consumers;
private static IPropagatorBlock<T1, T1> CreateThrottleBlock<T1>(TimeSpan Interval, Int32 MaxPerInterval)
{
SemaphoreSlim _sem = new SemaphoreSlim(MaxPerInterval);
return new TransformBlock<T1, T1>(async (x) =>
{
var sw = new Stopwatch();
sw.Start();
//Console.WriteLine($"Current count: {_sem.CurrentCount}");
await _sem.WaitAsync();
sw.Stop();
var now = DateTime.UtcNow;
var releaseTime = now.Add(Interval) - now;
//-- Using timer as opposed to Task.Delay as I do not want to await or wait for it to complete
var tm = new Timer((s) => {
var state = (TimerState<T1>)s;
//Console.WriteLine($"RELEASE: {state.Value} was released {DateTime.UtcNow:mm:ss:ff} Reset Sem");
state.Sem.Release();
}, new TimerState<T1> { Sem = _sem, Value = x }, (int)Interval.TotalMilliseconds,
-1);
/*
Task.Delay(delay).ContinueWith((t)=>
{
Console.WriteLine($"RELEASE(FAKE): {x} was released {DateTime.UtcNow:mm:ss:ff} Reset Sem");
//_sem.Release();
});
*/
//Console.WriteLine($"{x} was tramsformed in {sw.ElapsedMilliseconds}ms. Will release {now.Add(Interval):mm:ss:ff}");
return x;
},
//new ExecutionDataflowBlockOptions { BoundedCapacity = 1 });
//
new ExecutionDataflowBlockOptions { BoundedCapacity = 5, MaxDegreeOfParallelism = 10 });
}
public ThrottledProducerConsumer(TimeSpan Interval, int MaxPerInterval, Int32 QueueBoundedMax = 5, Action<T> ConsumerAction = null, Int32 MaxConsumers = 1)
{
var consumerOptions = new ExecutionDataflowBlockOptions { BoundedCapacity = 1, };
var linkOptions = new DataflowLinkOptions { PropagateCompletion = true, };
//-- Create the Queue
_queue = new BufferBlock<T>(new DataflowBlockOptions { BoundedCapacity = QueueBoundedMax, });
//-- Create and link the throttle block
_throttleBlock = CreateThrottleBlock<T>(Interval, MaxPerInterval);
_queue.LinkTo(_throttleBlock, linkOptions);
//-- Create and link the consumer(s) to the throttle block
var consumerAction = (ConsumerAction != null) ? ConsumerAction : new Action<T>(ConsumeItem);
_consumers = new List<Task>();
for (int i = 0; i < MaxConsumers; i++)
{
var consumer = new ActionBlock<T>(consumerAction, consumerOptions);
_throttleBlock.LinkTo(consumer, linkOptions);
_consumers.Add(consumer.Completion);
}
//-- TODO: Add some cancellation tokens to shut this thing down
}
/// <summary>
/// Default Consumer Action, just prints to console
/// </summary>
/// <param name="ItemToConsume"></param>
private void ConsumeItem(T ItemToConsume)
{
Console.WriteLine($"Consumed {ItemToConsume} at {DateTime.UtcNow}");
}
public async Task EnqueueAsync(T ItemToEnqueue)
{
await this._queue.SendAsync(ItemToEnqueue);
}
public async Task EnqueueItemsAsync(IEnumerable<T> ItemsToEnqueue)
{
foreach (var item in ItemsToEnqueue)
{
await this._queue.SendAsync(item);
}
}
public async Task CompleteAsync()
{
this._queue.Complete();
await Task.WhenAll(_consumers);
Console.WriteLine($"All consumers completed {DateTime.UtcNow}");
}
}
The test method
public class WorkItem<T>
{
public TaskCompletionSource<T> tcs;
//public T respone;
public string url;
public WorkItem(string Url)
{
tcs = new TaskCompletionSource<T>();
url = Url;
}
public override string ToString()
{
return $"{url}";
}
}
public static void TestQueue()
{
Console.WriteLine("Created the queue");
var defaultAction = new Action<WorkItem<String>>(async i => {
var taskItem = ((WorkItem<String>)i);
Console.WriteLine($"Consuming: {taskItem.url} {DateTime.UtcNow:mm:ss:ff}");
//-- Assume calling another async method e.g. await httpClient.DownloadStringTaskAsync(url);
await Task.Delay(5000);
taskItem.tcs.SetResult($"{taskItem.url}");
//Console.WriteLine($"Consumed: {taskItem.url} {DateTime.UtcNow}");
});
var queue = new ThrottledProducerConsumer<WorkItem<String>>(TimeSpan.FromMilliseconds(2000), 5, 2, defaultAction);
var results = new List<Task>();
foreach (var no in Enumerable.Range(0, 20))
{
var workItem = new WorkItem<String>($"http://someurl{no}.com");
results.Add(queue.EnqueueAsync(workItem));
results.Add(workItem.tcs.Task);
results.Add(workItem.tcs.Task.ContinueWith(response =>
{
Console.WriteLine($"Received: {response.Result} {DateTime.UtcNow:mm:ss:ff}");
}));
}
Task.WhenAll(results).Wait();
Console.WriteLine("All Work Items Have Been Processed");
}

