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  • How do I run a batch file async using PSExec?

    - by Paul Mrozowski
    I have a batch file I run that, among other things, reset's the NICs in the machine. I have some watchdog software running on another machine that monitors the first one. I'd like to run this batch file using PSExec when it detects certain types of failures. The problem I'm having is that since the batch file reset's the NIC's it kills the connection PSExec has (I'm OK with that). The real issue is that when PSExec dies the batch file stops running (leaving the NIC's disabled). I've tried using the -i option with PSExec with no luck. Any ideas about basically just fire off the batch file and NOT have it stop when PSExec is disconnected?

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  • Upload File to Windows Azure Blob in Chunks through ASP.NET MVC, JavaScript and HTML5

    - by Shaun
    Originally posted on: http://geekswithblogs.net/shaunxu/archive/2013/07/01/upload-file-to-windows-azure-blob-in-chunks-through-asp.net.aspxMany people are using Windows Azure Blob Storage to store their data in the cloud. Blob storage provides 99.9% availability with easy-to-use API through .NET SDK and HTTP REST. For example, we can store JavaScript files, images, documents in blob storage when we are building an ASP.NET web application on a Web Role in Windows Azure. Or we can store our VHD files in blob and mount it as a hard drive in our cloud service. If you are familiar with Windows Azure, you should know that there are two kinds of blob: page blob and block blob. The page blob is optimized for random read and write, which is very useful when you need to store VHD files. The block blob is optimized for sequential/chunk read and write, which has more common usage. Since we can upload block blob in blocks through BlockBlob.PutBlock, and them commit them as a whole blob with invoking the BlockBlob.PutBlockList, it is very powerful to upload large files, as we can upload blocks in parallel, and provide pause-resume feature. There are many documents, articles and blog posts described on how to upload a block blob. Most of them are focus on the server side, which means when you had received a big file, stream or binaries, how to upload them into blob storage in blocks through .NET SDK.  But the problem is, how can we upload these large files from client side, for example, a browser. This questioned to me when I was working with a Chinese customer to help them build a network disk production on top of azure. The end users upload their files from the web portal, and then the files will be stored in blob storage from the Web Role. My goal is to find the best way to transform the file from client (end user’s machine) to the server (Web Role) through browser. In this post I will demonstrate and describe what I had done, to upload large file in chunks with high speed, and save them as blocks into Windows Azure Blob Storage.   Traditional Upload, Works with Limitation The simplest way to implement this requirement is to create a web page with a form that contains a file input element and a submit button. 1: @using (Html.BeginForm("About", "Index", FormMethod.Post, new { enctype = "multipart/form-data" })) 2: { 3: <input type="file" name="file" /> 4: <input type="submit" value="upload" /> 5: } And then in the backend controller, we retrieve the whole content of this file and upload it in to the blob storage through .NET SDK. We can split the file in blocks and upload them in parallel and commit. The code had been well blogged in the community. 1: [HttpPost] 2: public ActionResult About(HttpPostedFileBase file) 3: { 4: var container = _client.GetContainerReference("test"); 5: container.CreateIfNotExists(); 6: var blob = container.GetBlockBlobReference(file.FileName); 7: var blockDataList = new Dictionary<string, byte[]>(); 8: using (var stream = file.InputStream) 9: { 10: var blockSizeInKB = 1024; 11: var offset = 0; 12: var index = 0; 13: while (offset < stream.Length) 14: { 15: var readLength = Math.Min(1024 * blockSizeInKB, (int)stream.Length - offset); 16: var blockData = new byte[readLength]; 17: offset += stream.Read(blockData, 0, readLength); 18: blockDataList.Add(Convert.ToBase64String(BitConverter.GetBytes(index)), blockData); 19:  20: index++; 21: } 22: } 23:  24: Parallel.ForEach(blockDataList, (bi) => 25: { 26: blob.PutBlock(bi.Key, new MemoryStream(bi.Value), null); 27: }); 28: blob.PutBlockList(blockDataList.Select(b => b.Key).ToArray()); 29:  30: return RedirectToAction("About"); 31: } This works perfect if we selected an image, a music or a small video to upload. But if I selected a large file, let’s say a 6GB HD-movie, after upload for about few minutes the page will be shown as below and the upload will be terminated. In ASP.NET there is a limitation of request length and the maximized request length is defined in the web.config file. It’s a number which less than about 4GB. So if we want to upload a really big file, we cannot simply implement in this way. Also, in Windows Azure, a cloud service network load balancer will terminate the connection if exceed the timeout period. From my test the timeout looks like 2 - 3 minutes. Hence, when we need to upload a large file we cannot just use the basic HTML elements. Besides the limitation mentioned above, the simple HTML file upload cannot provide rich upload experience such as chunk upload, pause and pause-resume. So we need to find a better way to upload large file from the client to the server.   Upload in Chunks through HTML5 and JavaScript In order to break those limitation mentioned above we will try to upload the large file in chunks. This takes some benefit to us such as - No request size limitation: Since we upload in chunks, we can define the request size for each chunks regardless how big the entire file is. - No timeout problem: The size of chunks are controlled by us, which means we should be able to make sure request for each chunk upload will not exceed the timeout period of both ASP.NET and Windows Azure load balancer. It was a big challenge to upload big file in chunks until we have HTML5. There are some new features and improvements introduced in HTML5 and we will use them to implement our solution.   In HTML5, the File interface had been improved with a new method called “slice”. It can be used to read part of the file by specifying the start byte index and the end byte index. For example if the entire file was 1024 bytes, file.slice(512, 768) will read the part of this file from the 512nd byte to 768th byte, and return a new object of interface called "Blob”, which you can treat as an array of bytes. In fact,  a Blob object represents a file-like object of immutable, raw data. The File interface is based on Blob, inheriting blob functionality and expanding it to support files on the user's system. For more information about the Blob please refer here. File and Blob is very useful to implement the chunk upload. We will use File interface to represent the file the user selected from the browser and then use File.slice to read the file in chunks in the size we wanted. For example, if we wanted to upload a 10MB file with 512KB chunks, then we can read it in 512KB blobs by using File.slice in a loop.   Assuming we have a web page as below. User can select a file, an input box to specify the block size in KB and a button to start upload. 1: <div> 2: <input type="file" id="upload_files" name="files[]" /><br /> 3: Block Size: <input type="number" id="block_size" value="512" name="block_size" />KB<br /> 4: <input type="button" id="upload_button_blob" name="upload" value="upload (blob)" /> 5: </div> Then we can have the JavaScript function to upload the file in chunks when user clicked the button. 1: <script type="text/javascript"> 1: 2: $(function () { 3: $("#upload_button_blob").click(function () { 4: }); 5: });</script> Firstly we need to ensure the client browser supports the interfaces we are going to use. Just try to invoke the File, Blob and FormData from the “window” object. If any of them is “undefined” the condition result will be “false” which means your browser doesn’t support these premium feature and it’s time for you to get your browser updated. FormData is another new feature we are going to use in the future. It could generate a temporary form for us. We will use this interface to create a form with chunk and associated metadata when invoked the service through ajax. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: if (window.File && window.Blob && window.FormData) { 4: alert("Your brwoser is awesome, let's rock!"); 5: } 6: else { 7: alert("Oh man plz update to a modern browser before try is cool stuff out."); 8: return; 9: } 10: }); Each browser supports these interfaces by their own implementation and currently the Blob, File and File.slice are supported by Chrome 21, FireFox 13, IE 10, Opera 12 and Safari 5.1 or higher. After that we worked on the files the user selected one by one since in HTML5, user can select multiple files in one file input box. 1: var files = $("#upload_files")[0].files; 2: for (var i = 0; i < files.length; i++) { 3: var file = files[i]; 4: var fileSize = file.size; 5: var fileName = file.name; 6: } Next, we calculated the start index and end index for each chunks based on the size the user specified from the browser. We put them into an array with the file name and the index, which will be used when we upload chunks into Windows Azure Blob Storage as blocks since we need to specify the target blob name and the block index. At the same time we will store the list of all indexes into another variant which will be used to commit blocks into blob in Azure Storage once all chunks had been uploaded successfully. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10:  11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: var blockSizeInKB = $("#block_size").val(); 14: var blockSize = blockSizeInKB * 1024; 15: var blocks = []; 16: var offset = 0; 17: var index = 0; 18: var list = ""; 19: while (offset < fileSize) { 20: var start = offset; 21: var end = Math.min(offset + blockSize, fileSize); 22:  23: blocks.push({ 24: name: fileName, 25: index: index, 26: start: start, 27: end: end 28: }); 29: list += index + ","; 30:  31: offset = end; 32: index++; 33: } 34: } 35: }); Now we have all chunks’ information ready. The next step should be upload them one by one to the server side, and at the server side when received a chunk it will upload as a block into Blob Storage, and finally commit them with the index list through BlockBlobClient.PutBlockList. But since all these invokes are ajax calling, which means not synchronized call. So we need to introduce a new JavaScript library to help us coordinate the asynchronize operation, which named “async.js”. You can download this JavaScript library here, and you can find the document here. I will not explain this library too much in this post. We will put all procedures we want to execute as a function array, and pass into the proper function defined in async.js to let it help us to control the execution sequence, in series or in parallel. Hence we will define an array and put the function for chunk upload into this array. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4:  5: // start to upload each files in chunks 6: var files = $("#upload_files")[0].files; 7: for (var i = 0; i < files.length; i++) { 8: var file = files[i]; 9: var fileSize = file.size; 10: var fileName = file.name; 11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: ... ... 14:  15: // define the function array and push all chunk upload operation into this array 16: blocks.forEach(function (block) { 17: putBlocks.push(function (callback) { 18: }); 19: }); 20: } 21: }); 22: }); As you can see, I used File.slice method to read each chunks based on the start and end byte index we calculated previously, and constructed a temporary HTML form with the file name, chunk index and chunk data through another new feature in HTML5 named FormData. Then post this form to the backend server through jQuery.ajax. This is the key part of our solution. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: blocks.forEach(function (block) { 15: putBlocks.push(function (callback) { 16: // load blob based on the start and end index for each chunks 17: var blob = file.slice(block.start, block.end); 18: // put the file name, index and blob into a temporary from 19: var fd = new FormData(); 20: fd.append("name", block.name); 21: fd.append("index", block.index); 22: fd.append("file", blob); 23: // post the form to backend service (asp.net mvc controller action) 24: $.ajax({ 25: url: "/Home/UploadInFormData", 26: data: fd, 27: processData: false, 28: contentType: "multipart/form-data", 29: type: "POST", 30: success: function (result) { 31: if (!result.success) { 32: alert(result.error); 33: } 34: callback(null, block.index); 35: } 36: }); 37: }); 38: }); 39: } 40: }); Then we will invoke these functions one by one by using the async.js. And once all functions had been executed successfully I invoked another ajax call to the backend service to commit all these chunks (blocks) as the blob in Windows Azure Storage. