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Task Factory for each loop with await

I'm new to tasks and have a question regarding the usage. Does the Task.Factory fire for all items in the foreach loop or block at the 'await' basically making the program single threaded? If I am thinking about this correctly, the foreach loop starts all the tasks and the .GetAwaiter().GetResult(); is blocking the main thread until the last task is complete.
Also, I'm just wanting some anonymous tasks to load the data. Would this be a correct implementation? I'm not referring to exception handling as this is simply an example.
To provide clarity, I am loading data into a database from an outside API. This one is using the FRED database. (https://fred.stlouisfed.org/), but I have several I will hit to complete the entire transfer (maybe 200k data points). Once they are done I update the tables, refresh market calculations, etc. Some of it is real time and some of it is End-of-day. I would also like to say, I currently have everything working in docker, but have been working to update the code using tasks to improve execution.
class Program
{
private async Task SQLBulkLoader()
{
foreach (var fileListObj in indicators.file_list)
{
await Task.Factory.StartNew( () =>
{
string json = this.GET(//API call);
SeriesObject obj = JsonConvert.DeserializeObject<SeriesObject>(json);
DataTable dataTableConversion = ConvertToDataTable(obj.observations);
dataTableConversion.TableName = fileListObj.series_id;
using (SqlConnection dbConnection = new SqlConnection("SQL Connection"))
{
dbConnection.Open();
using (SqlBulkCopy s = new SqlBulkCopy(dbConnection))
{
s.DestinationTableName = dataTableConversion.TableName;
foreach (var column in dataTableConversion.Columns)
s.ColumnMappings.Add(column.ToString(), column.ToString());
s.WriteToServer(dataTableConversion);
}
Console.WriteLine("File: {0} Complete", fileListObj.series_id);
}
});
}
}
static void Main(string[] args)
{
Program worker = new Program();
worker.SQLBulkLoader().GetAwaiter().GetResult();
}
}

Your awaiting the task returned from Task.Factory.StartNew does make it effectively single threaded. You can see a simple demonstration of this with this short LinqPad example:
for (var i = 0; i < 3; i++)
{
var index = i;
$"{index} inline".Dump();
await Task.Run(() =>
{
Thread.Sleep((3 - index) * 1000);
$"{index} in thread".Dump();
});
}
Here we wait less as we progress through the loop. The output is:
0 inline
0 in thread
1 inline
1 in thread
2 inline
2 in thread
If you remove the await in front of StartNew you'll see it runs in parallel. As others have mentioned, you can certainly use Parallel.ForEach, but for a demonstration of doing it a bit more manually, you can consider a solution like this:
var tasks = new List<Task>();
for (var i = 0; i < 3; i++)
{
var index = i;
$"{index} inline".Dump();
tasks.Add(Task.Factory.StartNew(() =>
{
Thread.Sleep((3 - index) * 1000);
$"{index} in thread".Dump();
}));
}
Task.WaitAll(tasks.ToArray());
Notice now how the result is:
0 inline
1 inline
2 inline
2 in thread
1 in thread
0 in thread

This is a typical problem that C# 8.0 Async Streams are going to solve very soon.
Until C# 8.0 is released, you can use the AsyncEnumarator library:
using System.Collections.Async;
class Program
{
private async Task SQLBulkLoader() {
await indicators.file_list.ParallelForEachAsync(async fileListObj =>
{
...
await s.WriteToServerAsync(dataTableConversion);
...
},
maxDegreeOfParalellism: 3,
cancellationToken: default);
}
static void Main(string[] args)
{
Program worker = new Program();
worker.SQLBulkLoader().GetAwaiter().GetResult();
}
}
I do not recommend using Parallel.ForEach and Task.WhenAll as those functions are not designed for asynchronous streaming.