Since asking, I have created a ThrottledConsumerProducer class based on TPL Dataflow. It was tested over a number of days which included concurrent producers which were queued and completed in order, approx 281k without any problems, however there my be bugs I've not discovered.
I am using a BufferBlock as an asynchronous queue, this is linked to:
A TransformBlock which provides the throttling and blocking I need. It is used in conjunction with a SempahoreSlim to control the max requests. As each item is passed through the block, it increments the semaphore and schedules a task to run X duration later to release the semaphore by one. This way I have a sliding window of X requests per duration; exactly what I wanted. Because of TPL I am also leveraging parallelism to the connected:
ActionBlock(s) which are responsible for performing the task I need.
The classes are generic, so it might be useful to others if they need something similar. I have not written cancellation or error handling, but thought I should just mark this as answered to move it along. I would be quite happy to see some alternatives and feedback, rather than mark mine as an accepted answer. Thanks for reading.
NOTE: I removed the Timer from the original implementation as it was doing weird stuff causing the semaphore to release more than the maximum, I am assuming it is dynamic context error, it occurred when I started running concurrent requests. I worked around it using Task.Delay to schedule a release of a semaphore lock.
Throttled Producer Consumer
public class ThrottledProducerConsumer<T>
{
private BufferBlock<T> _queue;
private IPropagatorBlock<T, T> _throttleBlock;
private List<Task> _consumers;
private static IPropagatorBlock<T1, T1> CreateThrottleBlock<T1>(TimeSpan Interval,
Int32 MaxPerInterval, Int32 BlockBoundedMax = 2, Int32 BlockMaxDegreeOfParallelism = 2)
{
SemaphoreSlim _sem = new SemaphoreSlim(MaxPerInterval, MaxPerInterval);
return new TransformBlock<T1, T1>(async (x) =>
{
//Log($"Transform blk: {x} {DateTime.UtcNow:mm:ss:ff} Semaphore Count: {_sem.CurrentCount}");
var sw = new Stopwatch();
sw.Start();
//Console.WriteLine($"Current count: {_sem.CurrentCount}");
await _sem.WaitAsync();
sw.Stop();
var delayTask = Task.Delay(Interval).ContinueWith((t) =>
{
//Log($"Pre-RELEASE: {x} {DateTime.UtcNow:mm:ss:ff} Semaphore Count {_sem.CurrentCount}");
_sem.Release();
//Log($"PostRELEASE: {x} {DateTime.UtcNow:mm:ss:ff} Semaphoere Count {_sem.CurrentCount}");
});
//},TaskScheduler.FromCurrentSynchronizationContext());
//Log($"Transformed: {x} in queue {sw.ElapsedMilliseconds}ms. {DateTime.Now:mm:ss:ff} will release {DateTime.Now.Add(Interval):mm:ss:ff} Semaphoere Count {_sem.CurrentCount}");
return x;
},
//-- Might be better to keep Bounded Capacity in sync with the semaphore
new ExecutionDataflowBlockOptions { BoundedCapacity = BlockBoundedMax,
MaxDegreeOfParallelism = BlockMaxDegreeOfParallelism });
}
public ThrottledProducerConsumer(TimeSpan Interval, int MaxPerInterval,
Int32 QueueBoundedMax = 5, Action<T> ConsumerAction = null, Int32 MaxConsumers = 1,
Int32 MaxThrottleBuffer = 20, Int32 MaxDegreeOfParallelism = 10)
{
//-- Probably best to link MaxPerInterval and MaxThrottleBuffer
// and MaxConsumers with MaxDegreeOfParallelism
var consumerOptions = new ExecutionDataflowBlockOptions { BoundedCapacity = 1, };
var linkOptions = new DataflowLinkOptions { PropagateCompletion = true, };
//-- Create the Queue
_queue = new BufferBlock<T>(new DataflowBlockOptions { BoundedCapacity = QueueBoundedMax, });
//-- Create and link the throttle block
_throttleBlock = CreateThrottleBlock<T>(Interval, MaxPerInterval);
_queue.LinkTo(_throttleBlock, linkOptions);
//-- Create and link the consumer(s) to the throttle block
var consumerAction = (ConsumerAction != null) ? ConsumerAction : new Action<T>(ConsumeItem);
_consumers = new List<Task>();
for (int i = 0; i < MaxConsumers; i++)
