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Brian Keating is a developer addicted to Microsoft Technologies.

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Async MVC Controllers

clock March 14, 2012 10:26 by author Brian Keating |

 

I previously wrote a post on MVC async controllers. Now I want to follow up on something that’s more cutting edge, unfortunately I’m one of those people that like nosing about with what new, how it will affect me and how I could leverage it in future etc., Often, I’ll readily admit it’s more bleeding edge than cutting edge (xaml support in Blend5 for example Thumbs down).

However in this post I want to show you something that you might agree is pretty nice.

A historical example

So lets take a look at the app we’re trying to build.

Client is a simple web form (yes it’s webforms but I’m trying to catch the non MVC microsofties too), the instruments are entered in the text box, fetch button is clicked and the result is output

image_thumb1

Now the workflow, for the purpose of this test I won’t be connecting to any database webservice etc, I’ve just added a delay of 10 seconds and and assign LastClose to RIC & " " & New Random().NextDouble()

image_thumb4

Here’s the code behind for the non-async button event handler

        protected void Button1_Click(object sender, EventArgs e)
        {
            Contract.Requires<ArgumentException>(!string.IsNullOrWhiteSpace(tbInst1.Text) || !string.IsNullOrWhiteSpace(tbInst2.Text), "Please enter values for instruments");
            // Make the call to the workflow to get the close for each instrument
            try
            {
                var args = new Dictionary<string, object>();
                args.Add("RIC", tbInst1.Text);                
                var res = WorkflowInvoker.Invoke(_getPricesWFDefinition, args);
                Label1.Text = res["LastClose"].ToString();
 
                args.Clear();
                args.Add("RIC", tbInst2.Text);
                res = WorkflowInvoker.Invoke(_getPricesWFDefinition, args);
                Label2.Text = res["LastClose"].ToString();
            }
            catch (Exception exp)
            {
                Trace.Warn(exp.Message);
            }
 
        }

So as you can see, we’re waiting at least 20 seconds for our page to return, nasty.
In theory we should be able to bring this down to 10 seconds as we can make the calls to the workflows in parallel.

Let me show you one way to “incorrectly” achieve this

        private void Option1()
        {
 
            var t1 = Option1TaskCreater(tbInst1, Label1);
            var t2 = Option1TaskCreater(tbInst2, Label2);
 
            Task.WaitAll(t1, t2);
 
        }
 
        private Task Option1TaskCreater(TextBox tb, Label lb)
        {
            var t1 = Task.Factory.StartNew(() =>
            {
                var args = new Dictionary<string, object>();
                args.Add("RIC", tb.Text);
                var res = WorkflowInvoker.Invoke(_getPricesWFDefinition, args);
                lb.Text = res["LastClose"].ToString();
            });
            return t1;
        } 

So we’re using the task library to make the two workflow requests async, and results look promising, down to about 10 seconds now… so any problems with doing this?

Well, tasks use threadpool threads to execute. So, the query will execute on a threadpool thread. To get true asynchronous execution(no worker threads blocked), we need to jump through a few more hoops. For webform people, there’s a very good explanation here. MVC people read on!

But Why/When use async?

The response to this question has been discussed many times in both books and magazines. ASP.NET uses threads from a common language runtime (CLR) thread pool to process requests. As long as there are threads available in the thread pool, ASP.NET has no trouble dispatching incoming requests. But once the thread pool becomes saturated-that is, all the threads inside it are busy processing requests and no free threads remain-new requests have to wait for threads to become free. If the jam becomes severe enough and the queue fills to capacity, ASP.NET throws up its hands and responds with a "Server Unavailable" to new requests.

Often to solve the problem of horizontal scalability more servers are added to the webfarm, however this only provided temporary relief to what in fact is an underlying design problem, it’s not a case of adding more processors(threads) but a case of using the threads more efficiently, as you can see from the diagram below, if you are CPU bound, there is little more you can do but add some more servers.

image_thumb2

So what is this telling us? Basically, if your app is I/O bound then you should use parallelism, if requests are computationally cheap to process, then parallelism is probably an unnecessary overhead.

If the incoming request rate is high, then adding more parallelism will likely yield few benefits and could actually decrease performance, since the incoming rate of work may be high enough to keep the CPUs busy.

If the incoming request rate is low, then the Web application could benefit from parallelism by using the idle CPU cycles to speed up the processing of an individual request. We can use either PLINQ or TPL (either Parallel loops or the Task class) to parallelize the computation over all the processors. Note that by default, however, the PLINQ implementation in .NET 4 will tie-up one ThreadPool worker per processor for the entire execution of the query. As such, it should only be used in Web applications that see few but expensive requests.

MVC4

In MVC4 it becomes even easier that my previous post on AsyncControllers, actually to me it looks quite like the WinRT async calls in .net 4.5 and C#5

Sample using task support for asynchronous controllers

You can now write asynchronous action methods as single methods that return an object of type Task or Task<ActionResult>.

e.g.

public async Task<ActionResult> Index(string city)
{    
    var newsService = new NewsService();    
    var sportsService = new SportsService();        
    return View("Common",        
                new PortalViewModel 
                {       
                    NewsHeadlines = await newsService.GetHeadlinesAsync(),        
                    SportsScores = await sportsService.GetScoresAsync()    
                });
}

In the previous action method, the calls to newsService.GetHeadlinesAsync andsportsService.GetScoresAsync are called asynchronously and do not block a thread from the thread pool.

