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  • ASP.NET and HTML5 Local Storage

    - by Stephen Walther
    My favorite feature of HTML5, hands-down, is HTML5 local storage (aka DOM storage). By taking advantage of HTML5 local storage, you can dramatically improve the performance of your data-driven ASP.NET applications by caching data in the browser persistently. Think of HTML5 local storage like browser cookies, but much better. Like cookies, local storage is persistent. When you add something to browser local storage, it remains there when the user returns to the website (possibly days or months later). Importantly, unlike the cookie storage limitation of 4KB, you can store up to 10 megabytes in HTML5 local storage. Because HTML5 local storage works with the latest versions of all modern browsers (IE, Firefox, Chrome, Safari), you can start taking advantage of this HTML5 feature in your applications right now. Why use HTML5 Local Storage? I use HTML5 Local Storage in the JavaScript Reference application: http://Superexpert.com/JavaScriptReference The JavaScript Reference application is an HTML5 app that provides an interactive reference for all of the syntax elements of JavaScript (You can read more about the application and download the source code for the application here). When you open the application for the first time, all of the entries are transferred from the server to the browser (all 300+ entries). All of the entries are stored in local storage. When you open the application in the future, only changes are transferred from the server to the browser. The benefit of this approach is that the application performs extremely fast. When you click the details link to view details on a particular entry, the entry details appear instantly because all of the entries are stored on the client machine. When you perform key-up searches, by typing in the filter textbox, matching entries are displayed very quickly because the entries are being filtered on the local machine. This approach can have a dramatic effect on the performance of any interactive data-driven web application. Interacting with data on the client is almost always faster than interacting with the same data on the server. Retrieving Data from the Server In the JavaScript Reference application, I use Microsoft WCF Data Services to expose data to the browser. WCF Data Services generates a REST interface for your data automatically. Here are the steps: Create your database tables in Microsoft SQL Server. For example, I created a database named ReferenceDB and a database table named Entities. Use the Entity Framework to generate your data model. For example, I used the Entity Framework to generate a class named ReferenceDBEntities and a class named Entities. Expose your data through WCF Data Services. I added a WCF Data Service to my project and modified the data service class to look like this:   using System.Data.Services; using System.Data.Services.Common; using System.Web; using JavaScriptReference.Models; namespace JavaScriptReference.Services { [System.ServiceModel.ServiceBehavior(IncludeExceptionDetailInFaults = true)] public class EntryService : DataService<ReferenceDBEntities> { // This method is called only once to initialize service-wide policies. public static void InitializeService(DataServiceConfiguration config) { config.UseVerboseErrors = true; config.SetEntitySetAccessRule("*", EntitySetRights.All); config.DataServiceBehavior.MaxProtocolVersion = DataServiceProtocolVersion.V2; } // Define a change interceptor for the Products entity set. [ChangeInterceptor("Entries")] public void OnChangeEntries(Entry entry, UpdateOperations operations) { if (!HttpContext.Current.Request.IsAuthenticated) { throw new DataServiceException("Cannot update reference unless authenticated."); } } } }     The WCF data service is named EntryService. Notice that it derives from DataService<ReferenceEntitites>. Because it derives from DataService<ReferenceEntities>, the data service exposes the contents of the ReferenceEntitiesDB database. In the code above, I defined a ChangeInterceptor to prevent un-authenticated users from making changes to the database. Anyone can retrieve data through the service, but only authenticated users are allowed to make changes. After you expose data through a WCF Data Service, you can use jQuery to retrieve the data by performing an Ajax call. For example, I am using an Ajax call that looks something like this to retrieve the JavaScript entries from the EntryService.svc data service: $.ajax({ dataType: "json", url: “/Services/EntryService.svc/Entries”, success: function (result) { var data = callback(result["d"]); } });     Notice that you must unwrap the data using result[“d”]. After you unwrap the data, you have a JavaScript array of the entries. I’m transferring all 300+ entries from the server to the client when the application is opened for the first time. In other words, I transfer the entire database from the server to the client, once and only once, when the application is opened for the first time. The data is transferred using JSON. Here is a fragment: { "d" : [ { "__metadata": { "uri": "http://superexpert.com/javascriptreference/Services/EntryService.svc/Entries(1)", "type": "ReferenceDBModel.Entry" }, "Id": 1, "Name": "Global", "Browsers": "ff3_6,ie8,ie9,c8,sf5,es3,es5", "Syntax": "object", "ShortDescription": "Contains global variables and functions", "FullDescription": "<p>\nThe Global object is determined by the host environment. In web browsers, the Global object is the same as the windows object.\n</p>\n<p>\nYou can use the keyword <code>this</code> to refer to the Global object when in the global context (outside of any function).\n</p>\n<p>\nThe Global object holds all global variables and functions. For example, the following code demonstrates that the global <code>movieTitle</code> variable refers to the same thing as <code>window.movieTitle</code> and <code>this.movieTitle</code>.\n</p>\n<pre>\nvar movieTitle = \"Star Wars\";\nconsole.log(movieTitle === this.movieTitle); // true\nconsole.log(movieTitle === window.movieTitle); // true\n</pre>\n", "LastUpdated": "634298578273756641", "IsDeleted": false, "OwnerId": null }, { "__metadata": { "uri": "http://superexpert.com/javascriptreference/Services/EntryService.svc/Entries(2)", "type": "ReferenceDBModel.Entry" }, "Id": 2, "Name": "eval(string)", "Browsers": "ff3_6,ie8,ie9,c8,sf5,es3,es5", "Syntax": "function", "ShortDescription": "Evaluates and executes JavaScript code dynamically", "FullDescription": "<p>\nThe following code evaluates and executes the string \"3+5\" at runtime.