{
var consumer = new ActionBlock<T>(consumerAction, consumerOptions);
_throttleBlock.LinkTo(consumer, linkOptions);
_consumers.Add(consumer.Completion);
}
//-- TODO: Add some cancellation tokens to shut this thing down
}
/// <summary>
/// Default Consumer Action, just prints to console
/// </summary>
/// <param name="ItemToConsume"></param>
private void ConsumeItem(T ItemToConsume)
{
Log($"Consumed {ItemToConsume} at {DateTime.UtcNow}");
}
public async Task EnqueueAsync(T ItemToEnqueue)
{
await this._queue.SendAsync(ItemToEnqueue);
}
public async Task EnqueueItemsAsync(IEnumerable<T> ItemsToEnqueue)
{
foreach (var item in ItemsToEnqueue)
{
await this._queue.SendAsync(item);
}
}
public async Task CompleteAsync()
{
this._queue.Complete();
await Task.WhenAll(_consumers);
Console.WriteLine($"All consumers completed {DateTime.UtcNow}");
}
private static void Log(String messageToLog)
{
System.Diagnostics.Trace.WriteLine(messageToLog);
Console.WriteLine(messageToLog);
}
}
- Example Usage -
A Generic WorkItem
public class WorkItem<Toutput,Tinput>
{
private TaskCompletionSource<Toutput> _tcs;
public Task<Toutput> Task { get { return _tcs.Task; } }
public Tinput InputData { get; private set; }
public Toutput OutputData { get; private set; }
public WorkItem(Tinput inputData)
{
_tcs = new TaskCompletionSource<Toutput>();
InputData = inputData;
}
public void Complete(Toutput result)
{
_tcs.SetResult(result);
}
public void Failed(Exception ex)
{
_tcs.SetException(ex);
}
public override string ToString()
{
return InputData.ToString();
}
}
Creating the action block executed in the pipeline
private Action<WorkItem<Location,PointToLocation>> CreateProcessingAction()
{
return new Action<WorkItem<Location,PointToLocation>>(async i => {
var sw = new Stopwatch();
sw.Start();
var taskItem = ((WorkItem<Location,PointToLocation>)i);
var inputData = taskItem.InputData;
//Log($"Consuming: {inputData.Latitude},{inputData.Longitude} {DateTime.UtcNow:mm:ss:ff}");
//-- Assume calling another async method e.g. await httpClient.DownloadStringTaskAsync(url);
await Task.Delay(500);
sw.Stop();
Location outData = new Location()
{
Latitude = inputData.Latitude,
Longitude = inputData.Longitude,
StreetAddress = $"Consumed: {inputData.Latitude},{inputData.Longitude} Duration(ms): {sw.ElapsedMilliseconds}"
};
taskItem.Complete(outData);
//Console.WriteLine($"Consumed: {taskItem.url} {DateTime.UtcNow}");
});
}
Test Method
You'll need to provide your own implementation for PointToLocation and Location. Just an example of how you'd use it with your own classes.
int startRange = 0;
int nextRange = 1000;
ThrottledProducerConsumer<WorkItem<Location,PointToLocation>> tpc;
private void cmdTestPipeline_Click(object sender, EventArgs e)
{
Log($"Pipeline test started {DateTime.Now:HH:mm:ss:ff}");
if(tpc == null)
{
tpc = new ThrottledProducerConsumer<WorkItem<Location, PointToLocation>>(
//1010, 2, 20000,
TimeSpan.FromMilliseconds(1010), 45, 100000,
CreateProcessingAction(),
2,45,10);
}
var workItems = new List<WorkItem<Models.Location, PointToLocation>>();
foreach (var i in Enumerable.Range(startRange, nextRange))
{
var ptToLoc = new PointToLocation() { Latitude = i + 101, Longitude = i + 100 };
var wrkItem = new WorkItem<Location, PointToLocation>(ptToLoc);
workItems.Add(wrkItem);
wrkItem.Task.ContinueWith(t =>
{
var loc = t.Result;
string line = $"[Simulated:{DateTime.Now:HH:mm:ss:ff}] - {loc.StreetAddress}";
//txtResponse.Text = String.Concat(txtResponse.Text, line, System.Environment.NewLine);
//var lines = txtResponse.Text.Split(new string[] { System.Environment.NewLine},
// StringSplitOptions.RemoveEmptyEntries).LongCount();
//lblLines.Text = lines.ToString();
//Log(line);
});
//}, TaskScheduler.FromCurrentSynchronizationContext());
}
startRange += nextRange;
tpc.EnqueueItemsAsync(workItems);
Log($"Pipeline test completed {DateTime.Now:HH:mm:ss:ff}");
}

