Hi I have a short question regarding tasks. As far as I understand Tasks can start multiple threads in itself.
Lets say I have two hardware sensors which give me data over two different dataports.
I want to implement them as producers in my c# project and then do something with the data. Would it make sense to start the data collection in two different tasks? Or should i implement them in the same task since c# will automatically put them on different threads?
var producerWorker = Task.Factory.StartNew(() => SensorB(number));
var producerWorker2 = Task.Factory.StartNew(() => SensorA(number));
or
var producerWorker = Task.Factory.StartNew(() => Sensor_A_AND_B(number));
My second problem is: When I have two different producers in two different tasks, how do I add their data to the same BlockingCollection queue if they have different datatypes but need to be at the same position in the queue?
For example if I have queueA for SensorA, queueB for SensorB, and queueC.
Both queues can be filled at different speeds. So lets say queueA has 50 elements, but SensorB is a lot faster and already has 100 elements stored in queueB. However I need to retrieve the data in a way, so that I can place queueA[33].data and queueB[33].data in queueC[33].data. Of course I would not like to start with element33, but always with the first element which was stored in queueA and queueB....
I hope you get what i mena
Tasks are executed in whatever way the runtime thinks is the best. Generally, there's a thread pool and both tasks run on available threads. If you really need to poll two sensors in parallel, I would recommend you to use two real threads to poll and use Reactive Extensions to process the readings in sync.
Judging by your question, you should do some reading on how tasks and Async work in C#, the topic is too large to answer on stack overflow. I would recommend picking up a book, because MS. documentation is rubbish when it comes to providing a solid block of knowledge.
Brifly, a task can not start multiple threads inside itself. Conceptually, a task is a smaller unit than a thread. A single thread can process multiple tasks, so lets say you have 20 tasks, the c# runtime will have a thread-pool of, for example, 4 threads, and they will take a task each, process it, then move on to the next task, and so on.
Perhaps what you are referring to is Asyncronous operations. That's a very different beast than a thread. Basically you are asking some part of the computer to go off, do an independent piece of work, for example send data over network and notify your program when it's done, without blocking the thread in the meantime.
Avoid using Task.Factory, because it has many ways of shooting yourself in the foot.Take a look at Stephen Cleary blog. Task.Run(... is a better choice most of the time.
My best guess is that when you say:
Or should i implement them in the same task since c# will automatically put them on different threads?
You are referring to async operations.
For simplicity's sake you could create two separate tasks, and as soon as they recieve data pop it into a queue.
Your question suggests that you need to synchronize the incoming data.If that's so, a blocking queue is probably the wrong choice. Use concurrent queue instead. A different task could read QueueA[x] and QueueB[x], and buffer the incoming data. Then you could pop then onto QueueC when both A and B supply N'th result.
Related
The app I'm developing is composed this way:
A producer task scan the file system for text files and put a reference to them in a bag.
Many consumer tasks take file refs from the bag concurrently and read the files (and do some short work with their content)
I must be able to pause and resume the whole process.
I've tried using TPL, creating a task for every file ref as they are put in the bag (in this case the bag is just a concept, the producer directly create the consumers task as it find files) but this way I don't have control over the task I create, I can't (or I don't know how to) pause them. I can write some code to suspend the thread currently executing the task but that will ruin the point of working with logical tasks instead of manully creating threads wouldn't it? I would want something like "task already assigned to phisical thread can complete but waiting logical tasks should not start until resume command"
How can I achive this? Can it be done with TPL or should I use something else?
EDIT:
Your answers are all valid but my main doubt remains unanswered. We are talking about tasks, if I use TPL my producer and my many consumer will be tasks (right?) not threads (well, ok at the moment of the execution tasks will be mapped on threads). Every synchronization mechanism i've found (like the one proposed in the comment "ManualResetEventSlim") work at thread level.
E.g. the description of the Wait() method of "ManualResetEventSlim" is "Blocks the current thread until the current ManualResetEventSlim is set."
My knowledge of task is purely academic, I don't know how things works in the "real world" but it seem logical to me that I need a way to coordinate (wait/signal/...) tasks at task level or things could get weird... like... two task may be mapped on the same thread but one was supposed to signal the other that was waiting then deadlock. I'm a bit confused. This is why I asked if my app could use TPL instead of old style simple threads.