You'll want to add each task to a collection and then use Task.WhenAll to await all of the tasks in that collection:
private async Task SQLBulkLoader()
{
var tasks = new List<Task>();
foreach (var fileListObj in indicators.file_list)
{
tasks.Add(Task.Factory.StartNew( () => { //Doing Stuff }));
}
await Task.WhenAll(tasks.ToArray());
}

My take on this: most time consuming operations will be getting the data using a GET operation and the actual call to WriteToServer using SqlBulkCopy. If you take a look at that class you will see that there is a native async method WriteToServerAsync method (docs here)
. Always use those before creating Tasks yourself using Task.Run.
The same applies to the http GET call. You can use the native HttpClient.GetAsync (docs here) for that.
Doing that you can rewrite your code to this:
private async Task ProcessFileAsync(string series_id)
{
string json = await GetAsync();
SeriesObject obj = JsonConvert.DeserializeObject<SeriesObject>(json);
DataTable dataTableConversion = ConvertToDataTable(obj.observations);
dataTableConversion.TableName = series_id;
using (SqlConnection dbConnection = new SqlConnection("SQL Connection"))
{
dbConnection.Open();
using (SqlBulkCopy s = new SqlBulkCopy(dbConnection))
{
s.DestinationTableName = dataTableConversion.TableName;
foreach (var column in dataTableConversion.Columns)
s.ColumnMappings.Add(column.ToString(), column.ToString());
await s.WriteToServerAsync(dataTableConversion);
}
Console.WriteLine("File: {0} Complete", series_id);
}
}
private async Task SQLBulkLoaderAsync()
{
var tasks = indicators.file_list.Select(f => ProcessFileAsync(f.series_id));
await Task.WhenAll(tasks);
}
Both operations (http call and sql server call) are I/O calls. Using the native async/await pattern there won't even be a thread created or used, see this question for a more in-depth explanation. That is why for IO bound operations you should never have to use Task.Run (or Task.Factory.StartNew. But do mind that Task.Run is the recommended approach).
Sidenote: if you are using HttpClient in a loop, please read this about how to correctly use it.
If you need to limit the number of parallel actions you could also use TPL Dataflow as it plays very nice with Task based IO bound operations. The SQLBulkLoaderAsyncshould then be modified to (leaving the ProcessFileAsync method from earlier this answer intact):
private async Task SQLBulkLoaderAsync()
{
var ab = new ActionBlock<string>(ProcessFileAsync, new ExecutionDataflowBlockOptions { MaxDegreeOfParallelism = 5 });
foreach (var file in indicators.file_list)
{
ab.Post(file.series_id);
}
ab.Complete();
await ab.Completion;
}

Use a Parallel.ForEach loop to enable data parallelism over any System.Collections.Generic.IEnumerable<T> source.
// Method signature: Parallel.ForEach(IEnumerable<TSource> source, Action<TSource> body)
Parallel.ForEach(fileList, (currentFile) =>
{
//Doing Stuff
Console.WriteLine("Processing {0} on thread {1}", currentFile, Thread.CurrentThread.ManagedThreadId);
});

Why didnt you try this :), this program will not start parallel tasks (in a foreach), it will be blocking but the logic in task will be done in separate thread from threadpool (only one at the time, but the main thread will be blocked).
The proper approach in your situation is to use Paraller.ForEach
How can I convert this foreach code to Parallel.ForEach?

C# .NET Parallel I/O operation (with throttling) [duplicate]

