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  • Using JSON.NET for dynamic JSON parsing

    - by Rick Strahl
    With the release of ASP.NET Web API as part of .NET 4.5 and MVC 4.0, JSON.NET has effectively pushed out the .NET native serializers to become the default serializer for Web API. JSON.NET is vastly more flexible than the built in DataContractJsonSerializer or the older JavaScript serializer. The DataContractSerializer in particular has been very problematic in the past because it can't deal with untyped objects for serialization - like values of type object, or anonymous types which are quite common these days. The JavaScript Serializer that came before it actually does support non-typed objects for serialization but it can't do anything with untyped data coming in from JavaScript and it's overall model of extensibility was pretty limited (JavaScript Serializer is what MVC uses for JSON responses). JSON.NET provides a robust JSON serializer that has both high level and low level components, supports binary JSON, JSON contracts, Xml to JSON conversion, LINQ to JSON and many, many more features than either of the built in serializers. ASP.NET Web API now uses JSON.NET as its default serializer and is now pulled in as a NuGet dependency into Web API projects, which is great. Dynamic JSON Parsing One of the features that I think is getting ever more important is the ability to serialize and deserialize arbitrary JSON content dynamically - that is without mapping the JSON captured directly into a .NET type as DataContractSerializer or the JavaScript Serializers do. Sometimes it isn't possible to map types due to the differences in languages (think collections, dictionaries etc), and other times you simply don't have the structures in place or don't want to create them to actually import the data. If this topic sounds familiar - you're right! I wrote about dynamic JSON parsing a few months back before JSON.NET was added to Web API and when Web API and the System.Net HttpClient libraries included the System.Json classes like JsonObject and JsonArray. With the inclusion of JSON.NET in Web API these classes are now obsolete and didn't ship with Web API or the client libraries. I re-linked my original post to this one. In this post I'll discus JToken, JObject and JArray which are the dynamic JSON objects that make it very easy to create and retrieve JSON content on the fly without underlying types. Why Dynamic JSON? So, why Dynamic JSON parsing rather than strongly typed parsing? Since applications are interacting more and more with third party services it becomes ever more important to have easy access to those services with easy JSON parsing. Sometimes it just makes lot of sense to pull just a small amount of data out of large JSON document received from a service, because the third party service isn't directly related to your application's logic most of the time - and it makes little sense to map the entire service structure in your application. For example, recently I worked with the Google Maps Places API to return information about businesses close to me (or rather the app's) location. The Google API returns a ton of information that my application had no interest in - all I needed was few values out of the data. Dynamic JSON parsing makes it possible to map this data, without having to map the entire API to a C# data structure. Instead I could pull out the three or four values I needed from the API and directly store it on my business entities that needed to receive the data - no need to map the entire Maps API structure. Getting JSON.NET The easiest way to use JSON.NET is to grab it via NuGet and add it as a reference to your project. You can add it to your project with: PM> Install-Package Newtonsoft.Json From the Package Manager Console or by using Manage NuGet Packages in your project References. As mentioned if you're using ASP.NET Web API or MVC 4 JSON.NET will be automatically added to your project. Alternately you can also go to the CodePlex site and download the latest version including source code: http://json.codeplex.com/ Creating JSON on the fly with JObject and JArray Let's start with creating some JSON on the fly. It's super easy to create a dynamic object structure with any of the JToken derived JSON.NET objects. The most common JToken derived classes you are likely to use are JObject and JArray. JToken implements IDynamicMetaProvider and so uses the dynamic  keyword extensively to make it intuitive to create object structures and turn them into JSON via dynamic object syntax. Here's an example of creating a music album structure with child songs using JObject for the base object and songs and JArray for the actual collection of songs:[TestMethod] public void JObjectOutputTest() { // strong typed instance var jsonObject = new JObject(); // you can explicitly add values here using class interface jsonObject.Add("Entered", DateTime.Now); // or cast to dynamic to dynamically add/read properties dynamic album = jsonObject; album.AlbumName = "Dirty Deeds Done Dirt Cheap"; album.Artist = "AC/DC"; album.YearReleased = 1976; album.Songs = new JArray() as dynamic; dynamic song = new JObject(); song.SongName = "Dirty Deeds Done Dirt Cheap"; song.SongLength = "4:11"; album.Songs.Add(song); song = new JObject(); song.SongName = "Love at First Feel"; song.SongLength = "3:10"; album.Songs.Add(song); Console.WriteLine(album.ToString()); } This produces a complete JSON structure: { "Entered": "2012-08-18T13:26:37.7137482-10:00", "AlbumName": "Dirty Deeds Done Dirt Cheap", "Artist": "AC/DC", "YearReleased": 