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  • Azure - Part 4 - Table Storage Service in Windows Azure

    - by Shaun
    In Windows Azure platform there are 3 storage we can use to save our data on the cloud. They are the Table, Blob and Queue. Before the Chinese New Year Microsoft announced that Azure SDK 1.1 had been released and it supports a new type of storage – Drive, which allows us to operate NTFS files on the cloud. I will cover it in the coming few posts but now I would like to talk a bit about the Table Storage.   Concept of Table Storage Service The most common development scenario is to retrieve, create, update and remove data from the data storage. In the normal way we communicate with database. When we attempt to move our application over to the cloud the most common requirement should be have a storage service. Windows Azure provides a in-build service that allow us to storage the structured data, which is called Windows Azure Table Storage Service. The data stored in the table service are like the collection of entities. And the entities are similar to rows or records in the tradtional database. An entity should had a partition key, a row key, a timestamp and set of properties. You can treat the partition key as a group name, the row key as a primary key and the timestamp as the identifer for solving the concurrency problem. Different with a table in a database, the table service does not enforce the schema for tables, which means you can have 2 entities in the same table with different property sets. The partition key is being used for the load balance of the Azure OS and the group entity transaction. As you know in the cloud you will never know which machine is hosting your application and your data. It could be moving based on the transaction weight and the number of the requests. If the Azure OS found that there are many requests connect to your Book entities with the partition key equals “Novel” it will move them to another idle machine to increase the performance. So when choosing the partition key for your entities you need to make sure they indecate the category or gourp information so that the Azure OS can perform the load balance as you wish.   Consuming the Table Although the table service looks like a database, you cannot access it through the way you are using now, neither ADO.NET nor ODBC. The table service exposed itself by ADO.NET Data Service protocol, which allows you can consume it through the RESTful style by Http requests. The Azure SDK provides a sets of classes for us to connect it. There are 2 classes we might need: TableServiceContext and TableServiceEntity. The TableServiceContext inherited from the DataServiceContext, which represents the runtime context of the ADO.NET data service. It provides 4 methods mainly used by us: CreateQuery: It will create a IQueryable instance from a given type of entity. AddObject: Add the specified entity into Table Service. UpdateObject: Update an existing entity in the Table Service. DeleteObject: Delete an entity from the Table Service. Beofre you operate the table service you need to provide the valid account information. It’s something like the connect string of the database but with your account name and the account key when you created the storage service on the Windows Azure Development Portal. After getting the CloudStorageAccount you can create the CloudTableClient instance which provides a set of methods for using the table service. A very useful method would be CreateTableIfNotExist. It will create the table container for you if it’s not exsited. And then you can operate the eneities to that table through the methods I mentioned above. Let me explain a bit more through an exmaple. We always like code rather than sentence.   Straightforward Accessing to the Table Here I would like to build a WCF service on the Windows Azure platform, and for now just one requirement: it would allow the client to create an account entity on the table service. The WCF service would have a method named Register and accept an instance of the account which the client wants to create. After perform some validation it will add the entity into the table service. So the first thing I should do is to create a Cloud Application on my VIstial Studio 2010 RC. (The Azure SDK 1.1 only supports VS2008 and VS2010 RC.) The solution should be like this below. Then I added a configuration items for the storage account through the Settings section under the cloud project. (Double click the Services file under Roles folder and navigate to the Setting section.) This setting will be used when to retrieve my storage account information. Since for now I just in the development phase I will select “UseDevelopmentStorage=true”. And then I navigated to the WebRole.cs file under my WCF project. If you have read my previous posts you would know that this file defines the process when the application start, and terminate on the cloud. What I need to do is to when the application start, set the configuration publisher to load my config file with the config name I specified. So the code would be like below. I removed the original service and contract created by the VS template and add my IAccountService contract and its implementation class - AccountService. And I add the service method Register with the parameters: email, password and it will return a boolean value to indicates the result which is very simple. At this moment if I press F5 the application will be established on my local development fabric and I can see my service runs well through the browser. Let’s implement the service method Rigister, add a new entity to the table service. As I said before the entities you want to store in the table service must have 3 properties: partition key, row key and timespan. You can create a class with these 3 properties. The Azure SDK provides us a base class for that named TableServiceEntity in Microsoft.WindowsAzure.StorageClient namespace. So what we need to do is more simply, create a class named Account and let it derived from the TableServiceEntity. And I need to add my own properties: Email, Password, DateCreated and DateDeleted. The DateDeleted is a nullable date time value to indecate whether this entity had been deleted and when. Do you notice that I missed something here? Yes it’s the partition key and row key I didn’t assigned. The TableServiceEntity base class defined 2 constructors one was a parameter-less constructor which will be used to fill values into the properties from the table service when retrieving data. The other was one with 2 parameters: partition key and row key. As I said below the partition key may affect the load balance and the row key must be unique so here I would like to use the email as the parition key and the email plus a Guid as the row key. OK now we finished the entity class we need to store onto the table service. The next step is to create a data access class for us to add it. Azure SDK gives us a base class for it named TableServiceContext as I mentioned below. So let’s create a class for operate the Account entities. The TableServiceContext need the storage account information for its constructor. It’s the combination of the storage service URI that we will create on Windows Azure platform, and the relevant account name and key. The TableServiceContext will use this information to find the related address and verify the account to operate the storage entities. Hence in my AccountDataContext class I need to override this constructor and pass the storage account into it. All entities will be saved in the table storage with one or many tables which we call them “table containers”. Before we operate an entity we need to make sure that the table container had been created on the storage. There’s a method we can use for that: CloudTableClient.CreateTableIfNotExist. So in the constructor I will perform it firstly to make sure all method will be invoked after the table had been created. Notice that I passed the storage account enpoint URI and the credentials to specify where my storage is located and who am I. Another advise is that, make your entity class name as the same as the table name when create the table. It will increase the performance when you operate it over the cloud especially querying. Since the Register WCF method will add a new account into the table service, here I will create a relevant method to add the account entity. Before implement, I should add a reference - System.Data.Services.Client to the project. This reference provides some common method within the ADO.NET Data Service which can be used in the Windows Azure Table Service. I will use its AddObject method to create my account entity. Since the table service are not fully implemented the ADO.NET Data Service, there are some methods in the System.Data.Services.Client that TableServiceContext doesn’t support, such as AddLinks, etc. Then I implemented the serivce method to add the account entity through the AccountDataContext. You can see in the service implmentation I load the storage account information through my configuration file and created the account table entity from the parameters. Then I created the AccountDataContext. If it’s my first time to invoke this method the constructor of the AccountDataContext will create a table container for me. Then I use Add method to add the account entity into the table. Next, let’s create a farely simple client application to test this service. I created a windows console application and added a service reference to my WCF service. The metadata information of the WCF service cannot be retrieved if it’s deployed on the Windows Azure even though the <serviceMetadata httpGetEnabled="true"/> had been set. If we need to get its metadata we can deploy it on the local development service and then changed the endpoint to the address which is on the cloud. In the client side app.config file I specified the endpoint to the local development fabric address. And the just implement the client to let me input an email and a password then invoke the WCF service to add my acocunt. Let’s run my application and see the result. Of course it should return TRUE to me. And in the local SQL Express I can see the data had been saved in the table.   Summary In this post I explained more about the Windows Azure Table Storage Service. I also created a small application for demostration of how to connect and consume it through the ADO.NET Data Service Managed Library provided within the Azure SDK. I only show how to create an eneity in the storage service. In the next post I would like to explain about how to query the entities with conditions thruogh LINQ. I also would like to refactor my AccountDataContext class to make it dyamic for any kinds of entities.   Hope this helps, Shaun   All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • Using Unity – Part 1

    - by nmarun
    I have been going through implementing some IoC pattern using Unity and so I decided to share my learnings (I know that’s not an English word, but you get the point). Ok, so I have an ASP.net project named ProductWeb and a class library called ProductModel. In the model library, I have a class called Product: 1: public class Product 2: { 3: public string Name { get; set; } 4: public string Description { get; set; } 5:  6: public Product() 7: { 8: Name = "iPad"; 9: Description = "Not just a reader!"; 10: } 11:  12: public string WriteProductDetails() 13: { 14: return string.Format("Name: {0} Description: {1}", Name, Description); 15: } 16: } In the Page_Load event of the default.aspx, I’ll need something like: 1: Product product = new Product(); 2: productDetailsLabel.Text = product.WriteProductDetails(); Now, let’s go ‘Unity’fy this application. I assume you have all the bits for the pattern. If not, get it from here. I found this schematic representation of Unity pattern from the above link. This image might not make much sense to you now, but as we proceed, things will get better. The first step to implement the Inversion of Control pattern is to create interfaces that your types will implement. An IProduct interface is added to the ProductModel project. 1: public interface IProduct 2: { 3: string WriteProductDetails(); 4: } Let’s make our Product class to implement the IProduct interface. The application will compile and run as before despite the changes made. Add the following references to your web project: Microsoft.Practices.Unity Microsoft.Practices.Unity.Configuration Microsoft.Practices.Unity.StaticFactory Microsoft.Practices.ObjectBuilder2 We need to add a few lines to the web.config file. The line below tells what version of Unity pattern we’ll be using. 1: <configSections> 2: <section name="unity" type="Microsoft.Practices.Unity.Configuration.UnityConfigurationSection, Microsoft.Practices.Unity.Configuration, Version=1.2.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35"/> 3: </configSections> Add another block with the same name as the section name declared above – ‘unity’. 1: <unity> 2: <typeAliases> 3: <!--Custom object types--> 4: <typeAlias alias="IProduct" type="ProductModel.IProduct, ProductModel"/> 5: <typeAlias alias="Product" type="ProductModel.Product, ProductModel"/> 6: </typeAliases> 7: <containers> 8: <container name="unityContainer"> 9: <types> 10: <type type="IProduct" mapTo="Product"/> 11: </types> 12: </container> 13: </containers> 14: </unity> From the Unity Configuration schematic shown above, you see that the ‘unity’ block has a ‘typeAliases’ and a ‘containers’ segment. The typeAlias element gives a ‘short-name’ for a type. This ‘short-name’ can be used to point to this type any where in the configuration file (web.config in our case, but all this information could be coming from an external xml file as well). The container element holds all the mapping information. This container is referenced through its name attribute in the code and you can have multiple of these container elements in the containers segment. The ‘type’ element in line 10 basically says: ‘When Unity requests to resolve the alias IProduct, return an instance of whatever the short-name of Product points to’. This is the most basic piece of Unity pattern and all of this is accomplished purely through configuration. So, in future you have a change in your model, all you need to do is - implement IProduct on the new model class and - either add a typeAlias for the new type and point the mapTo attribute to the new alias declared - or modify the mapTo attribute of the type element to point to the new alias (as the case may be). Now for the calling code. It’s a good idea to store your unity container details in the Application cache, as this is rarely bound to change and also adds for better performance. The Global.asax.cs file comes for our rescue: 1: protected void Application_Start(object sender, EventArgs e) 2: { 3: // create and populate a new Unity container from configuration 4: IUnityContainer unityContainer = new UnityContainer(); 5: UnityConfigurationSection section = (UnityConfigurationSection)ConfigurationManager.GetSection("unity"); 6: section.Containers["unityContainer"].Configure(unityContainer); 7: Application["UnityContainer"] = unityContainer; 8: } 9:  10: protected void Application_End(object sender, EventArgs e) 11: { 12: Application["UnityContainer"] = null; 13: } All this says is: create an instance of UnityContainer() and read the ‘unity’ section from the configSections segment of the web.config file. Then get the container named ‘unityContainer’ and store it in the Application cache. In my code-behind file, I’ll make use of this UnityContainer to create an instance of the Product type. 1: public partial class _Default : Page 2: { 3: private IUnityContainer unityContainer; 4: protected void Page_Load(object sender, EventArgs e) 5: { 6: unityContainer = Application["UnityContainer"] as IUnityContainer; 7: if (unityContainer == null) 8: { 9: productDetailsLabel.Text = "ERROR: Unity Container not populated in Global.asax.<p />"; 10: } 11: else 12: { 13: IProduct productInstance = unityContainer.Resolve<IProduct>(); 14: productDetailsLabel.Text = productInstance.WriteProductDetails(); 15: } 16: } 17: } Looking the ‘else’ block, I’m asking the unityContainer object to resolve the IProduct type. All this does, is to look at the matching type in the container, read its mapTo attribute value, get the full name from the alias and create an instance of the Product class. Fabulous!! I’ll go more in detail in the next blog. The code for this blog can be found here.