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.series(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); That’s all in the client side. The outline of our logic would be - Calculate the start and end byte index for each chunks based on the block size. - Defined the functions of reading the chunk form file and upload the content to the backend service through ajax. - Execute the functions defined in previous step with “async.js”. - Commit the chunks by invoking the backend service in Windows Azure Storage finally.   Save Chunks as Blocks into Blob Storage In above we finished the client size JavaScript code. It uploaded the file in chunks to the backend service which we are going to implement in this step. We will use ASP.NET MVC as our backend service, and it will receive the chunks, upload into Windows Azure Bob Storage in blocks, then finally commit as one blob. As in the client side we uploaded chunks by invoking the ajax call to the URL "/Home/UploadInFormData", I created a new action under the Index controller and it only accepts HTTP POST request. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: } 8: catch (Exception e) 9: { 10: error = e.ToString(); 11: } 12:  13: return new JsonResult() 14: { 15: Data = new 16: { 17: success = string.IsNullOrWhiteSpace(error), 18: error = error 19: } 20: }; 21: } Then I retrieved the file name, index and the chunk content from the Request.Form object, which was passed from our client side. And then, used the Windows Azure SDK to create a blob container (in this case we will use the container named “test”.) and create a blob reference with the blob name (same as the file name). Then uploaded the chunk as a block of this blob with the index, since in Blob Storage each block must have an index (ID) associated with so that finally we can put all blocks as one blob by specifying their block ID list. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var index = int.Parse(Request.Form["index"]); 9: var file = Request.Files[0]; 10: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 11:  12: var container = _client.GetContainerReference("test"); 13: container.CreateIfNotExists(); 14: var blob = container.GetBlockBlobReference(name); 15: blob.PutBlock(id, file.InputStream, null); 16: } 17: catch (Exception e) 18: { 19: error = e.ToString(); 20: } 21:  22: return new JsonResult() 23: { 24: Data = new 25: { 26: success = string.IsNullOrWhiteSpace(error), 27: error = error 28: } 29: }; 30: } Next, I created another action to commit the blocks into blob once all chunks had been uploaded. Similarly, I retrieved the blob name from the Request.Form. I also retrieved the chunks ID list, which is the block ID list from the Request.Form in a string format, split them as a list, then invoked the BlockBlob.PutBlockList method. After that our blob will be shown in the container and ready to be download. 1: [HttpPost] 2: public JsonResult Commit() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var list = Request.Form["list"]; 9: var ids = list 10: .Split(',') 11: .Where(id => !string.IsNullOrWhiteSpace(id)) 12: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 13: .ToArray(); 14:  15: var container = _client.GetContainerReference("test"); 16: container.CreateIfNotExists(); 17: var blob = container.GetBlockBlobReference(name); 18: blob.PutBlockList(ids); 19: } 20: catch (Exception e) 21: { 22: error = e.ToString(); 23: } 24:  25: return new JsonResult() 26: { 27: Data = new 28: { 29: success = string.IsNullOrWhiteSpace(error), 30: error = error 31: } 32: }; 33: } Now we finished all code we need. The whole process of uploading would be like this below. Below is the full client side JavaScript code. 1: <script type="text/javascript" src="~/Scripts/async.js"></script> 2: <script type="text/javascript"> 3: $(function () { 4: $("#upload_button_blob").click(function () { 5: // assert the browser support html5 6: if (window.File && window.Blob && window.FormData) { 7: alert("Your brwoser is awesome, let's rock!"); 8: } 9: else { 10: alert("Oh man plz update to a modern browser before try is cool stuff out."); 11: return; 12: } 13:  14: // start to upload each files in chunks 15: var files = $("#upload_files")[0].files; 16: for (var i = 0; i < files.length; i++) { 17: var file = files[i]; 18: var fileSize = file.size; 19: var fileName = file.name; 20:  21: // calculate the start and end byte index for each blocks(chunks) 22: // with the index, file name and index list for future using 23: var blockSizeInKB = $("#block_size").val(); 24: var blockSize = blockSizeInKB * 1024; 25: var blocks = []; 26: var offset = 0; 27: var index = 0; 28: var list = ""; 29: while (offset < fileSize) { 30: var start = offset; 31: var end = Math.min(offset + blockSize, fileSize); 32:  33: blocks.push({ 34: name: fileName, 35: index: index, 36: start: start, 37: end: end 38: }); 39: list += index + ","; 40:  41: offset = end; 42: index++; 43: } 44:  45: // define the function array and push all chunk upload operation into this array 46: var putBlocks = []; 47: blocks.forEach(function (block) { 48: putBlocks.push(function (callback) { 49: // load blob based on the start and end index for each chunks 50: var blob = file.slice(block.start, block.end); 51: // put the file name, index and blob into a temporary from 52: var fd = new FormData(); 53: fd.append("name", block.name); 54: fd.append("index", block.index); 55: fd.append("file", blob); 56: // post the form to backend service (asp.net mvc controller action) 57: $.ajax({ 58: url: "/Home/UploadInFormData", 59: data: fd, 60: processData: false, 61: contentType: "multipart/form-data", 62: type: "POST", 63: success: function (result) { 64: if (!result.success) { 65: alert(result.error); 66: } 67: callback(null, block.index); 68: } 69: }); 70: }); 71: }); 72:  73: // invoke the functions one by one 74: // then invoke the commit ajax call to put blocks into blob in azure storage 75: async.series(putBlocks, function (error, result) { 76: var data = { 77: name: fileName, 78: list: list 79: }; 80: $.post("/Home/Commit", data, function (result) { 81: if (!result.success) { 82: alert(result.error); 83: } 84: else { 85: alert("done!"); 86: } 87: }); 88: }); 89: } 90: }); 91: }); 92: </script> And below is the full ASP.NET MVC controller code. 1: public class HomeController : Controller 2: { 3: private CloudStorageAccount _account; 4: private CloudBlobClient _client; 5:  6: public HomeController() 7: : base() 8: { 9: _account = CloudStorageAccount.Parse(CloudConfigurationManager.GetSetting("DataConnectionString")); 10: _client = _account.CreateCloudBlobClient(); 11: } 12:  13: public ActionResult Index() 14: { 15: ViewBag.Message = "Modify this template to jump-start your ASP.NET MVC application."; 16:  17: return View(); 18: } 19:  20: [HttpPost] 21: public JsonResult UploadInFormData() 22: { 23: var error = string.Empty; 24: try 25: { 26: var name = Request.Form["name"]; 27: var index = int.Parse(Request.Form["index"]); 28: var file = Request.Files[0]; 29: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 30:  31: var container = _client.GetContainerReference("test"); 32: container.CreateIfNotExists(); 33: var blob = container.GetBlockBlobReference(name); 34: blob.PutBlock(id, file.InputStream, null); 35: } 36: catch (Exception e) 37: { 38: error = e.ToString(); 39: } 40:  41: return new JsonResult() 42: { 43: Data = new 44: { 45: success = string.IsNullOrWhiteSpace(error), 46: error = error 47: } 48: }; 49: } 50:  51: [HttpPost] 52: public JsonResult Commit() 53: { 54: var error = string.Empty; 55: try 56: { 57: var name = Request.Form["name"]; 58: var list = Request.Form["list"]; 59: var ids = list 60: .Split(',') 61: .Where(id => !string.IsNullOrWhiteSpace(id)) 62: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 63: .ToArray(); 64:  65: var container = _client.GetContainerReference("test"); 66: container.CreateIfNotExists(); 67: var blob = container.GetBlockBlobReference(name); 68: blob.PutBlockList(ids); 69: } 70: catch (Exception e) 71: { 72: error = e.ToString(); 73: } 74:  75: return new JsonResult() 76: { 77: Data = new 78: { 79: success = string.IsNullOrWhiteSpace(error), 80: error = error 81: } 82: }; 83: } 84: } And if we selected a file from the browser we will see our application will upload chunks in the size we specified to the server through ajax call in background, and then commit all chunks in one blob. Then we can find the blob in our Windows Azure Blob Storage.   Optimized by Parallel Upload In previous example we just uploaded our file in chunks. This solved the problem that ASP.NET MVC request content size limitation as well as the Windows Azure load balancer timeout. But it might introduce the performance problem since we uploaded chunks in sequence. In order to improve the upload performance we could modify our client side code a bit to make the upload operation invoked in parallel. The good news is that, “async.js” library provides the parallel execution function. If you remembered the code we invoke the service to upload chunks, it utilized “async.series” which means all functions will be executed in sequence. Now we will change this code to “async.parallel”. This will invoke all functions in parallel. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallel(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); In this way all chunks will be uploaded to the server side at the same time to maximize the bandwidth usage. This should work if the file was not very large and the chunk size was not very small. But for large file this might introduce another problem that too many ajax calls are sent to the server at the same time. So the best solution should be, upload the chunks in parallel with maximum concurrency limitation. The code below specified the concurrency limitation to 4, which means at the most only 4 ajax calls could be invoked at the same time. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallelLimit(putBlocks, 4, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: });   Summary In this post we discussed how to upload files in chunks to the backend service and then upload them into Windows Azure Blob Storage in blocks. We focused on the frontend side and leverage three new feature introduced in HTML 5 which are - File.slice: Read part of the file by specifying the start and end byte index. - Blob: File-like interface which contains the part of the file content. - FormData: Temporary form element that we can pass the chunk alone with some metadata to the backend service. Then we discussed the performance consideration of chunk uploading. Sequence upload cannot provide maximized upload speed, but the unlimited parallel upload might crash the browser and server if too many chunks. So we finally came up with the solution to upload chunks in parallel with the concurrency limitation. We also demonstrated how to utilize “async.js” JavaScript library to help us control the asynchronize call and the parallel limitation.   Regarding the chunk size and the parallel limitation value there is no “best” value. You need to test vary composition and find out the best one for your particular scenario. It depends on the local bandwidth, client machine cores and the server side (Windows Azure Cloud Service Virtual Machine) cores, memory and bandwidth. Below is one of my performance test result. The client machine was Windows 8 IE 10 with 4 cores. I was using Microsoft Cooperation Network. The web site was hosted on Windows Azure China North data center (in Beijing) with one small web role (1.7GB 1 core CPU, 1.75GB memory with 100Mbps bandwidth). The test cases were - Chunk size: 512KB, 1MB, 2MB, 4MB. - Upload Mode: Sequence, parallel (unlimited), parallel with limit (4 threads, 8 threads). - Chunk Format: base64 string, binaries. - Target file: 100MB. - Each case was tested 3 times. Below is the test result chart. Some thoughts, but not guidance or best practice: - Parallel gets better performance than series. - No significant performance improvement between parallel 4 threads and 8 threads. - Transform with binaries provides better performance than base64. - In all cases, chunk size in 1MB - 2MB gets better performance.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • Why is multithreading often preferred for improving performance?