1976, "Songs": [ { "SongName": "Dirty Deeds Done Dirt Cheap", "SongLength": "4:11" }, { "SongName": "Love at First Feel", "SongLength": "3:10" } ] } Notice that JSON.NET does a nice job formatting the JSON, so it's easy to read and paste into blog posts :-). JSON.NET includes a bunch of configuration options that control how JSON is generated. Typically the defaults are just fine, but you can override with the JsonSettings object for most operations. The important thing about this code is that there's no explicit type used for holding the values to serialize to JSON. Rather the JSON.NET objects are the containers that receive the data as I build up my JSON structure dynamically, simply by adding properties. This means this code can be entirely driven at runtime without compile time restraints of structure for the JSON output. Here I use JObject to create a album 'object' and immediately cast it to dynamic. JObject() is kind of similar in behavior to ExpandoObject in that it allows you to add properties by simply assigning to them. Internally, JObject values are stored in pseudo collections of key value pairs that are exposed as properties through the IDynamicMetaObject interface exposed in JSON.NET's JToken base class. For objects the syntax is very clean - you add simple typed values as properties. For objects and arrays you have to explicitly create new JObject or JArray, cast them to dynamic and then add properties and items to them. Always remember though these values are dynamic - which means no Intellisense and no compiler type checking. It's up to you to ensure that the names and values you create are accessed consistently and without typos in your code. Note that you can also access the JObject instance directly (not as dynamic) and get access to the underlying JObject type. This means you can assign properties by string, which can be useful for fully data driven JSON generation from other structures. Below you can see both styles of access next to each other:// strong type instance var jsonObject = new JObject(); // you can explicitly add values here jsonObject.Add("Entered", DateTime.Now); // expando style instance you can just 'use' properties dynamic album = jsonObject; album.AlbumName = "Dirty Deeds Done Dirt Cheap"; JContainer (the base class for JObject and JArray) is a collection so you can also iterate over the properties at runtime easily:foreach (var item in jsonObject) { Console.WriteLine(item.Key + " " + item.Value.ToString()); } The functionality of the JSON objects are very similar to .NET's ExpandObject and if you used it before, you're already familiar with how the dynamic interfaces to the JSON objects works. Importing JSON with JObject.Parse() and JArray.Parse() The JValue structure supports importing JSON via the Parse() and Load() methods which can read JSON data from a string or various streams respectively. Essentially JValue includes the core JSON parsing to turn a JSON string into a collection of JsonValue objects that can be then referenced using familiar dynamic object syntax. Here's a simple example:public void JValueParsingTest() { var jsonString = @"{""Name"":""Rick"",""Company"":""West Wind"", ""Entered"":""2012-03-16T00:03:33.245-10:00""}"; dynamic json = JValue.Parse(jsonString); // values require casting string name = json.Name; string company = json.Company; DateTime entered = json.Entered; Assert.AreEqual(name, "Rick"); Assert.AreEqual(company, "West Wind"); } The JSON string represents an object with three properties which is parsed into a JObject class and cast to dynamic. Once cast to dynamic I can then go ahead and access the object using familiar object syntax. Note that the actual values - json.Name, json.Company, json.Entered - are actually of type JToken and I have to cast them to their appropriate types first before I can do type comparisons as in the Asserts at the end of the test method. This is required because of the way that dynamic types work which can't determine the type based on the method signature of the Assert.AreEqual(object,object) method. I have to either assign the dynamic value to a variable as I did above, or explicitly cast ( (string) json.Name) in the actual method call. The JSON structure can be much more complex than this simple example. Here's another example of an array of albums serialized to JSON and then parsed through with JsonValue():[TestMethod] public void JsonArrayParsingTest() { var jsonString = @"[ { ""Id"": ""b3ec4e5c"", ""AlbumName"": ""Dirty Deeds Done Dirt Cheap"", ""Artist"": ""AC/DC"", ""YearReleased"": 1976, ""Entered"": ""2012-03-16T00:13:12.2810521-10:00"", ""AlbumImageUrl"": ""http://ecx.images-amazon.com/images/I/61kTaH-uZBL._AA115_.jpg"", ""AmazonUrl"": ""http://www.amazon.com/gp/product/…ASIN=B00008BXJ4"", ""Songs"": [ { ""AlbumId"": ""b3ec4e5c"", ""SongName"": ""Dirty Deeds Done Dirt Cheap"", ""SongLength"": ""4:11"" }, { ""AlbumId"": ""b3ec4e5c"", ""SongName"": ""Love at First Feel"", ""SongLength"": ""3:10"" }, { ""AlbumId"": ""b3ec4e5c"", ""SongName"": ""Big Balls"", ""SongLength"": ""2:38"" } ] }, { ""Id"": ""7b919432"", ""AlbumName"": ""End of the Silence"", ""Artist"": ""Henry Rollins Band"", ""YearReleased"": 1992, ""Entered"": ""2012-03-16T00:13:12.2800521-10:00"", ""AlbumImageUrl"": ""http://ecx.images-amazon.com/images/I/51FO3rb1tuL._SL160_AA160_.jpg"", ""AmazonUrl"": ""http://www.amazon.com/End-Silence-Rollins-Band/dp/B0000040OX/ref=sr_1_5?ie=UTF8&qid=1302232195&sr=8-5"", ""Songs"": [ { ""AlbumId"": ""7b919432"", ""SongName"": ""Low Self Opinion"", ""SongLength"": ""5:24"" }, { ""AlbumId"": ""7b919432"", ""SongName"": ""Grip"", ""SongLength"": ""4:51"" } ] } ]"; JArray