I would like to run a bunch of async tasks, with a limit on how many tasks may be pending completion at any given time.
Say you have 1000 URLs, and you only want to have 50 requests open at a time; but as soon as one request completes, you open up a connection to the next URL in the list. That way, there are always exactly 50 connections open at a time, until the URL list is exhausted.
I also want to utilize a given number of threads if possible.
I came up with an extension method, ThrottleTasksAsync that does what I want. Is there a simpler solution already out there? I would assume that this is a common scenario.
Usage:
class Program
{
static void Main(string[] args)
{
Enumerable.Range(1, 10).ThrottleTasksAsync(5, 2, async i => { Console.WriteLine(i); return i; }).Wait();
Console.WriteLine("Press a key to exit...");
Console.ReadKey(true);
}
}
Here is the code:
static class IEnumerableExtensions
{
public static async Task<Result_T[]> ThrottleTasksAsync<Enumerable_T, Result_T>(this IEnumerable<Enumerable_T> enumerable, int maxConcurrentTasks, int maxDegreeOfParallelism, Func<Enumerable_T, Task<Result_T>> taskToRun)
{
var blockingQueue = new BlockingCollection<Enumerable_T>(new ConcurrentBag<Enumerable_T>());
var semaphore = new SemaphoreSlim(maxConcurrentTasks);
// Run the throttler on a separate thread.
var t = Task.Run(() =>
{
foreach (var item in enumerable)
{
// Wait for the semaphore
semaphore.Wait();
blockingQueue.Add(item);
}
blockingQueue.CompleteAdding();
});
var taskList = new List<Task<Result_T>>();
Parallel.ForEach(IterateUntilTrue(() => blockingQueue.IsCompleted), new ParallelOptions { MaxDegreeOfParallelism = maxDegreeOfParallelism },
_ =>
{
Enumerable_T item;
if (blockingQueue.TryTake(out item, 100))
{
taskList.Add(
// Run the task
taskToRun(item)
.ContinueWith(tsk =>
{
// For effect
Thread.Sleep(2000);
// Release the semaphore
semaphore.Release();
return tsk.Result;
}
)
);
}
});
// Await all the tasks.
return await Task.WhenAll(taskList);
}
static IEnumerable<bool> IterateUntilTrue(Func<bool> condition)
{
while (!condition()) yield return true;
}
}
The method utilizes BlockingCollection and SemaphoreSlim to make it work. The throttler is run on one thread, and all the async tasks are run on the other thread. To achieve parallelism, I added a maxDegreeOfParallelism parameter that's passed to a Parallel.ForEach loop re-purposed as a while loop.
The old version was:
foreach (var master = ...)
{
var details = ...;
Parallel.ForEach(details, detail => {
// Process each detail record here
}, new ParallelOptions { MaxDegreeOfParallelism = 15 });
// Perform the final batch updates here
}
But, the thread pool gets exhausted fast, and you can't do async/await.
Bonus:
To get around the problem in BlockingCollection where an exception is thrown in Take() when CompleteAdding() is called, I'm using the TryTake overload with a timeout. If I didn't use the timeout in TryTake, it would defeat the purpose of using a BlockingCollection since TryTake won't block. Is there a better way? Ideally, there would be a TakeAsync method.

As suggested, use TPL Dataflow.
A TransformBlock<TInput, TOutput> may be what you're looking for.
You define a MaxDegreeOfParallelism to limit how many strings can be transformed (i.e., how many urls can be downloaded) in parallel. You then post urls to the block, and when you're done you tell the block you're done adding items and you fetch the responses.
var downloader = new TransformBlock<string, HttpResponse>(
url => Download(url),
new ExecutionDataflowBlockOptions { MaxDegreeOfParallelism = 50 }
);
var buffer = new BufferBlock<HttpResponse>();
downloader.LinkTo(buffer);
foreach(var url in urls)
downloader.Post(url);
//or await downloader.SendAsync(url);
downloader.Complete();
await downloader.Completion;
IList<HttpResponse> responses;
if (buffer.TryReceiveAll(out responses))
{
//process responses
}
Note: The TransformBlock buffers both its input and output. Why, then, do we need to link it to a BufferBlock?
Because the TransformBlock won't complete until all items (HttpResponse) have been consumed, and await downloader.Completion would hang. Instead, we let the downloader forward all its output to a dedicated buffer block - then we wait for the downloader to complete, and inspect the buffer block.