Yes, you can do that. First, you have a main thread, your application. There you have two workers, represented by threads. The first worker would be a producer and the second worker would be a consumer.
When your application starts, you start the workers. Both of them operates on the concurrency collection, the bag. Producer searches for files and puts references to the bag and consumer takes references from the bag and starts a task per reference.
When you want to signal pause, simply pause the producer. If you do that, consumer also stops working if there is nothing in the bag. If this is not a desired behaviour, you can simply define that pausing of the producer also clears the bag - backup your bag first and than clear it. This way all running tasks will finish their job and consumer will not start new tasks, but it can still run and wait for the results.
EDIT:
Based on your edit. I don't know how to achieve it the way you want, but although it is nice try to use new technologies, don't let your mind be clouded. Using a ThreadPool is also nice thing. It will take more time to start the application, but once it is running, consuming will be faster, because you already have workers ready.
It is not a bad idea, you can specify a maximum number of workers. If you create a task for every item in the bag, it will be more memory-consuming because you will still allocate and release memory. This will not happen with ThreadPool.
Sure you can use TPL for this. And may be also reactive extensions and LINQ to simplify grouping and pausing/resuming the thread works.
If you have just a short job on each file, it is pretty good idea to not to disturb the handler function with cancellations. You can just suspend queueing the workers instead.
I imagine something like this:
You directory scanner thread puts the found files into an observable collection.
The consumer thread subscribes the collection changes and gets/removes the files and assigns them to workers.
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Possible Duplicate:
Parallel.ForEach vs Task.Factory.StartNew
I need to run about 1,000 tasks in a ThreadPool on a nightly basis (the number may grow in the future). Each task is performing a long running operation (reading data from a web service) and is not CPU intensive. Async I/O is not an option for this particular use case.
Given an IList<string> of parameters, I need to DoSomething(string x). I am trying to pick between the following two options:
IList<Task> tasks = new List<Task>();
foreach (var p in parameters)
{
tasks.Add(Task.Factory.StartNew(() => DoSomething(p), TaskCreationOptions.LongRunning));
}
Task.WaitAll(tasks.ToArray());
OR
Parallel.ForEach(parameters, new ParallelOptions {MaxDegreeOfParallelism = Environment.ProcessorCount*32}, DoSomething);
Which option is better and why?
Note :
The answer should include a comparison between the usage of TaskCreationOptions.LongRunning and MaxDegreeOfParallelism = Environment.ProcessorCount * SomeConstant.
Perhaps you aren't aware of this, but the members in the Parallel class are simply (complicated) wrappers around Task objects. In case you're wondering, the Parallel class creates the Task objects with TaskCreationOptions.None. However, the MaxDegreeOfParallelism would affect those task objects no matter what creation options were passed to the task object's constructor.
TaskCreationOptions.LongRunning gives a "hint" to the underlying TaskScheduler that it might perform better with oversubscription of the threads. Oversubscription is good for threads with high-latency, for example I/O, because it will assign more than one thread (yes thread, not task) to a single core so that it will always have something to do, instead of waiting around for an operation to complete while the thread is in a waiting state. On the TaskScheduler that uses the ThreadPool, it will run LongRunning tasks on their own dedicated thread (the only case where you have a thread per task), otherwise it will run normally, with scheduling and work stealing (really, what you want here anyway)
MaxDegreeOfParallelism controls the number of concurrent operations run. It's similar to specifying the max number of paritions that the data will be split into and processed from. If TaskCreationOptions.LongRunning were able to be specified, all this would do would be to limit the number of tasks running at a single time, similar to a TaskScheduler whose maximum concurrency level is set to that value, similar to this example.
You might want the Parallel.ForEach. However, adding MaxDegreeOfParallelism equal to such a high number actually won't guarantee that there will be that many threads running at once, since the tasks will still be controlled by the ThreadPoolTaskScheduler. That scheduler will the number of threads running at once to the smallest amount possible, which I suppose is the biggest difference between the two methods. You could write (and specify) your own TaskScheduler that would mimic the max degree of parallelism behavior, and have the best of both worlds, but I'm doubting that something you're interested in doing.