jsonVal = JArray.Parse(jsonString) as JArray; dynamic albums = jsonVal; foreach (dynamic album in albums) { Console.WriteLine(album.AlbumName + " (" + album.YearReleased.ToString() + ")"); foreach (dynamic song in album.Songs) { Console.WriteLine("\t" + song.SongName); } } Console.WriteLine(albums[0].AlbumName); Console.WriteLine(albums[0].Songs[1].SongName); } JObject and JArray in ASP.NET Web API Of course these types also work in ASP.NET Web API controller methods. If you want you can accept parameters using these object or return them back to the server. The following contrived example receives dynamic JSON input, and then creates a new dynamic JSON object and returns it based on data from the first:[HttpPost] public JObject PostAlbumJObject(JObject jAlbum) { // dynamic input from inbound JSON dynamic album = jAlbum; // create a new JSON object to write out dynamic newAlbum = new JObject(); // Create properties on the new instance // with values from the first newAlbum.AlbumName = album.AlbumName + " New"; newAlbum.NewProperty = "something new"; newAlbum.Songs = new JArray(); foreach (dynamic song in album.Songs) { song.SongName = song.SongName + " New"; newAlbum.Songs.Add(song); } return newAlbum; } The raw POST request to the server looks something like this: POST http://localhost/aspnetwebapi/samples/PostAlbumJObject HTTP/1.1User-Agent: FiddlerContent-type: application/jsonHost: localhostContent-Length: 88 {AlbumName: "Dirty Deeds",Songs:[ { SongName: "Problem Child"},{ SongName: "Squealer"}]} and the output that comes back looks like this: {  "AlbumName": "Dirty Deeds New",  "NewProperty": "something new",  "Songs": [    {      "SongName": "Problem Child New"    },    {      "SongName": "Squealer New"    }  ]} The original values are echoed back with something extra appended to demonstrate that we're working with a new object. When you receive or return a JObject, JValue, JToken or JArray instance in a Web API method, Web API ignores normal content negotiation and assumes your content is going to be received and returned as JSON, so effectively the parameter and result type explicitly determines the input and output format which is nice. Dynamic to Strong Type Mapping You can also map JObject and JArray instances to a strongly typed object, so you can mix dynamic and static typing in the same piece of code. Using the 2 Album jsonString shown earlier, the code below takes an array of albums and picks out only a single album and casts that album to a static Album instance.[TestMethod] public void JsonParseToStrongTypeTest() { JArray albums = JArray.Parse(jsonString) as JArray; // pick out one album JObject jalbum = albums[0] as JObject; // Copy to a static Album instance Album album = jalbum.ToObject<Album>(); Assert.IsNotNull(album); Assert.AreEqual(album.AlbumName,jalbum.Value<string>("AlbumName")); Assert.IsTrue(album.Songs.Count > 0); } This is pretty damn useful for the scenario I mentioned earlier - you can read a large chunk of JSON and dynamically walk the property hierarchy down to the item you want to access, and then either access the specific item dynamically (as shown earlier) or map a part of the JSON to a strongly typed object. That's very powerful if you think about it - it leaves you in total control to decide what's dynamic and what's static. Strongly typed JSON Parsing With all this talk of dynamic let's not forget that JSON.NET of course also does strongly typed serialization which is drop dead easy. Here's a simple example on how to serialize and deserialize an object with JSON.NET:[TestMethod] public void StronglyTypedSerializationTest() { // Demonstrate deserialization from a raw string var album = new Album() { AlbumName = "Dirty Deeds Done Dirt Cheap", Artist = "AC/DC", Entered = DateTime.Now, YearReleased = 1976, Songs = new List<Song>() { new Song() { SongName = "Dirty Deeds Done Dirt Cheap", SongLength = "4:11" }, new Song() { SongName = "Love at First Feel", SongLength = "3:10" } } }; // serialize to string string json2 = JsonConvert.SerializeObject(album,Formatting.Indented); Console.WriteLine(json2); // make sure we can serialize back var album2 = JsonConvert.DeserializeObject<Album>(json2); Assert.IsNotNull(album2); Assert.IsTrue(album2.AlbumName == "Dirty Deeds Done Dirt Cheap"); Assert.IsTrue(album2.Songs.Count == 2); } JsonConvert is a high level static class that wraps lower level functionality, but you can also use the JsonSerializer class, which allows you to serialize/parse to and from streams. It's a little more work, but gives you a bit more control. The functionality available is easy to discover with Intellisense, and that's good because there's not a lot in the way of documentation that's actually useful. Summary JSON.NET is a pretty complete JSON implementation with lots of different choices for JSON parsing from dynamic parsing to static serialization, to complex querying of JSON objects using LINQ. It's good to see this open source library getting integrated into .NET, and pushing out the old and tired stock .NET parsers so that we finally have a bit more flexibility - and extensibility - in our JSON parsing. Good to go! Resources Sample Test Project http://json.codeplex.com/© Rick Strahl, West Wind Technologies, 2005-2012Posted in .NET  Web Api  AJAX   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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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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  • What I saw at TechEd North America 2014