Say you have 1000 URLs, and you only want to have 50 requests open at
a time; but as soon as one request completes, you open up a connection
to the next URL in the list. That way, there are always exactly 50
connections open at a time, until the URL list is exhausted.
The following simple solution has surfaced many times here on SO. It doesn't use blocking code and doesn't create threads explicitly, so it scales very well:
const int MAX_DOWNLOADS = 50;
static async Task DownloadAsync(string[] urls)
{
using (var semaphore = new SemaphoreSlim(MAX_DOWNLOADS))
using (var httpClient = new HttpClient())
{
var tasks = urls.Select(async url =>
{
await semaphore.WaitAsync();
try
{
var data = await httpClient.GetStringAsync(url);
Console.WriteLine(data);
}
finally
{
semaphore.Release();
}
});
await Task.WhenAll(tasks);
}
}
The thing is, the processing of the downloaded data should be done on a different pipeline, with a different level of parallelism, especially if it's a CPU-bound processing.
E.g., you'd probably want to have 4 threads concurrently doing the data processing (the number of CPU cores), and up to 50 pending requests for more data (which do not use threads at all). AFAICT, this is not what your code is currently doing.
That's where TPL Dataflow or Rx may come in handy as a preferred solution. Yet it is certainly possible to implement something like this with plain TPL. Note, the only blocking code here is the one doing the actual data processing inside Task.Run:
const int MAX_DOWNLOADS = 50;
const int MAX_PROCESSORS = 4;
// process data
class Processing
{
SemaphoreSlim _semaphore = new SemaphoreSlim(MAX_PROCESSORS);
HashSet<Task> _pending = new HashSet<Task>();
object _lock = new Object();
async Task ProcessAsync(string data)
{
await _semaphore.WaitAsync();
try
{
await Task.Run(() =>
{
// simuate work
Thread.Sleep(1000);
Console.WriteLine(data);
});
}
finally
{
_semaphore.Release();
}
}
public async void QueueItemAsync(string data)
{
var task = ProcessAsync(data);
lock (_lock)
_pending.Add(task);
try
{
await task;
}
catch
{
if (!task.IsCanceled && !task.IsFaulted)
throw; // not the task's exception, rethrow
// don't remove faulted/cancelled tasks from the list
return;
}
// remove successfully completed tasks from the list
lock (_lock)
_pending.Remove(task);
}
public async Task WaitForCompleteAsync()
{
Task[] tasks;
lock (_lock)
tasks = _pending.ToArray();
await Task.WhenAll(tasks);
}
}
// download data
static async Task DownloadAsync(string[] urls)
{
var processing = new Processing();
using (var semaphore = new SemaphoreSlim(MAX_DOWNLOADS))
using (var httpClient = new HttpClient())
{
var tasks = urls.Select(async (url) =>
{
await semaphore.WaitAsync();
try
{
var data = await httpClient.GetStringAsync(url);
// put the result on the processing pipeline
processing.QueueItemAsync(data);
}
finally
{
semaphore.Release();
}
});
await Task.WhenAll(tasks.ToArray());
await processing.WaitForCompleteAsync();
}
}