My guess is that, depending on latency and the number of actual requests you need to do, using tasks will perform better in many(?) cases, though wind up using more memory, while parallel will be more consistent in resource usage. Of course, async I/O will perform monstrously better than any of these two options, but I understand you can't do that because you're using legacy libraries. So, unfortunately, you'll be stuck with mediocre performance no matter which one of those you chose.
A real solution would be to figure out a way to make async I/O happen; since I don't know the situation, I don't think I can be more helpful than that. Your program (read, thread) will continue execution, and the kernel will wait for the I/O operation to complete (this is also known as using I/O completion ports). Because the thread is not in a waiting state, the runtime can do more work on less threads, which usually ends up in an optimal relationship between the number of cores and number of threads. Adding more threads, as much as I wish it would, does not equate to better performance (actually, it can often hurt performance, because of things like context switching).
However, this entire answer is useless in a determining a final answer for your question, though I hope it will give you some needed direction. You won't know what performs better until you profile it. If you don't try them both (I should clarify that I mean the Task without the LongRunning option, letting the scheduler handle thread switching) and profile them to determine what is best for your particular use case, you're selling yourself short.
Both options are entirely inappropriate for your scenario.
TaskCreationOptions.LongRunning is certainly a better choice for tasks that are not CPU-bound, as the TPL (Parallel classes/extensions) are almost exclusively meant for maximizing the throughput of a CPU-bound operation by running it on multiple cores (not threads).
However, 1000 tasks is an unacceptable number for this. Whether or not they're all running at once isn't exactly the issue; even 100 threads waiting on synchronous I/O is an untenable situation. As one of the comments suggests, your application will be using an enormous amount of memory and end up spending almost all of its time in context-switching. The TPL is not designed for this scale.
If your operations are I/O bound - and if you are using web services, they are - then async I/O is not only the correct solution, it's the only solution. If you have to re-architect some of your code (such as, for example, adding asynchronous methods to major interfaces where there were none originally), do it, because I/O completion ports are the only mechanism in Windows or .NET that can properly support this particular type of concurrency.
I've never heard of a situation where async I/O was somehow "not an option". I cannot even conceive of any valid use case for this constraint. If you are unable to use async I/O then this would indicate a serious design problem that must be fixed, ASAP.
While this is not a direct comparison, I think it may help you. I do something similar to what you describe (in my case I know there is a load balanced server cluster on the other end serving REST calls). I get good results using Parrallel.ForEach to spin up an optimal number of worker threads provided that I also use the following code to tell my operating system it can connect to more than usual number of endpoints.
var servicePointManager = System.Net.ServicePointManager.FindServicePoint(Uri);
servicePointManager.ConnectionLimit = 250;
Note you have to call that once for each unique URL you connect to.
I need a environment which needs to maintain different task queues, and for each of them to have a well defined number of concurrent threads that can execute for each queue. Something like this:
Queue 1 -> 3 threads;
Queue 2 -> 6 threads;
Kind of Task system. I have managed to implement by myself this using plain old c# code (aka System.Threading.Thread, lock and queue) which works more than fine for 1+ year. However, I keep reading articles about the wonders of TaskFactory and TaskScheduler, about being possible this things with built-in classes in .NET, but I have failed to find an example to prove this. I would like to test it and to compare with what I have right now to see if it's working better and if it does, to replace it.
More, I can live without having to limit/set the number of parallel threads for each queue as long as I can get the guarantee that if an item targeted for queue #2 is executed imediatly even if queue #1 is executing on full load.
So, my question is - is there something in .net 4 and more, can someone point me to a sample? I am looking for one an entire week and failed to get something relevant.
This is actually pretty trivial using the TPL and the new collections in System.Collections.Concurrent.
For your needs the BlockingCollection<T> is what I would recommend. By default it uses a ConcurrentQueue<T> as the underlying store which is perfect for what you want.
var queue = new BlockingCollection<Message>();
To set some code working on those messages, and control how many can execute in parallel is as simple as this:
//Set max parallel Tasks
var options = new ParallelOptions
{
MaxDegreeOfParallelism = 10
};
Parallel.ForEach(queue.GetConsumingEnumerable(), options, msg =>
{
//Do some stuff with this message
});
So what is going on here? Well...