    - by Brian Schroer
    Originally posted on: http://geekswithblogs.net/brians/archive/2014/05/19/teched-north-america-2014.aspxI was thrilled to be able to attend TechEd North America 2014 in Houston last week. I got to go to Orlando in 2008, and since then I’ve had to settle for watching the sessions online (which ain’t bad – They’re all available on Channel 9 for streaming or downloading. Here are links to the Developer Track sessions and to the sessions from all tracks.) The sessions I attended (with my favorites bolded) were: Shiny new stuff The Microsoft Application Platform for Developers: Create Applications That Span Devices and Services INTRODUCING: The Future of .NET on the Server DEEP DIVE: The Future of .NET on the Server ASP.NET: Building Web Application Using ASP.NET and Visual Studio The Next Generation of .NET for Building Applications The Future of Visual Basic and C# Stuff you can use now Building Rich Apps with AngularJS on ASP.NET Get the Most Out of Your Code Maps SignalR: Building Real-Time Applications with ASP.NET SignalR Performance Optimize Your ASP.NET Web App Modern Web and Visual Studio Visual Studio Power User: Tips and Tricks Debugging Tips and Tricks in Visual Studio 2013 In a world where the whole company uses TFS… Using Functional, Exploratory and Acceptance Testing to Release with Confidence A Practical View of Release Management for Visual Studio 2013 From Vanity to Value, Metrics That Matter: Improving Lean and Agile, Kanban, and Scrum Ain’t Nobody Got Time for That As usual, there were some time slots with nothing of interest and others with 5 things I wanted to see at the same time. Here are the sessions I’m still planning to watch… Getting Started with TypeScript Building a Large Scale JavaScript Application in TypeScript Modern Application Lifecycle Management Why a Hacker Can Own Your Web Servers in a Day! Async Best Practices for C# and Visual Basic Building Multi-Device Apps with the New Visual Studio Tooling for Apache Cordova Applying S.O.L.I.D. Principles in .NET/C# Native Mobile Application Development for iOS, Android, and Windows in C# and Visual Studio Using Xamarin Latest Innovations in Developing ASP.NET MVC Web Applications Zero to Hero: Untested to Tested with Microsoft Fakes Using Visual Studio Cool and Elegant ASP.NET Web Forms with HTML 5 for the Modern Web The Present and Future of .NET in a World of Devices and Services