    - by user1849534
    I have a question, it's about why programmers seems to love concurrency and multi-threaded programs in general. I'm considering 2 main approaches here: an async approach basically based on signals, or just an async approach as called by many papers and languages like the new C# 5.0 for example, and a "companion thread" that manages the policy of your pipeline a concurrent approach or multi-threading approach I will just say that I'm thinking about the hardware here and the worst case scenario, and I have tested this 2 paradigms myself, the async paradigm is a winner at the point that I don't get why people 90% of the time talk about multi-threading when they want to speed up things or make a good use of their resources. I have tested multi-threaded programs and async program on an old machine with an Intel quad-core that doesn't offer a memory controller inside the CPU, the memory is managed entirely by the motherboard, well in this case performances are horrible with a multi-threaded application, even a relatively low number of threads like 3-4-5 can be a problem, the application is unresponsive and is just slow and unpleasant. A good async approach is, on the other hand, probably not faster but it's not worst either, my application just waits for the result and doesn't hangs, it's responsive and there is a much better scaling going on. I have also discovered that a context change in the threading world it's not that cheap in real world scenario, it's in fact quite expensive especially when you have more than 2 threads that need to cycle and swap among each other to be computed. On modern CPUs the situation it's not really that different, the memory controller it's integrated but my point is that an x86 CPUs is basically a serial machine and the memory controller works the same way as with the old machine with an external memory controller on the motherboard. The context switch is still a relevant cost in my application and the fact that the memory controller it's integrated or that the newer CPU have more than 2 core it's not bargain for me. For what i have experienced the concurrent approach is good in theory but not that good in practice, with the memory model imposed by the hardware, it's hard to make a good use of this paradigm, also it introduces a lot of issues ranging from the use of my data structures to the join of multiple threads. Also both paradigms do not offer any security abut when the task or the job will be done in a certain point in time, making them really similar from a functional point of view. According to the X86 memory model, why the majority of people suggest to use concurrency with C++ and not just an async approach ? Also why not considering the worst case scenario of a computer where the context switch is probably more expensive than the computation itself ?

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  • Why C++ people loves multithreading when it comes to performances?

    - by user1849534
    I have a question, it's about why programmers seems to love concurrency and multi-threaded programs in general. I'm considering 2 main approach here: an async approach basically based on signals, or just an async approach as called by many papers and languages like the new C# 5.0 for example, and a "companion thread" that maanges the policy of your pipeline a concurrent approach or multi-threading approach I will just say that I'm thinking about the hardware here and the worst case scenario, and I have tested this 2 paradigms myself, the async paradigm is a winner at the point that I don't get why people 90% of the time talk about concurrency when they wont to speed up things or make a good use of their resources. I have tested multi-threaded programs and async program on an old machine with an Intel quad-core that doesn't offer a memory controller inside the CPU, the memory is managed entirely by the motherboard, well in this case performances are horrible with a multi-threaded application, even a relatively low number of threads like 3-4-5 can be a problem, the application is unresponsive and is just slow and unpleasant. A good async approach is, on the other hand, probably not faster but it's not worst either, my application just waits for the result and doesn't hangs, it's responsive and there is a much better scaling going on. I have also discovered that a context change in the threading world it's not that cheap in real world scenario, it's infact quite expensive especially when you have more than 2 threads that need to cycle and swap among each other to be computed. On modern CPUs the situation it's not really that different, the memory controller it's integrated but my point is that an x86 CPUs is basically a serial machine and the memory controller works the same way as with the old machine with an external memory controller on the motherboard. The context switch is still a relevant cost in my application and the fact that the memory controller it's integrated or that the newer CPU have more than 2 core it's not bargain for me. For what i have experienced the concurrent approach is good in theory but not that good in practice, with the memory model imposed by the hardware, it's hard to make a good use of this paradigm, also it introduces a lot of issues ranging from the use of my data structures to the join of multiple threads. Also both paradigms do not offer any security abut when the task or the job will be done in a certain point in time, making them really similar from a functional point of view. According to the X86 memory model, why the majority of people suggest to use concurrency with C++ and not just an async aproach ? Also why not considering the worst case scenario of a computer where the context switch is probably more expensive than the computation itself ?

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  • ?Oracle Database 12c????TTnn TMON??redo??????

    - by Liu Maclean(???)
    ?Oracle 11g? Data Guard?redo?????????3???????: ARCi (FAL – archived redo shipping, ping, local only archivals) NSAi (async) 12.1 name: TTnn , NSSi (sync) –– live redo shipping ????12c?? ??TTnn  ??TT00?????async ???redo??? ???????TMON????Redo transport monitor? SQL> select banner from v$version where rownum=1; BANNER -------------------------------------------------------------------------------- Oracle Database 12c Enterprise Edition Release 12.1.0.1.0 - 64bit Production SQL> select program,pid from v$process where program like '%TMON%' or Program like '%TT%'; PROGRAM PID ------------------------------ ---------- ORACLE.EXE (TMON) 7 ORACLE.EXE (TT00) 24 ??????? ?11g ???NSAi async redo ship?????????LGWR?????????,?????redo ????; ???12c?TTnn???redo???????LGWR? ???  ??????async redo ship ??redo??! 11g?: 12c?

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  • What is the fastest cyclic synchronization in Java (ExecutorService vs. CyclicBarrier vs. X)?