As requested, here's the code I ended up going with.
The work is set up in a master-detail configuration, and each master is processed as a batch. Each unit of work is queued up in this fashion:
var success = true;
// Start processing all the master records.
Master master;
while (null != (master = await StoredProcedures.ClaimRecordsAsync(...)))
{
await masterBuffer.SendAsync(master);
}
// Finished sending master records
masterBuffer.Complete();
// Now, wait for all the batches to complete.
await batchAction.Completion;
return success;
Masters are buffered one at a time to save work for other outside processes. The details for each master are dispatched for work via the masterTransform TransformManyBlock. A BatchedJoinBlock is also created to collect the details in one batch.
The actual work is done in the detailTransform TransformBlock, asynchronously, 150 at a time. BoundedCapacity is set to 300 to ensure that too many Masters don't get buffered at the beginning of the chain, while also leaving room for enough detail records to be queued to allow 150 records to be processed at one time. The block outputs an object to its targets, because it's filtered across the links depending on whether it's a Detail or Exception.
The batchAction ActionBlock collects the output from all the batches, and performs bulk database updates, error logging, etc. for each batch.
There will be several BatchedJoinBlocks, one for each master. Since each ISourceBlock is output sequentially and each batch only accepts the number of detail records associated with one master, the batches will be processed in order. Each block only outputs one group, and is unlinked on completion. Only the last batch block propagates its completion to the final ActionBlock.
The dataflow network:
// The dataflow network
BufferBlock<Master> masterBuffer = null;
TransformManyBlock<Master, Detail> masterTransform = null;
TransformBlock<Detail, object> detailTransform = null;
ActionBlock<Tuple<IList<object>, IList<object>>> batchAction = null;
// Buffer master records to enable efficient throttling.
masterBuffer = new BufferBlock<Master>(new DataflowBlockOptions { BoundedCapacity = 1 });
// Sequentially transform master records into a stream of detail records.
masterTransform = new TransformManyBlock<Master, Detail>(async masterRecord =>
{
var records = await StoredProcedures.GetObjectsAsync(masterRecord);
// Filter the master records based on some criteria here
var filteredRecords = records;
// Only propagate completion to the last batch
var propagateCompletion = masterBuffer.Completion.IsCompleted && masterTransform.InputCount == 0;
// Create a batch join block to encapsulate the results of the master record.
var batchjoinblock = new BatchedJoinBlock<object, object>(records.Count(), new GroupingDataflowBlockOptions { MaxNumberOfGroups = 1 });
// Add the batch block to the detail transform pipeline's link queue, and link the batch block to the the batch action block.
var detailLink1 = detailTransform.LinkTo(batchjoinblock.Target1, detailResult => detailResult is Detail);
var detailLink2 = detailTransform.LinkTo(batchjoinblock.Target2, detailResult => detailResult is Exception);
var batchLink = batchjoinblock.LinkTo(batchAction, new DataflowLinkOptions { PropagateCompletion = propagateCompletion });
// Unlink batchjoinblock upon completion.
// (the returned task does not need to be awaited, despite the warning.)
batchjoinblock.Completion.ContinueWith(task =>
{
detailLink1.Dispose();
detailLink2.Dispose();
batchLink.Dispose();
});
return filteredRecords;
}, new ExecutionDataflowBlockOptions { BoundedCapacity = 1 });
// Process each detail record asynchronously, 150 at a time.
detailTransform = new TransformBlock<Detail, object>(async detail => {
try
{
// Perform the action for each detail here asynchronously
await DoSomethingAsync();
return detail;
}
catch (Exception e)
{
success = false;
return e;
}
}, new ExecutionDataflowBlockOptions { MaxDegreeOfParallelism = 150, BoundedCapacity = 300 });
// Perform the proper action for each batch
batchAction = new ActionBlock<Tuple<IList<object>, IList<object>>>(async batch =>
{
var details = batch.Item1.Cast<Detail>();
var errors = batch.Item2.Cast<Exception>();
// Do something with the batch here
}, new ExecutionDataflowBlockOptions { MaxDegreeOfParallelism = 4 });
masterBuffer.LinkTo(masterTransform, new DataflowLinkOptions { PropagateCompletion = true });
masterTransform.LinkTo(detailTransform, new DataflowLinkOptions { PropagateCompletion = true });

Simple way to rate limit HttpClient requests

I am using the HTTPClient in System.Net.Http to make requests against an API. The API is limited to 10 requests per second.
My code is roughly like so:
List<Task> tasks = new List<Task>();
items..Select(i => tasks.Add(ProcessItem(i));
try
{
await Task.WhenAll(taskList.ToArray());
}
catch (Exception ex)
{
}
The ProcessItem method does a few things but always calls the API using the following:
await SendRequestAsync(..blah). Which looks like:
private async Task<Response> SendRequestAsync(HttpRequestMessage request, CancellationToken token)
{
token.ThrowIfCancellationRequested();
var response = await HttpClient
.SendAsync(request: request, cancellationToken: token).ConfigureAwait(continueOnCapturedContext: false);
token.ThrowIfCancellationRequested();
return await Response.BuildResponse(response);
}
Originally the code worked fine but when I started using Task.WhenAll I started getting 'Rate Limit Exceeded' messages from the API. How can I limit the rate at which requests are made?
Its worth noting that ProcessItem can make between 1-4 API calls depending on the item.