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  • Using Unity – Part 3

    - by nmarun
    The previous blog was about registering and invoking different types dynamically. In this one I’d like to show how Unity manages/disposes the instances – say hello to Lifetime Managers. When a type gets registered, either through the config file or when RegisterType method is explicitly called, the default behavior is that the container uses a transient lifetime manager. In other words, the unity container creates a new instance of the type when Resolve or ResolveAll method is called. Whereas, when you register an existing object using the RegisterInstance method, the container uses a container controlled lifetime manager - a singleton pattern. It does this by storing the reference of the object and that means so as long as the container is ‘alive’, your registered instance does not go out of scope and will be disposed only after the container either goes out of scope or when the code explicitly disposes the container. Let’s see how we can use these and test if something is a singleton or a transient instance. Continuing on the same solution used in the previous blogs, I have made the following changes: First is to add typeAlias elements for TransientLifetimeManager type: 1: <typeAlias alias="transient" type="Microsoft.Practices.Unity.TransientLifetimeManager, Microsoft.Practices.Unity"/> You then need to tell what type(s) you want to be transient by nature: 1: <type type="IProduct" mapTo="Product2"> 2: <lifetime type="transient" /> 3: </type> 4: <!--<type type="IProduct" mapTo="Product2" />--> The lifetime element’s type attribute matches with the alias attribute of the typeAlias element. Now since ‘transient’ is the default behavior, you can have a concise version of the same as line 4 shows. Also note that I’ve changed the mapTo attribute from ‘Product’ to ‘Product2’. I’ve done this to help understand the transient nature of the instance of the type Product2. By making this change, you are basically saying when a type of IProduct needs to be resolved, Unity should create an instance of Product2 by default. 1: public string WriteProductDetails() 2: { 3: return string.Format("Name: {0}<br/>Category: {1}<br/>Mfg Date: {2}<br/>Hash Code: {3}", 4: Name, Category, MfgDate.ToString("MM/dd/yyyy hh:mm:ss tt"), GetHashCode()); 5: } Again, the above change is purely for the purpose of making the example more clear to understand. The display will show the full date and also displays the hash code of the current instance. The GetHashCode() method returns an integer when an instance gets created – a new integer for every instance. When you run the application, you’ll see something like the below: Now when you click on the ‘Get Product2 Instance’ button, you’ll see that the Mfg Date (which is set in the constructor) and the Hash Code are different from the one created on page load. This proves to us that a new instance is created every single time. To make this a singleton, we need to add a type alias for the ContainerControlledLifetimeManager class and then change the type attribute of the lifetime element to singleton. 1: <typeAlias alias="singleton" type="Microsoft.Practices.Unity.ContainerControlledLifetimeManager, Microsoft.Practices.Unity"/> 2: ... 3: <type type="IProduct" mapTo="Product2"> 4: <lifetime type="singleton" /> 5: </type> Running the application now gets me the following output: Click on the button below and you’ll see that the Mfg Date and the Hash code remain unchanged => the unity container is storing the reference the first time it is created and then returns the same instance every time the type needs to be resolved. Digging more deeper into this, Unity provides more than the two lifetime managers. ExternallyControlledLifetimeManager – maintains a weak reference to type mappings and instances. Unity returns the same instance as long as the some code is holding a strong reference to this instance. For this, you need: 1: <typeAlias alias="external" type="Microsoft.Practices.Unity.ExternallyControlledLifetimeManager, Microsoft.Practices.Unity"/> 2: ... 3: <type type="IProduct" mapTo="Product2"> 4: <lifetime type="external" /> 5: </type> PerThreadLifetimeManager – Unity returns a unique instance of an object for each thread – so this effectively is a singleton behavior on a  per-thread basis. 1: <typeAlias alias="perThread" type="Microsoft.Practices.Unity.PerThreadLifetimeManager, Microsoft.Practices.Unity"/> 2: ... 3: <type type="IProduct" mapTo="Product2"> 4: <lifetime type="perThread" /> 5: </type> One thing to note about this is that if you use RegisterInstance method to register an existing object, this instance will be returned for every thread, making this a purely singleton behavior. Needless to say, this type of lifetime management is useful in multi-threaded applications (duh!!). I hope this blog provided some basics on lifetime management of objects resolved in Unity and in the next blog, I’ll talk about Injection. Please see the code used here.

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  • Parallelism in .NET – Part 3, Imperative Data Parallelism: Early Termination

    - by Reed
    Although simple data parallelism allows us to easily parallelize many of our iteration statements, there are cases that it does not handle well.  In my previous discussion, I focused on data parallelism with no shared state, and where every element is being processed exactly the same. Unfortunately, there are many common cases where this does not happen.  If we are dealing with a loop that requires early termination, extra care is required when parallelizing. Often, while processing in a loop, once a certain condition is met, it is no longer necessary to continue processing.  This may be a matter of finding a specific element within the collection, or reaching some error case.  The important distinction here is that, it is often impossible to know until runtime, what set of elements needs to be processed. In my initial discussion of data parallelism, I mentioned that this technique is a candidate when you can decompose the problem based on the data involved, and you wish to apply a single operation concurrently on all of the elements of a collection.  This covers many of the potential cases, but sometimes, after processing some of the elements, we need to stop processing. As an example, lets go back to our previous Parallel.ForEach example with contacting a customer.  However, this time, we’ll change the requirements slightly.  In this case, we’ll add an extra condition – if the store is unable to email the customer, we will exit gracefully.  The thinking here, of course, is that if the store is currently unable to email, the next time this operation runs, it will handle the same situation, so we can just skip our processing entirely.  The original, serial case, with this extra condition, might look something like the following: foreach(var customer in customers) { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { // Exit gracefully if we fail to email, since this // entire process can be repeated later without issue. if (theStore.EmailCustomer(customer) == false) break; customer.LastEmailContact = DateTime.Now; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here, we’re processing our loop, but at any point, if we fail to send our email successfully, we just abandon this process, and assume that it will get handled correctly the next time our routine is run.  If we try to parallelize this using Parallel.ForEach, as we did previously, we’ll run into an error almost immediately: the break statement we’re using is only valid when enclosed within an iteration statement, such as foreach.  When we switch to Parallel.ForEach, we’re no longer within an iteration statement – we’re a delegate running in a method. This needs to be handled slightly differently when parallelized.  Instead of using the break statement, we need to utilize a new class in the Task Parallel Library: ParallelLoopState.  The ParallelLoopState class is intended to allow concurrently running loop bodies a way to interact with each other, and provides us with a way to break out of a loop.  In order to use this, we will use a different overload of Parallel.ForEach which takes an IEnumerable<T> and an Action<T, ParallelLoopState> instead of an Action<T>.  Using this, we can parallelize the above operation by doing: Parallel.ForEach(customers, (customer, parallelLoopState) => { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { // Exit gracefully if we fail to email, since this // entire process can be repeated later without issue. if (theStore.EmailCustomer(customer) == false) parallelLoopState.Break(); else customer.LastEmailContact = DateTime.Now; } }); There are a couple of important points here.  First, we didn’t actually instantiate the ParallelLoopState instance.  It was provided directly to us via the Parallel class.  All we needed to do was change our lambda expression to reflect that we want to use the loop state, and the Parallel class creates an instance for our use.  We also needed to change our logic slightly when we call Break().  Since Break() doesn’t stop the program flow within our block, we needed to add an else case to only set the property in customer when we succeeded.  This same technique can be used to break out of a Parallel.For loop. That being said, there is a huge difference between using ParallelLoopState to cause early termination and to use break in a standard iteration statement.  When dealing with a loop serially, break will immediately terminate the processing within the closest enclosing loop statement.  Calling ParallelLoopState.Break(), however, has a very different behavior. The issue is that, now, we’re no longer processing one element at a time.  If we break in one of our threads, there are other threads that will likely still be executing.  This leads to an important observation about termination of parallel code: Early termination in parallel routines is not immediate.  Code will continue to run after you request a termination. This may seem problematic at first, but it is something you just need to keep in mind while designing your routine.  ParallelLoopState.Break() should be thought of as a request.  We are telling the runtime that no elements that were in the collection past the element we’re currently processing need to be processed, and leaving it up to the runtime to decide how to handle this as gracefully as possible.  Although this may seem problematic at first, it is a good thing.  If the runtime tried to immediately stop processing, many of our elements would be partially processed.  It would be like putting a return statement in a random location throughout our loop body – which could have horrific consequences to our code’s maintainability. In order to understand and effectively write parallel routines, we, as developers, need a subtle, but profound shift in our thinking.  We can no longer think in terms of sequential processes, but rather need to think in terms of requests to the system that may be handled differently than we’d first expect.  This is more natural to developers who have dealt with asynchronous models previously, but is an important distinction when moving to concurrent programming models. As an example, I’ll discuss the Break() method.  ParallelLoopState.Break() functions in a way that may be unexpected at first.  When you call Break() from a loop body, the runtime will continue to process all elements of the collection that were found prior to the element that was being processed when the Break() method was called.  This is done to keep the behavior of the Break() method as close to the behavior of the break statement as possible. We can see the behavior in this simple code: var collection = Enumerable.Range(0, 20); var pResult = Parallel.ForEach(collection, (element, state) => { if (element > 10) { Console.WriteLine("Breaking on {0}", element); state.Break(); } Console.WriteLine(element); }); If we run this, we get a result that may seem unexpected at first: 0 2 1 5 6 3 4 10 Breaking on 11 11 Breaking on 12 12 9 Breaking on 13 13 7 8 Breaking on 15 15 What is occurring here is that we loop until we find the first element where the element is greater than 10.  In this case, this was found, the first time, when one of our threads reached element 11.  It requested that the loop stop by calling Break() at this point.  However, the loop continued processing until all of the elements less than 11 were completed, then terminated.  This means that it will guarantee that elements 9, 7, and 8 are completed before it stops processing.  You can see our other threads that were running each tried to break as well, but since Break() was called on the element with a value of 11, it decides which elements (0-10) must be processed. If this behavior is not desirable, there is another option.  Instead of calling ParallelLoopState.Break(), you can call ParallelLoopState.Stop().  The Stop() method requests that the runtime terminate as soon as possible , without guaranteeing that any other elements are processed.  Stop() will not stop the processing within an element, so elements already being processed will continue to be processed.  It will prevent new elements, even ones found earlier in the collection, from being processed.  Also, when Stop() is called, the ParallelLoopState’s IsStopped property will return true.  This lets longer running processes poll for this value, and return after performing any necessary cleanup. The basic rule of thumb for choosing between Break() and Stop() is the following. Use ParallelLoopState.Stop() when possible, since it terminates more quickly.  This is particularly useful in situations where you are searching for an element or a condition in the collection.  Once you’ve found it, you do not need to do any other processing, so Stop() is more appropriate. Use ParallelLoopState.Break() if you need to more closely match the behavior of the C# break statement. Both methods behave differently than our C# break statement.  Unfortunately, when parallelizing a routine, more thought and care needs to be put into every aspect of your routine than you may otherwise expect.  This is due to my second observation: Parallelizing a routine will almost always change its behavior. This sounds crazy at first, but it’s a concept that’s so simple its easy to forget.  We’re purposely telling the system to process more than one thing at the same time, which means that the sequence in which things get processed is no longer deterministic.  It is easy to change the behavior of your routine in very subtle ways by introducing parallelism.  Often, the changes are not avoidable, even if they don’t have any adverse side effects.  This leads to my final observation for this post: Parallelization is something that should be handled with care and forethought, added by design, and not just introduced casually.

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  • Parallelism in .NET – Part 14, The Different Forms of Task

    - by Reed
    Before discussing Task creation and actual usage in concurrent environments, I will briefly expand upon my introduction of the Task class and provide a short explanation of the distinct forms of Task.  The Task Parallel Library includes four distinct, though related, variations on the Task class. In my introduction to the Task class, I focused on the most basic version of Task.  This version of Task, the standard Task class, is most often used with an Action delegate.  This allows you to implement for each task within the task decomposition as a single delegate. Typically, when using the new threading constructs in .NET 4 and the Task Parallel Library, we use lambda expressions to define anonymous methods.  The advantage of using a lambda expression is that it allows the Action delegate to directly use variables in the calling scope.  This eliminates the need to make separate Task classes for Action<T>, Action<T1,T2>, and all of the other Action<…> delegate types.  As an example, suppose we wanted to make a Task to handle the ”Show Splash” task from our earlier decomposition.  Even if this task required parameters, such as a message to display, we could still use an Action delegate specified via a lambda: // Store this as a local variable string messageForSplashScreen = GetSplashScreenMessage(); // Create our task Task showSplashTask = new Task( () => { // We can use variables in our outer scope, // as well as methods scoped to our class! this.DisplaySplashScreen(messageForSplashScreen); }); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This provides a huge amount of flexibility.  We can use this single form of task for any task which performs an operation, provided the only information we need to track is whether the task has completed successfully or not.  This leads to my first observation: Use a Task with a System.Action delegate for any task for which no result is generated. This observation leads to an obvious corollary: we also need a way to define a task which generates a result.  The Task Parallel Library provides this via the Task<TResult> class. Task<TResult> subclasses the standard Task class, providing one additional feature – the ability to return a value back to the user of the task.  This is done by switching from providing an Action delegate to providing a Func<TResult> delegate.  If we decompose our problem, and we realize we have one task where its result is required by a future operation, this can be handled via Task<TResult>.  For example, suppose we want to make a task for our “Check for Update” task, we could do: Task<bool> checkForUpdateTask = new Task<bool>( () => { return this.CheckWebsiteForUpdate(); }); Later, we would start this task, and perform some other work.  At any point in the future, we could get the value from the Task<TResult>.Result property, which will cause our thread to block until the task has finished processing: // This uses Task<bool> checkForUpdateTask generated above... // Start the task, typically on a background thread checkForUpdateTask.Start(); // Do some other work on our current thread this.DoSomeWork(); // Discover, from our background task, whether an update is available // This will block until our task completes bool updateAvailable = checkForUpdateTask.Result; This leads me to my second observation: Use a Task<TResult> with a System.Func<TResult> delegate for any task which generates a result. Task and Task<TResult> provide a much cleaner alternative to the previous Asynchronous Programming design patterns in the .NET framework.  Instead of trying to implement IAsyncResult, and providing BeginXXX() and EndXXX() methods, implementing an asynchronous programming API can be as simple as creating a method that returns a Task or Task<TResult>.  The client side of the pattern also is dramatically simplified – the client can call a method, then either choose to call task.Wait() or use task.Result when it needs to wait for the operation’s completion. While this provides a much cleaner model for future APIs, there is quite a bit of infrastructure built around the current Asynchronous Programming design patterns.  In order to provide a model to work with existing APIs, two other forms of Task exist.  There is a constructor for Task which takes an Action<Object> and a state parameter.  In addition, there is a constructor for creating a Task<TResult> which takes a Func<Object, TResult> as well as a state parameter.  When using these constructors, the state parameter is stored in the Task.AsyncState property. While these two overloads exist, and are usable directly, I strongly recommend avoiding this for new development.  The two forms of Task which take an object state parameter exist primarily for interoperability with traditional .NET Asynchronous Programming methodologies.  Using lambda expressions to capture variables from the scope of the creator is a much cleaner approach than using the untyped state parameters, since lambda expressions provide full type safety without introducing new variables.

