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  • ConcurrenctBag(Of MyType) Vs List(Of MyType)

    - by Ben
    What is the advantage of using a ConcurrentBag(Of MyType) against just using a List(Of MyType)? The MSDN page on the CB (http://msdn.microsoft.com/en-us/library/dd381779(v=VS.100).aspx) states that ConcurrentBag(Of T) is a thread-safe bag implementation, optimized for scenarios where the same thread will be both producing and consuming data stored in the bag So what is the advantage? I can understand the advantage of the other collection types in the Concurrency namespace, but this one puzzled me. Thanks.

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  • How solve consumer/producer task using semaphores

    - by user1074896
    I have SimpleProducerConsumer class that illustrate consumer/producer problem (I am not sure that it's correct). public class SimpleProducerConsumer { private Stack<Object> stack = new Stack<Object>(); private static final int STACK_MAX_SIZE = 10; public static void main(String[] args) { SimpleProducerConsumer pc = new SimpleProducerConsumer(); new Thread(pc.new Producer(), "p1").start(); new Thread(pc.new Producer(), "p2").start(); new Thread(pc.new Consumer(), "c1").start(); new Thread(pc.new Consumer(), "c2").start(); new Thread(pc.new Consumer(), "c3").start(); } public synchronized void push(Object d) { while (stack.size() >= STACK_MAX_SIZE) try { wait(); } catch (InterruptedException e) { e.printStackTrace(); } try { Thread.sleep(1000); } catch (InterruptedException e) { e.printStackTrace(); } stack.push(new Object()); System.out.println("push " + Thread.currentThread().getName() + " " + stack.size()); notify(); } public synchronized Object pop() { while (stack.size() == 0) try { wait(); } catch (InterruptedException e) { e.printStackTrace(); } try { Thread.sleep(50); } catch (InterruptedException e) { e.printStackTrace(); } stack.pop(); System.out.println("pop " + Thread.currentThread().getName() + " " + stack.size()); notify(); return null; } class Consumer implements Runnable { @Override public void run() { while (true) { pop(); } } } class Producer implements Runnable { @Override public void run() { while (true) { push(new Object()); } } } } I found simple realization of semaphore(here:http://docs.oracle.com/javase/tutorial/essential/concurrency/guardmeth.html I know that there is concurrency package) How I need to change code to exchange java objects monitors to my custom semaphore. (To illustrate C/P problem using semaphores) Semaphore: class Semaphore { private int counter; public Semaphore() { this(0); } public Semaphore(int i) { if (i < 0) throw new IllegalArgumentException(i + " < 0"); counter = i; } public synchronized void release() { if (counter == 0) { notify(); } counter++; } public synchronized void acquire() throws InterruptedException { while (counter == 0) { wait(); } counter--; } }

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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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  • Michael Crump&rsquo;s notes for 70-563 PRO &ndash; Designing and Developing Windows Applications usi