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  • How do you handle EF Data Contexts combined with asp.net custom membership/role providers

    - by KallDrexx
    I can't seem to get my head around how to implement a custom membership provider with Entity Framework data contexts into my asp.net MVC application. I understand how to create a custom membership/role provider by itself (using this as a reference). Here's my current setup: As of now I have a repository factory interface that allows different repository factories to be created (right now I only have a factory for EF repositories and and in memory repositories). The repository factory looks like this: public class EFRepositoryFactory : IRepositoryFactory { private EntitiesContainer _entitiesContext; /// <summary> /// Constructor that generates the necessary object contexts /// </summary> public EFRepositoryFactory() { _entitiesContext = new EntitiesContainer(); } /// <summary> /// Generates a new entity framework repository for the specified entity type /// </summary> /// <typeparam name="T">Type of entity to generate a repository for </typeparam> /// <returns>Returns an EFRepository</returns> public IRepository<T> GenerateRepository<T>() where T : class { return new EFRepository<T>(_entitiesContext); } } Controllers are passed an EF repository factory via castle Windsor. The controller then creates all the service/business layer objects it requires and passes in the repository factory into it. This means that all service objects are using the same EF data contexts and I do not have to worry about objects being used in more than one data context (which of course is not allowed and causes an exception). As of right now I am trying to decide how to generate my user and authorization service layers, and have run against a design roadblock. The User/Authization service will be a central class that handles the logic for logging in, changing user details, managing roles and determining what users have access to what. The problem is, using the current methodology the asp.net mvc controllers will initialize it's own EF repository factory via Windsor and the asp.net membership/role provider will have to initialize it's own EF repository factory. This means that each part of the site will then have it's own data context. This seems to mean that if asp.net authenticates a user, that user's object will be in the membership provider's data context and thus if I try to retrieve that user object in the service layer (say to change the user's name) I will get a duplication exception. I thought of making the repository factory class a singleton, but I don't see a way for that to work with castle Windsor. How do other people handle asp.net custom providers in a MVC (or any n-tier) architecture without having object duplication issues?

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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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  • ASP.NET: Custom MembershipProvider with a custom user table

    - by blahblah
    I've recently started tinkering with ASP.NET MVC, but this question should apply to classic ASP.NET as well. For what it's worth, I don't know very much about forms authentication and membership providers either. I'm trying to write my own MembershipProvider which will be connected to my own custom user table in my database. My user table contains all of the basic user information such as usernames, passwords, password salts, e-mail addresses and so on, but also information such as first name, last name and country of residence. As far as I understand, the standard way of doing this in ASP.NET is to create a user table without the extra information and then a "profile" table with the extra information. However, this doesn't sound very good to me, because whenever I need to access that extra information I would have to make one extra database query to get it. I read in the book "Pro ASP.NET 3.5 in C# 2008" that having a separate table for the profiles is not a very good idea if you need to access the profile table a lot and have many different pages in your website. Now for the problem at hand... As I said, I'm writing my own custom MembershipProvider subclass and it's going pretty well so far, but now I've come to realize that the CreateUser doesn't allow me to create users in the way I'd like. The method only takes a fixed number of arguments and first name, last name and country of residence are not part of them. So how would I create an entry for the new user in my custom table without this information at hand in CreateUser of my MembershipProvider?