TaskFactory, Starting a new Task when one ends

I have found many methods of using the TaskFactory but I could not find anything about starting more tasks and watching when one ends and starting another one.
I always want to have 10 tasks working.
I want something like this
int nTotalTasks=10;
int nCurrentTask=0;
Task<bool>[] tasks=new Task<bool>[nThreadsNum];
for (int i=0; i<1000; i++)
{
string param1="test";
string param2="test";
if (nCurrentTask<10) // if there are less than 10 tasks then start another one
tasks[nCurrentThread++] = Task.Factory.StartNew<bool>(() =>
{
MyClass cls = new MyClass();
bool bRet = cls.Method1(param1, param2, i); // takes up to 2 minutes to finish
return bRet;
});
// How can I stop the for loop until a new task is finished and start a new one?
}

Check out the Task.WaitAny method:
Waits for any of the provided Task objects to complete execution.
Example from the documentation:
var t1 = Task.Factory.StartNew(() => DoOperation1());
var t2 = Task.Factory.StartNew(() => DoOperation2());
Task.WaitAny(t1, t2)

I would use a combination of Microsoft's Reactive Framework (NuGet "Rx-Main") and TPL for this. It becomes very simple.
Here's the code:
int nTotalTasks=10;
string param1="test";
string param2="test";
IDisposable subscription =
Observable
.Range(0, 1000)
.Select(i => Observable.FromAsync(() => Task.Factory.StartNew<bool>(() =>
{
MyClass cls = new MyClass();
bool bRet = cls.Method1(param1, param2, i); // takes up to 2 minutes to finish
return bRet;
})))
.Merge(nTotalTasks)
.ToArray()
.Subscribe((bool[] results) =>
{
/* Do something with the results. */
});
The key part here is the .Merge(nTotalTasks) which limits the number of concurrent tasks.
If you need to stop the processing part way thru just call subscription.Dispose() and everything gets cleaned up for you.
If you want to process each result as they are produced you can change the code from the .Merge(...) like this:
.Merge(nTotalTasks)
.Subscribe((bool result) =>
{
/* Do something with each result. */
});

This should be all you need, not complete, but all you need to do is wait on the first to complete and then run the second.
Task.WaitAny(task to wait on);
Task.Factory.StartNew()

Have you seen the BlockingCollection class? It allows you to have multiple threads running in parallel and you can wait from results from one task to execute another. See more information here.