    - by Alex Dunlop
    Which Java synchronization construct is likely to provide the best performance for a concurrent, iterative processing scenario with a fixed number of threads like the one outlined below? After experimenting on my own for a while (using ExecutorService and CyclicBarrier) and being somewhat surprised by the results, I would be grateful for some expert advice and maybe some new ideas. Existing questions here do not seem to focus primarily on performance, hence this new one. Thanks in advance! The core of the app is a simple iterative data processing algorithm, parallelized to the spread the computational load across 8 cores on a Mac Pro, running OS X 10.6 and Java 1.6.0_07. The data to be processed is split into 8 blocks and each block is fed to a Runnable to be executed by one of a fixed number of threads. Parallelizing the algorithm was fairly straightforward, and it functionally works as desired, but its performance is not yet what I think it could be. The app seems to spend a lot of time in system calls synchronizing, so after some profiling I wonder whether I selected the most appropriate synchronization mechanism(s). A key requirement of the algorithm is that it needs to proceed in stages, so the threads need to sync up at the end of each stage. The main thread prepares the work (very low overhead), passes it to the threads, lets them work on it, then proceeds when all threads are done, rearranges the work (again very low overhead) and repeats the cycle. The machine is dedicated to this task, Garbage Collection is minimized by using per-thread pools of pre-allocated items, and the number of threads can be fixed (no incoming requests or the like, just one thread per CPU core). V1 - ExecutorService My first implementation used an ExecutorService with 8 worker threads. The program creates 8 tasks holding the work and then lets them work on it, roughly like this: // create one thread per CPU executorService = Executors.newFixedThreadPool( 8 ); ... // now process data in cycles while( ...) { // package data into 8 work items ... // create one Callable task per work item ... // submit the Callables to the worker threads executorService.invokeAll( taskList ); } This works well functionally (it does what it should), and for very large work items indeed all 8 CPUs become highly loaded, as much as the processing algorithm would be expected to allow (some work items will finish faster than others, then idle). However, as the work items become smaller (and this is not really under the program's control), the user CPU load shrinks dramatically: blocksize | system | user | cycles/sec 256k 1.8% 85% 1.30 64k 2.5% 77% 5.6 16k 4% 64% 22.5 4096 8% 56% 86 1024 13% 38% 227 256 17% 19% 420 64 19% 17% 948 16 19% 13% 1626 Legend: - block size = size of the work item (= computational steps) - system = system load, as shown in OS X Activity Monitor (red bar) - user = user load, as shown in OS X Activity Monitor (green bar) - cycles/sec = iterations through the main while loop, more is better The primary area of concern here is the high percentage of time spent in the system, which appears to be driven by thread synchronization calls. As expected, for smaller work items, ExecutorService.invokeAll() will require relatively more effort to sync up the threads versus the amount of work being performed in each thread. But since ExecutorService is more generic than it would need to be for this use case (it can queue tasks for threads if there are more tasks than cores), I though maybe there would be a leaner synchronization construct. V2 - CyclicBarrier The next implementation used a CyclicBarrier to sync up the threads before receiving work and after completing it, roughly as follows: main() { // create the barrier barrier = new CyclicBarrier( 8 + 1 ); // create Runable for thread, tell it about the barrier Runnable task = new WorkerThreadRunnable( barrier ); // start the threads for( int i = 0; i < 8; i++ ) { // create one thread per core new Thread( task ).start(); } while( ... ) { // tell threads about the work ... // N threads + this will call await(), then system proceeds barrier.await(); // ... now worker threads work on the work... // wait for worker threads to finish barrier.await(); } } class WorkerThreadRunnable implements Runnable { CyclicBarrier barrier; WorkerThreadRunnable( CyclicBarrier barrier ) { this.barrier = barrier; } public void run() { while( true ) { // wait for work barrier.await(); // do the work ... // wait for everyone else to finish barrier.await(); } } } Again, this works well functionally (it does what it should), and for very large work items indeed all 8 CPUs become highly loaded, as before. However, as the work items become smaller, the load still shrinks dramatically: blocksize | system | user | cycles/sec 256k 1.9% 85% 1.30 64k 2.7% 78% 6.1 16k 5.5% 52% 25 4096 9% 29% 64 1024 11% 15% 117 256 12% 8% 169 64 12% 6.5% 285 16 12% 6% 377 For large work items, synchronization is negligible and the performance is identical to V1. But unexpectedly, the results of the (highly specialized) CyclicBarrier seem MUCH WORSE than those for the (generic) ExecutorService: throughput (cycles/sec) is only about 1/4th of V1. A preliminary conclusion would be that even though this seems to be the advertised ideal use case for CyclicBarrier, it performs much worse than the generic ExecutorService. V3 - Wait/Notify + CyclicBarrier It seemed worth a try to replace the first cyclic barrier await() with a simple wait/notify mechanism: main() { // create the barrier // create Runable for thread, tell it about the barrier // start the threads while( ... ) { // tell threads about the work // for each: workerThreadRunnable.setWorkItem( ... ); // ... now worker threads work on the work... // wait for worker threads to finish barrier.await(); } } class WorkerThreadRunnable implements Runnable { CyclicBarrier barrier; @NotNull volatile private Callable<Integer> workItem; WorkerThreadRunnable( CyclicBarrier barrier ) { this.barrier = barrier; this.workItem = NO_WORK; } final protected void setWorkItem( @NotNull final Callable<Integer> callable ) { synchronized( this ) { workItem = callable; notify(); } } public void run() { while( true ) { // wait for work while( true ) { synchronized( this ) { if( workItem != NO_WORK ) break; try { wait(); } catch( InterruptedException e ) { e.printStackTrace(); } } } // do the work ... // wait for everyone else to finish barrier.await(); } } } Again, this works well functionally (it does what it should). blocksize | system | user | cycles/sec 256k 1.9% 85% 1.30 64k 2.4% 80% 6.3 16k 4.6% 60% 30.1 4096 8.6% 41% 98.5 1024 12% 23% 202 256 14% 11.6% 299 64 14% 10.0% 518 16 14.8% 8.7% 679 The throughput for small work items is still much worse than that of the ExecutorService, but about 2x that of the CyclicBarrier. Eliminating one CyclicBarrier eliminates half of the gap. V4 - Busy wait instead of wait/notify Since this app is the primary one running on the system and the cores idle anyway if they're not busy with a work item, why not try a busy wait for work items in each thread, even if that spins the CPU needlessly. The worker thread code changes as follows: class WorkerThreadRunnable implements Runnable { // as before final protected void setWorkItem( @NotNull final Callable<Integer> callable ) { workItem = callable; } public void run() { while( true ) { // busy-wait for work while( true ) { if( workItem != NO_WORK ) break; } // do the work ... // wait for everyone else to finish barrier.await(); } } } Also works well functionally (it does what it should). blocksize | system | user | cycles/sec 256k 1.9% 85% 1.30 64k 2.2% 81% 6.3 16k 4.2% 62% 33 4096 7.5% 40% 107 1024 10.4% 23% 210 256 12.0% 12.0% 310 64 11.9% 10.2% 550 16 12.2% 8.6% 741 For small work items, this increases throughput by a further 10% over the CyclicBarrier + wait/notify variant, which is not insignificant. But it is still much lower-throughput than V1 with the ExecutorService. V5 - ? So what is the best synchronization mechanism for such a (presumably not uncommon) problem? I am weary of writing my own sync mechanism to completely replace ExecutorService (assuming that it is too generic and there has to be something that can still be taken out to make it more efficient). It is not my area of expertise and I'm concerned that I'd spend a lot of time debugging it (since I'm not even sure my wait/notify and busy wait variants are correct) for uncertain gain. Any advice would be greatly appreciated.

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  • Memory management, and async operations: when does an object become nil?

    - by Kenny Winker
    I have a view that will be displaying downloaded images and text. I'd like to handle all the downloading asynchronously using ASIHTTPRequest, but I'm not sure how to go about notifying the view when downloads are finished... If I pass my view controller as the delegate of the ASIHTTPRequest, and then my view is destroyed (user navigates away) will it fail gracefully when it tries to message my view controller because the delegate is now nil? i.e. if i do this: UIViewController *myvc = [[UIViewController alloc] init]; request.delegate = myvc; [myvc release]; Do myvc, and request.delegate now == a pointer to nil? This is the problem with being self-taught... I'm kinda fuzzy on some basic concepts. Other ideas of how to handle this are welcome.

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  • Async trigger for an update panel refreshes entire page when triggering too much in too short of tim

    - by Matt
    I have a search button tied to an update panel as a trigger like so: <asp:Panel ID="CRM_Search" runat="server"> <p>Search:&nbsp;<asp:TextBox ID="CRM_Search_Box" CssClass="CRM_Search_Box" runat="server"></asp:TextBox> <asp:Button ID="CRM_Search_Button" CssClass="CRM_Search_Button" runat="server" Text="Search" OnClick="SearchLeads" /></p> </asp:Panel> <asp:UpdatePanel ID="UpdatePanel1" runat="server"> <Triggers> <asp:AsyncPostBackTrigger ControlID="CRM_Search_Button" /> </Triggers> <ContentTemplate> /* Content Here */ </ContentTemplate> </asp:UpdatePanel> In my javascript I use jQuery to grab the search box and tie it's keyup to make the search button click: $($(".CRM_Search_Box")[0]).keyup( function () { $($(".CRM_Search_Button")[0]).click(); } ); This works perfectly, except when I start typing too fast. As soon as I type too fast (my guess is if it's any faster than the data actually returns) the entire page refreshes (doing a postback?) instead of just the update panel. I've also found that instead of typing, if I just click the button really fast it starts doing the same thing. Is there any way to prevent it from doing this? Possibly prevent 2nd requests until the first has been completed? If I'm not on the right track then anyone have any other ideas? Thanks, Matt

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  • What happens to orphaned/killed async AJAX WebMethod or PageMethod calls?

    - by Armchair Bronco
    What happens behind the scenes if I make an AJAX PageMethod or WebMethod call from, say, "Default.aspx" and then I quickly navigate away to a different page, say, "Settings.aspx" before the initial PageMethod has returned? What kind of housekeeping, if any, takes place on either the browser or the ASP.NET back end? In other words, where do abandoned AJAX PageMethod calls go to die...and what is their funeral like?

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  • How to implement async pattern in windows forms application?

    - by Alkersan
    I'm using an MVC pattern in winforms application. I need to call remote service asynchronously. So On some event in View I invoke corresponding Presenter method. In Presenter I call BeginInvoke method of service. But to View must be updated only in Main Thread. I could actualy point CallBack to some function in View, and update it`s controls state, but this conflicts with MVP pattern - View must not be responsible for data it carries. This callback function must be in Presenter. But how then invoke View in Main Thread?

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  • Async task ASP.net HttpContext.Current.Items is empty - How do handle this?

    - by GuruC
    We are running a very large web application in asp.net MVC .NET 4.0. Recently we had an audit done and the performance team says that there were a lot of null reference exceptions. So I started investigating it from the dumps and event viewer. My understanding was as follows: We are using Asyn Tasks in our controllers. We rely on HttpContext.Current.Items hashtable to store a lot of Application level values. Task<Articles>.Factory.StartNew(() => { System.Web.HttpContext.Current = ControllerContext.HttpContext.ApplicationInstance.Context; var service = new ArticlesService(page); return service.GetArticles(); }).ContinueWith(t => SetResult(t, "articles")); So we are copying the context object onto the new thread that is spawned from Task factory. This context.Items is used again in the thread wherever necessary. Say for ex: public class SomeClass { internal static int StreamID { get { if (HttpContext.Current != null) { return (int)HttpContext.Current.Items["StreamID"]; } else { return DEFAULT_STREAM_ID; } } } This runs fine as long as number of parallel requests are optimal. My questions are as follows: 1. When the load is more and there are too many parallel requests, I notice that HttpContext.Current.Items is empty. I am not able to figure out a reason for this and this causes all the null reference exceptions. 2. How do we make sure it is not null ? Any workaround if present ? NOTE: I read through in StackOverflow and people have questions like HttpContext.Current is null - but in my case it is not null and its empty. I was reading one more article where the author says that sometimes request object is terminated and it may cause problems since dispose is already called on objects. I am doing a copy of Context object - its just a shallow copy and not a deep copy.