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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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  • 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 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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  • Reuse security code between WCF and MVC.NET

    - by mrjoltcola
    First the background: I jumped into MVC.NET from the Java MVC world, so my implementation below is possibly cheating, I don't know. I avoided fooling with a custom membership provider and I just implemented the base code needed to authenticate and load roles in my LogOn action. Typically I just need to check roles programatically, and have no use for all of the other membership features, so I didn't originally think I needed a full Membership provider. I have a successful WCF project with a custom authentication and authorization layer that I did at least write per the proper API. I implemented it with custom IPrincipal, UserNamePasswordValidator and IAuthorizationPolicy classes to load from an Oracle database. In my WCF services, I use declarative security: [PrincipalPermission(SecurityAction.Demand, Role="ADMIN")]. The question (on the ASP.NET/MCV.NET side): All my reading indicates I should implement a custom Membership/Roles provider, and use [Authorize(Roles="ADMIN")] on my controller actions. At this point, I don't have a true Membership provider, but I'm using the same User class that implements the IPrincipal interface that works with the WCF security. I plan to share common code between the WCF and ASP.NET modules. So my LogOn action is not using the FormsService (and I assume this is bad). I had commented it out, and just used my "UserService" to access the Oracle db. Note my "TODO" comment below. public ActionResult LogOn(LogOnModel model, string returnUrl) { log.Info("Login attempt by " + model.UserName); if (ModelState.IsValid) { User user = userService.findByUserName(model.UserName); // Commented original MemberShipService code, this is probably bad // if (MembershipService.ValidateUser(model.UserName, model.Password)) if (user != null && user.Authenticate(model.Password) == true) { log.Info("Login success by " + model.UserName); FormsService.SignIn(model.UserName, model.RememberMe); // TODO: Override with Custom identity / roles? user.AddRoles(userService.listRolesByUser(user)); // pull in roles from db if (!String.IsNullOrEmpty(returnUrl)) return Redirect(returnUrl); else return RedirectToAction("Index", "Home"); } else { log.Info("Login failure by " + model.UserName); ModelState.AddModelError("", "The user name or password provided is incorrect."); } } // If we got this far, something failed, redisplay form return View(model); } So can I make the above work? Can I stick the IPrincipal (User) into the CurrentContext or HttpContext? Can I integrate the custom IPrincipal I've already created without writing a full Membership/Roles Provider? I currently stick the User object into the session and access it from all MVC.NET controllers with "CurrentUser" property which grabs it from the session on demand. But this doesn't work with the [Authorize] attribute; I assume that is because it knows nothing about my custom Principal in the session, and is instead using whatever FormsService.SignIn() produces. I also found that session timeouts screw up the login redirect, the user doesn't get forwarded, instead we get a null exception accessing User from the session, and I assume it is related to my "skipping steps" to get a quick implementation. Thanks.

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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 12, More on Task Decomposition