Asynchronous action methods that return Task instances can also support timeouts. To make your action method cancellable, add a parameter of type CancellationToken to the action method signature. The following example shows an asynchronous action method that has a timeout of 2500 milliseconds and that displays a TimedOut view to the client if a timeout occurs.

[AsyncTimeout(2500)] 
[HandleError(ExceptionType = typeof(TaskCanceledException), View = "TimedOut")]
public async Task<ActionResult> Index(string city, CancellationToken cancellationToken) 
{    
    var newsService = new NewsService();    
    var sportsService = new SportsService();       
    return View("Common",        
        new PortalViewModel 
        {        
            NewsHeadlines = await newsService.GetHeadlinesAsync(cancellationToken),        
            SportsScores = await sportsService.GetScoresAsync(cancellationToken)    
        });
}
 
 
 

Conculsion

MVC4 Makes programming of async controllers even easier.




Strategy pattern

clock January 30, 2012 12:43 by author Brian Keating |

 

So what is the strategy pattern? It’s one of the simplest object orientated design patterns, I find that it helps clean up day to day object orientated design. It’s purpose is to

  • Encapsulate a family of related algorithms such that they are callable through a common interface.
  • Independent evolution, algorithms can vary and evolve separately from classes using them.
  • Allow a class to serve a single purpose
  • Separates the calculation from the the delivery of it’s results. (separation of concerns)
How do we know when we should consider the strategy pattern?
  • Look for switch statements with possible common interface
  • Adding a new calculation to a class could break existing calculations (breaking the Open-Close principle, i.e. a class should be open for extension, but closed for modification.

UML – Strategy model

image

Consequences:

  • Strategies may not use class members from context
  • Tests may now be written for individual concrete strategies
  • Strategies may be mocked when testing the Context class
  • Adding a new Strategy does not modify the Context class

How to implement:

  • Class based
  • Functional programming approach with anonymous methods (Delegates and Funcs as opposed to new classes), I like this when the calculations are trivial
  • Property injection
  • Method strategy (passed to a method and not to the context class constructor)

Show me the code:

    Context class

    image

    Here we see the strategy is getting passed to the context in the constructor, this class should be closed to modification, Trip is just an empty class for my demo and it’s not actually used in the calculation sample.

      Strategy interface
    image

      Sample Strategy

      image

       

      So, what is the strategy pattern again? It’s something you possibly do on a day to day basis and you don’t even realise it.

      e.g. If you write ASP.MVC code, you quite likely are passing interfaces to you controllers for dependency injection and testability ---> Strategy pattern.

      Regds,
      Brian.



    An invisible Azure Message

    clock December 29, 2011 22:22 by author Brian Keating |

     

    When creating an Azure queue, you specify a lock duration, once a message is read from the queue it’s marked as invisible for other readers for a period of time, e.g. one minute.

    image

    Choosing the invisibility time is a trade-off between expected processing time and application recovery time.

    When a message is dequeued, the application specifies the amount of time for which the message is invisible to workers dequeueing messages from the same queue. This time should be large enough to complete the operation specified by the queue message.

    If the timeout is too large, the time it takes to finish processing the message is affected when there are failures. For example, if the invisibility time is set at 30 minutes , and the application crashes after 10 minutes, the message will not have a chance of being started again for another 20 minutes.

    If the invisibility time is too small, the message may become visible when someone is still processing it. Thus, multiple workers could end up processing the same message, and one may not be able to delete the message from the queue (see the next section).

    The application could address this as follows

    1. If the amount of time to process a message is predictable, set the invisibility timeout large enough so that a message can be completed within that time.

    2. Sometimes the processing time for different types of messages may vary significantly. In that case, one can use separate queues for different types of messages, where messages in each queue take a similar amount of time to be processed. Appropriate invisibility timeout value can then be set to each queue.

    3. Furthermore, ensure that the operations performed on the messages are idempotent and resume-able. The following can be done to improve efficiency

    a. The processing should be stopped before the invisibility time is reached to avoid redundant work.

    b. The work for a message can be done in small chunks, where a small invisibility time may be sufficient. In this way, the next time the work is picked up from the queue after it becomes visible again, the work can be resumed from where it is left off.

    4. Finally, if the message invisibility time is too short and too many dequeued messages are becoming visible before they can be deleted, applications may want to dynamically change the invisibility time that is being set for new messages put onto the queues. This could be detected by counting at the worker roles how many times message deletes are failing due to messages becoming visible. Then based on a threshold communicate that back to the front-end web roles, so they can increase the invisibility time for new messages put into the queue if the invisibility time needs to be tuned.

    Manage the invisibility on the fly

    The “Update Message” REST API is used to extend the lease period (aka visibility timeout) and/or update the message content. A worker that is processing a message can now determine the extra processing time it needs based on the content of a message. The lease period, specified in seconds, must be >= 0 and is relative to the current time. 0 makes the message visible at that time in the queue as a candidate for processing. The maximum value for lease period is 7 days. Note, when updating the visibility timeout it can go beyond the expiry time (or time to live) that is defined when the message was added to the queue. But the expiry time will take precedence and the message will be deleted at that time from the queue.