The answer depends on whether the tasks to be scheduled are CPU or I/O bound.
For CPU-intensive work I would use Parallel.For() API setting the number of thread/tasks through MaxDegreeOfParallelism property of ParallelOptions
For I/O bound work the number of concurrently executing tasks can be significantly larger than the number of available CPUs, so the strategy is to rely on async methods as much as possible, which reduces the total number of threads waiting for completion.
How can I stop the for loop until a new task is finished and start a
new one?
The loop can be throttled by using await:
static void Main(string[] args)
{
var task = DoWorkAsync();
task.Wait();
// handle results
// task.Result;
Console.WriteLine("Done.");
}
async static Task<bool> DoWorkAsync()
{
const int NUMBER_OF_SLOTS = 10;
string param1="test";
string param2="test";
var results = new bool[NUMBER_OF_SLOTS];
AsyncWorkScheduler ws = new AsyncWorkScheduler(NUMBER_OF_SLOTS);
for (int i = 0; i < 1000; ++i)
{
await ws.ScheduleAsync((slotNumber) => DoWorkAsync(i, slotNumber, param1, param2, results));
}
ws.Complete();
await ws.Completion;
}
async static Task DoWorkAsync(int index, int slotNumber, string param1, string param2, bool[] results)
{
results[slotNumber] = results[slotNumber} && await Task.Factory.StartNew<bool>(() =>
{
MyClass cls = new MyClass();
bool bRet = cls.Method1(param1, param2, i); // takes up to 2 minutes to finish
return bRet;
}));
}
A helper class AsyncWorkScheduler uses TPL.DataFlow components as well as Task.WhenAll():
class AsyncWorkScheduler
{
public AsyncWorkScheduler(int numberOfSlots)
{
m_slots = new Task[numberOfSlots];
m_availableSlots = new BufferBlock<int>();
m_errors = new List<Exception>();
m_tcs = new TaskCompletionSource<bool>();
m_completionPending = 0;
// Initial state: all slots are available
for(int i = 0; i < m_slots.Length; ++i)
{
m_slots[i] = Task.FromResult(false);
m_availableSlots.Post(i);
}
}
public async Task ScheduleAsync(Func<int, Task> action)
{
if (Volatile.Read(ref m_completionPending) != 0)
{
throw new InvalidOperationException("Unable to schedule new items.");
}
// Acquire a slot
int slotNumber = await m_availableSlots.ReceiveAsync().ConfigureAwait(false);
// Schedule a new task for a given slot
var task = action(slotNumber);
// Store a continuation on the task to handle completion events
m_slots[slotNumber] = task.ContinueWith(t => HandleCompletedTask(t, slotNumber), TaskContinuationOptions.ExecuteSynchronously);
}
public async void Complete()
{
if (Interlocked.CompareExchange(ref m_completionPending, 1, 0) != 0)
{
return;
}
// Signal the queue's completion
m_availableSlots.Complete();
await Task.WhenAll(m_slots).ConfigureAwait(false);
// Set completion
if (m_errors.Count != 0)
{
m_tcs.TrySetException(m_errors);
}
else
{
m_tcs.TrySetResult(true);
}
}
public Task Completion
{
get
{
return m_tcs.Task;
}
}
void SetFailed(Exception error)
{
lock(m_errors)
{
m_errors.Add(error);
}
}
void HandleCompletedTask(Task task, int slotNumber)
{
if (task.IsFaulted || task.IsCanceled)
{
SetFailed(task.Exception);
return;
}
if (Volatile.Read(ref m_completionPending) == 1)
{
return;
}
// Release a slot
m_availableSlots.Post(slotNumber);
}
int m_completionPending;
List<Exception> m_errors;
BufferBlock<int> m_availableSlots;
TaskCompletionSource<bool> m_tcs;
Task[] m_slots;
}