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  • C# 5 Async, Part 1: Simplifying Asynchrony – That for which we await

    - by Reed
    Today’s announcement at PDC of the future directions C# is taking excite me greatly.  The new Visual Studio Async CTP is amazing.  Asynchronous code – code which frustrates and demoralizes even the most advanced of developers, is taking a huge leap forward in terms of usability.  This is handled by building on the Task functionality in .NET 4, as well as the addition of two new keywords being added to the C# language: async and await. This core of the new asynchronous functionality is built upon three key features.  First is the Task functionality in .NET 4, and based on Task and Task<TResult>.  While Task was intended to be the primary means of asynchronous programming with .NET 4, the .NET Framework was still based mainly on the Asynchronous Pattern and the Event-based Asynchronous Pattern. The .NET Framework added functionality and guidance for wrapping existing APIs into a Task based API, but the framework itself didn’t really adopt Task or Task<TResult> in any meaningful way.  The CTP shows that, going forward, this is changing. One of the three key new features coming in C# is actually a .NET Framework feature.  Nearly every asynchronous API in the .NET Framework has been wrapped into a new, Task-based method calls.  In the CTP, this is done via as external assembly (AsyncCtpLibrary.dll) which uses Extension Methods to wrap the existing APIs.  However, going forward, this will be handled directly within the Framework.  This will have a unifying effect throughout the .NET Framework.  This is the first building block of the new features for asynchronous programming: Going forward, all asynchronous operations will work via a method that returns Task or Task<TResult> The second key feature is the new async contextual keyword being added to the language.  The async keyword is used to declare an asynchronous function, which is a method that either returns void, a Task, or a Task<T>. Inside the asynchronous function, there must be at least one await expression.  This is a new C# keyword (await) that is used to automatically take a series of statements and break it up to potentially use discontinuous evaluation.  This is done by using await on any expression that evaluates to a Task or Task<T>. For example, suppose we want to download a webpage as a string.  There is a new method added to WebClient: Task<string> WebClient.DownloadStringTaskAsync(Uri).  Since this returns a Task<string> we can use it within an asynchronous function.  Suppose, for example, that we wanted to do something similar to my asynchronous Task example – download a web page asynchronously and check to see if it supports XHTML 1.0, then report this into a TextBox.  This could be done like so: private async void button1_Click(object sender, RoutedEventArgs e) { string url = "http://reedcopsey.com"; string content = await new WebClient().DownloadStringTaskAsync(url); this.textBox1.Text = string.Format("Page {0} supports XHTML 1.0: {1}", url, content.Contains("XHTML 1.0")); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Let’s walk through what’s happening here, step by step.  By adding the async contextual keyword to the method definition, we are able to use the await keyword on our WebClient.DownloadStringTaskAsync method call. When the user clicks this button, the new method (Task<string> WebClient.DownloadStringTaskAsync(string)) is called, which returns a Task<string>.  By adding the await keyword, the runtime will call this method that returns Task<string>, and execution will return to the caller at this point.  This means that our UI is not blocked while the webpage is downloaded.  Instead, the UI thread will “await” at this point, and let the WebClient do it’s thing asynchronously. When the WebClient finishes downloading the string, the user interface’s synchronization context will automatically be used to “pick up” where it left off, and the Task<string> returned from DownloadStringTaskAsync is automatically unwrapped and set into the content variable.  At this point, we can use that and set our text box content. There are a couple of key points here: Asynchronous functions are declared with the async keyword, and contain one or more await expressions In addition to the obvious benefits of shorter, simpler code – there are some subtle but tremendous benefits in this approach.  When the execution of this asynchronous function continues after the first await statement, the initial synchronization context is used to continue the execution of this function.  That means that we don’t have to explicitly marshal the call that sets textbox1.Text back to the UI thread – it’s handled automatically by the language and framework!  Exception handling around asynchronous method calls also just works. I’d recommend every C# developer take a look at the documentation on the new Asynchronous Programming for C# and Visual Basic page, download the Visual Studio Async CTP, and try it out.

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  • Parallelism in .NET – Part 15, Making Tasks Run: The TaskScheduler

    - by Reed
    In my introduction to the Task class, I specifically made mention that the Task class does not directly provide it’s own execution.  In addition, I made a strong point that the Task class itself is not directly related to threads or multithreading.  Rather, the Task class is used to implement our decomposition of tasks.  Once we’ve implemented our tasks, we need to execute them.  In the Task Parallel Library, the execution of Tasks is handled via an instance of the TaskScheduler class. The TaskScheduler class is an abstract class which provides a single function: it schedules the tasks and executes them within an appropriate context.  This class is the class which actually runs individual Task instances.  The .NET Framework provides two (internal) implementations of the TaskScheduler class. Since a Task, based on our decomposition, should be a self-contained piece of code, parallel execution makes sense when executing tasks.  The default implementation of the TaskScheduler class, and the one most often used, is based on the ThreadPool.  This can be retrieved via the TaskScheduler.Default property, and is, by default, what is used when we just start a Task instance with Task.Start(). Normally, when a Task is started by the default TaskScheduler, the task will be treated as a single work item, and run on a ThreadPool thread.  This pools tasks, and provides Task instances all of the advantages of the ThreadPool, including thread pooling for reduced resource usage, and an upper cap on the number of work items.  In addition, .NET 4 brings us a much improved thread pool, providing work stealing and reduced locking within the thread pool queues.  By using the default TaskScheduler, our Tasks are run asynchronously on the ThreadPool. There is one notable exception to my above statements when using the default TaskScheduler.  If a Task is created with the TaskCreationOptions set to TaskCreationOptions.LongRunning, the default TaskScheduler will generate a new thread for that Task, at least in the current implementation.  This is useful for Tasks which will persist for most of the lifetime of your application, since it prevents your Task from starving the ThreadPool of one of it’s work threads. The Task Parallel Library provides one other implementation of the TaskScheduler class.  In addition to providing a way to schedule tasks on the ThreadPool, the framework allows you to create a TaskScheduler which works within a specified SynchronizationContext.  This scheduler can be retrieved within a thread that provides a valid SynchronizationContext by calling the TaskScheduler.FromCurrentSynchronizationContext() method. This implementation of TaskScheduler is intended for use with user interface development.  Windows Forms and Windows Presentation Foundation both require any access to user interface controls to occur on the same thread that created the control.  For example, if you want to set the text within a Windows Forms TextBox, and you’re working on a background thread, that UI call must be marshaled back onto the UI thread.  The most common way this is handled depends on the framework being used.  In Windows Forms, Control.Invoke or Control.BeginInvoke is most often used.  In WPF, the equivelent calls are Dispatcher.Invoke or Dispatcher.BeginInvoke. As an example, say we’re working on a background thread, and we want to update a TextBlock in our user interface with a status label.  The code would typically look something like: // Within background thread work... string status = GetUpdatedStatus(); Dispatcher.BeginInvoke(DispatcherPriority.Normal, new Action( () => { statusLabel.Text = status; })); // Continue on in background method .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This works fine, but forces your method to take a dependency on WPF or Windows Forms.  There is an alternative option, however.  Both Windows Forms and WPF, when initialized, setup a SynchronizationContext in their thread, which is available on the UI thread via the SynchronizationContext.Current property.  This context is used by classes such as BackgroundWorker to marshal calls back onto the UI thread in a framework-agnostic manner. The Task Parallel Library provides the same functionality via the TaskScheduler.FromCurrentSynchronizationContext() method.  When setting up our Tasks, as long as we’re working on the UI thread, we can construct a TaskScheduler via: TaskScheduler uiScheduler = TaskScheduler.FromCurrentSynchronizationContext(); We then can use this scheduler on any thread to marshal data back onto the UI thread.  For example, our code above can then be rewritten as: string status = GetUpdatedStatus(); (new Task(() => { statusLabel.Text = status; })) .Start(uiScheduler); // Continue on in background method This is nice since it allows us to write code that isn’t tied to Windows Forms or WPF, but is still fully functional with those technologies.  I’ll discuss even more uses for the SynchronizationContext based TaskScheduler when I demonstrate task continuations, but even without continuations, this is a very useful construct. In addition to the two implementations provided by the Task Parallel Library, it is possible to implement your own TaskScheduler.  The ParallelExtensionsExtras project within the Samples for Parallel Programming provides nine sample TaskScheduler implementations.  These include schedulers which restrict the maximum number of concurrent tasks, run tasks on a single threaded apartment thread, use a new thread per task, and more.

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  • Parallelism in .NET – Part 7, Some Differences between PLINQ and LINQ to Objects

    - by Reed
    In my previous post on Declarative Data Parallelism, I mentioned that PLINQ extends LINQ to Objects to support parallel operations.  Although nearly all of the same operations are supported, there are some differences between PLINQ and LINQ to Objects.  By introducing Parallelism to our declarative model, we add some extra complexity.  This, in turn, adds some extra requirements that must be addressed. In order to illustrate the main differences, and why they exist, let’s begin by discussing some differences in how the two technologies operate, and look at the underlying types involved in LINQ to Objects and PLINQ . LINQ to Objects is mainly built upon a single class: Enumerable.  The Enumerable class is a static class that defines a large set of extension methods, nearly all of which work upon an IEnumerable<T>.  Many of these methods return a new IEnumerable<T>, allowing the methods to be chained together into a fluent style interface.  This is what allows us to write statements that chain together, and lead to the nice declarative programming model of LINQ: double min = collection .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Other LINQ variants work in a similar fashion.  For example, most data-oriented LINQ providers are built upon an implementation of IQueryable<T>, which allows the database provider to turn a LINQ statement into an underlying SQL query, to be performed directly on the remote database. PLINQ is similar, but instead of being built upon the Enumerable class, most of PLINQ is built upon a new static class: ParallelEnumerable.  When using PLINQ, you typically begin with any collection which implements IEnumerable<T>, and convert it to a new type using an extension method defined on ParallelEnumerable: AsParallel().  This method takes any IEnumerable<T>, and converts it into a ParallelQuery<T>, the core class for PLINQ.  There is a similar ParallelQuery class for working with non-generic IEnumerable implementations. This brings us to our first subtle, but important difference between PLINQ and LINQ – PLINQ always works upon specific types, which must be explicitly created. Typically, the type you’ll use with PLINQ is ParallelQuery<T>, but it can sometimes be a ParallelQuery or an OrderedParallelQuery<T>.  Instead of dealing with an interface, implemented by an unknown class, we’re dealing with a specific class type.  This works seamlessly from a usage standpoint – ParallelQuery<T> implements IEnumerable<T>, so you can always “switch back” to an IEnumerable<T>.  The difference only arises at the beginning of our parallelization.  When we’re using LINQ, and we want to process a normal collection via PLINQ, we need to explicitly convert the collection into a ParallelQuery<T> by calling AsParallel().  There is an important consideration here – AsParallel() does not need to be called on your specific collection, but rather any IEnumerable<T>.  This allows you to place it anywhere in the chain of methods involved in a LINQ statement, not just at the beginning.  This can be useful if you have an operation which will not parallelize well or is not thread safe.  For example, the following is perfectly valid, and similar to our previous examples: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); However, if SomeOperation() is not thread safe, we could just as easily do: double min = collection .Select(item => item.SomeOperation()) .AsParallel() .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); In this case, we’re using standard LINQ to Objects for the Select(…) method, then converting the results of that map routine to a ParallelQuery<T>, and processing our filter (the Where method) and our aggregation (the Min method) in parallel. PLINQ also provides us with a way to convert a ParallelQuery<T> back into a standard IEnumerable<T>, forcing sequential processing via standard LINQ to Objects.  If SomeOperation() was thread-safe, but PerformComputation() was not thread-safe, we would need to handle this by using the AsEnumerable() method: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .AsEnumerable() .Min(item => item.PerformComputation()); Here, we’re converting our collection into a ParallelQuery<T>, doing our map operation (the Select(…) method) and our filtering in parallel, then converting the collection back into a standard IEnumerable<T>, which causes our aggregation via Min() to be performed sequentially. This could also be written as two statements, as well, which would allow us to use the language integrated syntax for the first portion: var tempCollection = from item in collection.AsParallel() let e = item.SomeOperation() where (e.SomeProperty > 6 && e.SomeProperty < 24) select e; double min = tempCollection.AsEnumerable().Min(item => item.PerformComputation()); This allows us to use the standard LINQ style language integrated query syntax, but control whether it’s performed in parallel or serial by adding AsParallel() and AsEnumerable() appropriately. The second important difference between PLINQ and LINQ deals with order preservation.  PLINQ, by default, does not preserve the order of of source collection. This is by design.  In order to process a collection in parallel, the system needs to naturally deal with multiple elements at the same time.  Maintaining the original ordering of the sequence adds overhead, which is, in many cases, unnecessary.  Therefore, by default, the system is allowed to completely change the order of your sequence during processing.  If you are doing a standard query operation, this is usually not an issue.  However, there are times when keeping a specific ordering in place is important.  If this is required, you can explicitly request the ordering be preserved throughout all operations done on a ParallelQuery<T> by using the AsOrdered() extension method.  This will cause our sequence ordering to be preserved. For example, suppose we wanted to take a collection, perform an expensive operation which converts it to a new type, and display the first 100 elements.  In LINQ to Objects, our code might look something like: // Using IEnumerable<SourceClass> collection IEnumerable<ResultClass> results = collection .Select(e => e.CreateResult()) .Take(100); If we just converted this to a parallel query naively, like so: IEnumerable<ResultClass> results = collection .AsParallel() .Select(e => e.CreateResult()) .Take(100); We could very easily get a very different, and non-reproducable, set of results, since the ordering of elements in the input collection is not preserved.  To get the same results as our original query, we need to use: IEnumerable<ResultClass> results = collection .AsParallel() .AsOrdered() .Select(e => e.CreateResult()) .Take(100); This requests that PLINQ process our sequence in a way that verifies that our resulting collection is ordered as if it were processed serially.  This will cause our query to run slower, since there is overhead involved in maintaining the ordering.  However, in this case, it is required, since the ordering is required for correctness. PLINQ is incredibly useful.  It allows us to easily take nearly any LINQ to Objects query and run it in parallel, using the same methods and syntax we’ve used previously.  There are some important differences in operation that must be considered, however – it is not a free pass to parallelize everything.  When using PLINQ in order to parallelize your routines declaratively, the same guideline I mentioned before still applies: Parallelization is something that should be handled with care and forethought, added by design, and not just introduced casually.