\n</p>\n<pre>\nvar result = eval(\"3+5\");\nconsole.log(result); // returns 8\n</pre>\n<p>\nYou can rewrite the code above like this:\n</p>\n<pre>\nvar result;\neval(\"result = 3+5\");\nconsole.log(result);\n</pre>", "LastUpdated": "634298580913817644", "IsDeleted": false, "OwnerId": 1 } … ]} I worried about the amount of time that it would take to transfer the records. According to Google Chome, it takes about 5 seconds to retrieve all 300+ records on a broadband connection over the Internet. 5 seconds is a small price to pay to avoid performing any server fetches of the data in the future. And here are the estimated times using different types of connections using Fiddler: Notice that using a modem, it takes 33 seconds to download the database. 33 seconds is a significant chunk of time. So, I would not use the approach of transferring the entire database up front if you expect a significant portion of your website audience to connect to your website with a modem. Adding Data to HTML5 Local Storage After the JavaScript entries are retrieved from the server, the entries are stored in HTML5 local storage. Here’s the reference documentation for HTML5 storage for Internet Explorer: http://msdn.microsoft.com/en-us/library/cc197062(VS.85).aspx You access local storage by accessing the windows.localStorage object in JavaScript. This object contains key/value pairs. For example, you can use the following JavaScript code to add a new item to local storage: <script type="text/javascript"> window.localStorage.setItem("message", "Hello World!"); </script>   You can use the Google Chrome Storage tab in the Developer Tools (hit CTRL-SHIFT I in Chrome) to view items added to local storage: After you add an item to local storage, you can read it at any time in the future by using the window.localStorage.getItem() method: <script type="text/javascript"> window.localStorage.setItem("message", "Hello World!"); </script>   You only can add strings to local storage and not JavaScript objects such as arrays. Therefore, before adding a JavaScript object to local storage, you need to convert it into a JSON string. In the JavaScript Reference application, I use a wrapper around local storage that looks something like this: function Storage() { this.get = function (name) { return JSON.parse(window.localStorage.getItem(name)); }; this.set = function (name, value) { window.localStorage.setItem(name, JSON.stringify(value)); }; this.clear = function () { window.localStorage.clear(); }; }   If you use the wrapper above, then you can add arbitrary JavaScript objects to local storage like this: var store = new Storage(); // Add array to storage var products = [ {name:"Fish", price:2.33}, {name:"Bacon", price:1.33} ]; store.set("products", products); // Retrieve items from storage var products = store.get("products");   Modern browsers support the JSON object natively. If you need the script above to work with older browsers then you should download the JSON2.js library from: https://github.com/douglascrockford/JSON-js The JSON2 library will use the native JSON object if a browser already supports JSON. Merging Server Changes with Browser Local Storage When you first open the JavaScript Reference application, the entire database of JavaScript entries is transferred from the server to the browser. Two items are added to local storage: entries and entriesLastUpdated. The first item contains the entire entries database (a big JSON string of entries). The second item, a timestamp, represents the version of the entries. Whenever you open the JavaScript Reference in the future, the entriesLastUpdated timestamp is passed to the server. Only records that have been deleted, updated, or added since entriesLastUpdated are transferred to the browser. The OData query to get the latest updates looks like this: http://superexpert.com/javascriptreference/Services/EntryService.svc/Entries?$filter=(LastUpdated%20gt%20634301199890494792L) If you remove URL encoding, the query looks like this: http://superexpert.com/javascriptreference/Services/EntryService.svc/Entries?$filter=(LastUpdated gt 634301199890494792L) This query returns only those entries where the value of LastUpdated > 634301199890494792 (the version timestamp). The changes – new JavaScript entries, deleted entries, and updated entries – are merged with the existing entries in local storage. The JavaScript code for performing the merge is contained in the EntriesHelper.js file. The merge() method looks like this:   merge: function (oldEntries, newEntries) { // concat (this performs the add) oldEntries = oldEntries || []; var mergedEntries = oldEntries.concat(newEntries); // sort this.sortByIdThenLastUpdated(mergedEntries); // prune duplicates (this performs the update) mergedEntries = this.pruneDuplicates(mergedEntries); // delete mergedEntries = this.removeIsDeleted(mergedEntries); // Sort this.sortByName(mergedEntries); return mergedEntries; },   The contents of local storage are then updated with the merged entries. I spent several hours writing the merge() method (much longer than I expected). I found two resources to be extremely useful. First, I wrote extensive unit tests for the merge() method. I wrote the unit tests using server-side JavaScript. I describe this approach to writing unit tests in this blog entry. The unit tests are included in the JavaScript Reference source code. Second, I found the following blog entry to be super useful (thanks Nick!): http://nicksnettravels.builttoroam.com/post/2010/08/03/OData-Synchronization-with-WCF-Data-Services.aspx One big challenge that I encountered involved timestamps. I originally tried to store an actual UTC time as the value of the entriesLastUpdated item. I quickly discovered that trying to work with dates in JSON turned out to be a big can of worms that I did not want to open. Next, I tried to use a SQL timestamp column. However, I learned that OData cannot handle the timestamp data type when doing a filter query. Therefore, I ended up using a bigint column in SQL and manually creating the value when a record is updated. I overrode the SaveChanges() method to look something like this: public override int SaveChanges(SaveOptions options) { var changes = this.ObjectStateManager.GetObjectStateEntries( EntityState.Modified | EntityState.Added | EntityState.Deleted); foreach (var change in changes) { var entity = change.Entity as IEntityTracking; if (entity != null) { entity.LastUpdated = DateTime.Now.Ticks; } } return base.SaveChanges(options); }   Notice that I assign Date.Now.Ticks to the entity.LastUpdated property whenever an entry is modified, added, or deleted. Summary After building the JavaScript Reference application, I am convinced that HTML5 local storage can have a dramatic impact on the performance of any data-driven web application. If you are building a web application that involves extensive interaction with data then I recommend that you take advantage of this new feature included in the HTML5 standard.