The API is limited to 10 requests per second.
Then just have your code do a batch of 10 requests, ensuring they take at least one second:
Items[] items = ...;
int index = 0;
while (index < items.Length)
{
var timer = Task.Delay(TimeSpan.FromSeconds(1.2)); // ".2" to make sure
var tasks = items.Skip(index).Take(10).Select(i => ProcessItemsAsync(i));
var tasksAndTimer = tasks.Concat(new[] { timer });
await Task.WhenAll(tasksAndTimer);
index += 10;
}
Update
My ProcessItems method makes 1-4 API calls depending on the item.
In this case, batching is not an appropriate solution. You need to limit an asynchronous method to a certain number, which implies a SemaphoreSlim. The tricky part is that you want to allow more calls over time.
I haven't tried this code, but the general idea I would go with is to have a periodic function that releases the semaphore up to 10 times. So, something like this:
private readonly SemaphoreSlim _semaphore = new SemaphoreSlim(10);
private async Task<Response> ThrottledSendRequestAsync(HttpRequestMessage request, CancellationToken token)
{
await _semaphore.WaitAsync(token);
return await SendRequestAsync(request, token);
}
private async Task PeriodicallyReleaseAsync(Task stop)
{
while (true)
{
var timer = Task.Delay(TimeSpan.FromSeconds(1.2));
if (await Task.WhenAny(timer, stop) == stop)
return;
// Release the semaphore at most 10 times.
for (int i = 0; i != 10; ++i)
{
try
{
_semaphore.Release();
}
catch (SemaphoreFullException)
{
break;
}
}
}
}
Usage:
// Start the periodic task, with a signal that we can use to stop it.
var stop = new TaskCompletionSource<object>();
var periodicTask = PeriodicallyReleaseAsync(stop.Task);
// Wait for all item processing.
await Task.WhenAll(taskList);
// Stop the periodic task.
stop.SetResult(null);
await periodicTask;

The answer is similar to this one.
Instead of using a list of tasks and WhenAll, use Parallel.ForEach and use ParallelOptions to limit the number of concurrent tasks to 10, and make sure each one takes at least 1 second:
Parallel.ForEach(
items,
new ParallelOptions { MaxDegreeOfParallelism = 10 },
async item => {
ProcessItems(item);
await Task.Delay(1000);
}
);
Or if you want to make sure each item takes as close to 1 second as possible:
Parallel.ForEach(
searches,
new ParallelOptions { MaxDegreeOfParallelism = 10 },
async item => {
var watch = new Stopwatch();
watch.Start();
ProcessItems(item);
watch.Stop();
if (watch.ElapsedMilliseconds < 1000) await Task.Delay((int)(1000 - watch.ElapsedMilliseconds));
}
);
Or:
Parallel.ForEach(
searches,
new ParallelOptions { MaxDegreeOfParallelism = 10 },
async item => {
await Task.WhenAll(
Task.Delay(1000),
Task.Run(() => { ProcessItems(item); })
);
}
);

UPDATED ANSWER
My ProcessItems method makes 1-4 API calls depending on the item. So with a batch size of 10 I still exceed the rate limit.
You need to implement a rolling window in SendRequestAsync. A queue containing timestamps of each request is a suitable data structure. You dequeue entries with a timestamp older than 10 seconds. As it so happens, there is an implementation as an answer to a similar question on SO.
ORIGINAL ANSWER
May still be useful to others
One straightforward way to handle this is to batch your requests in groups of 10, run those concurrently, and then wait until a total of 10 seconds has elapsed (if it hasn't already). This will bring you in right at the rate limit if the batch of requests can complete in 10 seconds, but is less than optimal if the batch of requests takes longer. Have a look at the .Batch() extension method in MoreLinq. Code would look approximately like
foreach (var taskList in tasks.Batch(10))
{
Stopwatch sw = Stopwatch.StartNew(); // From System.Diagnostics
await Task.WhenAll(taskList.ToArray());
if (sw.Elapsed.TotalSeconds < 10.0)
{
// Calculate how long you still have to wait and sleep that long
// You might want to wait 10.5 or 11 seconds just in case the rate
// limiting on the other side isn't perfectly implemented
}
}

https://github.com/thomhurst/EnumerableAsyncProcessor
I've written a library to help with this sort of logic.
Usage would be:
var responses = await AsyncProcessorBuilder.WithItems(items) // Or Extension Method: items.ToAsyncProcessorBuilder()
.SelectAsync(item => ProcessItem(item), CancellationToken.None)
.ProcessInParallel(levelOfParallelism: 10, TimeSpan.FromSeconds(1));