    - by mbcrump
    TIME TO GO PRO! This is my notes for 70-563 PRO – Designing and Developing Windows Applications using .NET Framework 3.5 I created it using several resources (various certification web sites, msdn, official ms 70-548 book). The reason that I created this review is because a) I am taking the exam. b) MS did not create a book for this exam. Use the(MS 70-548)book. c) To make sure I am familiar with each before the exam. I hope that it provides a good start for your own notes. I hope that someone finds this useful. At least, it will give you a starting point of what to expect to know on the PRO exam. Also, for those wondering, the PRO exam does contains very little code. It is basically all theory. 1. Validation Controls – How to prevent users from entering invalid data on forms. (MaskedTextBox control and RegEx) 2. ServiceController – used to start and control the behavior of existing services. 3. User Feedback (know winforms Status Bar, Tool Tips, Color, Error Provider, Context-Sensitive and Accessibility) 4. Specific (derived) exceptions must be handled before general (base class) exceptions. By moving the exception handling for the base type Exception to after exception handling of ArgumentNullException, all ArgumentNullException thrown by the Helper method will be caught and logged correctly. 5. A heartbeat method is a method exposed by a Web service that allows external applications to check on the status of the service. 6. New users must master key tasks quickly. Giving these tasks context and appropriate detail will help. However, advanced users will demand quicker paths. Shortcuts, accelerators, or toolbar buttons will speed things along for the advanced user. 7. MSBuild uses project files to instruct the build engine what to build and how to build it. MSBuild project files are XML files that adhere to the MSBuild XML schema. The MSBuild project files contain complete file, build action, and dependency information for each individual projects. 8. Evaluating whether or not to fix a bug involves a triage process. You must identify the bug's impact, set the priority, categorize it, and assign a developer. Many times the person doing the triage work will assign the bug to a developer for further investigation. In fact, the workflow for the bug work item inside of Team System supports this step. Developers are often asked to assess the impact of a given bug. This assessment helps the person doing the triage make a decision on how to proceed. When assessing the impact of a bug, you should consider time and resources to fix it, bug risk, and impacts of the bug. 9. In large projects it is generally impossible and unfeasible to fix all bugs because of the impact on schedule and budget. 10. Code reviews should be conducted by a technical lead or a technical peer. 11. Testing Applications 12. WCF Services – application state 13. SQL Server 2005 / 2008 Express Edition – reliable storage of data / Microsoft SQL Server 3.5 Compact Database– used for client computers to retrieve and save data from a shared location. 14. SQL Server 2008 Compact Edition – used for minimum possible memory and can synchronize data with a corporate SQL Server 2008 Database. Supports offline user and minimum dependency on external components. 15. MDI and SDI Forms (specifically IsMDIContainer) 16. GUID – in the case of data warehousing, it is important to define unique keys. 17. Encrypting / Security Data 18. Understanding of Isolated Storage/Proper location to store items 19. LINQ to SQL 20. Multithreaded access 21. ADO.NET Entity Framework model 22. Marshal.ReleaseComObject 23. Common User Interface Layout (ComboBox, ListBox, Listview, MaskedTextBox, TextBox, RichTextBox, SplitContainer, TableLayoutPanel, TabControl) 24. DataSets Class - http://msdn.microsoft.com/en-us/library/system.data.dataset%28VS.71%29.aspx 25. SQL Server 2008 Reporting Services (SSRS) 26. SystemIcons.Shield (Vista UAC) 27. Leverging stored procedures to perform data manipulation for a database schema that can change. 28. DataContext 29. Microsoft Windows Installer Packages, ClickOnce(bootstrapping features), XCopy. 30. Client Application Services – will authenticate users by using the same data source as a ASP.NET web application. 31. SQL Server 2008 Caching 32. StringBuilder 33. Accessibility Guidelines for Windows Applications http://msdn.microsoft.com/en-us/library/ms228004.aspx 34. Logging erros 35. Testing performance related issues. 36. Role Based Security, GenericIdentity and GenericPrincipal 37. System.Net.CookieContainer will store session data for webapps (see isolated storage for winforms) 38. .NET CLR Profiler tool will identify objects that cause performance issues. 39. ADO.NET Synchronization (SyncGroup) 40. Globalization - CultureInfo 41. IDisposable Interface- reports on several questions relating to this. 42. Adding timestamps to determine whether data has changed or not. 43. Converting applications to .NET Framework 3.5 44. MicrosoftReportViewer 45. Composite Controls 46. Windows Vista KNOWN folders. 47. Microsoft Sync Framework 48. TypeConverter -Provides a unified way of converting types of values to other types, as well as for accessing standard values and sub properties. http://msdn.microsoft.com/en-us/library/system.componentmodel.typeconverter.aspx 49. Concurrency control mechanisms The main categories of concurrency control mechanisms are: Optimistic - Delay the checking of whether a transaction meets the isolation rules (e.g., serializability and recoverability) until its end, without blocking any of its (read, write) operations, and then abort a transaction, if the desired rules are violated. Pessimistic - Block operations of a transaction, if they may cause violation of the rules. Semi-optimistic - Block operations in some situations, and do not block in other situations, while delaying rules checking to transaction's end, as done with optimistic. 50. AutoResetEvent 51. Microsoft Messaging Queue (MSMQ) 4.0 52. Bulk imports 53. KeyDown event of controls 54. WPF UI components 55. UI process layer 56. GAC (installing, removing and queuing) 57. Use a local database cache to reduce the network bandwidth used by applications. 58. Sound can easily be annoying and distracting to users, so use it judiciously. Always give users the option to turn sound off. Because a user might have sound off, never convey important information through sound alone.

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  • ODI 12c - Parallel Table Load