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  • Regexp that matches user-agents of end-user browsers but NOT crawlers with >90 % accuracy

    - by knorv
    I'm trying to construct a regexp that will evaluate to true for User-Agent:s of "browsers navigated by humans", but false for bots. Needless to say the matching will not be exact, but if it gets things right in say 90 % of cases that is more than good enough. My approach so far is to target the User-Agent string of the the five major desktop browsers (MSIE, Firefox, Chrome, Safari, Opera). Specifically I want the regexp NOT to match if the user-agent is a bot (Googlebot, msnbot, etc.). Currently I'm using the following regexp which appears to achieve the desired precision: ^(Mozilla.*(Gecko|KHTML|MSIE|Presto|Trident)|Opera).*$ I've observed small number of false negatives which are mostly mobile browsers. The exceptions all match: (BlackBerry|HTC|LG|MOT|Nokia|NOKIAN|PLAYSTATION|PSP|SAMSUNG|SonyEricsson) My question is: Given the desired accuracy level, how would you improve the regexp? Can you think of any major false positives or false negatives to the given regexp? Please note that the question is specifically about regexp-based User-Agent matching. There are a bunch of other approaches to solving this problem, but those are out of the scope of this question.

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  • How do I create a dynamic data transfer object dynamically from ADO.net model

    - by Richard
    I have a pretty simple database with 5 tables, PK's and relationships setup, etc. I also have an ASP.net MVC3 project I'm using to create simple web services to feed JSON/XML to a mobile app using post/get. To access my data I'm using an ADO.net entity model class to handle generation of the entities, etc. Due to issues with serialization/circular references created by the auto-generated relations from ADO.net entity model, I've been forced to create "Data transfer objects" to strip out the relations and data that doesn't need to be transferred. Question 1: is there an easier way to create DTOs using the entity framework itself? IE, specify only the entity properties I want to convert to Jsonresults? I don't wish to use any 3rd party frameworks if I can help it. Question 2: A side question for Entity Framework, say I create an ADO.net entity model in one project within a solution. Because that model relies on the connection to the database specified in project A, can project B somehow use that model with a similar connection? Both projects are in the same solution. Thanks!

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  • List of Hidden / Virtual Windows User Accounts

    - by Synetech inc.
    I’m trying to find a way to get a comprehensive list of user accounts on a Windows 7 system, including hidden ones. The User Accounts dialog (>control userpasswords2) only shows the normal user accounts, and even the Local User and Groups editor only shows normal user accounts and standard hidden/disabled ones like Administrator and Guest. The Select Users or Groups dialog has a Find Now button which which combines users and groups, but alas, it has the same contents as the LUG. I’m looking for a more comprehensive list that includes “super-hidden” / virtual user accounts like TrustedInstaller (or to be more accurate, NT Service\TrustedInstaller—notice the different “domain”). I checked HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon\SpecialAccounts\UserList, but the SpecialAccounts key does not exist. I also checked HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\ProfileList, and while it does have the SystemProfile, LocalService, and NetworkService accounts listed, it does not have others (like TrustedInstaller and its ilk). TrustedInstaller specifically is a little confusing because it is a user, a service, and an executable file. I am using it as an example because it is “super hidden” in that it does not seem to be listed in any sort of user list. (As an experiment, I tried searching the whole registry for “trustedinstaller” to see if I could find a place where it is listed as a user, but found none.) To be clear, what I am looking for is a list of all accounts that can be used in a user input-field such as in permissions dialogs or as a runas argument.

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  • Asp.net: Replace GenericPrincipal

    - by Pickels
    Hello, I was wondering what the best way is to replace the genericPrincipal with my own CustomGenericPrincipal. At the moment I have something like this but I aint sure if it's correct. protected void Application_AuthenticateRequest(Object sender, EventArgs e) { HttpCookie authCookie = Request.Cookies[FormsAuthentication.FormsCookieName]; if (authCookie != null) { FormsAuthenticationTicket authTicket = FormsAuthentication.Decrypt(authCookie.Value); var identity = new CustomIdentity(authTicket); var principal = new CustomPrincipal(identity); Context.User = principal; } else { //Todo: check if this is correct var genericIdentity = new CustomGenericIdentity(); Context.User = new CustomPrincipal(genericIdentity); } } I need to replace it because I need a Principal that implements my ICustomPrincipal interface because I am doing the following with Ninject: Bind<ICustomPrincipal>().ToMethod(x => (ICustomPrincipal)HttpContext.Current.User) .InRequestScope(); So what's the best way to replace the GenericPrincipal? Thanks in advance, Pickels

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  • Is there any real benefit to using ASP.Net Authentication with ASP.Net MVC?