The call to GetConsumingEnumerable() will actually block until there is something in queue to consume. This is great because no extra code is necessary for signaling that new work is ready to be done. Rather, as queue fills up, a new Task with your (anonymous) delegate will be kicked off with an item.
The ParallelOptions object allows you to control how Parallel.ForEach operates. In this case, you are telling it you never want more than 10 Tasks executing at any one time. It is important to note that Tasks != Threads. The details are murky, but needless to say there is a lot of optimization going on under the hood. It's all pluggable mind you, but that is not for the faint of heart.
There are obviously a lot of details I haven't covered here, but hopefully you can see how simple and expressive using the Task Parallel Library can be.
Currently, I have a large number of C# computations (method calls) residing in a queue that will be run sequentially. Each computation will use some high-latency service (network, disk...).
I was going to use Mono coroutines to allow the next computation in the computation queue to continue while a previous computation is waiting for the high latency service to return. However, I prefer to not depend on Mono coroutines.
Is there a design pattern that's implementable in pure C# that will enable me to process additional computations while waiting for high latency services to return?
Thanks
Update:
I need to execute a huge number (>10000) of tasks, and each task will be using some high-latency service. On Windows, you can't create that much threads.
Update:
Basically, I need a design pattern that emulates the advantages (as follows) of tasklets in Stackless Python (http://www.stackless.com/)
Huge # of tasks
If a task blocks the next task in the queue executes
No wasted cpu cycle
Minimal overhead switching between tasks
You can simulate cooperative microthreading using IEnumerable. Unfortunately this won't work with blocking APIs, so you need to find APIs that you can poll, or which have callbacks that you can use for signalling.
Consider a method
IEnumerable Thread ()
{
//do some stuff
Foo ();
//co-operatively yield
yield null;
//do some more stuff
Bar ();
//sleep 2 seconds
yield new TimeSpan (2000);
}
The C# compiler will unwrap this into a state machine - but the appearance is that of a co-operative microthread.
The pattern is quite straightforward. You implement a "scheduler" that keeps a list of all the active IEnumerators. As it cycles through the list, it "runs" each one using MoveNext (). If the value of MoveNext is false, the thread has ended, and the scheduler removes it from the list. If it's true, then the scheduler accesses the Current property to determine the current state of the thread. If it's a TimeSpan, the thread wishes to sleep, and the scheduler moved it onto some queue that can be flushed back into the main list when the sleep timespans have ended.
You can use other return objects to implement other signalling mechanisms. For example, define some kind of WaitHandle. If the thread yields one of these, it can be moved to a waiting queue until the handle is signalled. Or you could support WaitAll by yielding an array of wait handles. You could even implement priorities.
I did a simple implementation of this scheduler in about 150LOC but I haven't got round to blogging the code yet. It was for our PhyreSharp PhyreEngine wrapper (which won't be public), where it seems to work pretty well for controlling a couple of hundred characters in one of our demos. We borrowed the concept from the Unity3D engine -- they have some online docs that explain it from a user point of view.
.NET 4.0 comes with extensive support for Task parallelism:
How to: Use Parallel.Invoke to Execute Simple Parallel Tasks
How to: Return a Value from a Task
How to: Chain Multiple Tasks with Continuations
I'd recommend using the Thread Pool to execute multiple tasks from your queue at once in manageable batches using a list of active tasks that feeds off of the task queue.
In this scenario your main worker thread would initially pop N tasks from the queue into the active tasks list to be dispatched to the thread pool (most likely using QueueUserWorkItem), where N represents a manageable amount that won't overload the thread pool, bog your app down with thread scheduling and synchronization costs, or suck up available memory due to the combined I/O memory overhead of each task.
Whenever a task signals completion to the worker thread, you can remove it from the active tasks list and add the next one from your task queue to be executed.
This will allow you to have a rolling set of N tasks from your queue. You can manipulate N to affect the performance characteristics and find what is best in your particular circumstances.
Since you are ultimately bottlenecked by hardware operations (disk I/O and network I/O, CPU) I imagine smaller is better. Two thread pool tasks working on disk I/O most likely won't execute faster than one.
You could also implement flexibility in the size and contents of the active task list by restricting it to a set number of particular type of task. For example if you are running on a machine with 4 cores, you might find that the highest performing configuration is four CPU-bound tasks running concurrently along with one disk-bound task and a network task.