    - by David Allan
    In this post we will look at the ODI 12c capability of parallel table load from the aspect of the mapping developer and the knowledge module developer - two quite different viewpoints. This is about parallel table loading which isn't to be confused with loading multiple targets per se. It supports the ability for ODI mappings to be executed concurrently especially if there is an overlap of the datastores that they access, so any temporary resources created may be uniquely constructed by ODI. Temporary objects can be anything basically - common examples are staging tables, indexes, views, directories - anything in the ETL to help the data integration flow do its job. In ODI 11g users found a few workarounds (such as changing the technology prefixes - see here) to build unique temporary names but it was more of a challenge in error cases. ODI 12c mappings by default operate exactly as they did in ODI 11g with respect to these temporary names (this is also true for upgraded interfaces and scenarios) but can be configured to support the uniqueness capabilities. We will look at this feature from two aspects; that of a mapping developer and that of a developer (of procedures or KMs). 1. Firstly as a Mapping Developer..... 1.1 Control when uniqueness is enabled A new property is available to set unique name generation on/off. When unique names have been enabled for a mapping, all temporary names used by the collection and integration objects will be generated using unique names. This property is presented as a check-box in the Property Inspector for a deployment specification. 1.2 Handle cleanup after successful execution Provided that all temporary objects that are created have a corresponding drop statement then all of the temporary objects should be removed during a successful execution. This should be the case with the KMs developed by Oracle. 1.3 Handle cleanup after unsuccessful execution If an execution failed in ODI 11g then temporary tables would have been left around and cleaned up in the subsequent run. In ODI 12c, KM tasks can now have a cleanup-type task which is executed even after a failure in the main tasks. These cleanup tasks will be executed even on failure if the property 'Remove Temporary Objects on Error' is set. If the agent was to crash and not be able to execute this task, then there is an ODI tool (OdiRemoveTemporaryObjects here) you can invoke to cleanup the tables - it supports date ranges and the like. That's all there is to it from the aspect of the mapping developer it's much, much simpler and straightforward. You can now execute the same mapping concurrently or execute many mappings using the same resource concurrently without worrying about conflict.  2. Secondly as a Procedure or KM Developer..... In the ODI Operator the executed code shows the actual name that is generated - you can also see the runtime code prior to execution (introduced in 11.1.1.7), for example below in the code type I selected 'Pre-executed Code' this lets you see the code about to be processed and you can also see the executed code (which is the default view). References to the collection (C$) and integration (I$) names will be automatically made unique by using the odiRef APIs - these objects will have unique names whenever concurrency has been enabled for a particular mapping deployment specification. It's also possible to use name uniqueness functions in procedures and your own KMs. 2.1 New uniqueness tags  You can also make your own temporary objects have unique names by explicitly including either %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG in the name passed to calls to the odiRef APIs. Such names would always include the unique tag regardless of the concurrency setting. To illustrate, let's look at the getObjectName() method. At <% expansion time, this API will append %UNIQUE_STEP_TAG to the object name for collection and integration tables. The name parameter passed to this API may contain  %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG. This API always generates to the <? version of getObjectName() At execution time this API will replace the unique tag macros with a string that is unique to the current execution scope. The returned name will conform to the name-length restriction for the target technology, and its pattern for the unique tag. Any necessary truncation will be performed against the initial name for the object and any other fixed text that may have been specified. Examples are:- <?=odiRef.getObjectName("L", "%COL_PRFEMP%UNIQUE_STEP_TAG", "D")?> SCOTT.C$_EABH7QI1BR1EQI3M76PG9SIMBQQ <?=odiRef.getObjectName("L", "EMP%UNIQUE_STEP_TAG_AE", "D")?> SCOTT.EMPAO96Q2JEKO0FTHQP77TMSAIOSR_ Methods which have this kind of support include getFrom, getTableName, getTable, getObjectShortName and getTemporaryIndex. There are APIs for retrieving this tag info also, the getInfo API has been extended with the following properties (the UNIQUE* properties can also be used in ODI procedures); UNIQUE_STEP_TAG - Returns the unique value for the current step scope, e.g. 5rvmd8hOIy7OU2o1FhsF61 Note that this will be a different value for each loop-iteration when the step is in a loop. UNIQUE_SESSION_TAG - Returns the unique value for the current session scope, e.g. 6N38vXLrgjwUwT5MseHHY9 IS_CONCURRENT - Returns info about the current mapping, will return 0 or 1 (only in % phase) GUID_SRC_SET - Returns the UUID for the current source set/execution unit (only in % phase) The getPop API has been extended with the IS_CONCURRENT property which returns info about an mapping, will return 0 or 1.  2.2 Additional APIs Some new APIs are provided including getFormattedName which will allow KM developers to construct a name from fixed-text or ODI symbols that can be optionally truncate to a max length and use a specific encoding for the unique tag. It has syntax getFormattedName(String pName[, String pTechnologyCode]) This API is available at both the % and the ? phase.  The format string can contain the ODI prefixes that are available for getObjectName(), e.g. %INT_PRF, %COL_PRF, %ERR_PRF, %IDX_PRF alongwith %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG. The latter tags will be expanded into a unique string according to the specified technology. Calls to this API within the same execution context are guaranteed to return the same unique name provided that the same parameters are passed to the call. e.g. <%=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG_AE", "ORACLE")%> <?=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG_AE", "ORACLE")?> C$_MY_TAB7wDiBe80vBog1auacS1xB_AE <?=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG.log", "FILE")?> C2_MY_TAB7wDiBe80vBog1auacS1xB.log 2.3 Name length generation  As part of name generation, the length of the generated name will be compared with the maximum length for the target technology and truncation may need to be applied. When a unique tag is included in the generated string it is important that uniqueness is not compromised by truncation of the unique tag. When a unique tag is NOT part of the generated name, the name will be truncated by removing characters from the end - this is the existing 11g algorithm. When a unique tag is included, the algorithm will first truncate the <postfix> and if necessary  the <prefix>. It is recommended that users will ensure there is sufficient uniqueness in the <prefix> section to ensure uniqueness of the final resultant name. SUMMARY To summarize, ODI 12c make it much simpler to utilize mappings in concurrent cases and provides APIs for helping developing any procedures or custom knowledge modules in such a way they can be used in highly concurrent, parallel scenarios. 