    - by alchemical
    I've been researching this intensely for the past few days. We're developing an ASP.Net MVC site that needs to support 100,000+ users. We'd like to keep it fast, scalable, and simple. We have our own SQL database tables for user and user_role, etc. We are not using server controls. Given that there are no server controls, and a custom membershipProvider would need to be created, where is there any benefit left to use ASP.Net Auth/Membership? The other alternative would seem to be to create custom code to drop a UniqueID CustomerID in a cookie and authenticate with that. Or, if we're paranoid about sniffers, we could encrypt the cookie as well. Is there any real benefit in this scenario (MVC and customer data is in our own tables) to using the ASP.Net auth/membership framework, or is the fully custom solution a viable route?

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  • ASP.NET Membership Provider - Single Login

    - by RSolberg
    I'm considering utilizing the ASP.NET Membership Provider for a few different web apps/tools with a single login approach. REQUIREMENTS User logs in to my.domain.com and sees a list of apps/tools that they have permission to use. The user selects the tool they'd like to use and clicks the link. When the tool opens, it is able to identify that they are currently logged in and who they are to identify any unique permissions to the application. I know that each app could simply point to the same back end Membership Provider DB, however will each app require a login or will it be able to identify if the user is already logged in?

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  • Asp.Net MVC - Plugins Directory, Community etc?

    - by Jörg Battermann
    Good evening everyone, I am currently starting to dive into asp.net mvc and I really like what I see so far.. BUT I am somewhat confused about 'drop-in' functionality (similiar to what rails and it's plugins and nowadays gems are), an active community to contact etc. For rails there's github with one massiv index of plugins/gems/code-examples regarding mostly rails (despite their goal being generic source-code hosting..), for blogs, mailing lists etc it's also pretty easy to find the places the other developers flock around, but... for asp.net mvc I am somewhat lost where to go/look. It all seems scattered across codeplex and private sites, google code hosting etc etc.. but is there one (or few places) where to turn to regarding asp.net mvc development, sample code etc? Cheers and thanks, -Jörg

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  • advise how to implement a code generator for asp.NET mvc 2

    - by loviji
    Hello, I would like your advice about how best to solve my problem. In a Web server is running. NET Framework 4.0. Whatever the methods and technologies you would advise me. applications built on the basis Asp.NET MVC 2. I have a database table in MS SQL Server. For each database, I must implement the interface for viewing, editing, and deleting. So code generator must generate model, controller and views.. Generation should happen after clicking on the button. as model I use .NET Entity Framework. Now, I need to generate controllers and views. So if i have a table with name tableN1. and below its colums: [ID] [bigint] IDENTITY(1,1) NOT NULL, [name] [nvarchar 20] NOT NULL, [fullName] [nvarchar 50] NOT NULL, [age] [int] NOT NULL [active] [bit] NULL for this table, i want to generate views and controller. thanks.

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  • My first .net web app - should I go straight to MVC framework (c.f. ASP.net)

    - by Greg
    Hi, I'm done some WinForms work in C# but now moving to have to develop a web application front end in .NET (C#). I have experience developing web apps in Ruby on Rails (& a little with Java with JSP pages & struts mvc). Should I jump straight to MVC framework? (as opposed to going ASP.net) That is from the point of view of future direction for Microsoft & as well ease in ramping up from myself. Or if you like, given my experience to date, what would the pros/cons for me re MVC versus ASP.net? thanks

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  • Simple way of converting server side objects into client side using JSON serialization for asp.net websites

    - by anil.kasalanati
     Introduction:- With the growth of Web2.0 and the need for faster user experience the spotlight has shifted onto javascript based applications built using REST pattern or asp.net AJAX Pagerequest manager. And when we are working with javascript wouldn’t it be much better if we could create objects in an OOAD way and easily push it to the client side.  Following are the reasons why you would push the server side objects onto client side -          Easy availability of the complex object. -          Use C# compiler and rick intellisense to create and maintain the objects but use them in the javascript. You could run code analysis etc. -          Reduce the number of calls we make to the server side by loading data on the pageload.   I would like to explain about the 3rd point because that proved to be highly beneficial to me when I was fixing the performance issues of a major website. There could be a scenario where in you be making multiple AJAX based webrequestmanager calls in order to get the same response in a single page. This happens in the case of widget based framework when all the widgets are independent but they need some common information available in the framework to load the data. So instead of making n multiple calls we could load the data needed during pageload. The above picture shows the scenario where in all the widgets need the common information and then call GetData webservice on the server side. Ofcourse the result can be cached on the client side but a better solution would be to avoid the call completely.  In order to do that we need to JSONSerialize the content and send it in the DOM.                                                                                                                                                                                                                                                                                                                                                                                            Example:- I have developed a simple application to demonstrate the idea and I would explaining that in detail here. The class called SimpleClass would be sent as serialized JSON to the client side .   And this inherits from the base class which has the implementation for the GetJSONString method. You can create a single base class and all the object which need to be pushed to the client side can inherit from that class. The important thing to note is that the class should be annotated with DataContract attribute and the methods should have the Data Member attribute. This is needed by the .Net DataContractSerializer and this follows the opt-in mode so if you want to send an attribute to the client side then you need to annotate the DataMember attribute. So if I didn’t want to send the Result I would simple remove the DataMember attribute. This is default WCF/.Net 3.5 stuff but it provides the flexibility of have a fullfledged object on the server side but sending a smaller object to the client side. Sometimes you may hide some values due to security constraints. And thing you will notice is that I have marked the class as Serializable so that it can be stored in the Session and used in webfarm deployment scenarios. Following is the implementation of the base class –  This implements the default DataContractJsonSerializer and for more information or customization refer to following blogs – http://softcero.blogspot.com/2010/03/optimizing-net-json-serializing-and-ii.html http://weblogs.asp.net/gunnarpeipman/archive/2010/12/28/asp-net-serializing-and-deserializing-json-objects.aspx The next part is pretty simple, I just need to inject this object into the aspx page.   And in the aspx markup I have the following line – <script type="text/javascript"> var data =(<%=SimpleClassJSON  %>);   alert(data.ResultText); </script>   This will output the content as JSON into the variable data and this can be any element in the DOM. And you can verify the element by checking data in the Firebug console.    Design Consideration – If you have a lot of javascripts then you need to think about using Script # and you can write javascript in C#. Refer to Nikhil’s blog – http://projects.nikhilk.net/ScriptSharp Ensure that you are taking security into consideration while exposing server side objects on to client side. I have seen application exposing passwords, secret key so it is not a good practice.   The application can be tested using the following url – http://techconsulting.vpscustomer.com/Samples/JsonTest.aspx The source code is available at http://techconsulting.vpscustomer.com/Source/HistoryTest.zip