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  • How to split and dispatch an async control-flow using Continuations?

    - by hotzen
    Hello, I have an asynchronous control-flow like the following: ActorA ! DoA(dataA, callback1, callbackOnErrorA) def callback1() = { ... ActorB ! DoB(dataB, callback2, callbackOnErrorB) } def callback2() = { ActorC ! DoC(dataC, callback3, callbackOnErrorC) } ... How would I divide this flow into several parts (continuations) and sequentially dispatch these to different actors (or threads/tasks) while maintaining the overall state? Any hint appreciated, Thanks

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  • how to deal with async calls in Ajax 4.0(using jquery?)

    - by dexter
    in my code i have done something like this. $.get('/Home/Module/Submit', { moduleName: ModName, moduleParameters: moduleParameters }, function(result) { $("#" + target).html(result); }); when i put alert in the function(result) {..} it shows html perfectly(both in alert and at the 'target'-on the .aspx page) BUT when i remove the alert.. on the page the 'html' don't appear or appear randomly (this method is called multiple times) i think that the 'result' comes to function asynchronously thats why it is not bind with the respective 'div' however in the last iteration it gets bind every time. can we make process stop until data gets bind? or is there any functionality (like alert) which can make data bind.. without disturbing UI (unlike alert)?

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  • Entity Framework 6: Alpha2 Now Available

    - by ScottGu
    The Entity Framework team recently announced the 2nd alpha release of EF6.   The alpha 2 package is available for download from NuGet. Since this is a pre-release package make sure to select “Include Prereleases” in the NuGet package manager, or execute the following from the package manager console to install it: PM> Install-Package EntityFramework -Pre This week’s alpha release includes a bunch of great improvements in the following areas: Async language support is now available for queries and updates when running on .NET 4.5. Custom conventions now provide the ability to override the default conventions that Code First uses for mapping types, properties, etc. to your database. Multi-tenant migrations allow the same database to be used by multiple contexts with full Code First Migrations support for independently evolving the model backing each context. Using Enumerable.Contains in a LINQ query is now handled much more efficiently by EF and the SQL Server provider resulting greatly improved performance. All features of EF6 (except async) are available on both .NET 4 and .NET 4.5. This includes support for enums and spatial types and the performance improvements that were previously only available when using .NET 4.5. Start-up time for many large models has been dramatically improved thanks to improved view generation performance. Below are some additional details about a few of the improvements above: Async Support .NET 4.5 introduced the Task-Based Asynchronous Pattern that uses the async and await keywords to help make writing asynchronous code easier. EF 6 now supports this pattern. This is great for ASP.NET applications as database calls made through EF can now be processed asynchronously – avoiding any blocking of worker threads. This can increase scalability on the server by allowing more requests to be processed while waiting for the database to respond. The following code shows an MVC controller that is querying a database for a list of location entities:     public class HomeController : Controller     {         LocationContext db = new LocationContext();           public async Task<ActionResult> Index()         {             var locations = await db.Locations.ToListAsync();               return View(locations);         }     } Notice above the call to the new ToListAsync method with the await keyword. When the web server reaches this code it initiates the database request, but rather than blocking while waiting for the results to come back, the thread that is processing the request returns to the thread pool, allowing ASP.NET to process another incoming request with the same thread. In other words, a thread is only consumed when there is actual processing work to do, allowing the web server to handle more concurrent requests with the same resources. A more detailed walkthrough covering async in EF is available with additional information and examples. Also a walkthrough is available showing how to use async in an ASP.NET MVC application. Custom Conventions When working with EF Code First, the default behavior is to map .NET classes to tables using a set of conventions baked into EF. For example, Code First will detect properties that end with “ID” and configure them automatically as primary keys. However, sometimes you cannot or do not want to follow those conventions and would rather provide your own. For example, maybe your primary key properties all end in “Key” instead of “Id”. Custom conventions allow the default conventions to be overridden or new conventions to be added so that Code First can map by convention using whatever rules make sense for your project. The following code demonstrates using custom conventions to set the precision of all decimals to 5. As with other Code First configuration, this code is placed in the OnModelCreating method which is overridden on your derived DbContext class:         protected override void OnModelCreating(DbModelBuilder modelBuilder)         {             modelBuilder.Properties<decimal>()                 .Configure(x => x.HasPrecision(5));           } But what if there are a couple of places where a decimal property should have a different precision? Just as with all the existing Code First conventions, this new convention can be overridden for a particular property simply by explicitly configuring that property using either the fluent API or a data annotation. A more detailed description of custom code first conventions is available here. Community Involvement I blogged a while ago about EF being released under an open source license.  Since then a number of community members have made contributions and these are included in EF6 alpha 2. Two examples of community contributions are: AlirezaHaghshenas contributed a change that increases the startup performance of EF for larger models by improving the performance of view generation. The change means that it is less often necessary to use of pre-generated views. UnaiZorrilla contributed the first community feature to EF: the ability to load all Code First configuration classes in an assembly with a single method call like the following: protected override void OnModelCreating(DbModelBuilder modelBuilder) {        modelBuilder.Configurations            .AddFromAssembly(typeof(LocationContext).Assembly); } This code will find and load all the classes that inherit from EntityTypeConfiguration<T> or ComplexTypeConfiguration<T> in the assembly where LocationContext is defined. This reduces the amount of coupling between the context and Code First configuration classes, and is also a very convenient shortcut for large models. Other upcoming features coming in EF 6 Lots of information about the development of EF6 can be found on the EF CodePlex site, including a roadmap showing the other features that are planned for EF6. One of of the nice upcoming features is connection resiliency, which will automate the process of retying database operations on transient failures common in cloud environments and with databases such as the Windows Azure SQL Database. Another often requested feature that will be included in EF6 is the ability to map stored procedures to query and update operations on entities when using Code First. Summary EF6 is the first open source release of Entity Framework being developed in CodePlex. The alpha 2 preview release of EF6 is now available on NuGet, and contains some really great features for you to try. The EF team are always looking for feedback from developers - especially on the new features such as custom Code First conventions and async support. To provide feedback you can post a comment on the EF6 alpha 2 announcement post, start a discussion or file a bug on the CodePlex site. Hope this helps, Scott P.S. In addition to blogging, I am also now using Twitter for quick updates and to share links. Follow me at: twitter.com/scottgu

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  • Understanding F# Asynchronous Programming

    - by Yin Zhu
    I kind of know the syntax of asynchronous programming in F#. E.g. let downloadUrl(url:string) = async { let req = HttpWebRequest.Create(url) // Run operation asynchronously let! resp = req.AsyncGetResponse() let stream = resp.GetResponseStream() // Dispose 'StreamReader' when completed use reader = new StreamReader(stream) // Run asynchronously and then return the result return! reader.AsyncReadToEnd() } In F# expert book (and many other sources), they say like let! var = expr simply means "perform the asynchronous operation expr and bind the result to var when the operation completes. Then continue by executing the rest of the computation body" I also know that a new thread is created when performing async operation. My original understanding was that there are two parallel threads after the async operation, one doing I/O and one continuing to execute the async body at the same time. But in this example, I am confused at let! resp = req.AsyncGetResponse() let stream = resp.GetResponseStream() What happens if resp has not started yet and the thread in the async body wants to GetResponseStream? Is this a possible error? So maybe my original understanding was wrong. The quoted sentences in the F# expert book actually means that "creating a new thread, hang the current thread up, when the new thread finishes, wake up the body thread and continue", but in this case I don't see we could save any time. In the original understanding, the time is saved when there are several independent IO operations in one async block so that they could be done at the same time without intervention with each other. But here, if I don't get the response, I cannot create the stream; only I have stream, I can start reading the stream. Where's the time gained?

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  • Stuck at the STARTUP [closed]