    - by Reed
    Many tasks can be decomposed using a Data Decomposition approach, but often, this is not appropriate.  Frequently, decomposing the problem into distinctive tasks that must be performed is a more natural abstraction. However, as I mentioned in Part 1, Task Decomposition tends to be a bit more difficult than data decomposition, and can require a bit more effort.  Before we being parallelizing our algorithm based on the tasks being performed, we need to decompose our problem, and take special care of certain considerations such as ordering and grouping of tasks. Up to this point in this series, I’ve focused on parallelization techniques which are most appropriate when a problem space can be decomposed by data.  Using PLINQ and the Parallel class, I’ve shown how problem spaces where there is a collection of data, and each element needs to be processed, can potentially be parallelized. However, there are many other routines where this is not appropriate.  Often, instead of working on a collection of data, there is a single piece of data which must be processed using an algorithm or series of algorithms.  Here, there is no collection of data, but there may still be opportunities for parallelism. As I mentioned before, in cases like this, the approach is to look at your overall routine, and decompose your problem space based on tasks.  The idea here is to look for discrete “tasks,” individual pieces of work which can be conceptually thought of as a single operation. Let’s revisit the example I used in Part 1, an application startup path.  Say we want our program, at startup, to do a bunch of individual actions, or “tasks”.  The following is our list of duties we must perform right at startup: Display a splash screen Request a license from our license manager Check for an update to the software from our web server If an update is available, download it Setup our menu structure based on our current license Open and display our main, welcome Window Hide the splash screen The first step in Task Decomposition is breaking up the problem space into discrete tasks. This, naturally, can be abstracted as seven discrete tasks.  In the serial version of our program, if we were to diagram this, the general process would appear as: These tasks, obviously, provide some opportunities for parallelism.  Before we can parallelize this routine, we need to analyze these tasks, and find any dependencies between tasks.  In this case, our dependencies include: The splash screen must be displayed first, and as quickly as possible. We can’t download an update before we see whether one exists. Our menu structure depends on our license, so we must check for the license before setting up the menus. Since our welcome screen will notify the user of an update, we can’t show it until we’ve downloaded the update. Since our welcome screen includes menus that are customized based off the licensing, we can’t display it until we’ve received a license. We can’t hide the splash until our welcome screen is displayed. By listing our dependencies, we start to see the natural ordering that must occur for the tasks to be processed correctly. The second step in Task Decomposition is determining the dependencies between tasks, and ordering tasks based on their dependencies. Looking at these tasks, and looking at all the dependencies, we quickly see that even a simple decomposition such as this one can get quite complicated.  In order to simplify the problem of defining the dependencies, it’s often a useful practice to group our tasks into larger, discrete tasks.  The goal when grouping tasks is that you want to make each task “group” have as few dependencies as possible to other tasks or groups, and then work out the dependencies within that group.  Typically, this works best when any external dependency is based on the “last” task within the group when it’s ordered, although that is not a firm requirement.  This process is often called Grouping Tasks.  In our case, we can easily group together tasks, effectively turning this into four discrete task groups: 1. Show our splash screen – This needs to be left as its own task.  First, multiple things depend on this task, mainly because we want this to start before any other action, and start as quickly as possible. 2. Check for Update and Download the Update if it Exists - These two tasks logically group together.  We know we only download an update if the update exists, so that naturally follows.  This task has one dependency as an input, and other tasks only rely on the final task within this group. 3. Request a License, and then Setup the Menus – Here, we can group these two tasks together.  Although we mentioned that our welcome screen depends on the license returned, it also depends on setting up the menu, which is the final task here.  Setting up our menus cannot happen until after our license is requested.  By grouping these together, we further reduce our problem space. 4. Display welcome and hide splash - Finally, we can display our welcome window and hide our splash screen.  This task group depends on all three previous task groups – it cannot happen until all three of the previous groups have completed. By grouping the tasks together, we reduce our problem space, and can naturally see a pattern for how this process can be parallelized.  The diagram below shows one approach: The orange boxes show each task group, with each task represented within.  We can, now, effectively take these tasks, and run a large portion of this process in parallel, including the portions which may be the most time consuming.  We’ve now created two parallel paths which our process execution can follow, hopefully speeding up the application startup time dramatically. The main point to remember here is that, when decomposing your problem space by tasks, you need to: Define each discrete action as an individual Task Discover dependencies between your tasks Group tasks based on their dependencies Order the tasks and groups of tasks

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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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  • Parallelism in .NET – Part 19, TaskContinuationOptions