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  • Custom ASPNetMembership FailureInformation always null, OnValidatingPassword issue

    - by bigb
    As stated here http://msdn.microsoft.com/en-us/library/system.web.security.membershipprovider.onvalidatingpassword.aspx "When the ValidatingPassword event has completed, the properties of the ValidatePasswordEventArgs object supplied as the e parameter can be examined to determine whether the current action should be canceled and if a particular Exception, stored in the FailureInformation property, should be thrown." Here is some details/code which really shows why FailureInformation shouldn't be always null http://forums.asp.net/t/991002.aspx if any password security conditions not matched. According with my Membership settings i should get an exception that password does not match password security conditions, but it is not happened. Then i did try to debug System.Web.ApplicationServices.dll(in .NET 4.0 System.Web.Security located here) Framework Code to see whats really happens there, but i cant step into this assembly, may be because of this [TypeForwardedFrom("System.Web, Version=2.0.0.0, Culture=Neutral, PublicKeyToken=b03f5f7f11d50a3a")] public abstract class MembershipProvider : ProviderBase Easily i may step into any another .NET 4.0 assembly, but in this one not. I did check, symbols for System.Web.ApplicationServices.dll loaded. Now i have only one idea how ti fix it - to override method OnValidatingPassword(ValidatePasswordEventArgs e). Thats my story. May be some one may help: 1) Any ideas why OnValidatingPassword not working? 2) Any ideas how to step into it?

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  • Guessess of my session value conflicts

    - by SmartestVEGA
    I have a asp.net web form which will submit information to come as emails. Whenever user fill the form and click on submit button,the information user entered will be sent as email. This web form has 4 page. but the web form will not use all 4 page on all requests. if the user select a particular value in first page, the form will bypass the 3rd page and go the last 4th page(like...page1,2,4). IF it is any other values selected in the first page. form will navigate as page1,2,3,4. So now my problem is when multiple users access the same website, the value in the first page get combines from different users and the form will act abnormally.Sometime it will bypass sometimes it will not bypass the page3 Show below is the variable decalrations: Public strRoleType As String = String.Empty Protected Shared isAreaSelected As Integer = 0 Protected Shared isStoreSelected As Integer = 0 Protected Shared isHeadOfficeSelected As Integer = 0 Protected Shared isRegionSelected As Integer = 0 I guess the problem is with strRoleType variable whether it is getting values from different users. Do any have any work around?

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  • Parallelism in .NET – Part 5, Partitioning of Work