Mixing async tasks with blocking sync task

I am writing a set of async tasks that go away an download and parse data, however I am running in to a bit of a blank with the next step where I am updating a database.
The issue is that for the sake of performance I am using a TableLock to load rather large datasets, so what I am wanting to do is have my import service wait for the first Task to return, start the import. Should another Task complete while the first import is running the process joins a queue and waits for the import service is complete for task 1.
eg.
Async
- Task1
- Task2
- Task3
Sync
- ImportService
RunAsync Tasks
Task3 returns first > ImportService.Import(Task3)
Task1 return, ImportService is still running. Wait()
ImportService.Complete() event
Task2 returns. Wait()
ImportService.Import(Task1)
ImportService.Complete() event
ImportService.Import(Task2)
ImportService.Complete() event
Hope this makes sense!

You can't really use await here, but you can wait on multiple tasks to complete:
var tasks = new List<Task)();
// start the tasks however
tasks.Add(Task.Run(Task1Function);
tasks.Add(Task.Run(Task2Function);
tasks.Add(Task.Run(Task2Function);
while (tasks.Count > 0)
{
var i = Task.WaitAny(tasks.ToArray()); // yes this is ugly but an array is required
var task = tasks[i];
tasks.RemoveAt(i);
ImportService.Import(task); // do you need to pass the task or the task.Result
}
Seems to me however that there should be a better option. You could let the tasks and the import run and add a lock on the ImportService part for instance:
// This is the task code doing whatever
....
// Task finishes and calls ImportService.Import
lock(typeof(ImportService)) // actually the lock should probably be inside the Import method
{
ImportService.Import(....);
}
There are several things bothering me with your requirements (including using a static ImportService, static classes are rarely a good idea), but without further details I can't provide better advice.

While this is likely not the most graceful solution, I would try launching the work tasks and have them place their output in a ConcurrentQueue. You could check the queue for work on a timer until all tasks are completed.
var rand = new Random();
var importedData = new List<string>();
var results = new ConcurrentQueue<string>();
var tasks = new List<Task<string>>
{
new Task<string>(() =>
{
Thread.Sleep(rand.Next(1000, 5000));
Debug.WriteLine("Task 1 Completed");
return "ABC";
}),
new Task<string>(() =>
{
Thread.Sleep(rand.Next(1000, 5000));
Debug.WriteLine("Task 2 Completed");
return "FOO";
}),
new Task<string>(() =>
{
Thread.Sleep(rand.Next(1000, 5000));
Debug.WriteLine("Task 3 Completed");
return "BAR";
})
};
tasks.ForEach(t =>
{
t.ContinueWith(r => results.Enqueue(r.Result));
t.Start();
});
var allTasksCompleted = new AutoResetEvent(false);
new Timer(state =>
{
var timer = (Timer) state;
string item;
if (!results.TryDequeue(out item))
return;
importedData.Add(item);
Debug.WriteLine("Imported " + item);
if (importedData.Count == tasks.Count)
{
timer.Dispose();
Debug.WriteLine("Completed.");
allTasksCompleted.Set();
}
}).Change(1000, 100);
allTasksCompleted.WaitOne();

How do you use AsParallel with the async and await keywords?