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  • Service Discovery in WCF 4.0 &ndash; Part 1

    - by Shaun
    When designing a service oriented architecture (SOA) system, there will be a lot of services with many service contracts, endpoints and behaviors. Besides the client calling the service, in a large distributed system a service may invoke other services. In this case, one service might need to know the endpoints it invokes. This might not be a problem in a small system. But when you have more than 10 services this might be a problem. For example in my current product, there are around 10 services, such as the user authentication service, UI integration service, location service, license service, device monitor service, event monitor service, schedule job service, accounting service, player management service, etc..   Benefit of Discovery Service Since almost all my services need to invoke at least one other service. This would be a difficult task to make sure all services endpoints are configured correctly in every service. And furthermore, it would be a nightmare when a service changed its endpoint at runtime. Hence, we need a discovery service to remove the dependency (configuration dependency). A discovery service plays as a service dictionary which stores the relationship between the contracts and the endpoints for every service. By using the discovery service, when service X wants to invoke service Y, it just need to ask the discovery service where is service Y, then the discovery service will return all proper endpoints of service Y, then service X can use the endpoint to send the request to service Y. And when some services changed their endpoint address, all need to do is to update its records in the discovery service then all others will know its new endpoint. In WCF 4.0 Discovery it supports both managed proxy discovery mode and ad-hoc discovery mode. In ad-hoc mode there is no standalone discovery service. When a client wanted to invoke a service, it will broadcast an message (normally in UDP protocol) to the entire network with the service match criteria. All services which enabled the discovery behavior will receive this message and only those matched services will send their endpoint back to the client. The managed proxy discovery service works as I described above. In this post I will only cover the managed proxy mode, where there’s a discovery service. For more information about the ad-hoc mode please refer to the MSDN.   Service Announcement and Probe The main functionality of discovery service should be return the proper endpoint addresses back to the service who is looking for. In most cases the consume service (as a client) will send the contract which it wanted to request to the discovery service. And then the discovery service will find the endpoint and respond. Sometimes the contract and endpoint are not enough. It also contains versioning, extensions attributes. This post I will only cover the case includes contract and endpoint. When a client (or sometimes a service who need to invoke another service) need to connect to a target service, it will firstly request the discovery service through the “Probe” method with the criteria. Basically the criteria contains the contract type name of the target service. Then the discovery service will search its endpoint repository by the criteria. The repository might be a database, a distributed cache or a flat XML file. If it matches, the discovery service will grab the endpoint information (it’s called discovery endpoint metadata in WCF) and send back. And this is called “Probe”. Finally the client received the discovery endpoint metadata and will use the endpoint to connect to the target service. Besides the probe, discovery service should take the responsible to know there is a new service available when it goes online, as well as stopped when it goes offline. This feature is named “Announcement”. When a service started and stopped, it will announce to the discovery service. So the basic functionality of a discovery service should includes: 1, An endpoint which receive the service online message, and add the service endpoint information in the discovery repository. 2, An endpoint which receive the service offline message, and remove the service endpoint information from the discovery repository. 3, An endpoint which receive the client probe message, and return the matches service endpoints, and return the discovery endpoint metadata. WCF 4.0 discovery service just covers all these features in it's infrastructure classes.   Discovery Service in WCF 4.0 WCF 4.0 introduced a new assembly named System.ServiceModel.Discovery which has all necessary classes and interfaces to build a WS-Discovery compliant discovery service. It supports ad-hoc and managed proxy modes. For the case mentioned in this post, what we need to build is a standalone discovery service, which is the managed proxy discovery service mode. To build a managed discovery service in WCF 4.0 just create a new class inherits from the abstract class System.ServiceModel.Discovery.DiscoveryProxy. This class implemented and abstracted the procedures of service announcement and probe. And it exposes 8 abstract methods where we can implement our own endpoint register, unregister and find logic. These 8 methods are asynchronized, which means all invokes to the discovery service are asynchronously, for better service capability and performance. 1, OnBeginOnlineAnnouncement, OnEndOnlineAnnouncement: Invoked when a service sent the online announcement message. We need to add the endpoint information to the repository in this method. 2, OnBeginOfflineAnnouncement, OnEndOfflineAnnouncement: Invoked when a service sent the offline announcement message. We need to remove the endpoint information from the repository in this method. 3, OnBeginFind, OnEndFind: Invoked when a client sent the probe message that want to find the service endpoint information. We need to look for the proper endpoints by matching the client’s criteria through the repository in this method. 4, OnBeginResolve, OnEndResolve: Invoked then a client sent the resolve message. Different from the find method, when using resolve method the discovery service will return the exactly one service endpoint metadata to the client. In our example we will NOT implement this method.   Let’s create our own discovery service, inherit the base System.ServiceModel.Discovery.DiscoveryProxy. We also need to specify the service behavior in this class. Since the build-in discovery service host class only support the singleton mode, we must set its instance context mode to single. 1: using System; 2: using System.Collections.Generic; 3: using System.Linq; 4: using System.Text; 5: using System.ServiceModel.Discovery; 6: using System.ServiceModel; 7:  8: namespace Phare.Service 9: { 10: [ServiceBehavior(InstanceContextMode = InstanceContextMode.Single, ConcurrencyMode = ConcurrencyMode.Multiple)] 11: public class ManagedProxyDiscoveryService : DiscoveryProxy 12: { 13: protected override IAsyncResult OnBeginFind(FindRequestContext findRequestContext, AsyncCallback callback, object state) 14: { 15: throw new NotImplementedException(); 16: } 17:  18: protected override IAsyncResult OnBeginOfflineAnnouncement(DiscoveryMessageSequence messageSequence, EndpointDiscoveryMetadata endpointDiscoveryMetadata, AsyncCallback callback, object state) 19: { 20: throw new NotImplementedException(); 21: } 22:  23: protected override IAsyncResult OnBeginOnlineAnnouncement(DiscoveryMessageSequence messageSequence, EndpointDiscoveryMetadata endpointDiscoveryMetadata, AsyncCallback callback, object state) 24: { 25: throw new NotImplementedException(); 26: } 27:  28: protected override IAsyncResult OnBeginResolve(ResolveCriteria resolveCriteria, AsyncCallback callback, object state) 29: { 30: throw new NotImplementedException(); 31: } 32:  33: protected override void OnEndFind(IAsyncResult result) 34: { 35: throw new NotImplementedException(); 36: } 37:  38: protected override void OnEndOfflineAnnouncement(IAsyncResult result) 39: { 40: throw new NotImplementedException(); 41: } 42:  43: protected override void OnEndOnlineAnnouncement(IAsyncResult result) 44: { 45: throw new NotImplementedException(); 46: } 47:  48: protected override EndpointDiscoveryMetadata OnEndResolve(IAsyncResult result) 49: { 50: throw new NotImplementedException(); 51: } 52: } 53: } Then let’s implement the online, offline and find methods one by one. WCF discovery service gives us full flexibility to implement the endpoint add, remove and find logic. For the demo purpose we will use an internal dictionary to store the services’ endpoint metadata. In the next post we will see how to serialize and store these information in database. Define a concurrent dictionary inside the service class since our it will be used in the multiple threads scenario. 1: [ServiceBehavior(InstanceContextMode = InstanceContextMode.Single, ConcurrencyMode = ConcurrencyMode.Multiple)] 2: public class ManagedProxyDiscoveryService : DiscoveryProxy 3: { 4: private ConcurrentDictionary<EndpointAddress, EndpointDiscoveryMetadata> _services; 5:  6: public ManagedProxyDiscoveryService() 7: { 8: _services = new ConcurrentDictionary<EndpointAddress, EndpointDiscoveryMetadata>(); 9: } 10: } Then we can simply implement the logic of service online and offline. 1: protected override IAsyncResult OnBeginOnlineAnnouncement(DiscoveryMessageSequence messageSequence, EndpointDiscoveryMetadata endpointDiscoveryMetadata, AsyncCallback callback, object state) 2: { 3: _services.AddOrUpdate(endpointDiscoveryMetadata.Address, endpointDiscoveryMetadata, (key, value) => endpointDiscoveryMetadata); 4: return new OnOnlineAnnouncementAsyncResult(callback, state); 5: } 6:  7: protected override void OnEndOnlineAnnouncement(IAsyncResult result) 8: { 9: OnOnlineAnnouncementAsyncResult.End(result); 10: } 11:  12: protected override IAsyncResult OnBeginOfflineAnnouncement(DiscoveryMessageSequence messageSequence, EndpointDiscoveryMetadata endpointDiscoveryMetadata, AsyncCallback callback, object state) 13: { 14: EndpointDiscoveryMetadata endpoint = null; 15: _services.TryRemove(endpointDiscoveryMetadata.Address, out endpoint); 16: return new OnOfflineAnnouncementAsyncResult(callback, state); 17: } 18:  19: protected override void OnEndOfflineAnnouncement(IAsyncResult result) 20: { 21: OnOfflineAnnouncementAsyncResult.End(result); 22: } Regards the find method, the parameter FindRequestContext.Criteria has a method named IsMatch, which can be use for us to evaluate which service metadata is satisfied with the criteria. So the implementation of find method would be like this. 1: protected override IAsyncResult OnBeginFind(FindRequestContext findRequestContext, AsyncCallback callback, object state) 2: { 3: _services.Where(s => findRequestContext.Criteria.IsMatch(s.Value)) 4: .Select(s => s.Value) 5: .All(meta => 6: { 7: findRequestContext.AddMatchingEndpoint(meta); 8: return true; 9: }); 10: return new OnFindAsyncResult(callback, state); 11: } 12:  13: protected override void OnEndFind(IAsyncResult result) 14: { 15: OnFindAsyncResult.End(result); 16: } As you can see, we checked all endpoints metadata in repository by invoking the IsMatch method. Then add all proper endpoints metadata into the parameter. Finally since all these methods are asynchronized we need some AsyncResult classes as well. Below are the base class and the inherited classes used in previous methods. 1: using System; 2: using System.Collections.Generic; 3: using System.Linq; 4: using System.Text; 5: using System.Threading; 6:  7: namespace Phare.Service 8: { 9: abstract internal class AsyncResult : IAsyncResult 10: { 11: AsyncCallback callback; 12: bool completedSynchronously; 13: bool endCalled; 14: Exception exception; 15: bool isCompleted; 16: ManualResetEvent manualResetEvent; 17: object state; 18: object thisLock; 19:  20: protected AsyncResult(AsyncCallback callback, object state) 21: { 22: this.callback = callback; 23: this.state = state; 24: this.thisLock = new object(); 25: } 26:  27: public object AsyncState 28: { 29: get 30: { 31: return state; 32: } 33: } 34:  35: public WaitHandle AsyncWaitHandle 36: { 37: get 38: { 39: if (manualResetEvent != null) 40: { 41: return manualResetEvent; 42: } 43: lock (ThisLock) 44: { 45: if (manualResetEvent == null) 46: { 47: manualResetEvent = new ManualResetEvent(isCompleted); 48: } 49: } 50: return manualResetEvent; 51: } 52: } 53:  54: public bool CompletedSynchronously 55: { 56: get 57: { 58: return completedSynchronously; 59: } 60: } 61:  62: public bool IsCompleted 63: { 64: get 65: { 66: return isCompleted; 67: } 68: } 69:  70: object ThisLock 71: { 72: get 73: { 74: return this.thisLock; 75: } 76: } 77:  78: protected static TAsyncResult End<TAsyncResult>(IAsyncResult result) 79: where TAsyncResult : AsyncResult 80: { 81: if (result == null) 82: { 83: throw new ArgumentNullException("result"); 84: } 85:  86: TAsyncResult asyncResult = result as TAsyncResult; 87:  88: if (asyncResult == null) 89: { 90: throw new ArgumentException("Invalid async result.", "result"); 91: } 92:  93: if (asyncResult.endCalled) 94: { 95: throw new InvalidOperationException("Async object already ended."); 96: } 97:  98: asyncResult.endCalled = true; 99:  100: if (!asyncResult.isCompleted) 101: { 102: asyncResult.AsyncWaitHandle.WaitOne(); 103: } 104:  105: if (asyncResult.manualResetEvent != null) 106: { 107: asyncResult.manualResetEvent.Close(); 108: } 109:  110: if (asyncResult.exception != null) 111: { 112: throw asyncResult.exception; 113: } 114:  115: return asyncResult; 116: } 117:  118: protected void Complete(bool completedSynchronously) 119: { 120: if (isCompleted) 121: { 122: throw new InvalidOperationException("This async result is already completed."); 123: } 124:  125: this.completedSynchronously = completedSynchronously; 126:  127: if (completedSynchronously) 128: { 129: this.isCompleted = true; 130: } 131: else 132: { 133: lock (ThisLock) 134: { 135: this.isCompleted = true; 136: if (this.manualResetEvent != null) 137: { 138: this.manualResetEvent.Set(); 139: } 140: } 141: } 142:  143: if (callback != null) 144: { 145: callback(this); 146: } 147: } 148:  149: protected void Complete(bool completedSynchronously, Exception exception) 150: { 151: this.exception = exception; 152: Complete(completedSynchronously); 153: } 154: } 155: } 1: using System; 2: using System.Collections.Generic; 3: using System.Linq; 4: using System.Text; 5: using System.ServiceModel.Discovery; 6: using Phare.Service; 7:  8: namespace Phare.Service 9: { 10: internal sealed class OnOnlineAnnouncementAsyncResult : AsyncResult 11: { 12: public OnOnlineAnnouncementAsyncResult(AsyncCallback callback, object state) 13: : base(callback, state) 14: { 15: this.Complete(true); 16: } 17:  18: public static void End(IAsyncResult result) 19: { 20: AsyncResult.End<OnOnlineAnnouncementAsyncResult>(result); 21: } 22:  23: } 24:  25: sealed class OnOfflineAnnouncementAsyncResult : AsyncResult 26: { 27: public OnOfflineAnnouncementAsyncResult(AsyncCallback callback, object state) 28: : base(callback, state) 29: { 30: this.Complete(true); 31: } 32:  33: public static void End(IAsyncResult result) 34: { 35: AsyncResult.End<OnOfflineAnnouncementAsyncResult>(result); 36: } 37: } 38:  39: sealed class OnFindAsyncResult : AsyncResult 40: { 41: public OnFindAsyncResult(AsyncCallback callback, object state) 42: : base(callback, state) 43: { 44: this.Complete(true); 45: } 46:  47: public static void End(IAsyncResult result) 48: { 49: AsyncResult.End<OnFindAsyncResult>(result); 50: } 51: } 52:  53: sealed class OnResolveAsyncResult : AsyncResult 54: { 55: EndpointDiscoveryMetadata matchingEndpoint; 56:  57: public OnResolveAsyncResult(EndpointDiscoveryMetadata matchingEndpoint, AsyncCallback callback, object state) 58: : base(callback, state) 59: { 60: this.matchingEndpoint = matchingEndpoint; 61: this.Complete(true); 62: } 63:  64: public static EndpointDiscoveryMetadata End(IAsyncResult result) 65: { 66: OnResolveAsyncResult thisPtr = AsyncResult.End<OnResolveAsyncResult>(result); 67: return thisPtr.matchingEndpoint; 68: } 69: } 70: } Now we have finished the discovery service. The next step is to host it. The discovery service is a standard WCF service. So we can use ServiceHost on a console application, windows service, or in IIS as usual. The following code is how to host the discovery service we had just created in a console application. 1: static void Main(string[] args) 2: { 3: using (var host = new ServiceHost(new ManagedProxyDiscoveryService())) 4: { 5: host.Opened += (sender, e) => 6: { 7: host.Description.Endpoints.All((ep) => 8: { 9: Console.WriteLine(ep.ListenUri); 10: return true; 11: }); 12: }; 13:  14: try 15: { 16: // retrieve the announcement, probe endpoint and binding from configuration 17: var announcementEndpointAddress = new EndpointAddress(ConfigurationManager.AppSettings["announcementEndpointAddress"]); 18: var probeEndpointAddress = new EndpointAddress(ConfigurationManager.AppSettings["probeEndpointAddress"]); 19: var binding = Activator.CreateInstance(Type.GetType(ConfigurationManager.AppSettings["bindingType"], true, true)) as Binding; 20: var announcementEndpoint = new AnnouncementEndpoint(binding, announcementEndpointAddress); 21: var probeEndpoint = new DiscoveryEndpoint(binding, probeEndpointAddress); 22: probeEndpoint.IsSystemEndpoint = false; 23: // append the service endpoint for announcement and probe 24: host.AddServiceEndpoint(announcementEndpoint); 25: host.AddServiceEndpoint(probeEndpoint); 26:  27: host.Open(); 28:  29: Console.WriteLine("Press any key to exit."); 30: Console.ReadKey(); 31: } 32: catch (Exception ex) 33: { 34: Console.WriteLine(ex.ToString()); 35: } 36: } 37:  38: Console.WriteLine("Done."); 39: Console.ReadKey(); 40: } What we need to notice is that, the discovery service needs two endpoints for announcement and probe. In this example I just retrieve them from the configuration file. I also specified the binding of these two endpoints in configuration file as well. 1: <?xml version="1.0"?> 2: <configuration> 3: <startup> 4: <supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.0"/> 5: </startup> 6: <appSettings> 7: <add key="announcementEndpointAddress" value="net.tcp://localhost:10010/announcement"/> 8: <add key="probeEndpointAddress" value="net.tcp://localhost:10011/probe"/> 9: <add key="bindingType" value="System.ServiceModel.NetTcpBinding, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/> 10: </appSettings> 11: </configuration> And this is the console screen when I ran my discovery service. As you can see there are two endpoints listening for announcement message and probe message.   Discoverable Service and Client Next, let’s create a WCF service that is discoverable, which means it can be found by the discovery service. To do so, we need to let the service send the online announcement message to the discovery service, as well as offline message before it shutdown. Just create a simple service which can make the incoming string to upper. The service contract and implementation would be like this. 1: [ServiceContract] 2: public interface IStringService 3: { 4: [OperationContract] 5: string ToUpper(string content); 6: } 1: public class StringService : IStringService 2: { 3: public string ToUpper(string content) 4: { 5: return content.ToUpper(); 6: } 7: } Then host this service in the console application. In order to make the discovery service easy to be tested the service address will be changed each time it’s started. 1: static void Main(string[] args) 2: { 3: var baseAddress = new Uri(string.Format("net.tcp://localhost:11001/stringservice/{0}/", Guid.NewGuid().ToString())); 4:  5: using (var host = new ServiceHost(typeof(StringService), baseAddress)) 6: { 7: host.Opened += (sender, e) => 8: { 9: Console.WriteLine("Service opened at {0}", host.Description.Endpoints.First().ListenUri); 10: }; 11:  12: host.AddServiceEndpoint(typeof(IStringService), new NetTcpBinding(), string.Empty); 13:  14: host.Open(); 15:  16: Console.WriteLine("Press any key to exit."); 17: Console.ReadKey(); 18: } 19: } Currently this service is NOT discoverable. We need to add a special service behavior so that it could send the online and offline message to the discovery service announcement endpoint when the host is opened and closed. WCF 4.0 introduced a service behavior named ServiceDiscoveryBehavior. When we specified the announcement endpoint address and appended it to the service behaviors this service will be discoverable. 1: var announcementAddress = new EndpointAddress(ConfigurationManager.AppSettings["announcementEndpointAddress"]); 2: var announcementBinding = Activator.CreateInstance(Type.GetType(ConfigurationManager.AppSettings["bindingType"], true, true)) as Binding; 3: var announcementEndpoint = new AnnouncementEndpoint(announcementBinding, announcementAddress); 4: var discoveryBehavior = new ServiceDiscoveryBehavior(); 5: discoveryBehavior.AnnouncementEndpoints.Add(announcementEndpoint); 6: host.Description.Behaviors.Add(discoveryBehavior); The ServiceDiscoveryBehavior utilizes the service extension and channel dispatcher to implement the online and offline announcement logic. In short, it injected the channel open and close procedure and send the online and offline message to the announcement endpoint.   On client side, when we have the discovery service, a client can invoke a service without knowing its endpoint. WCF discovery assembly provides a class named DiscoveryClient, which can be used to find the proper service endpoint by passing the criteria. In the code below I initialized the DiscoveryClient, specified the discovery service probe endpoint address. Then I created the find criteria by specifying the service contract I wanted to use and invoke the Find method. This will send the probe message to the discovery service and it will find the endpoints back to me. The discovery service will return all endpoints that matches the find criteria, which means in the result of the find method there might be more than one endpoints. In this example I just returned the first matched one back. In the next post I will show how to extend our discovery service to make it work like a service load balancer. 1: static EndpointAddress FindServiceEndpoint() 2: { 3: var probeEndpointAddress = new EndpointAddress(ConfigurationManager.AppSettings["probeEndpointAddress"]); 4: var probeBinding = Activator.CreateInstance(Type.GetType(ConfigurationManager.AppSettings["bindingType"], true, true)) as Binding; 5: var discoveryEndpoint = new DiscoveryEndpoint(probeBinding, probeEndpointAddress); 6:  7: EndpointAddress address = null; 8: FindResponse result = null; 9: using (var discoveryClient = new DiscoveryClient(discoveryEndpoint)) 10: { 11: result = discoveryClient.Find(new FindCriteria(typeof(IStringService))); 12: } 13:  14: if (result != null && result.Endpoints.Any()) 15: { 16: var endpointMetadata = result.Endpoints.First(); 17: address = endpointMetadata.Address; 18: } 19: return address; 20: } Once we probed the discovery service we will receive the endpoint. So in the client code we can created the channel factory from the endpoint and binding, and invoke to the service. When creating the client side channel factory we need to make sure that the client side binding should be the same as the service side. WCF discovery service can be used to find the endpoint for a service contract, but the binding is NOT included. This is because the binding was not in the WS-Discovery specification. In the next post I will demonstrate how to add the binding information into the discovery service. At that moment the client don’t need to create the binding by itself. Instead it will use the binding received from the discovery service. 1: static void Main(string[] args) 2: { 3: Console.WriteLine("Say something..."); 4: var content = Console.ReadLine(); 5: while (!string.IsNullOrWhiteSpace(content)) 6: { 7: Console.WriteLine("Finding the service endpoint..."); 8: var address = FindServiceEndpoint(); 9: if (address == null) 10: { 11: Console.WriteLine("There is no endpoint matches the criteria."); 12: } 13: else 14: { 15: Console.WriteLine("Found the endpoint {0}", address.Uri); 16:  17: var factory = new ChannelFactory<IStringService>(new NetTcpBinding(), address); 18: factory.Opened += (sender, e) => 19: { 20: Console.WriteLine("Connecting to {0}.", factory.Endpoint.ListenUri); 21: }; 22: var proxy = factory.CreateChannel(); 23: using (proxy as IDisposable) 24: { 25: Console.WriteLine("ToUpper: {0} => {1}", content, proxy.ToUpper(content)); 26: } 27: } 28:  29: Console.WriteLine("Say something..."); 30: content = Console.ReadLine(); 31: } 32: } Similarly, the discovery service probe endpoint and binding were defined in the configuration file. 1: <?xml version="1.0"?> 2: <configuration> 3: <startup> 4: <supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.0"/> 5: </startup> 6: <appSettings> 7: <add key="announcementEndpointAddress" value="net.tcp://localhost:10010/announcement"/> 8: <add key="probeEndpointAddress" value="net.tcp://localhost:10011/probe"/> 9: <add key="bindingType" value="System.ServiceModel.NetTcpBinding, System.ServiceModel, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089"/> 10: </appSettings> 11: </configuration> OK, now let’s have a test. Firstly start the discovery service, and then start our discoverable service. When it started it will announced to the discovery service and registered its endpoint into the repository, which is the local dictionary. And then start the client and type something. As you can see the client asked the discovery service for the endpoint and then establish the connection to the discoverable service. And more interesting, do NOT close the client console but terminate the discoverable service but press the enter key. This will make the service send the offline message to the discovery service. Then start the discoverable service again. Since we made it use a different address each time it started, currently it should be hosted on another address. If we enter something in the client we could see that it asked the discovery service and retrieve the new endpoint, and connect the the service.   Summary In this post I discussed the benefit of using the discovery service and the procedures of service announcement and probe. I also demonstrated how to leverage the WCF Discovery feature in WCF 4.0 to build a simple managed discovery service. For test purpose, in this example I used the in memory dictionary as the discovery endpoint metadata repository. And when finding I also just return the first matched endpoint back. I also hard coded the bindings between the discoverable service and the client. In next post I will show you how to solve the problem mentioned above, as well as some additional feature for production usage. You can download the code here.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • Parallelism in .NET – Part 2, Simple Imperative Data Parallelism