Guarantee TransformBlock output sequence

From the TPL documentation
As with ActionBlock<TInput>, TransformBlock<TInput,TOutput> defaults
to processing one message at a time, maintaining strict FIFO ordering.
However, in a multi-threaded scenario, i.e. if multiple threads are "simultaneously" doing SendAsync and then "awaiting" for a result by calling ReceiveAsync, how do we guarantee that the thread that posted something into the TransformBlock<TInput,TOutput> actually gets the intended result that it is waiting for?
In my experiments, it seems like the way to "guarantee" my desired outcome, is to add the option BoundedCapacity = 1. At least the thread(s) still doesn't get blocked when sending and receiving.
If I don't do this, some threads will receive the result intended for another thread.
Is this the right approach in this particular use case?
Here is some code that illustrates my concern:
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Threading.Tasks.Dataflow;
namespace ConsoleTransformBlock
{
class Program
{
private readonly static TransformBlock<int, int> _pipeline;
static Program()
{
_pipeline = new TransformBlock<int, int>(async (input) =>
{
await Task.Delay(RandomGen2.Next(5, 100)).ConfigureAwait(false);
return input;
},
new ExecutionDataflowBlockOptions() { BoundedCapacity = 1 }); // this is the fix???
}
static void Main(string[] args)
{
var dop = System.Environment.ProcessorCount;// 8-core
Parallel.For(0, dop, new ParallelOptions() { MaxDegreeOfParallelism = dop },
(d) =>
{
DoStuff().Wait();
});
Console.WriteLine("Parallel For Done ...");
var tasks = new Task[dop];
for (var i = 0; i < dop; i++)
{
var temp = i;
tasks[temp] = Task.Factory.StartNew
(async () => await DoStuff().ConfigureAwait(false),
CancellationToken.None,
TaskCreationOptions.LongRunning,
TaskScheduler.Default).Unwrap();
}
Task.WaitAll(tasks);
}
private static async Task DoStuff()
{
for (var i = 0; i < 100; i++)
{
var temp = RandomGen2.Next();
await _pipeline.SendAsync(temp).ConfigureAwait(false);
Console.WriteLine("Just sent {0}, now waiting {1}...", new object[] { temp, System.Threading.Thread.CurrentThread.ManagedThreadId });
await Task.Delay(RandomGen2.Next(5, 50)).ConfigureAwait(false);
var result = await _pipeline.ReceiveAsync().ConfigureAwait(false);
Console.WriteLine("Received {0}... {1}", new object[] { result, System.Threading.Thread.CurrentThread.ManagedThreadId });
if (result != temp)
{
var error = string.Format("************** Sent {0} But Received {1}", temp, result, System.Threading.Thread.CurrentThread.ManagedThreadId);
Console.WriteLine(error);
break;
}
}
}
/// <summary>
/// Thread-Safe Random Generator
/// </summary>
public static class RandomGen2
{
private static Random _global = new Random();
[ThreadStatic]
private static Random _local;
public static int Next()
{
return Next(0, int.MaxValue);
}
public static int Next(int max)
{
return Next(0, max);
}
public static int Next(int min, int max)
{
Random inst = _local;
if (inst == null)
{
int seed;
lock (_global) seed = _global.Next();
_local = inst = new Random(seed);
}
return inst.Next(min, max);
}
}
}
}

TransformBlock already maintains FIFO order. The order in which you post items to the block is the exact order in which the items will be returned from the block.
When you specify a maximum degree of parallelism that is larger than 1, multiple messages are processed simultaneously, and therefore, messages might not be processed in the order in which they are received. The order in which the messages are output from the block will, however, be correctly ordered.
From Dataflow (Task Parallel Library)
You can see that with this example:
private static async Task MainAsync()
{
var transformBlock = new TransformBlock<int, int>(async input =>
{
await Task.Delay(RandomGen2.Next(5, 100));
return input;
}, new ExecutionDataflowBlockOptions {MaxDegreeOfParallelism = 10});
foreach (var number in Enumerable.Range(0,100))
{
await transformBlock.SendAsync(number);
}
for (int i = 0; i < 100; i++)
{
var result = await transformBlock.ReceiveAsync();
Console.WriteLine(result);
}
}
In which the order will be ordered 0-99.
However, what you seem to want is some correlation with threads, so a thread will post an item to the block and then receive its result. This doesn't really fit into TPL Dataflow which should be more a pipeline of blocks. You can hack it with BoundedCapacity = 1 but you probably shouldn't.

Limited number of concurent threads C# [duplicate]

Let's say I have 100 tasks that do something that takes 10 seconds.
Now I want to only run 10 at a time like when 1 of those 10 finishes another task gets executed till all are finished.
Now I always used ThreadPool.QueueUserWorkItem() for such task but I've read that it is bad practice to do so and that I should use Tasks instead.
My problem is that I nowhere found a good example for my scenario so could you get me started on how to achieve this goal with Tasks?