    - by Tarik Setia
    I started with "Getting started with asp mvc4 tutorial". I just created the project and when I pressed F5 I got this: Server Error in '/' Application. -------------------------------------------------------------------------------- Could not load type 'System.Web.WebPages.DisplayModes' from assembly 'System.Web.WebPages, Version=2.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35'. Description: An unhandled exception occurred during the execution of the current web request. Please review the stack trace for more information about the error and where it originated in the code. Exception Details: System.TypeLoadException: Could not load type 'System.Web.WebPages.DisplayModes' from assembly 'System.Web.WebPages, Version=2.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35'. Source Error: An unhandled exception was generated during the execution of the current web request. Information regarding the origin and location of the exception can be identified using the exception stack trace below. Stack Trace: [TypeLoadException: Could not load type 'System.Web.WebPages.DisplayModes' from assembly 'System.Web.WebPages, Version=2.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35'.] System.Web.Mvc.VirtualPathProviderViewEngine.GetPath(ControllerContext controllerContext, String[] locations, String[] areaLocations, String locationsPropertyName, String name, String controllerName, String cacheKeyPrefix, Boolean useCache, String[]& searchedLocations) +0 System.Web.Mvc.VirtualPathProviderViewEngine.FindView(ControllerContext controllerContext, String viewName, String masterName, Boolean useCache) +315 System.Web.Mvc.c__DisplayClassc.b__a(IViewEngine e) +68 System.Web.Mvc.ViewEngineCollection.Find(Func`2 lookup, Boolean trackSearchedPaths) +182 System.Web.Mvc.ViewEngineCollection.Find(Func`2 cacheLocator, Func`2 locator) +67 System.Web.Mvc.ViewEngineCollection.FindView(ControllerContext controllerContext, String viewName, String masterName) +329 System.Web.Mvc.ViewResult.FindView(ControllerContext context) +135 System.Web.Mvc.ViewResultBase.ExecuteResult(ControllerContext context) +230 System.Web.Mvc.ControllerActionInvoker.InvokeActionResult(ControllerContext controllerContext, ActionResult actionResult) +39 System.Web.Mvc.c__DisplayClass1c.b__19() +74 System.Web.Mvc.ControllerActionInvoker.InvokeActionResultFilter(IResultFilter filter, ResultExecutingContext preContext, Func`1 continuation) +388 System.Web.Mvc.c__DisplayClass1e.b__1b() +72 System.Web.Mvc.ControllerActionInvoker.InvokeActionResultWithFilters(ControllerContext controllerContext, IList`1 filters, ActionResult actionResult) +303 System.Web.Mvc.ControllerActionInvoker.InvokeAction(ControllerContext controllerContext, String actionName) +844 System.Web.Mvc.Controller.ExecuteCore() +130 System.Web.Mvc.ControllerBase.Execute(RequestContext requestContext) +229 System.Web.Mvc.ControllerBase.System.Web.Mvc.IController.Execute(RequestContext requestContext) +39 System.Web.Mvc.c__DisplayClassb.b__5() +71 System.Web.Mvc.Async.c__DisplayClass1.b__0() +44 System.Web.Mvc.Async.c__DisplayClass8`1.b__7(IAsyncResult _) +42 System.Web.Mvc.Async.WrappedAsyncResult`1.End() +152 System.Web.Mvc.Async.AsyncResultWrapper.End(IAsyncResult asyncResult, Object tag) +59 System.Web.Mvc.Async.AsyncResultWrapper.End(IAsyncResult asyncResult, Object tag) +40 System.Web.Mvc.c__DisplayClasse.b__d() +75 System.Web.Mvc.SecurityUtil.b__0(Action f) +31 System.Web.Mvc.SecurityUtil.ProcessInApplicationTrust(Action action) +61 System.Web.Mvc.MvcHandler.EndProcessRequest(IAsyncResult asyncResult) +118 System.Web.Mvc.MvcHandler.System.Web.IHttpAsyncHandler.EndProcessRequest(IAsyncResult result) +38 System.Web.CallHandlerExecutionStep.System.Web.HttpApplication.IExecutionStep.Execute() +10303829 System.Web.HttpApplication.ExecuteStep(IExecutionStep step, Boolean& completedSynchronously) +178 -------------------------------------------------------------------------------- Version Information: Microsoft .NET Framework Version:4.0.30319; ASP.NET Version:4.0.30319.17020

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  • LINQ to Twitter v2.1.09 Released

    - by Joe Mayo
    Originally posted on: http://geekswithblogs.net/WinAZ/archive/2013/10/15/linq-to-twitter-v2.1.09-released.aspxToday, I released LINQ to Twitter v2.1.09. Here are important new changes. Bug Fixes This is primarily a bug fix release. Most notably, there were authentication problems in WinRT apps. This is now fixed. New Features One new feature is the addition of ApplicationOnlyAuthentication for WinRT. It is fully async.  Here’s how it works: var auth = new WinRtApplicationOnlyAuthorizer { Credentials = new InMemoryCredentials { ConsumerKey = "", ConsumerSecret = "" } }; if (auth == null || !auth.IsAuthorized) { await auth.AuthorizeAsync(); } var twitterCtx = new TwitterContext(auth); (from search in twitterCtx.Search where search.Type == SearchType.Search && search.Query == SearchTextBox.Text select search) .MaterializedAsyncCallback( async response => await Dispatcher.RunAsync( CoreDispatcherPriority.Normal, async () => { Search searchResponse = response.State.Single(); string message = string.Format( "Search returned {0} statuses", searchResponse.Statuses.Count); await new MessageDialog(message, "Search Complete").ShowAsync(); })); It’s called the WinRtApplicationOnlyAuthorizer. You only need two tokens, ConsumerKey and ConsumerSecret, which come from your Twitter API application settings page. Note: You need a Twitter Application, which you can create at https://dev.twitter.com/. The MaterializedAsyncCallback materializes your query and handles the response. I put everything together in a lambda for demonstration purposes, but you can always replace the callback with a handler of type Action<TwitterAsyncResponse<IEnumerable<T>>>, where T is Search for this example. On the Horizon The next version of LINQ to Twitter is in development. I discussed it at LINQ to Twitter Async. This isn’t complete, but you can download the source code at the LINQ to Twitter site on CodePlex. I’ve competed all the spikes for what I thought would be the hard parts and now have prototypes of queries and commands working. This would be a good time to provide feedback if there are features in the current version that you think could be improved. The current driving forces for the next version will be async and PCL.   @JoeMayo

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  • Error "fileid changed" when accessing files over NFS

    - by Roman Prikhodchenko
    I have an nfs-kernel-server configured and running on Ubuntu 10.04 Server. /export THIRD_SERVER_IP(rw,fsid=0,insecure,no_subtree_check,async) SECOND_SERVER_IP(rw,fsid=0,insecure,no_subtree_check,async) /export/ebs THIRD_SERVER_IP(rw,fsid=0,insecure,no_subtree_check,async) SECOND_SERVER_IP(rw,nohide,insecure,no_subtree_check,async) I mounted the exported folder to the second server: mount -t nfs4 -o proto=tcp,port=2049 NFS_SERVER_IP_HERE:/ebs /ebs and it works just fine. I mounted it to the third server but I cannot access files from it. ls -l /ebs ls: reading directory /ebs: Stale NFS file handle total 0 The syslog on the third server says: kernel: [11575.483720] NFS: server NFS_SERVER_IP_HERE error: fileid changed kernel: [11575.483722] fsid 0:14: expected fileid 0x2, got 0x6e001 Some info: uname -r 2.6.32-312-ec2 uname -m i686

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  • Error "fileid changed" when accessing files over NFS

    - by Roman Prikhodchenko
    I have an nfs-kernel-server configured and running on Ubuntu 10.04 Server. /export THIRD_SERVER_IP(rw,fsid=0,insecure,no_subtree_check,async) SECOND_SERVER_IP(rw,fsid=0,insecure,no_subtree_check,async) /export/ebs THIRD_SERVER_IP(rw,fsid=0,insecure,no_subtree_check,async) SECOND_SERVER_IP(rw,nohide,insecure,no_subtree_check,async) I mounted the exported folder to the second server: mount -t nfs4 -o proto=tcp,port=2049 NFS_SERVER_IP_HERE:/ebs /ebs and it works just fine. I mounted it to the third server but I cannot access files from it. ls -l /ebs ls: reading directory /ebs: Stale NFS file handle total 0 The syslog on the third server says: kernel: [11575.483720] NFS: server NFS_SERVER_IP_HERE error: fileid changed kernel: [11575.483722] fsid 0:14: expected fileid 0x2, got 0x6e001 Some info: uname -r 2.6.32-312-ec2 uname -m i686

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  • Azure Mobile Services: what files does it consist of?