    - by Reed
    In my discussion of Decomposition of the problem space, I mentioned that Data Decomposition is often the simplest abstraction to use when trying to parallelize a routine.  If a problem can be decomposed based off the data, we will often want to use what MSDN refers to as Data Parallelism as our strategy for implementing our routine.  The Task Parallel Library in .NET 4 makes implementing Data Parallelism, for most cases, very simple. Data Parallelism is the main technique we use to parallelize a routine which can be decomposed based off data.  Data Parallelism refers to taking a single collection of data, and having a single operation be performed concurrently on elements in the collection.  One side note here: Data Parallelism is also sometimes referred to as the Loop Parallelism Pattern or Loop-level Parallelism.  In general, for this series, I will try to use the terminology used in the MSDN Documentation for the Task Parallel Library.  This should make it easier to investigate these topics in more detail. Once we’ve determined we have a problem that, potentially, can be decomposed based on data, implementation using Data Parallelism in the TPL is quite simple.  Let’s take our example from the Data Decomposition discussion – a simple contrast stretching filter.  Here, we have a collection of data (pixels), and we need to run a simple operation on each element of the pixel.  Once we know the minimum and maximum values, we most likely would have some simple code like the following: for (int row=0; row < pixelData.GetUpperBound(0); ++row) { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This simple routine loops through a two dimensional array of pixelData, and calls the AdjustContrast routine on each pixel. As I mentioned, when you’re decomposing a problem space, most iteration statements are potentially candidates for data decomposition.  Here, we’re using two for loops – one looping through rows in the image, and a second nested loop iterating through the columns.  We then perform one, independent operation on each element based on those loop positions. This is a prime candidate – we have no shared data, no dependencies on anything but the pixel which we want to change.  Since we’re using a for loop, we can easily parallelize this using the Parallel.For method in the TPL: Parallel.For(0, pixelData.GetUpperBound(0), row => { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } }); Here, by simply changing our first for loop to a call to Parallel.For, we can parallelize this portion of our routine.  Parallel.For works, as do many methods in the TPL, by creating a delegate and using it as an argument to a method.  In this case, our for loop iteration block becomes a delegate creating via a lambda expression.  This lets you write code that, superficially, looks similar to the familiar for loop, but functions quite differently at runtime. We could easily do this to our second for loop as well, but that may not be a good idea.  There is a balance to be struck when writing parallel code.  We want to have enough work items to keep all of our processors busy, but the more we partition our data, the more overhead we introduce.  In this case, we have an image of data – most likely hundreds of pixels in both dimensions.  By just parallelizing our first loop, each row of pixels can be run as a single task.  With hundreds of rows of data, we are providing fine enough granularity to keep all of our processors busy. If we parallelize both loops, we’re potentially creating millions of independent tasks.  This introduces extra overhead with no extra gain, and will actually reduce our overall performance.  This leads to my first guideline when writing parallel code: Partition your problem into enough tasks to keep each processor busy throughout the operation, but not more than necessary to keep each processor busy. Also note that I parallelized the outer loop.  I could have just as easily partitioned the inner loop.  However, partitioning the inner loop would have led to many more discrete work items, each with a smaller amount of work (operate on one pixel instead of one row of pixels).  My second guideline when writing parallel code reflects this: Partition your problem in a way to place the most work possible into each task. This typically means, in practice, that you will want to parallelize the routine at the “highest” point possible in the routine, typically the outermost loop.  If you’re looking at parallelizing methods which call other methods, you’ll want to try to partition your work high up in the stack – as you get into lower level methods, the performance impact of parallelizing your routines may not overcome the overhead introduced. Parallel.For works great for situations where we know the number of elements we’re going to process in advance.  If we’re iterating through an IList<T> or an array, this is a typical approach.  However, there are other iteration statements common in C#.  In many situations, we’ll use foreach instead of a for loop.  This can be more understandable and easier to read, but also has the advantage of working with collections which only implement IEnumerable<T>, where we do not know the number of elements involved in advance. As an example, lets take the following situation.  Say we have a collection of Customers, and we want to iterate through each customer, check some information about the customer, and if a certain case is met, send an email to the customer and update our instance to reflect this change.  Normally, this might look something like: foreach(var customer in customers) { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { theStore.EmailCustomer(customer); customer.LastEmailContact = DateTime.Now; } } Here, we’re doing a fair amount of work for each customer in our collection, but we don’t know how many customers exist.  If we assume that theStore.GetLastContact(customer) and theStore.EmailCustomer(customer) are both side-effect free, thread safe operations, we could parallelize this using Parallel.ForEach: Parallel.ForEach(customers, customer => { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { theStore.EmailCustomer(customer); customer.LastEmailContact = DateTime.Now; } }); Just like Parallel.For, we rework our loop into a method call accepting a delegate created via a lambda expression.  This keeps our new code very similar to our original iteration statement, however, this will now execute in parallel.  The same guidelines apply with Parallel.ForEach as with Parallel.For. The other iteration statements, do and while, do not have direct equivalents in the Task Parallel Library.  These, however, are very easy to implement using Parallel.ForEach and the yield keyword. Most applications can benefit from implementing some form of Data Parallelism.  Iterating through collections and performing “work” is a very common pattern in nearly every application.  When the problem can be decomposed by data, we often can parallelize the workload by merely changing foreach statements to Parallel.ForEach method calls, and for loops to Parallel.For method calls.  Any time your program operates on a collection, and does a set of work on each item in the collection where that work is not dependent on other information, you very likely have an opportunity to parallelize your routine.