I was looking at someone sample code for async and noticed a few issues with the way it was implemented. Whilst looking at the code I wondered if it would be more efficient to loop through a list using as parallel, rather than just looping through the list normally.
As far as I can tell there is very little difference in performance, both use up every processor, and both talk around the same amount of time to completed.
This is the first way of doing it
var tasks= Client.GetClients().Select(async p => await p.Initialize());
And this is the second
var tasks = Client.GetClients().AsParallel().Select(async p => await p.Initialize());
Am I correct in assuming there is no difference between the two?
The full program can be found below
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace ConsoleApplication2
{
class Program
{
static void Main(string[] args)
{
RunCode1();
Console.WriteLine("Here");
Console.ReadLine();
RunCode2();
Console.WriteLine("Here");
Console.ReadLine();
}
private async static void RunCode1()
{
Stopwatch myStopWatch = new Stopwatch();
myStopWatch.Start();
var tasks= Client.GetClients().Select(async p => await p.Initialize());
Task.WaitAll(tasks.ToArray());
Console.WriteLine("Time ellapsed(ms): " + myStopWatch.ElapsedMilliseconds);
myStopWatch.Stop();
}
private async static void RunCode2()
{
Stopwatch myStopWatch = new Stopwatch();
myStopWatch.Start();
var tasks = Client.GetClients().AsParallel().Select(async p => await p.Initialize());
Task.WaitAll(tasks.ToArray());
Console.WriteLine("Time ellapsed(ms): " + myStopWatch.ElapsedMilliseconds);
myStopWatch.Stop();
}
}
class Client
{
public static IEnumerable<Client> GetClients()
{
for (int i = 0; i < 100; i++)
{
yield return new Client() { Id = Guid.NewGuid() };
}
}
public Guid Id { get; set; }
//This method has to be called before you use a client
//For the sample, I don't put it on the constructor
public async Task Initialize()
{
await Task.Factory.StartNew(() =>
{
Stopwatch timer = new Stopwatch();
timer.Start();
while(timer.ElapsedMilliseconds<1000)
{}
timer.Stop();
});
Console.WriteLine("Completed: " + Id);
}
}
}

There should be very little discernible difference.
In your first case:
var tasks = Client.GetClients().Select(async p => await p.Initialize());
The executing thread will (one at a time) start executing Initialize for each element in the client list. Initialize immediately queues a method to the thread pool and returns an uncompleted Task.
In your second case:
var tasks = Client.GetClients().AsParallel().Select(async p => await p.Initialize());
The executing thread will fork to the thread pool and (in parallel) start executing Initialize for each element in the client list. Initialize has the same behavior: it immediately queues a method to the thread pool and returns.
The two timings are nearly identical because you're only parallelizing a small amount of code: the queueing of the method to the thread pool and the return of an uncompleted Task.
If Initialize did some longer (synchronous) work before its first await, it may make sense to use AsParallel.
Remember, all async methods (and lambdas) start out being executed synchronously (see the official FAQ or my own intro post).

There's a singular major difference.
In the following code, you are taking it upon yourself to perform the partitioning. In other words, you're creating one Task object per item from the IEnumerable<T> that is returned from the call to GetClients():
var tasks= Client.GetClients().Select(async p => await p.Initialize());
In the second, the call to AsParallel is internally going to use Task instances to execute partitions of the IEnumerable<T> and you're going to have the initial Task that is returned from the lambda async p => await p.Initialize():
var tasks = Client.GetClients().AsParallel().
Select(async p => await p.Initialize());
Finally, you're not really doing anything by using async/await here. Granted, the compiler might optimize this out, but you're just waiting on a method that returns a Task and then returning a continuation that does nothing back through the lambda. That said, since the call to Initialize is already returning a Task, it's best to keep it simple and just do:
var tasks = Client.GetClients().Select(p => p.Initialize());
Which will return the sequence of Task instances for you.

To improve on the above 2 answers this is the simplest way to get an async/threaded execution that is awaitable:
var results = await Task.WhenAll(Client.GetClients()
.Select(async p => p.Initialize()));
This will ensure that it spins separate threads and that you get the results at the end. Hope that helps someone. Took me quite a while to figure this out properly since this is very not obvious and the AsParallel() function seems to be what you want but doesn't use async/await.