    - by svdoever
    Azure Mobile Services is a platform that provides a small set of functionality consisting of authentication, custom data tables, custom API’s, scheduling scripts and push notifications to be used as the back-end of a mobile application or if you want, any application or web site. As described in my previous post Azure Mobile Services: lessons learned the documentation on what can be used in the custom scripts is a bit minimalistic. The list below of all files the complete Azure Mobile Services platform consists of ca shed some light on what is available in the platform. In following posts I will provide more detailed information on what we can conclude from this list of files. Below are the available files as available in the Azure Mobile Services platform. The bold files are files that describe your data model, api scripts, scheduler scripts and table scripts. Those are the files you configure/construct to provide the “configuration”/implementation of you mobile service. The files are located in a folder like C:\DWASFiles\Sites\youreservice\VirtualDirectory0\site\wwwroot. One file is missing in the list below and that is the event log file C:\DWASFiles\Sites\youreservice\VirtualDirectory0\site\LogFiles\eventlog.xml where your messages written with for example console.log() and exception catched by the system are written. NOTA BENE: the Azure Mobile Services system is a system that is under full development, new releases may change the list of files. ./app.js ./App_Data/config/datamodel.json ./App_Data/config/scripts/api/youreapi.js ./App_Data/config/scripts/api/youreapi.json ./App_Data/config/scripts/scheduler/placeholder ./App_Data/config/scripts/scheduler/youresheduler.js ./App_Data/config/scripts/shared/placeholder ./App_Data/config/scripts/table/placeholder ./App_Data/config/scripts/table/yourtable.insert.js ./App_Data/config/scripts/table/yourtable.update.js ./App_Data/config/scripts/table/yourtable.delete.js ./App_Data/config/scripts/table/yourtable.read.js ./node_modules/apn/index.js ./node_modules/apn/lib/connection.js ./node_modules/apn/lib/device.js ./node_modules/apn/lib/errors.js ./node_modules/apn/lib/feedback.js ./node_modules/apn/lib/notification.js ./node_modules/apn/lib/util.js ./node_modules/apn/node_modules/q/package.json ./node_modules/apn/node_modules/q/q.js ./node_modules/apn/package.json ./node_modules/azure/lib/azure.js ./node_modules/azure/lib/cli/blobUtils.js ./node_modules/azure/lib/cli/cacheUtils.js ./node_modules/azure/lib/cli/callbackAggregator.js ./node_modules/azure/lib/cli/cert.js ./node_modules/azure/lib/cli/channel.js ./node_modules/azure/lib/cli/cli.js ./node_modules/azure/lib/cli/commands/account.js ./node_modules/azure/lib/cli/commands/config.js ./node_modules/azure/lib/cli/commands/deployment.js ./node_modules/azure/lib/cli/commands/deployment_.js ./node_modules/azure/lib/cli/commands/help.js ./node_modules/azure/lib/cli/commands/log.js ./node_modules/azure/lib/cli/commands/log_.js ./node_modules/azure/lib/cli/commands/repository.js ./node_modules/azure/lib/cli/commands/repository_.js ./node_modules/azure/lib/cli/commands/service.js ./node_modules/azure/lib/cli/commands/site.js ./node_modules/azure/lib/cli/commands/site_.js ./node_modules/azure/lib/cli/commands/vm.js ./node_modules/azure/lib/cli/common.js ./node_modules/azure/lib/cli/constants.js ./node_modules/azure/lib/cli/generate-psm1-utils.js ./node_modules/azure/lib/cli/generate-psm1.js ./node_modules/azure/lib/cli/iaas/blobserviceex.js ./node_modules/azure/lib/cli/iaas/deleteImage.js ./node_modules/azure/lib/cli/iaas/image.js ./node_modules/azure/lib/cli/iaas/upload/blobInfo.js ./node_modules/azure/lib/cli/iaas/upload/bufferStream.js ./node_modules/azure/lib/cli/iaas/upload/intSet.js ./node_modules/azure/lib/cli/iaas/upload/jobTracker.js ./node_modules/azure/lib/cli/iaas/upload/pageBlob.js ./node_modules/azure/lib/cli/iaas/upload/streamMerger.js ./node_modules/azure/lib/cli/iaas/upload/uploadVMImage.js ./node_modules/azure/lib/cli/iaas/upload/vhdTools.js ./node_modules/azure/lib/cli/keyFiles.js ./node_modules/azure/lib/cli/patch-winston.js ./node_modules/azure/lib/cli/templates/node/iisnode.yml ./node_modules/azure/lib/cli/utils.js ./node_modules/azure/lib/diagnostics/logger.js ./node_modules/azure/lib/http/webresource.js ./node_modules/azure/lib/serviceruntime/fileinputchannel.js ./node_modules/azure/lib/serviceruntime/goalstatedeserializer.js ./node_modules/azure/lib/serviceruntime/namedpipeinputchannel.js ./node_modules/azure/lib/serviceruntime/namedpipeoutputchannel.js ./node_modules/azure/lib/serviceruntime/protocol1runtimeclient.js ./node_modules/azure/lib/serviceruntime/protocol1runtimecurrentstateclient.js ./node_modules/azure/lib/serviceruntime/protocol1runtimegoalstateclient.js ./node_modules/azure/lib/serviceruntime/roleenvironment.js ./node_modules/azure/lib/serviceruntime/runtimekernel.js ./node_modules/azure/lib/serviceruntime/runtimeversionmanager.js ./node_modules/azure/lib/serviceruntime/runtimeversionprotocolclient.js ./node_modules/azure/lib/serviceruntime/xmlcurrentstateserializer.js ./node_modules/azure/lib/serviceruntime/xmlgoalstatedeserializer.js ./node_modules/azure/lib/serviceruntime/xmlroleenvironmentdatadeserializer.js ./node_modules/azure/lib/services/blob/blobservice.js ./node_modules/azure/lib/services/blob/hmacsha256sign.js ./node_modules/azure/lib/services/blob/models/blobresult.js ./node_modules/azure/lib/services/blob/models/blocklistresult.js ./node_modules/azure/lib/services/blob/models/containeraclresult.js ./node_modules/azure/lib/services/blob/models/containerresult.js ./node_modules/azure/lib/services/blob/models/leaseresult.js ./node_modules/azure/lib/services/blob/models/listblobsresultcontinuation.js ./node_modules/azure/lib/services/blob/models/listcontainersresultcontinuation.js ./node_modules/azure/lib/services/blob/models/servicepropertiesresult.js ./node_modules/azure/lib/services/blob/sharedaccesssignature.js ./node_modules/azure/lib/services/blob/sharedkey.js ./node_modules/azure/lib/services/blob/sharedkeylite.js ./node_modules/azure/lib/services/core/connectionstringparser.js ./node_modules/azure/lib/services/core/exponentialretrypolicyfilter.js ./node_modules/azure/lib/services/core/linearretrypolicyfilter.js ./node_modules/azure/lib/services/core/servicebusserviceclient.js ./node_modules/azure/lib/services/core/servicebussettings.js ./node_modules/azure/lib/services/core/serviceclient.js ./node_modules/azure/lib/services/core/servicemanagementclient.js ./node_modules/azure/lib/services/core/servicemanagementsettings.js ./node_modules/azure/lib/services/core/servicesettings.js ./node_modules/azure/lib/services/core/storageserviceclient.js ./node_modules/azure/lib/services/core/storageservicesettings.js ./node_modules/azure/lib/services/queue/models/listqueuesresultcontinuation.js ./node_modules/azure/lib/services/queue/models/queuemessageresult.js ./node_modules/azure/lib/services/queue/models/queueresult.js ./node_modules/azure/lib/services/queue/models/servicepropertiesresult.js ./node_modules/azure/lib/services/queue/queueservice.js ./node_modules/azure/lib/services/serviceBus/models/acstokenresult.js ./node_modules/azure/lib/services/serviceBus/models/queuemessageresult.js ./node_modules/azure/lib/services/serviceBus/models/queueresult.js ./node_modules/azure/lib/services/serviceBus/models/ruleresult.js ./node_modules/azure/lib/services/serviceBus/models/subscriptionresult.js ./node_modules/azure/lib/services/serviceBus/models/topicresult.js ./node_modules/azure/lib/services/serviceBus/servicebusservice.js ./node_modules/azure/lib/services/serviceBus/wrap.js ./node_modules/azure/lib/services/serviceBus/wrapservice.js ./node_modules/azure/lib/services/serviceBus/wraptokenmanager.js ./node_modules/azure/lib/services/serviceManagement/models/roleparser.js ./node_modules/azure/lib/services/serviceManagement/models/roleschema.json ./node_modules/azure/lib/services/serviceManagement/models/servicemanagementserialize.js ./node_modules/azure/lib/services/serviceManagement/servicemanagementservice.js ./node_modules/azure/lib/services/table/batchserviceclient.js ./node_modules/azure/lib/services/table/models/entityresult.js ./node_modules/azure/lib/services/table/models/queryentitiesresultcontinuation.js ./node_modules/azure/lib/services/table/models/querytablesresultcontinuation.js ./node_modules/azure/lib/services/table/models/servicepropertiesresult.js ./node_modules/azure/lib/services/table/models/tableresult.js ./node_modules/azure/lib/services/table/sharedkeylitetable.js ./node_modules/azure/lib/services/table/sharedkeytable.js ./node_modules/azure/lib/services/table/tablequery.js ./node_modules/azure/lib/services/table/tableservice.js ./node_modules/azure/lib/util/atomhandler.js ./node_modules/azure/lib/util/certificates/der.js ./node_modules/azure/lib/util/certificates/pkcs.js ./node_modules/azure/lib/util/constants.js ./node_modules/azure/lib/util/iso8061date.js ./node_modules/azure/lib/util/js2xml.js ./node_modules/azure/lib/util/rfc1123date.js ./node_modules/azure/lib/util/util.js ./node_modules/azure/lib/util/validate.js ./node_modules/azure/LICENSE.txt ./node_modules/azure/node_modules/async/index.js ./node_modules/azure/node_modules/async/lib/async.js ./node_modules/azure/node_modules/async/LICENSE ./node_modules/azure/node_modules/async/package.json ./node_modules/azure/node_modules/azure/lib/azure.js ./node_modules/azure/node_modules/azure/lib/diagnostics/logger.js ./node_modules/azure/node_modules/azure/lib/http/webresource.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/fileinputchannel.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/goalstatedeserializer.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/namedpipeinputchannel.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/namedpipeoutputchannel.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/protocol1runtimeclient.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/protocol1runtimecurrentstateclient.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/protocol1runtimegoalstateclient.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/roleenvironment.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/runtimekernel.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/runtimeversionmanager.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/runtimeversionprotocolclient.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/xmlcurrentstateserializer.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/xmlgoalstatedeserializer.js ./node_modules/azure/node_modules/azure/lib/serviceruntime/xmlroleenvironmentdatadeserializer.js ./node_modules/azure/node_modules/azure/lib/services/blob/blobservice.js ./node_modules/azure/node_modules/azure/lib/services/blob/internal/sharedaccesssignature.js ./node_modules/azure/node_modules/azure/lib/services/blob/internal/sharedkey.js ./node_modules/azure/node_modules/azure/lib/services/blob/internal/sharedkeylite.js ./node_modules/azure/node_modules/azure/lib/services/blob/models/blobresult.js ./node_modules/azure/node_modules/azure/lib/services/blob/models/blocklistresult.js ./node_modules/azure/node_modules/azure/lib/services/blob/models/containeraclresult.js ./node_modules/azure/node_modules/azure/lib/services/blob/models/containerresult.js ./node_modules/azure/node_modules/azure/lib/services/blob/models/leaseresult.js ./node_modules/azure/node_modules/azure/lib/services/blob/models/listblobsresultcontinuation.js ./node_modules/azure/node_modules/azure/lib/services/blob/models/listcontainersresultcontinuation.js ./node_modules/azure/node_modules/azure/lib/services/blob/models/servicepropertiesresult.js ./node_modules/azure/node_modules/azure/lib/services/core/connectionstringparser.js ./node_modules/azure/node_modules/azure/lib/services/core/exponentialretrypolicyfilter.js ./node_modules/azure/node_modules/azure/lib/services/core/hmacsha256sign.js ./node_modules/azure/node_modules/azure/lib/services/core/linearretrypolicyfilter.js ./node_modules/azure/node_modules/azure/lib/services/core/servicebusserviceclient.js ./node_modules/azure/node_modules/azure/lib/services/core/servicebussettings.js ./node_modules/azure/node_modules/azure/lib/services/core/serviceclient.js ./node_modules/azure/node_modules/azure/lib/services/core/servicemanagementclient.js ./node_modules/azure/node_modules/azure/lib/services/core/servicemanagementsettings.js ./node_modules/azure/node_modules/azure/lib/services/core/servicesettings.js ./node_modules/azure/node_modules/azure/lib/services/core/sqlserviceclient.js ./node_modules/azure/node_modules/azure/lib/services/core/storageserviceclient.js ./node_modules/azure/node_modules/azure/lib/services/core/storageservicesettings.js ./node_modules/azure/node_modules/azure/lib/services/queue/models/listqueuesresultcontinuation.js ./node_modules/azure/node_modules/azure/lib/services/queue/models/queuemessageresult.js ./node_modules/azure/node_modules/azure/lib/services/queue/models/queueresult.js ./node_modules/azure/node_modules/azure/lib/services/queue/models/servicepropertiesresult.js ./node_modules/azure/node_modules/azure/lib/services/queue/queueservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/apnsservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/gcmservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/internal/sharedaccesssignature.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/internal/wrap.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/internal/wraptokenmanager.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/models/acstokenresult.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/models/notificationhubresult.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/models/queuemessageresult.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/models/queueresult.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/models/registrationresult.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/models/resourceresult.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/models/ruleresult.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/models/subscriptionresult.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/models/topicresult.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/notificationhubservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/servicebusservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/servicebusservicebase.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/wnsservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceBus/wrapservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceManagement/hdinsightservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceManagement/models/roleparser.js ./node_modules/azure/node_modules/azure/lib/services/serviceManagement/models/roleschema.json ./node_modules/azure/node_modules/azure/lib/services/serviceManagement/models/servicemanagementserialize.js ./node_modules/azure/node_modules/azure/lib/services/serviceManagement/servicebusmanagementservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceManagement/servicemanagementservice.js ./node_modules/azure/node_modules/azure/lib/services/serviceManagement/sqlmanagementservice.js ./node_modules/azure/node_modules/azure/lib/services/sqlAzure/models/databaseresult.js ./node_modules/azure/node_modules/azure/lib/services/sqlAzure/sqlserveracs.js ./node_modules/azure/node_modules/azure/lib/services/sqlAzure/sqlservice.js ./node_modules/azure/node_modules/azure/lib/services/table/batchserviceclient.js ./node_modules/azure/node_modules/azure/lib/services/table/internal/sharedkeylitetable.js ./node_modules/azure/node_modules/azure/lib/services/table/internal/sharedkeytable.js ./node_modules/azure/node_modules/azure/lib/services/table/models/entityresult.js ./node_modules/azure/node_modules/azure/lib/services/table/models/listresult.js ./node_modules/azure/node_modules/azure/lib/services/table/models/queryentitiesresultcontinuation.js ./node_modules/azure/node_modules/azure/lib/services/table/models/querytablesresultcontinuation.js ./node_modules/azure/node_modules/azure/lib/services/table/models/servicepropertiesresult.js ./node_modules/azure/node_modules/azure/lib/services/table/models/tableresult.js ./node_modules/azure/node_modules/azure/lib/services/table/tablequery.js ./node_modules/azure/node_modules/azure/lib/services/table/tableservice.js 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./runtime/script/metadata.js ./runtime/script/push/notify-apns.js ./runtime/script/push/notify-gcm.js ./runtime/script/push/notify-mpns.js ./runtime/script/push/notify-wns.js ./runtime/script/push/notify.js ./runtime/script/scriptcache.js ./runtime/script/scripterror.js ./runtime/script/scriptloader.js ./runtime/script/scriptmanager.js ./runtime/script/scriptstate.js ./runtime/script/sqladapter.js ./runtime/script/table.js ./runtime/server.js ./runtime/statuscodes.js ./runtime/storage/sqlbooleanizer.js ./runtime/storage/sqlformatter.js ./runtime/storage/sqlhelpers.js ./runtime/storage/storage.js ./runtime/Zumo.Node.js ./static/client/MobileServices.Web-1.0.0.js ./static/client/MobileServices.Web-1.0.0.min.js ./static/default.htm ./static/robots.txt ./Web.config