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  • Parallelism in .NET – Part 13, Introducing the Task class

    - by Reed
    Once we’ve used a task-based decomposition to decompose a problem, we need a clean abstraction usable to implement the resulting decomposition.  Given that task decomposition is founded upon defining discrete tasks, .NET 4 has introduced a new API for dealing with task related issues, the aptly named Task class. The Task class is a wrapper for a delegate representing a single, discrete task within your decomposition.  We will go into various methods of construction for tasks later, but, when reduced to its fundamentals, an instance of a Task is nothing more than a wrapper around a delegate with some utility functionality added.  In order to fully understand the Task class within the new Task Parallel Library, it is important to realize that a task really is just a delegate – nothing more.  In particular, note that I never mentioned threading or parallelism in my description of a Task.  Although the Task class exists in the new System.Threading.Tasks namespace: Tasks are not directly related to threads or multithreading. Of course, Task instances will typically be used in our implementation of concurrency within an application, but the Task class itself does not provide the concurrency used.  The Task API supports using Tasks in an entirely single threaded, synchronous manner. Tasks are very much like standard delegates.  You can execute a task synchronously via Task.RunSynchronously(), or you can use Task.Start() to schedule a task to run, typically asynchronously.  This is very similar to using delegate.Invoke to execute a delegate synchronously, or using delegate.BeginInvoke to execute it asynchronously. The Task class adds some nice functionality on top of a standard delegate which improves usability in both synchronous and multithreaded environments. The first addition provided by Task is a means of handling cancellation via the new unified cancellation mechanism of .NET 4.  If the wrapped delegate within a Task raises an OperationCanceledException during it’s operation, which is typically generated via calling ThrowIfCancellationRequested on a CancellationToken, or if the CancellationToken used to construct a Task instance is flagged as canceled, the Task’s IsCanceled property will be set to true automatically.  This provides a clean way to determine whether a Task has been canceled, often without requiring specific exception handling. Tasks also provide a clean API which can be used for waiting on a task.  Although the Task class explicitly implements IAsyncResult, Tasks provide a nicer usage model than the traditional .NET Asynchronous Programming Model.  Instead of needing to track an IAsyncResult handle, you can just directly call Task.Wait() to block until a Task has completed.  Overloads exist for providing a timeout, a CancellationToken, or both to prevent waiting indefinitely.  In addition, the Task class provides static methods for waiting on multiple tasks – Task.WaitAll and Task.WaitAny, again with overloads providing time out options.  This provides a very simple, clean API for waiting on single or multiple tasks. Finally, Tasks provide a much nicer model for Exception handling.  If the delegate wrapped within a Task raises an exception, the exception will automatically get wrapped into an AggregateException and exposed via the Task.Exception property.  This exception is stored with the Task directly, and does not tear down the application.  Later, when Task.Wait() (or Task.WaitAll or Task.WaitAny) is called on this task, an AggregateException will be raised at that point if any of the tasks raised an exception.  For example, suppose we have the following code: Task taskOne = new Task( () => { throw new ApplicationException("Random Exception!"); }); Task taskTwo = new Task( () => { throw new ArgumentException("Different exception here"); }); // Start the tasks taskOne.Start(); taskTwo.Start(); try { Task.WaitAll(new[] { taskOne, taskTwo }); } catch (AggregateException e) { Console.WriteLine(e.InnerExceptions.Count); foreach (var inner in e.InnerExceptions) Console.WriteLine(inner.Message); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here, our routine will print: 2 Different exception here Random Exception! Note that we had two separate tasks, each of which raised two distinctly different types of exceptions.  We can handle this cleanly, with very little code, in a much nicer manner than the Asynchronous Programming API.  We no longer need to handle TargetInvocationException or worry about implementing the Event-based Asynchronous Pattern properly by setting the AsyncCompletedEventArgs.Error property.  Instead, we just raise our exception as normal, and handle AggregateException in a single location in our calling code.

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  • Parallelism in .NET – Part 18, Task Continuations with Multiple Tasks

    - by Reed
    In my introduction to Task continuations I demonstrated how the Task class provides a more expressive alternative to traditional callbacks.  Task continuations provide a much cleaner syntax to traditional callbacks, but there are other reasons to switch to using continuations… Task continuations provide a clean syntax, and a very simple, elegant means of synchronizing asynchronous method results with the user interface.  In addition, continuations provide a very simple, elegant means of working with collections of tasks. Prior to .NET 4, working with multiple related asynchronous method calls was very tricky.  If, for example, we wanted to run two asynchronous operations, followed by a single method call which we wanted to run when the first two methods completed, we’d have to program all of the handling ourselves.  We would likely need to take some approach such as using a shared callback which synchronized against a common variable, or using a WaitHandle shared within the callbacks to allow one to wait for the second.  Although this could be accomplished easily enough, it requires manually placing this handling into every algorithm which requires this form of blocking.  This is error prone, difficult, and can easily lead to subtle bugs. Similar to how the Task class static methods providing a way to block until multiple tasks have completed, TaskFactory contains static methods which allow a continuation to be scheduled upon the completion of multiple tasks: TaskFactory.ContinueWhenAll. This allows you to easily specify a single delegate to run when a collection of tasks has completed.  For example, suppose we have a class which fetches data from the network.  This can be a long running operation, and potentially fail in certain situations, such as a server being down.  As a result, we have three separate servers which we will “query” for our information.  Now, suppose we want to grab data from all three servers, and verify that the results are the same from all three. With traditional asynchronous programming in .NET, this would require using three separate callbacks, and managing the synchronization between the various operations ourselves.  The Task and TaskFactory classes simplify this for us, allowing us to write: var server1 = Task.Factory.StartNew( () => networkClass.GetResults(firstServer) ); var server2 = Task.Factory.StartNew( () => networkClass.GetResults(secondServer) ); var server3 = Task.Factory.StartNew( () => networkClass.GetResults(thirdServer) ); var result = Task.Factory.ContinueWhenAll( new[] {server1, server2, server3 }, (tasks) => { // Propogate exceptions (see below) Task.WaitAll(tasks); return this.CompareTaskResults( tasks[0].Result, tasks[1].Result, tasks[2].Result); }); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This is clean, simple, and elegant.  The one complication is the Task.WaitAll(tasks); statement. Although the continuation will not complete until all three tasks (server1, server2, and server3) have completed, there is a potential snag.  If the networkClass.GetResults method fails, and raises an exception, we want to make sure to handle it cleanly.  By using Task.WaitAll, any exceptions raised within any of our original tasks will get wrapped into a single AggregateException by the WaitAll method, providing us a simplified means of handling the exceptions.  If we wait on the continuation, we can trap this AggregateException, and handle it cleanly.  Without this line, it’s possible that an exception could remain uncaught and unhandled by a task, which later might trigger a nasty UnobservedTaskException.  This would happen any time two of our original tasks failed. Just as we can schedule a continuation to occur when an entire collection of tasks has completed, we can just as easily setup a continuation to run when any single task within a collection completes.  If, for example, we didn’t need to compare the results of all three network locations, but only use one, we could still schedule three tasks.  We could then have our completion logic work on the first task which completed, and ignore the others.  This is done via TaskFactory.ContinueWhenAny: var server1 = Task.Factory.StartNew( () => networkClass.GetResults(firstServer) ); var server2 = Task.Factory.StartNew( () => networkClass.GetResults(secondServer) ); var server3 = Task.Factory.StartNew( () => networkClass.GetResults(thirdServer) ); var result = Task.Factory.ContinueWhenAny( new[] {server1, server2, server3 }, (firstTask) => { return this.ProcessTaskResult(firstTask.Result); }); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here, instead of working with all three tasks, we’re just using the first task which finishes.  This is very useful, as it allows us to easily work with results of multiple operations, and “throw away” the others.  However, you must take care when using ContinueWhenAny to properly handle exceptions.  At some point, you should always wait on each task (or use the Task.Result property) in order to propogate any exceptions raised from within the task.  Failing to do so can lead to an UnobservedTaskException.

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  • Parallelism in .NET – Part 4, Imperative Data Parallelism: Aggregation

    - by Reed
    In the article on simple data parallelism, I described how to perform an operation on an entire collection of elements in parallel.  Often, this is not adequate, as the parallel operation is going to be performing some form of aggregation. Simple examples of this might include taking the sum of the results of processing a function on each element in the collection, or finding the minimum of the collection given some criteria.  This can be done using the techniques described in simple data parallelism, however, special care needs to be taken into account to synchronize the shared data appropriately.  The Task Parallel Library has tools to assist in this synchronization. The main issue with aggregation when parallelizing a routine is that you need to handle synchronization of data.  Since multiple threads will need to write to a shared portion of data.  Suppose, for example, that we wanted to parallelize a simple loop that looked for the minimum value within a dataset: double min = double.MaxValue; foreach(var item in collection) { double value = item.PerformComputation(); min = System.Math.Min(min, value); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This seems like a good candidate for parallelization, but there is a problem here.  If we just wrap this into a call to Parallel.ForEach, we’ll introduce a critical race condition, and get the wrong answer.  Let’s look at what happens here: // Buggy code! Do not use! double min = double.MaxValue; Parallel.ForEach(collection, item => { double value = item.PerformComputation(); min = System.Math.Min(min, value); }); This code has a fatal flaw: min will be checked, then set, by multiple threads simultaneously.  Two threads may perform the check at the same time, and set the wrong value for min.  Say we get a value of 1 in thread 1, and a value of 2 in thread 2, and these two elements are the first two to run.  If both hit the min check line at the same time, both will determine that min should change, to 1 and 2 respectively.  If element 1 happens to set the variable first, then element 2 sets the min variable, we’ll detect a min value of 2 instead of 1.  This can lead to wrong answers. Unfortunately, fixing this, with the Parallel.ForEach call we’re using, would require adding locking.  We would need to rewrite this like: // Safe, but slow double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach(collection, item => { double value = item.PerformComputation(); lock(syncObject) min = System.Math.Min(min, value); }); This will potentially add a huge amount of overhead to our calculation.  Since we can potentially block while waiting on the lock for every single iteration, we will most likely slow this down to where it is actually quite a bit slower than our serial implementation.  The problem is the lock statement – any time you use lock(object), you’re almost assuring reduced performance in a parallel situation.  This leads to two observations I’ll make: When parallelizing a routine, try to avoid locks. That being said: Always add any and all required synchronization to avoid race conditions. These two observations tend to be opposing forces – we often need to synchronize our algorithms, but we also want to avoid the synchronization when possible.  Looking at our routine, there is no way to directly avoid this lock, since each element is potentially being run on a separate thread, and this lock is necessary in order for our routine to function correctly every time. However, this isn’t the only way to design this routine to implement this algorithm.  Realize that, although our collection may have thousands or even millions of elements, we have a limited number of Processing Elements (PE).  Processing Element is the standard term for a hardware element which can process and execute instructions.  This typically is a core in your processor, but many modern systems have multiple hardware execution threads per core.  The Task Parallel Library will not execute the work for each item in the collection as a separate work item. Instead, when Parallel.ForEach executes, it will partition the collection into larger “chunks” which get processed on different threads via the ThreadPool.  This helps reduce the threading overhead, and help the overall speed.  In general, the Parallel class will only use one thread per PE in the system. Given the fact that there are typically fewer threads than work items, we can rethink our algorithm design.  We can parallelize our algorithm more effectively by approaching it differently.  Because the basic aggregation we are doing here (Min) is communitive, we do not need to perform this in a given order.  We knew this to be true already – otherwise, we wouldn’t have been able to parallelize this routine in the first place.  With this in mind, we can treat each thread’s work independently, allowing each thread to serially process many elements with no locking, then, after all the threads are complete, “merge” together the results. This can be accomplished via a different set of overloads in the Parallel class: Parallel.ForEach<TSource,TLocal>.  The idea behind these overloads is to allow each thread to begin by initializing some local state (TLocal).  The thread will then process an entire set of items in the source collection, providing that state to the delegate which processes an individual item.  Finally, at the end, a separate delegate is run which allows you to handle merging that local state into your final results. To rewriting our routine using Parallel.ForEach<TSource,TLocal>, we need to provide three delegates instead of one.  The most basic version of this function is declared as: public static ParallelLoopResult ForEach<TSource, TLocal>( IEnumerable<TSource> source, Func<TLocal> localInit, Func<TSource, ParallelLoopState, TLocal, TLocal> body, Action<TLocal> localFinally ) The first delegate (the localInit argument) is defined as Func<TLocal>.  This delegate initializes our local state.  It should return some object we can use to track the results of a single thread’s operations. The second delegate (the body argument) is where our main processing occurs, although now, instead of being an Action<T>, we actually provide a Func<TSource, ParallelLoopState, TLocal, TLocal> delegate.  This delegate will receive three arguments: our original element from the collection (TSource), a ParallelLoopState which we can use for early termination, and the instance of our local state we created (TLocal).  It should do whatever processing you wish to occur per element, then return the value of the local state after processing is completed. The third delegate (the localFinally argument) is defined as Action<TLocal>.  This delegate is passed our local state after it’s been processed by all of the elements this thread will handle.  This is where you can merge your final results together.  This may require synchronization, but now, instead of synchronizing once per element (potentially millions of times), you’ll only have to synchronize once per thread, which is an ideal situation. Now that I’ve explained how this works, lets look at the code: // Safe, and fast! double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach( collection, // First, we provide a local state initialization delegate. () => double.MaxValue, // Next, we supply the body, which takes the original item, loop state, // and local state, and returns a new local state (item, loopState, localState) => { double value = item.PerformComputation(); return System.Math.Min(localState, value); }, // Finally, we provide an Action<TLocal>, to "merge" results together localState => { // This requires locking, but it's only once per used thread lock(syncObj) min = System.Math.Min(min, localState); } ); Although this is a bit more complicated than the previous version, it is now both thread-safe, and has minimal locking.  This same approach can be used by Parallel.For, although now, it’s Parallel.For<TLocal>.  When working with Parallel.For<TLocal>, you use the same triplet of delegates, with the same purpose and results. Also, many times, you can completely avoid locking by using a method of the Interlocked class to perform the final aggregation in an atomic operation.  The MSDN example demonstrating this same technique using Parallel.For uses the Interlocked class instead of a lock, since they are doing a sum operation on a long variable, which is possible via Interlocked.Add. By taking advantage of local state, we can use the Parallel class methods to parallelize algorithms such as aggregation, which, at first, may seem like poor candidates for parallelization.  Doing so requires careful consideration, and often requires a slight redesign of the algorithm, but the performance gains can be significant if handled in a way to avoid excessive synchronization.