SemaphoreSlim maxThread = new SemaphoreSlim(10);
for (int i = 0; i < 115; i++)
{
maxThread.Wait();
Task.Factory.StartNew(() =>
{
//Your Works
}
, TaskCreationOptions.LongRunning)
.ContinueWith( (task) => maxThread.Release() );
}

TPL Dataflow is great for doing things like this. You can create a 100% async version of Parallel.Invoke pretty easily:
async Task ProcessTenAtOnce<T>(IEnumerable<T> items, Func<T, Task> func)
{
ExecutionDataflowBlockOptions edfbo = new ExecutionDataflowBlockOptions
{
MaxDegreeOfParallelism = 10
};
ActionBlock<T> ab = new ActionBlock<T>(func, edfbo);
foreach (T item in items)
{
await ab.SendAsync(item);
}
ab.Complete();
await ab.Completion;
}

You have several options. You can use Parallel.Invoke for starters:
public void DoWork(IEnumerable<Action> actions)
{
Parallel.Invoke(new ParallelOptions() { MaxDegreeOfParallelism = 10 }
, actions.ToArray());
}
Here is an alternate option that will work much harder to have exactly 10 tasks running (although the number of threads in the thread pool processing those tasks may be different) and that returns a Task indicating when it finishes, rather than blocking until done.
public Task DoWork(IList<Action> actions)
{
List<Task> tasks = new List<Task>();
int numWorkers = 10;
int batchSize = (int)Math.Ceiling(actions.Count / (double)numWorkers);
foreach (var batch in actions.Batch(actions.Count / 10))
{
tasks.Add(Task.Factory.StartNew(() =>
{
foreach (var action in batch)
{
action();
}
}));
}
return Task.WhenAll(tasks);
}
If you don't have MoreLinq, for the Batch function, here's my simpler implementation:
public static IEnumerable<IEnumerable<T>> Batch<T>(this IEnumerable<T> source, int batchSize)
{
List<T> buffer = new List<T>(batchSize);
foreach (T item in source)
{
buffer.Add(item);
if (buffer.Count >= batchSize)
{
yield return buffer;
buffer = new List<T>();
}
}
if (buffer.Count >= 0)
{
yield return buffer;
}
}

You can create a method like this:
public static async Task RunLimitedNumberAtATime<T>(int numberOfTasksConcurrent,
IEnumerable<T> inputList, Func<T, Task> asyncFunc)
{
Queue<T> inputQueue = new Queue<T>(inputList);
List<Task> runningTasks = new List<Task>(numberOfTasksConcurrent);
for (int i = 0; i < numberOfTasksConcurrent && inputQueue.Count > 0; i++)
runningTasks.Add(asyncFunc(inputQueue.Dequeue()));
while (inputQueue.Count > 0)
{
Task task = await Task.WhenAny(runningTasks);
runningTasks.Remove(task);
runningTasks.Add(asyncFunc(inputQueue.Dequeue()));
}
await Task.WhenAll(runningTasks);
}
And then you can call any async method n times with a limit like this:
Task task = RunLimitedNumberAtATime(10,
Enumerable.Range(1, 100),
async x =>
{
Console.WriteLine($"Starting task {x}");
await Task.Delay(100);
Console.WriteLine($"Finishing task {x}");
});
Or if you want to run long running non async methods, you can do it that way:
Task task = RunLimitedNumberAtATime(10,
Enumerable.Range(1, 100),
x => Task.Factory.StartNew(() => {
Console.WriteLine($"Starting task {x}");
System.Threading.Thread.Sleep(100);
Console.WriteLine($"Finishing task {x}");
}, TaskCreationOptions.LongRunning));
Maybe there is a similar method somewhere in the framework, but I didn't find it yet.

I would love to use the simplest solution I can think of which as I think using the TPL:
string[] urls={};
Parallel.ForEach(urls, new ParallelOptions() { MaxDegreeOfParallelism = 2}, url =>
{
//Download the content or do whatever you want with each URL
});