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  • ASP.NET Asynchronous Tasks - Worker Thread Not Releasing?

    - by user296752
    I am having an issue with testing asynchronous tasks in ASP.NET & IIS7. From what I have read, the worker thread should be released back into the thread pool while the I/O thread performs the async work, allowing ASP.NET to server other incoming requests. But when I simulate heavy load on the web application by making 20 simultaneous requests to a page (async.aspx) that performs long running async tasks, I am unable to browse to some other normal aspx page until the requests are just about done. Am I misunderstanding or missing something here? I am running Vista Biz x64, VS2008 + IIS7. I have the Async attribute applied to the Page directive.

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  • Why does use of H264 in sender/receiver pipelines introduce just HUGE delay?

    - by Serguey Zefirov
    When I try to create pipeline that uses H264 to transmit video, I get some enormous delay, up to 10 seconds to transmit video from my machine to... my machine! This is unacceptable for my goals and I'd like to consult StackOverflow over what I (or someone else) do wrong. I took pipelines from gstrtpbin documentation page and slightly modified them to use Speex: This is sender pipeline: #!/bin/sh gst-launch -v gstrtpbin name=rtpbin \ v4l2src ! ffmpegcolorspace ! ffenc_h263 ! rtph263ppay ! rtpbin.send_rtp_sink_0 \ rtpbin.send_rtp_src_0 ! udpsink host=127.0.0.1 port=5000 \ rtpbin.send_rtcp_src_0 ! udpsink host=127.0.0.1 port=5001 sync=false async=false \ udpsrc port=5005 ! rtpbin.recv_rtcp_sink_0 \ pulsesrc ! audioconvert ! audioresample ! audio/x-raw-int,rate=16000 ! \ speexenc bitrate=16000 ! rtpspeexpay ! rtpbin.send_rtp_sink_1 \ rtpbin.send_rtp_src_1 ! udpsink host=127.0.0.1 port=5002 \ rtpbin.send_rtcp_src_1 ! udpsink host=127.0.0.1 port=5003 sync=false async=false \ udpsrc port=5007 ! rtpbin.recv_rtcp_sink_1 Receiver pipeline: !/bin/sh gst-launch -v\ gstrtpbin name=rtpbin \ udpsrc caps="application/x-rtp,media=(string)video, clock-rate=(int)90000, encoding-name=(string)H263-1998" \ port=5000 ! rtpbin.recv_rtp_sink_0 \ rtpbin. ! rtph263pdepay ! ffdec_h263 ! xvimagesink \ udpsrc port=5001 ! rtpbin.recv_rtcp_sink_0 \ rtpbin.send_rtcp_src_0 ! udpsink port=5005 sync=false async=false \ udpsrc caps="application/x-rtp,media=(string)audio, clock-rate=(int)16000, encoding-name=(string)SPEEX, encoding-params=(string)1, payload=(int)110" \ port=5002 ! rtpbin.recv_rtp_sink_1 \ rtpbin. ! rtpspeexdepay ! speexdec ! audioresample ! audioconvert ! alsasink \ udpsrc port=5003 ! rtpbin.recv_rtcp_sink_1 \ rtpbin.send_rtcp_src_1 ! udpsink host=127.0.0.1 port=5007 sync=false async=false Those pipelines, a combination of H263 and Speex, work fine enough. I snap my fingers near camera and micropohne and then I see movement and hear sound at the same time. Then I changed pipelines to use H264 along the video path. The sender becomes: #!/bin/sh gst-launch -v gstrtpbin name=rtpbin \ v4l2src ! ffmpegcolorspace ! x264enc bitrate=300 ! rtph264pay ! rtpbin.send_rtp_sink_0 \ rtpbin.send_rtp_src_0 ! udpsink host=127.0.0.1 port=5000 \ rtpbin.send_rtcp_src_0 ! udpsink host=127.0.0.1 port=5001 sync=false async=false \ udpsrc port=5005 ! rtpbin.recv_rtcp_sink_0 \ pulsesrc ! audioconvert ! audioresample ! audio/x-raw-int,rate=16000 ! \ speexenc bitrate=16000 ! rtpspeexpay ! rtpbin.send_rtp_sink_1 \ rtpbin.send_rtp_src_1 ! udpsink host=127.0.0.1 port=5002 \ rtpbin.send_rtcp_src_1 ! udpsink host=127.0.0.1 port=5003 sync=false async=false \ udpsrc port=5007 ! rtpbin.recv_rtcp_sink_1 And receiver becomes: #!/bin/sh gst-launch -v\ gstrtpbin name=rtpbin \ udpsrc caps="application/x-rtp,media=(string)video, clock-rate=(int)90000, encoding-name=(string)H264" \ port=5000 ! rtpbin.recv_rtp_sink_0 \ rtpbin. ! rtph264depay ! ffdec_h264 ! xvimagesink \ udpsrc port=5001 ! rtpbin.recv_rtcp_sink_0 \ rtpbin.send_rtcp_src_0 ! udpsink port=5005 sync=false async=false \ udpsrc caps="application/x-rtp,media=(string)audio, clock-rate=(int)16000, encoding-name=(string)SPEEX, encoding-params=(string)1, payload=(int)110" \ port=5002 ! rtpbin.recv_rtp_sink_1 \ rtpbin. ! rtpspeexdepay ! speexdec ! audioresample ! audioconvert ! alsasink \ udpsrc port=5003 ! rtpbin.recv_rtcp_sink_1 \ rtpbin.send_rtcp_src_1 ! udpsink host=127.0.0.1 port=5007 sync=false async=false This is what happen under Ubuntu 10.04. I didn't noticed such huge delays on Ubuntu 9.04 - the delays there was in range 2-3 seconds, AFAIR.

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  • What is the purpose of "do!" notation in F#?

    - by Yacoder
    I'm a beginner in F#, so it's a simple question and maybe a duplicate, but I couldn't find the answer anywhere... I'm reading this LOGO DSL implementation and I don't understand, what is the meaning of the "do!" notation in here: this.Loaded.Add (fun _ -> async { do! Async.Sleep 200 for cmd in theDrawing do do! this.Execute(cmd) } |> Async.StartImmediate ) Can you help?

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