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  • Parallelism in .NET – Part 11, Divide and Conquer via Parallel.Invoke

    - by Reed
    Many algorithms are easily written to work via recursion.  For example, most data-oriented tasks where a tree of data must be processed are much more easily handled by starting at the root, and recursively “walking” the tree.  Some algorithms work this way on flat data structures, such as arrays, as well.  This is a form of divide and conquer: an algorithm design which is based around breaking up a set of work recursively, “dividing” the total work in each recursive step, and “conquering” the work when the remaining work is small enough to be solved easily. Recursive algorithms, especially ones based on a form of divide and conquer, are often a very good candidate for parallelization. This is apparent from a common sense standpoint.  Since we’re dividing up the total work in the algorithm, we have an obvious, built-in partitioning scheme.  Once partitioned, the data can be worked upon independently, so there is good, clean isolation of data. Implementing this type of algorithm is fairly simple.  The Parallel class in .NET 4 includes a method suited for this type of operation: Parallel.Invoke.  This method works by taking any number of delegates defined as an Action, and operating them all in parallel.  The method returns when every delegate has completed: Parallel.Invoke( () => { Console.WriteLine("Action 1 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 2 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 3 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); } ); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Running this simple example demonstrates the ease of using this method.  For example, on my system, I get three separate thread IDs when running the above code.  By allowing any number of delegates to be executed directly, concurrently, the Parallel.Invoke method provides us an easy way to parallelize any algorithm based on divide and conquer.  We can divide our work in each step, and execute each task in parallel, recursively. For example, suppose we wanted to implement our own quicksort routine.  The quicksort algorithm can be designed based on divide and conquer.  In each iteration, we pick a pivot point, and use that to partition the total array.  We swap the elements around the pivot, then recursively sort the lists on each side of the pivot.  For example, let’s look at this simple, sequential implementation of quicksort: public static void QuickSort<T>(T[] array) where T : IComparable<T> { QuickSortInternal(array, 0, array.Length - 1); } private static void QuickSortInternal<T>(T[] array, int left, int right) where T : IComparable<T> { if (left >= right) { return; } SwapElements(array, left, (left + right) / 2); int last = left; for (int current = left + 1; current <= right; ++current) { if (array[current].CompareTo(array[left]) < 0) { ++last; SwapElements(array, last, current); } } SwapElements(array, left, last); QuickSortInternal(array, left, last - 1); QuickSortInternal(array, last + 1, right); } static void SwapElements<T>(T[] array, int i, int j) { T temp = array[i]; array[i] = array[j]; array[j] = temp; } Here, we implement the quicksort algorithm in a very common, divide and conquer approach.  Running this against the built-in Array.Sort routine shows that we get the exact same answers (although the framework’s sort routine is slightly faster).  On my system, for example, I can use framework’s sort to sort ten million random doubles in about 7.3s, and this implementation takes about 9.3s on average. Looking at this routine, though, there is a clear opportunity to parallelize.  At the end of QuickSortInternal, we recursively call into QuickSortInternal with each partition of the array after the pivot is chosen.  This can be rewritten to use Parallel.Invoke by simply changing it to: // Code above is unchanged... SwapElements(array, left, last); Parallel.Invoke( () => QuickSortInternal(array, left, last - 1), () => QuickSortInternal(array, last + 1, right) ); } This routine will now run in parallel.  When executing, we now see the CPU usage across all cores spike while it executes.  However, there is a significant problem here – by parallelizing this routine, we took it from an execution time of 9.3s to an execution time of approximately 14 seconds!  We’re using more resources as seen in the CPU usage, but the overall result is a dramatic slowdown in overall processing time. This occurs because parallelization adds overhead.  Each time we split this array, we spawn two new tasks to parallelize this algorithm!  This is far, far too many tasks for our cores to operate upon at a single time.  In effect, we’re “over-parallelizing” this routine.  This is a common problem when working with divide and conquer algorithms, and leads to an important observation: When parallelizing a recursive routine, take special care not to add more tasks than necessary to fully utilize your system. This can be done with a few different approaches, in this case.  Typically, the way to handle this is to stop parallelizing the routine at a certain point, and revert back to the serial approach.  Since the first few recursions will all still be parallelized, our “deeper” recursive tasks will be running in parallel, and can take full advantage of the machine.  This also dramatically reduces the overhead added by parallelizing, since we’re only adding overhead for the first few recursive calls.  There are two basic approaches we can take here.  The first approach would be to look at the total work size, and if it’s smaller than a specific threshold, revert to our serial implementation.  In this case, we could just check right-left, and if it’s under a threshold, call the methods directly instead of using Parallel.Invoke. The second approach is to track how “deep” in the “tree” we are currently at, and if we are below some number of levels, stop parallelizing.  This approach is a more general-purpose approach, since it works on routines which parse trees as well as routines working off of a single array, but may not work as well if a poor partitioning strategy is chosen or the tree is not balanced evenly. This can be written very easily.  If we pass a maxDepth parameter into our internal routine, we can restrict the amount of times we parallelize by changing the recursive call to: // Code above is unchanged... SwapElements(array, left, last); if (maxDepth < 1) { QuickSortInternal(array, left, last - 1, maxDepth); QuickSortInternal(array, last + 1, right, maxDepth); } else { --maxDepth; Parallel.Invoke( () => QuickSortInternal(array, left, last - 1, maxDepth), () => QuickSortInternal(array, last + 1, right, maxDepth)); } We no longer allow this to parallelize indefinitely – only to a specific depth, at which time we revert to a serial implementation.  By starting the routine with a maxDepth equal to Environment.ProcessorCount, we can restrict the total amount of parallel operations significantly, but still provide adequate work for each processing core. With this final change, my timings are much better.  On average, I get the following timings: Framework via Array.Sort: 7.3 seconds Serial Quicksort Implementation: 9.3 seconds Naive Parallel Implementation: 14 seconds Parallel Implementation Restricting Depth: 4.7 seconds Finally, we are now faster than the framework’s Array.Sort implementation.

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  • Looking into ASP.Net MVC 4.0 Mobile Development - part 1

    - by nikolaosk
    In this post I will be looking how ASP.Net MVC 4.0 helps us to create web solutions that target mobile devices.We all experience the magic that is the World Wide Web through mobile devices. Millions of people around the world, use tablets and smartphones to view the contents of websites,e-shops and portals.ASP.Net MVC 4.0 includes a new mobile project template and the ability to render a different set of views for different types of devices.There is a new feature that is called browser overriding which allows us to control exactly what a user is going to see from your web application regardless of what type of device he is using.In order to follow along this post you must have Visual Studio 2012 and .Net Framework 4.5 installed in your machine.Download and install VS 2012 using this link.My machine runs on Windows 8 and Visual Studio 2012 works just fine.It will work fine in Windows 7 as well so do not worry if you do not have the latest Microsoft operating system.1) Launch VS 2012 and create a new Web Forms application by going to File - >New Project - > ASP.Net MVC 4 Web Application and then click OKHave a look at the picture below  2) From the available templates select Mobile Application and then click OK.Have a look at the picture below 3) When I run the application I get the mobile view of the page. I would like to show you what a typical ASP.Net MVC 4.0 application looks like. So I will create a new simple ASP.Net MVC 4.0 Web Application. When I run the application I get the normal page view.Have a look at the picture below.On the left is the mobile view and on the right the normal view. As you can see we have more or less the same content in our mobile application (log in,register) compared with the normal ASP.Net MVC 4.0 application but it is optimised for mobile devices. 4) Let me explain how and when the mobile view is selected and finally rendered.There is a feature in MVC 4.0 that is called Display Modes and with this feature the runtime will select a view.If we have 2 views e.g contact.mobile.cshtml and contact.cshtml in our application the Controller at some point will instruct the runtime to select and render a view named contact.The runtime will look at the browser making the request and will determine if it is a mobile browser or a desktop browser. So if there is a request from my IPhone Safari browser for a particular site, if there is a mobile view the MVC 4.0 will select it and render it. If there is not a mobile view, the normal view will be rendered.5) In the  ASP.Net MVC 4.0 (Internet application) I created earlier (not the first project which was a mobile one) I can run it once more and see how it looks on the browser. If I want to view it with a mobile browser I must download one emulator like Opera Mobile.You can download Opera Mobile hereWhen I run the application I get the same view in both the desktop and the mobile browser. That was to be expected. Have a look at the picture below 6) Then I create another version of the _Layout.mobile.cshtml view in the Shared folder.I simply copy and paste the _Layout.cshtml  into the same folder and then rename it to _Layout.mobile.cshtml and then just alter the contents of the _Layout.mobile.cshtml.When I run again the application I get a different view on the desktop browser and a different one on the Opera mobile browser.Have a look at the picture below ?he Controller will instruct the ASP.Net runtime to select and render a view named _Layout.mobile.cshtml when the request will come from a mobile browser.?he runtime knows that a browser is a mobile one through the ASP.Net browser capability provider. Hope it helps!!!

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  • Parallelism in .NET – Part 20, Using Task with Existing APIs

    - by Reed
    Although the Task class provides a huge amount of flexibility for handling asynchronous actions, the .NET Framework still contains a large number of APIs that are based on the previous asynchronous programming model.  While Task and Task<T> provide a much nicer syntax as well as extending the flexibility, allowing features such as continuations based on multiple tasks, the existing APIs don’t directly support this workflow. There is a method in the TaskFactory class which can be used to adapt the existing APIs to the new Task class: TaskFactory.FromAsync.  This method provides a way to convert from the BeginOperation/EndOperation method pair syntax common through .NET Framework directly to a Task<T> containing the results of the operation in the task’s Result parameter. While this method does exist, it unfortunately comes at a cost – the method overloads are far from simple to decipher, and the resulting code is not always as easily understood as newer code based directly on the Task class.  For example, a single call to handle WebRequest.BeginGetResponse/EndGetReponse, one of the easiest “pairs” of methods to use, looks like the following: var task = Task.Factory.FromAsync<WebResponse>( request.BeginGetResponse, request.EndGetResponse, null); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } The compiler is unfortunately unable to infer the correct type, and, as a result, the WebReponse must be explicitly mentioned in the method call.  As a result, I typically recommend wrapping this into an extension method to ease use.  For example, I would place the above in an extension method like: public static class WebRequestExtensions { public static Task<WebResponse> GetReponseAsync(this WebRequest request) { return Task.Factory.FromAsync<WebResponse>( request.BeginGetResponse, request.EndGetResponse, null); } } This dramatically simplifies usage.  For example, if we wanted to asynchronously check to see if this blog supported XHTML 1.0, and report that in a text box to the user, we could do: var webRequest = WebRequest.Create("http://www.reedcopsey.com"); webRequest.GetReponseAsync().ContinueWith(t => { using (var sr = new StreamReader(t.Result.GetResponseStream())) { string str = sr.ReadLine();; this.textBox1.Text = string.Format("Page at {0} supports XHTML 1.0: {1}", t.Result.ResponseUri, str.Contains("XHTML 1.0")); } }, TaskScheduler.FromCurrentSynchronizationContext());   By using a continuation with a TaskScheduler based on the current synchronization context, we can keep this request asynchronous, check based on the first line of the response string, and report the results back on our UI directly.

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  • TFS 2010 and SSL Configuration: Part 1 Certificates in Place

    - by Enrique Lima
    What is needed?  For starters, an understanding on the how to properly configure a certificate in IIS. Many people have found challenges in working with certificates in IIS 7 First thing is to get your certificate created, and then proceed to add it to IIS 7 By clicking on Server Certificates, we will get to this And we will be able to see the certificate or certificates installed. What options do we have to get certificates? They can be generated “in-house” or purchase them from certification authorities.  If it is “in-house”, you will those options in the Server Certificates area of IIS Manager.

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  • Parallelism in .NET – Part 1, Decomposition

    - by Reed
    The first step in designing any parallelized system is Decomposition.  Decomposition is nothing more than taking a problem space and breaking it into discrete parts.  When we want to work in parallel, we need to have at least two separate things that we are trying to run.  We do this by taking our problem and decomposing it into parts. There are two common abstractions that are useful when discussing parallel decomposition: Data Decomposition and Task Decomposition.  These two abstractions allow us to think about our problem in a way that helps leads us to correct decision making in terms of the algorithms we’ll use to parallelize our routine. To start, I will make a couple of minor points. I’d like to stress that Decomposition has nothing to do with specific algorithms or techniques.  It’s about how you approach and think about the problem, not how you solve the problem using a specific tool, technique, or library.  Decomposing the problem is about constructing the appropriate mental model: once this is done, you can choose the appropriate design and tools, which is a subject for future posts. Decomposition, being unrelated to tools or specific techniques, is not specific to .NET in any way.  This should be the first step to parallelizing a problem, and is valid using any framework, language, or toolset.  However, this gives us a starting point – without a proper understanding of decomposition, it is difficult to understand the proper usage of specific classes and tools within the .NET framework. Data Decomposition is often the simpler abstraction to use when trying to parallelize a routine.  In order to decompose our problem domain by data, we take our entire set of data and break it into smaller, discrete portions, or chunks.  We then work on each chunk in the data set in parallel. This is particularly useful if we can process each element of data independently of the rest of the data.  In a situation like this, there are some wonderfully simple techniques we can use to take advantage of our data.  By decomposing our domain by data, we can very simply parallelize our routines.  In general, we, as developers, should be always searching for data that can be decomposed. Finding data to decompose if fairly simple, in many instances.  Data decomposition is typically used with collections of data.  Any time you have a collection of items, and you’re going to perform work on or with each of the items, you potentially have a situation where parallelism can be exploited.  This is fairly easy to do in practice: look for iteration statements in your code, such as for and foreach. Granted, every for loop is not a candidate to be parallelized.  If the collection is being modified as it’s iterated, or the processing of elements depends on other elements, the iteration block may need to be processed in serial.  However, if this is not the case, data decomposition may be possible. Let’s look at one example of how we might use data decomposition.  Suppose we were working with an image, and we were applying a simple contrast stretching filter.  When we go to apply the filter, once we know the minimum and maximum values, we can apply this to each pixel independently of the other pixels.  This means that we can easily decompose this problem based off data – we will do the same operation, in parallel, on individual chunks of data (each pixel). Task Decomposition, on the other hand, is focused on the individual tasks that need to be performed instead of focusing on the data.  In order to decompose our problem domain by tasks, we need to think about our algorithm in terms of discrete operations, or tasks, which can then later be parallelized. Task decomposition, in practice, can be a bit more tricky than data decomposition.  Here, we need to look at what our algorithm actually does, and how it performs its actions.  Once we have all of the basic steps taken into account, we can try to analyze them and determine whether there are any constraints in terms of shared data or ordering.  There are no simple things to look for in terms of finding tasks we can decompose for parallelism; every algorithm is unique in terms of its tasks, so every algorithm will have unique opportunities for task decomposition. For example, say we want our software to perform some customized actions on startup, prior to showing our main screen.  Perhaps we want to check for proper licensing, notify the user if the license is not valid, and also check for updates to the program.  Once we verify the license, and that there are no updates, we’ll start normally.  In this case, we can decompose this problem into tasks – we have a few tasks, but there are at least two discrete, independent tasks (check licensing, check for updates) which we can perform in parallel.  Once those are completed, we will continue on with our other tasks. One final note – Data Decomposition and Task Decomposition are not mutually exclusive.  Often, you’ll mix the two approaches while trying to parallelize a single routine.  It’s possible to decompose your problem based off data, then further decompose the processing of each element of data based on tasks.  This just provides a framework for thinking about our algorithms, and for discussing the problem.