    - by Reed
    When parallelizing any routine, we start by decomposing the problem.  Once the problem is understood, we need to break our work into separate tasks, so each task can be run on a different processing element.  This process is called partitioning. Partitioning our tasks is a challenging feat.  There are opposing forces at work here: too many partitions adds overhead, too few partitions leaves processors idle.  Trying to work the perfect balance between the two extremes is the goal for which we should aim.  Luckily, the Task Parallel Library automatically handles much of this process.  However, there are situations where the default partitioning may not be appropriate, and knowledge of our routines may allow us to guide the framework to making better decisions. First off, I’d like to say that this is a more advanced topic.  It is perfectly acceptable to use the parallel constructs in the framework without considering the partitioning taking place.  The default behavior in the Task Parallel Library is very well-behaved, even for unusual work loads, and should rarely be adjusted.  I have found few situations where the default partitioning behavior in the TPL is not as good or better than my own hand-written partitioning routines, and recommend using the defaults unless there is a strong, measured, and profiled reason to avoid using them.  However, understanding partitioning, and how the TPL partitions your data, helps in understanding the proper usage of the TPL. I indirectly mentioned partitioning while discussing aggregation.  Typically, our systems will have a limited number of Processing Elements (PE), which is the terminology used for hardware capable of processing a stream of instructions.  For example, in a standard Intel i7 system, there are four processor cores, each of which has two potential hardware threads due to Hyperthreading.  This gives us a total of 8 PEs – theoretically, we can have up to eight operations occurring concurrently within our system. In order to fully exploit this power, we need to partition our work into Tasks.  A task is a simple set of instructions that can be run on a PE.  Ideally, we want to have at least one task per PE in the system, since fewer tasks means that some of our processing power will be sitting idle.  A naive implementation would be to just take our data, and partition it with one element in our collection being treated as one task.  When we loop through our collection in parallel, using this approach, we’d just process one item at a time, then reuse that thread to process the next, etc.  There’s a flaw in this approach, however.  It will tend to be slower than necessary, often slower than processing the data serially. The problem is that there is overhead associated with each task.  When we take a simple foreach loop body and implement it using the TPL, we add overhead.  First, we change the body from a simple statement to a delegate, which must be invoked.  In order to invoke the delegate on a separate thread, the delegate gets added to the ThreadPool’s current work queue, and the ThreadPool must pull this off the queue, assign it to a free thread, then execute it.  If our collection had one million elements, the overhead of trying to spawn one million tasks would destroy our performance. The answer, here, is to partition our collection into groups, and have each group of elements treated as a single task.  By adding a partitioning step, we can break our total work into small enough tasks to keep our processors busy, but large enough tasks to avoid overburdening the ThreadPool.  There are two clear, opposing goals here: Always try to keep each processor working, but also try to keep the individual partitions as large as possible. When using Parallel.For, the partitioning is always handled automatically.  At first, partitioning here seems simple.  A naive implementation would merely split the total element count up by the number of PEs in the system, and assign a chunk of data to each processor.  Many hand-written partitioning schemes work in this exactly manner.  This perfectly balanced, static partitioning scheme works very well if the amount of work is constant for each element.  However, this is rarely the case.  Often, the length of time required to process an element grows as we progress through the collection, especially if we’re doing numerical computations.  In this case, the first PEs will finish early, and sit idle waiting on the last chunks to finish.  Sometimes, work can decrease as we progress, since previous computations may be used to speed up later computations.  In this situation, the first chunks will be working far longer than the last chunks.  In order to balance the workload, many implementations create many small chunks, and reuse threads.  This adds overhead, but does provide better load balancing, which in turn improves performance. The Task Parallel Library handles this more elaborately.  Chunks are determined at runtime, and start small.  They grow slowly over time, getting larger and larger.  This tends to lead to a near optimum load balancing, even in odd cases such as increasing or decreasing workloads.  Parallel.ForEach is a bit more complicated, however. When working with a generic IEnumerable<T>, the number of items required for processing is not known in advance, and must be discovered at runtime.  In addition, since we don’t have direct access to each element, the scheduler must enumerate the collection to process it.  Since IEnumerable<T> is not thread safe, it must lock on elements as it enumerates, create temporary collections for each chunk to process, and schedule this out.  By default, it uses a partitioning method similar to the one described above.  We can see this directly by looking at the Visual Partitioning sample shipped by the Task Parallel Library team, and available as part of the Samples for Parallel Programming.  When we run the sample, with four cores and the default, Load Balancing partitioning scheme, we see this: The colored bands represent each processing core.  You can see that, when we started (at the top), we begin with very small bands of color.  As the routine progresses through the Parallel.ForEach, the chunks get larger and larger (seen by larger and larger stripes). Most of the time, this is fantastic behavior, and most likely will out perform any custom written partitioning.  However, if your routine is not scaling well, it may be due to a failure in the default partitioning to handle your specific case.  With prior knowledge about your work, it may be possible to partition data more meaningfully than the default Partitioner. There is the option to use an overload of Parallel.ForEach which takes a Partitioner<T> instance.  The Partitioner<T> class is an abstract class which allows for both static and dynamic partitioning.  By overriding Partitioner<T>.SupportsDynamicPartitions, you can specify whether a dynamic approach is available.  If not, your custom Partitioner<T> subclass would override GetPartitions(int), which returns a list of IEnumerator<T> instances.  These are then used by the Parallel class to split work up amongst processors.  When dynamic partitioning is available, GetDynamicPartitions() is used, which returns an IEnumerable<T> for each partition.  If you do decide to implement your own Partitioner<T>, keep in mind the goals and tradeoffs of different partitioning strategies, and design appropriately. The Samples for Parallel Programming project includes a ChunkPartitioner class in the ParallelExtensionsExtras project.  This provides example code for implementing your own, custom allocation strategies, including a static allocator of a given chunk size.  Although implementing your own Partitioner<T> is possible, as I mentioned above, this is rarely required or useful in practice.  The default behavior of the TPL is very good, often better than any hand written partitioning strategy.

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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 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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  • Parallelism in .NET – Part 9, Configuration in PLINQ and TPL