If you already have one task classified as a disk IO task, you may choose to wait until it is complete before adding another disk IO task, and you may choose to schedule a CPU-bound or network-bound task in the meanwhile.
Hope this makes sense!
PS: Do you have any dependancies on the order of tasks?
You should definitely check out the Concurrency and Coordination Runtime. One of their samples describes exactly what you're talking about: you call out to long-latency services, and the CCR efficiently allows some other task to run while you wait. It can handle huge number of tasks because it doesn't need to spawn a thread for each one, though it will use all your cores if you ask it to.
Isn't this a conventional use of multi-threaded processing?
Have a look at patterns such as Reactor here
Writing it to use Async IO might be sufficient.
This can lead to nasy, hard to debug code without strong structure in the design.
You should take a look at this:
http://www.replicator.org/node/80
This should do exactly what you want. It is a hack, though.
Some more information about the "Reactive" pattern (as mentioned by another poster) with respect to an implementation in .NET; aka "Linq to Events"
http://themechanicalbride.blogspot.com/2009/07/introducing-rx-linq-to-events.html
-Oisin
In fact, if you use one thread for a task, you will lose the game. Think about why Node.js can support huge number of conections. Using a few number of thread with async IO!!! Async and await functions can help on this.
foreach (var task in tasks)
{
await SendAsync(task.value);
ReadAsync();
}
SendAsync() and ReadAsync() are faked functions to async IO call.
Task parallelism is also a good choose. But I am not sure which one is faster. You can test both of them
in your case.
Yes of course you can. You just need to build a dispatcher mechanism that will call back on a lambda that you provide and goes into a queue. All the code I write in unity uses this approach and I never use coroutines. I wrap methods that use coroutines such as WWW stuff to just get rid of it. In theory, coroutines can be faster because there is less overhead. Practically they introduce new syntax to a language to do a fairly trivial task and furthermore you can't follow the stack trace properly on an error in a co-routine because all you'll see is ->Next. You'll have to then implement the ability to run the tasks in the queue on another thread. However, there is parallel functions in the latest .net and you'd be essentially writing similar functionality. It wouldn't be many lines of code really.
If anyone is interested I would send the code, don't have it on me.
I've got a program I'm creating(in C#) and I see two approaches..
1) A job manager that waits for any number of X threads to finish, when finished it gets the next chunk of work and creates a new thread and gives it that chunk
or
2) We create X threads to start, give them each a chunk of work, and when a thread finishes a chunk its asks the job manager for more work. If there isn't any more work it sleeps and then asks again, with the sleep becoming progressively longer.
This program will be a run and done, tho I could see it turning into a service that continually looks for more jobs.
Each chunk will consists of a number of data ids, a call to the database to get some info or perform an operation on the data id, and then writing to the database info on the data id.
Assuming you are aware of the additional precautions that need to be taken when dealing with multithreaded database operations, it sounds like you're describing two different scenarios. In the first, you have several threads running, and once ALL of them finish it will look for new work. In the second, you have several threads running and their operations are completely parallel. Your environment is going to be what determines the proper approach to take; if there is something tying all of the work in the several threads where additional work cannot continue until all of them are finished, then with the former. If they don't have much affect on each other, go with the latter.
The second option isn't really right, as making the sleep time progressively longer means that you will unnecessarily keep those threads blocked.
Rather, you should have a pooled set of threads like the second option, but they use WaitHandles to wait for work and use a producer/consumer pattern. Basically, when the producer indicates that there is work, it sends a signal to a consumer (there will be a manager which will determine which thread will get the work, and then signal that thread) which will wake up and start working.
You might want to look into the Parallel Task Library. It's in beta now, but if you can use it and are comfortable with it, I would recommend it, as it will manage a great deal of this for you (and much better, taking into account the number of cores on a machine, the optimal number of threads, etc, etc).
The former solution (spawn a thread for each new piece of work), is easier to code, and not too bad, if the units of work are large enough.
The second solution (thread-pool, with a queue of work), is more complicated to code, but supports smaller units of work.
Instead of rolling your own solution, you should look at the ThreadPool class in the .NET framework. You could use the QueueUserWorkItem method. It should do exactly what you want to accomplish.