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  • WPF Databinding- Not your fathers databinding Part 1-3

    - by Shervin Shakibi
    As Promised here is my advanced databinding presentation from South Florida Code camp and also Orlando Code camp. you can find the demo files here. http://ssccinc.com/wpfdatabinding.zip Here is a quick description of the first demos, there will be 2 other Blogposting in the next few days getting into more advance databinding topics.   Example00 Here we have 3 textboxes, The first textbox mySourceElement Second textbox has a binding to mySourceElement and Path= Text <Binding ElementName="mySourceElement" Path="Text"  />   Third textbox is also bound to the Text property but we use inline Binding <TextBlock Text="{Binding ElementName=mySourceElement,Path=Text }" Grid.Row="2" /> Here is the entire XAML     <Grid  >           <Grid.RowDefinitions >             <RowDefinition Height="*" />             <RowDefinition Height="*" />             <RowDefinition Height="*" />         </Grid.RowDefinitions>         <TextBox Name="mySourceElement" Grid.Row="0"                  TextChanged="mySourceElement_TextChanged">Hello Orlnado</TextBox>         <TextBlock Grid.Row="1">                        <TextBlock.Text>                 <Binding ElementName="mySourceElement" Path="Text"  />             </TextBlock.Text>         </TextBlock>         <TextBlock Text="{Binding ElementName=mySourceElement,Path=Text }" Grid.Row="2" />     </Grid> </Window> Example01 we have a slider control, then we have two textboxes bound to the value property of the slider. one has its text property bound, the second has its fontsize property bound. <Grid>      <Grid.RowDefinitions >          <RowDefinition Height="40px" />          <RowDefinition Height="40px" />          <RowDefinition Height="*" />      </Grid.RowDefinitions>      <Slider Name="fontSizeSlider" Minimum="5" Maximum="100"              Value="10" Grid.Row="0" />      <TextBox Name="SizeTextBox"                    Text="{Binding ElementName=fontSizeSlider, Path=Value}" Grid.Row="1"/>      <TextBlock Text="Example 01"                 FontSize="{Binding ElementName=SizeTextBox,  Path=Text}"  Grid.Row="2"/> </Grid> Example02 very much like the previous example but it also has a font dropdown <Grid>      <Grid.RowDefinitions >          <RowDefinition Height="20px" />          <RowDefinition Height="40px" />          <RowDefinition Height="40px" />          <RowDefinition Height="*" />      </Grid.RowDefinitions>      <ComboBox Name="FontNameList" SelectedIndex="0" Grid.Row="0">          <ComboBoxItem Content="Arial" />          <ComboBoxItem Content="Calibri" />          <ComboBoxItem Content="Times New Roman" />          <ComboBoxItem Content="Verdana" />      </ComboBox>      <Slider Name="fontSizeSlider" Minimum="5" Maximum="100" Value="10" Grid.Row="1" />      <TextBox Name="SizeTextBox"      Text="{Binding ElementName=fontSizeSlider, Path=Value}" Grid.Row="2"/>      <TextBlock Text="Example 01" FontFamily="{Binding ElementName=FontNameList, Path=Text}"                 FontSize="{Binding ElementName=SizeTextBox,  Path=Text}"  Grid.Row="3"/> </Grid> Example03 In this example we bind to an object Employee.cs Notice we added a directive to our xaml which is clr-namespace and the namespace for our employee Class xmlns:local="clr-namespace:Example03" In Our windows Resources we create an instance of our object <Window.Resources>     <local:Employee x:Key="MyEmployee" EmployeeNumber="145"                     FirstName="John"                     LastName="Doe"                     Department="Product Development"                     Title="QA Manager" /> </Window.Resources> then we bind our container to the that instance of the data <Grid DataContext="{StaticResource MyEmployee}">         <Grid.RowDefinitions>             <RowDefinition Height="*" />             <RowDefinition Height="*" />             <RowDefinition Height="*" />             <RowDefinition Height="*" />             <RowDefinition Height="*" />         </Grid.RowDefinitions>         <Grid.ColumnDefinitions >             <ColumnDefinition Width="130px" />             <ColumnDefinition Width="178*" />         </Grid.ColumnDefinitions>     </Grid> and Finally we have textboxes that will bind to that textbox         <Label Grid.Row="0" Grid.Column="0">Employee Number</Label>         <TextBox Grid.Row="0" Grid.Column="1" Text="{Binding Path=EmployeeNumber}"></TextBox>         <Label Grid.Row="1" Grid.Column="0">First Name</Label>         <TextBox Grid.Row="1" Grid.Column="1" Text="{Binding Path=FirstName}"></TextBox>         <Label Grid.Row="2" Grid.Column="0">Last Name</Label>         <TextBox Grid.Row="2" Grid.Column="1" Text="{Binding Path=LastName}" />         <Label Grid.Row="3" Grid.Column="0">Title</Label>         <TextBox Grid.Row="3" Grid.Column="1" Text="{Binding Path=Title}"></TextBox>         <Label Grid.Row="4" Grid.Column="0">Department</Label>         <TextBox Grid.Row="4" Grid.Column="1" Text="{Binding Path=Department}" />

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  • Raspberry Pi entrance signed backed by Umbraco - Part 1

    - by Chris Houston
    Being experts on all things Umbraco, we jumped at the chance to help our client, QV Offices, with their pressing signage predicament. They needed to display a sign in the entrance to their building and approached us for our advice. Of course it had to be electronic: displaying multiple names of their serviced office clients, meeting room bookings and on-the-pulse promotions. But with a winding Victorian staircase and minimal storage space how could the monitor be run, updated and managed? That’s where we came in…Raspberry PiUmbraco CMSAutomatic updatesAutomated monitor of the signPower saving when the screen is not in useMounting the screenThe screen that has been used is a standard LED low energy Full HD screen and has been mounted on the wall using it's VESA mounting points, as the wall is a stud wall we were able to add an access panel behind the screen to feed through the mains, HDMI and sensor cables.The Raspberry Pi is then tucked away out of sight in the main electrical cupboard which just happens to be next to the sign, we had an electrician add a power point inside this cupboard to allow us to power the screen and the Raspberry Pi.Designing the interface and editing the contentAlthough a room sign was the initial requirement from QV Offices, their medium term goal has always been to add online meeting booking to their website and hence we suggested adding information about the current and next day's meetings to the sign that would be pulled directly from their online booking system.We produced the design and built the web page to fit exactly on a 1920 x 1080 screen (Full HD in Portrait)As you would expect all the information can be edited via an Umbraco CMS, they are able to add floors, rooms, clients and virtual clients as well as add meeting bookings to their meeting diary.How we configured the Raspberry PiAfter receiving a new Raspberry Pi we downloaded the latest release of Raspbian operating system and followed the official guide which shows how to copy the OS onto an SD card from a Mac, we then followed the majority of steps on this useful guide: 10 Things to Do After Buying a Raspberry Pi.Installing ChromiumWe chose to use the Chromium web browser which for those who do not know is the open sourced version of Google Chrome. You can install this from the terminal with the following command:sudo apt-get install chromium-browserInstalling UnclutterWe found this little application which automatically hides the mouse pointer, it is used in the script below and is installed using the following command:sudo apt-get install unclutterAuto start Chromium and disabling the screen saver, power saving and mouseWhen the Raspberry Pi has been installed it will not have a keyboard or mouse and hence if their was a power cut we needed it to always boot and re-loaded Chromium with the correct URL.Our preferred command line text editor is Nano and I have assumed you know how to use this editor or will be able to work it out pretty quickly.So using the following command:sudo nano /etc/xdg/lxsession/LXDE/autostartWe then changed the autostart file content to:@lxpanel --profile LXDE@pcmanfm --desktop --profile LXDE@xscreensaver -no-splash@xset s off@xset -dpms@xset s noblank@chromium --kiosk --incognito http://www.qvoffices.com/someURL@unclutter -idle 0The first few commands turn off the screen saver and power saving, we then open Cromium in Kiosk Mode (full screen with no menu etc) and pass in the URL to use (I have changed the URL in this example) We found a useful blog post with the Cromium command line switches.Finally we also open an application called Unclutter which auto hides the mouse after 0 seconds, so you will never see a mouse on the sign.We also had to edit the following file:sudo nano /etc/lightdm/lightdm.confAnd added the following line under the [SeatDefault] section:xserver-command=X -s 0 dpmsRefreshing the screenWe decided to try and add a scheduled task that would trigger Chromium to reload the page, at some point in the future we might well change this to using Javascript to update the content, but for now this works fine.First we installed the XDOTool which enables you to script Keyboard commands:sudo apt-get install xdotoolWe used the Refreshing Chromium Browser by Shell Script post as a reference and created the following shell script (which we called refreshing.sh):export DISPLAY=":0"WID=$(xdotool search --onlyvisible --class chromium|head -1)xdotool windowactivate ${WID}xdotool key ctrl+F5This selects the correct display and then sends a CTRL + F5 to refresh Chromium.You will need to give this file execute permissions:chmod a=rwx refreshing.shNow we have the script file setup we just need to schedule it to call this script periodically which is done by using Crontab, to edit this you use the following command:crontab -eAnd we added the following:*/5 * * * * DISPLAY=":.0" /home/pi/scripts/refreshing.sh >/home/pi/cronlog.log 2>&1This calls our script every 5 minutes to refresh the display and it logs any errors to the cronlog.log file.SummaryQV Offices now have a richer and more manageable booking system than they did before we started, and a great new sign to boot.How could we make sure that the sign was running smoothly downstairs in a busy office centre? A second post will follow outlining exactly how Vizioz enabled QV Offices to monitor their sign simply and remotely, from the comfort of their desks.

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  • Using a service registry that doesn’t suck part I: UDDI is dead

    - by gsusx
    This is the first of a series of posts on which I am hoping to detail some of the most common SOA governance scenarios in the real world, their challenges and the approach we’ve taken to address them in SO-Aware. This series does not intend to be a marketing pitch about SO-Aware. Instead, I would like to use this to foment an honest dialog between SOA governance technologists. For the starting post I decided to focus on the aspect that was once considered the keystone of SOA governance: service discovery...(read more)

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  • Getting started with Blocks and namespaces - Enterprise Library 5.0 Tutorial Part 2

    This is my second post in this series. In first blog post I explained how to install Enterprise Library 5.0 and provided links to various resources. Enterprise Library is divided into various blocks. Simply we can say, a block is a ready made solution for a particular common problem across various applications. So instead focusing on implementation of common problem across various applications, we can reuse these fully tested and extendable blocks to increase the productivity and also extendibility as these blocks are made with good design principles and patterns. Major blocks of Enterprise Library 5.0 are as follows.   Core infrastructure Functional Application Blocks Caching Data Exception Handling Logging Security Cryptography Validation Wiring Application Blocks Unity Policy Injection/Interception   Each block resides in its own assembly, and also some extra assemblies for common infrastructure. Assemblies are as follows. Microsoft.Practices.EnterpriseLibrary.Caching.Cryptography.dll Microsoft.Practices.EnterpriseLibrary.Caching.Database.dll Microsoft.Practices.EnterpriseLibrary.Caching.dll Microsoft.Practices.EnterpriseLibrary.Common.dll Microsoft.Practices.EnterpriseLibrary.Configuration.Design.HostAdapter.dll Microsoft.Practices.EnterpriseLibrary.Configuration.Design.HostAdapterV5.dll Microsoft.Practices.EnterpriseLibrary.Configuration.DesignTime.dll Microsoft.Practices.EnterpriseLibrary.Configuration.EnvironmentalOverrides.dll Microsoft.Practices.EnterpriseLibrary.Data.dll Microsoft.Practices.EnterpriseLibrary.Data.SqlCe.dll Microsoft.Practices.EnterpriseLibrary.ExceptionHandling.dll Microsoft.Practices.EnterpriseLibrary.ExceptionHandling.Logging.dll Microsoft.Practices.EnterpriseLibrary.ExceptionHandling.WCF.dll Microsoft.Practices.EnterpriseLibrary.Logging.Database.dll Microsoft.Practices.EnterpriseLibrary.Logging.dll Microsoft.Practices.EnterpriseLibrary.PolicyInjection.dll Microsoft.Practices.EnterpriseLibrary.Security.Cache.CachingStore.dll Microsoft.Practices.EnterpriseLibrary.Security.Cryptography.dll Microsoft.Practices.EnterpriseLibrary.Security.dll Microsoft.Practices.EnterpriseLibrary.Validation.dll Microsoft.Practices.EnterpriseLibrary.Validation.Integration.AspNet.dll Microsoft.Practices.EnterpriseLibrary.Validation.Integration.WCF.dll Microsoft.Practices.EnterpriseLibrary.Validation.Integration.WinForms.dll Microsoft.Practices.ServiceLocation.dll Microsoft.Practices.Unity.Configuration.dll Microsoft.Practices.Unity.dll Microsoft.Practices.Unity.Interception.dll Enterprise Library Configuration Tool In addition to these assemblies you would get configuration tool “EntLibConfig-32.exe”. If you are targeting your application to .NET 4.0 framework then you would need to use “EntLibConfig.NET4.exe”. Optionally you can install Visual Studio 2008 and Visual Studio 2010 add-ins whilst installing of Enterprise Library. So that you can invoke the enterprise Library configuration from Visual Studio by right clicking on “app.config” or “web.config” file as shown below. I would suggest you to download the documentation from Codeplex which was released on May 2010. It consists 3MB of information. you can also find issue tracker to know various issues/bugs currently people talking about enterprise library. There is also discussion link takes you to community site where you can post your questions. In my next blog post, I would cover more on each block. span.fullpost {display:none;}

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