    - by Reed
    Parallel LINQ and the Task Parallel Library contain many options for configuration.  Although the default configuration options are often ideal, there are times when customizing the behavior is desirable.  Both frameworks provide full configuration support. When working with Data Parallelism, there is one primary configuration option we often need to control – the number of threads we want the system to use when parallelizing our routine.  By default, PLINQ and the TPL both use the ThreadPool to schedule tasks.  Given the major improvements in the ThreadPool in CLR 4, this default behavior is often ideal.  However, there are times that the default behavior is not appropriate.  For example, if you are working on multiple threads simultaneously, and want to schedule parallel operations from within both threads, you might want to consider restricting each parallel operation to using a subset of the processing cores of the system.  Not doing this might over-parallelize your routine, which leads to inefficiencies from having too many context switches. In the Task Parallel Library, configuration is handled via the ParallelOptions class.  All of the methods of the Parallel class have an overload which accepts a ParallelOptions argument. We configure the Parallel class by setting the ParallelOptions.MaxDegreeOfParallelism property.  For example, let’s revisit one of the simple data parallel examples from Part 2: 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); } }); .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 looping through an image, and calling a method on each pixel in the image.  If this was being done on a separate thread, and we knew another thread within our system was going to be doing a similar operation, we likely would want to restrict this to using half of the cores on the system.  This could be accomplished easily by doing: var options = new ParallelOptions(); options.MaxDegreeOfParallelism = Math.Max(Environment.ProcessorCount / 2, 1); Parallel.For(0, pixelData.GetUpperBound(0), options, row => { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } }); Now, we’re restricting this routine to using no more than half the cores in our system.  Note that I included a check to prevent a single core system from supplying zero; without this check, we’d potentially cause an exception.  I also did not hard code a specific value for the MaxDegreeOfParallelism property.  One of our goals when parallelizing a routine is allowing it to scale on better hardware.  Specifying a hard-coded value would contradict that goal. Parallel LINQ also supports configuration, and in fact, has quite a few more options for configuring the system.  The main configuration option we most often need is the same as our TPL option: we need to supply the maximum number of processing threads.  In PLINQ, this is done via a new extension method on ParallelQuery<T>: ParallelEnumerable.WithDegreeOfParallelism. Let’s revisit our declarative data parallelism sample from Part 6: double min = collection.AsParallel().Min(item => item.PerformComputation()); Here, we’re performing a computation on each element in the collection, and saving the minimum value of this operation.  If we wanted to restrict this to a limited number of threads, we would add our new extension method: int maxThreads = Math.Max(Environment.ProcessorCount / 2, 1); double min = collection .AsParallel() .WithDegreeOfParallelism(maxThreads) .Min(item => item.PerformComputation()); This automatically restricts the PLINQ query to half of the threads on the system. PLINQ provides some additional configuration options.  By default, PLINQ will occasionally revert to processing a query in parallel.  This occurs because many queries, if parallelized, typically actually cause an overall slowdown compared to a serial processing equivalent.  By analyzing the “shape” of the query, PLINQ often decides to run a query serially instead of in parallel.  This can occur for (taken from MSDN): Queries that contain a Select, indexed Where, indexed SelectMany, or ElementAt clause after an ordering or filtering operator that has removed or rearranged original indices. Queries that contain a Take, TakeWhile, Skip, SkipWhile operator and where indices in the source sequence are not in the original order. Queries that contain Zip or SequenceEquals, unless one of the data sources has an originally ordered index and the other data source is indexable (i.e. an array or IList(T)). Queries that contain Concat, unless it is applied to indexable data sources. Queries that contain Reverse, unless applied to an indexable data source. If the specific query follows these rules, PLINQ will run the query on a single thread.  However, none of these rules look at the specific work being done in the delegates, only at the “shape” of the query.  There are cases where running in parallel may still be beneficial, even if the shape is one where it typically parallelizes poorly.  In these cases, you can override the default behavior by using the WithExecutionMode extension method.  This would be done like so: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .Select(i => i.PerformComputation()) .Reverse(); Here, the default behavior would be to not parallelize the query unless collection implemented IList<T>.  We can force this to run in parallel by adding the WithExecutionMode extension method in the method chain. Finally, PLINQ has the ability to configure how results are returned.  When a query is filtering or selecting an input collection, the results will need to be streamed back into a single IEnumerable<T> result.  For example, the method above returns a new, reversed collection.  In this case, the processing of the collection will be done in parallel, but the results need to be streamed back to the caller serially, so they can be enumerated on a single thread. This streaming introduces overhead.  IEnumerable<T> isn’t designed with thread safety in mind, so the system needs to handle merging the parallel processes back into a single stream, which introduces synchronization issues.  There are two extremes of how this could be accomplished, but both extremes have disadvantages. The system could watch each thread, and whenever a thread produces a result, take that result and send it back to the caller.  This would mean that the calling thread would have access to the data as soon as data is available, which is the benefit of this approach.  However, it also means that every item is introducing synchronization overhead, since each item needs to be merged individually. On the other extreme, the system could wait until all of the results from all of the threads were ready, then push all of the results back to the calling thread in one shot.  The advantage here is that the least amount of synchronization is added to the system, which means the query will, on a whole, run the fastest.  However, the calling thread will have to wait for all elements to be processed, so this could introduce a long delay between when a parallel query begins and when results are returned. The default behavior in PLINQ is actually between these two extremes.  By default, PLINQ maintains an internal buffer, and chooses an optimal buffer size to maintain.  Query results are accumulated into the buffer, then returned in the IEnumerable<T> result in chunks.  This provides reasonably fast access to the results, as well as good overall throughput, in most scenarios. However, if we know the nature of our algorithm, we may decide we would prefer one of the other extremes.  This can be done by using the WithMergeOptions extension method.  For example, if we know that our PerformComputation() routine is very slow, but also variable in runtime, we may want to retrieve results as they are available, with no bufferring.  This can be done by changing our above routine to: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .WithMergeOptions(ParallelMergeOptions.NotBuffered) .Select(i => i.PerformComputation()) .Reverse(); On the other hand, if are already on a background thread, and we want to allow the system to maximize its speed, we might want to allow the system to fully buffer the results: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .WithMergeOptions(ParallelMergeOptions.FullyBuffered) .Select(i => i.PerformComputation()) .Reverse(); Notice, also, that you can specify multiple configuration options in a parallel query.  By chaining these extension methods together, we generate a query that will always run in parallel, and will always complete before making the results available in our IEnumerable<T>.

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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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  • Should a c# dev switch to VB.net when the team language base is mixed?

    - by jjr2527
    I recently joined a new development team where the language preferences are mixed on the .net platform. Dev 1: Knows VB.net, does not know c# Dev 2: Knows VB.net, does not know c# Dev 3: Knows c# and VB.net, prefers c# Dev 4: Knows c# and VB6(VB.net should be pretty easy to pick up), prefers c# It seems to me that the thought leaders in the .net space are c# devs almost universally. I also thought that some 3rd party tools didn't support VB.net but when I started looking into it I didn't find any good examples. I would prefer to get the whole team on c# but if there isn't any good reason to force the issue aside from preference then I don't think that is the right choice. Are there any reasons I should lead folks away from VB.net?