    - by Reed
    My introduction to Task continuations demonstrates continuations on the Task class.  In addition, I’ve shown how continuations allow handling of multiple tasks in a clean, concise manner.  Continuations can also be used to handle exceptional situations using a clean, simple syntax. In addition to standard Task continuations , the Task class provides some options for filtering continuations automatically.  This is handled via the TaskContinationOptions enumeration, which provides hints to the TaskScheduler that it should only continue based on the operation of the antecedent task. This is especially useful when dealing with exceptions.  For example, we can extend the sample from our earlier continuation discussion to include support for handling exceptions thrown by the Factorize method: // Get a copy of the UI-thread task scheduler up front to use later var uiScheduler = TaskScheduler.FromCurrentSynchronizationContext(); // Start our task var factorize = Task.Factory.StartNew( () => { int primeFactor1 = 0; int primeFactor2 = 0; bool result = Factorize(10298312, ref primeFactor1, ref primeFactor2); return new { Result = result, Factor1 = primeFactor1, Factor2 = primeFactor2 }; }); // When we succeed, report the results to the UI factorize.ContinueWith(task => textBox1.Text = string.Format("{0}/{1} [Succeeded {2}]", task.Result.Factor1, task.Result.Factor2, task.Result.Result), CancellationToken.None, TaskContinuationOptions.NotOnFaulted, uiScheduler); // When we have an exception, report it factorize.ContinueWith(task => textBox1.Text = string.Format("Error: {0}", task.Exception.Message), CancellationToken.None, TaskContinuationOptions.OnlyOnFaulted, uiScheduler); .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 above code works by using a combination of features.  First, we schedule our task, the same way as in the previous example.  However, in this case, we use a different overload of Task.ContinueWith which allows us to specify both a specific TaskScheduler (in order to have your continuation run on the UI’s synchronization context) as well as a TaskContinuationOption.  In the first continuation, we tell the continuation that we only want it to run when there was not an exception by specifying TaskContinuationOptions.NotOnFaulted.  When our factorize task completes successfully, this continuation will automatically run on the UI thread, and provide the appropriate feedback. However, if the factorize task has an exception – for example, if the Factorize method throws an exception due to an improper input value, the second continuation will run.  This occurs due to the specification of TaskContinuationOptions.OnlyOnFaulted in the options.  In this case, we’ll report the error received to the user. We can use TaskContinuationOptions to filter our continuations by whether or not an exception occurred and whether or not a task was cancelled.  This allows us to handle many situations, and is especially useful when trying to maintain a valid application state without ever blocking the user interface.  The same concepts can be extended even further, and allow you to chain together many tasks based on the success of the previous ones.  Continuations can even be used to create a state machine with full error handling, all without blocking the user interface thread.

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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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  • .NET Properties - Use Private Set or ReadOnly Property?

    - by tgxiii
    In what situation should I use a Private Set on a property versus making it a ReadOnly property? Take into consideration the two very simplistic examples below. First example: Public Class Person Private _name As String Public Property Name As String Get Return _name End Get Private Set(ByVal value As String) _name = value End Set End Property Public Sub WorkOnName() Dim txtInfo As TextInfo = _ Threading.Thread.CurrentThread.CurrentCulture.TextInfo Me.Name = txtInfo.ToTitleCase(Me.Name) End Sub End Class // ---------- public class Person { private string _name; public string Name { get { return _name; } private set { _name = value; } } public void WorkOnName() { TextInfo txtInfo = System.Threading.Thread.CurrentThread.CurrentCulture.TextInfo; this.Name = txtInfo.ToTitleCase(this.Name); } } Second example: Public Class AnotherPerson Private _name As String Public ReadOnly Property Name As String Get Return _name End Get End Property Public Sub WorkOnName() Dim txtInfo As TextInfo = _ Threading.Thread.CurrentThread.CurrentCulture.TextInfo _name = txtInfo.ToTitleCase(_name) End Sub End Class // --------------- public class AnotherPerson { private string _name; public string Name { get { return _name; } } public void WorkOnName() { TextInfo txtInfo = System.Threading.Thread.CurrentThread.CurrentCulture.TextInfo; _name = txtInfo.ToTitleCase(_name); } } They both yield the same results. Is this a situation where there's no right and wrong, and it's just a matter of preference?

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  • ASP.NET ViewState Tips and Tricks #1

    - by João Angelo
    In User Controls or Custom Controls DO NOT use ViewState to store non public properties. Persisting non public properties in ViewState results in loss of functionality if the Page hosting the controls has ViewState disabled since it can no longer reset values of non public properties on page load. Example: public class ExampleControl : WebControl { private const string PublicViewStateKey = "Example_Public"; private const string NonPublicViewStateKey = "Example_NonPublic"; // DO public int Public { get { object o = this.ViewState[PublicViewStateKey]; if (o == null) return default(int); return (int)o; } set { this.ViewState[PublicViewStateKey] = value; } } // DO NOT private int NonPublic { get { object o = this.ViewState[NonPublicViewStateKey]; if (o == null) return default(int); return (int)o; } set { this.ViewState[NonPublicViewStateKey] = value; } } } // Page with ViewState disabled public partial class ExamplePage : Page { protected override void OnLoad(EventArgs e) { base.OnLoad(e); this.Example.Public = 10; // Restore Public value this.Example.NonPublic = 20; // Compile Error! } }

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