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  • .NET Weak Event Handlers – Part II

    - by João Angelo
    On the first part of this article I showed two possible ways to create weak event handlers. One using reflection and the other using a delegate. For this performance analysis we will further differentiate between creating a delegate by providing the type of the listener at compile time (Explicit Delegate) vs creating the delegate with the type of the listener being only obtained at runtime (Implicit Delegate). As expected, the performance between reflection/delegate differ significantly. With the reflection based approach, creating a weak event handler is just storing a MethodInfo reference while with the delegate based approach there is the need to create the delegate which will be invoked later. So, at creating the weak event handler reflection clearly wins, but what about when the handler is invoked. No surprises there, performing a call through reflection every time a handler is invoked is costly. In conclusion, if you want good performance when creating handlers that only sporadically get triggered use reflection, otherwise use the delegate based approach. The explicit delegate approach always wins against the implicit delegate, but I find the syntax for the latter much more intuitive. // Implicit delegate - The listener type is inferred at runtime from the handler parameter public static EventHandler WrapInDelegateCall(EventHandler handler); public static EventHandler<TArgs> WrapInDelegateCall<TArgs>(EventHandler<TArgs> handler) where TArgs : EventArgs; // Explicite delegate - TListener is the type that defines the handler public static EventHandler WrapInDelegateCall<TListener>(EventHandler handler); public static EventHandler<TArgs> WrapInDelegateCall<TArgs, TListener>(EventHandler<TArgs> handler) where TArgs : EventArgs;

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  • West Palm Beach .Net User Group May 25th User Group Meeting Update

    - by Sam Abraham
    Just returned from the West Palm Beach .Net User Group Meeting featuring Shervin Shakibi who spoke to us about What’s New in Silverlight 4.0.  It was a great talk where the audience was fully engaged with Shervin as he spoke about and demonstrated the various new features of Silverlight 4.0. We enjoyed free pizza and soda as well as a free raffle with every attendee leaving home with a freebie.   For our June Meeting, Don Demsak, Microsoft MVP, will be speaking to us about WCF Data Services.  We will continue to have free pizza and a free raffle with great prizes, so hope to see you all there. Below are some photos from The West Palm Beach .Net User Group May 25th meeting with Shervin Shakibi. See you next Month for our June 22nd meeting, 6:30 PM at CompTec   Sam Abraham Site Director - West Palm Beach .Net User Group

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  • Free SQL Server training? Now you’re talking.

    - by Fatherjack
    SQL Server user groups are everywhere, literally all over the globe there are SQL Server professionals meeting on a regular basis, sharing ideas, solving problems, learning about how to do new stuff and new ways to do old stuff and it’s all for free. I don’t have detailed figures but of all the SQL Server professionals there are only a small number of them attend these user groups. Those people are the people that are taking the time and making then effort to make themselves better at their chosen trade, more employable and having a good time. For free. I don’t know why but there are many people that don’t seem to want to be the best they can be. Some of you enlightened people that do already attend could be doing more though. Have you ever spoken at  your group? Not just in the break while you have a mouthful of pizza and a drink in your hand but had the attention of the whole group listen to you speak. It doesn’t need to be a full hour, it doesn’t need to be some obscure deeply technical demonstration of SQL Server internals, just a few minutes on something that you do that might help other people with their daily work. A neat process that helps you get from Problem A to Solution B. There is no need to get concerned that becoming a speaker means that you suddenly have to know more than anyone else in the room. This is you talking about something that you experienced. What you did, what you would repeat, what you might do differently next time. No one in the audience can pick you up on a technicality. If someone comes out with a great idea that you hadn’t thought of, say “That’s a great idea, I didn’t think of that while we had the problem on our hands. I’ll try to remember that for next time”. If someone is looking to show you up for picking the wrong decision (and this, in my experience, is very uncommon indeed) then you simply give a reply like “Well, at the time we chose that option. Perhaps another time then we would tackle things differently but we were happy with how our solution worked”. It’s sharing things like this that makes user groups have a real value, talking about how you coped with or averted a disaster, a handy little section of code or using a tool in a particular way that you take for granted that might, just might, be something that other people haven’t thought of that solves a problem or saves some time for them. At the next meeting you might get the same benefit from a different person and so it goes on. As individuals benefits so the community benefits. For free. Things I encourage you to do; If you are a chapter or user group leader; encourage someone from your group who has never spoken before to start speaking. If you are a chapter or user group attendee that hasn’t spoken before; speak for at least 5 minutes on something related to SQL Server at any group meeting. If you don’t currently attend a user group; please go along to you nearest one when they are meeting next and invest in yourself and your future. UK user group details are here: http://sqlsouthwest.co.uk/national_ug.htm , PASS chapters outside the UK are found via http://www.sqlpass.org/PASSChapters/LocalChapters.aspx. If you are unsure of how you might achieve any of these things then get in touch with me*, I’ll give you specific advice on getting started on any of the above points and help you prove to yourself what you are capable of. SQL Community – be part of it and make it better. Let me know how you get on in the comments.

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  • Building a better .NET Application Configuration Class - revisited

    - by Rick Strahl
    Managing configuration settings is an important part of successful applications. It should be easy to ensure that you can easily access and modify configuration values within your applications. If it's not - well things don't get parameterized as much as they should. In this post I discuss a custom Application Configuration class that makes it super easy to create reusable configuration objects in your applications using a code-first approach and the ability to persist configuration information into various types of configuration stores.

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