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  • SQL SERVER – Introduction to SQL Server 2014 In-Memory OLTP

    - by Pinal Dave
    In SQL Server 2014 Microsoft has introduced a new database engine component called In-Memory OLTP aka project “Hekaton” which is fully integrated into the SQL Server Database Engine. It is optimized for OLTP workloads accessing memory resident data. In-memory OLTP helps us create memory optimized tables which in turn offer significant performance improvement for our typical OLTP workload. The main objective of memory optimized table is to ensure that highly transactional tables could live in memory and remain in memory forever without even losing out a single record. The most significant part is that it still supports majority of our Transact-SQL statement. Transact-SQL stored procedures can be compiled to machine code for further performance improvements on memory-optimized tables. This engine is designed to ensure higher concurrency and minimal blocking. In-Memory OLTP alleviates the issue of locking, using a new type of multi-version optimistic concurrency control. It also substantially reduces waiting for log writes by generating far less log data and needing fewer log writes. Points to remember Memory-optimized tables refer to tables using the new data structures and key words added as part of In-Memory OLTP. Disk-based tables refer to your normal tables which we used to create in SQL Server since its inception. These tables use a fixed size 8 KB pages that need to be read from and written to disk as a unit. Natively compiled stored procedures refer to an object Type which is new and is supported by in-memory OLTP engine which convert it into machine code, which can further improve the data access performance for memory –optimized tables. Natively compiled stored procedures can only reference memory-optimized tables, they can’t be used to reference any disk –based table. Interpreted Transact-SQL stored procedures, which is what SQL Server has always used. Cross-container transactions refer to transactions that reference both memory-optimized tables and disk-based tables. Interop refers to interpreted Transact-SQL that references memory-optimized tables. Using In-Memory OLTP In-Memory OLTP engine has been available as part of SQL Server 2014 since June 2013 CTPs. Installation of In-Memory OLTP is part of the SQL Server setup application. The In-Memory OLTP components can only be installed with a 64-bit edition of SQL Server 2014 hence they are not available with 32-bit editions. Creating Databases Any database that will store memory-optimized tables must have a MEMORY_OPTIMIZED_DATA filegroup. This filegroup is specifically designed to store the checkpoint files needed by SQL Server to recover the memory-optimized tables, and although the syntax for creating the filegroup is almost the same as for creating a regular filestream filegroup, it must also specify the option CONTAINS MEMORY_OPTIMIZED_DATA. Here is an example of a CREATE DATABASE statement for a database that can support memory-optimized tables: CREATE DATABASE InMemoryDB ON PRIMARY(NAME = [InMemoryDB_data], FILENAME = 'D:\data\InMemoryDB_data.mdf', size=500MB), FILEGROUP [SampleDB_mod_fg] CONTAINS MEMORY_OPTIMIZED_DATA (NAME = [InMemoryDB_mod_dir], FILENAME = 'S:\data\InMemoryDB_mod_dir'), (NAME = [InMemoryDB_mod_dir], FILENAME = 'R:\data\InMemoryDB_mod_dir') LOG ON (name = [SampleDB_log], Filename='L:\log\InMemoryDB_log.ldf', size=500MB) COLLATE Latin1_General_100_BIN2; Above example code creates files on three different drives (D:  S: and R:) for the data files and in memory storage so if you would like to run this code kindly change the drive and folder locations as per your convenience. Also notice that binary collation was specified as Windows (non-SQL). BIN2 collation is the only collation support at this point for any indexes on memory optimized tables. It is also possible to add a MEMORY_OPTIMIZED_DATA file group to an existing database, use the below command to achieve the same. ALTER DATABASE AdventureWorks2012 ADD FILEGROUP hekaton_mod CONTAINS MEMORY_OPTIMIZED_DATA; GO ALTER DATABASE AdventureWorks2012 ADD FILE (NAME='hekaton_mod', FILENAME='S:\data\hekaton_mod') TO FILEGROUP hekaton_mod; GO Creating Tables There is no major syntactical difference between creating a disk based table or a memory –optimized table but yes there are a few restrictions and a few new essential extensions. Essentially any memory-optimized table should use the MEMORY_OPTIMIZED = ON clause as shown in the Create Table query example. DURABILITY clause (SCHEMA_AND_DATA or SCHEMA_ONLY) Memory-optimized table should always be defined with a DURABILITY value which can be either SCHEMA_AND_DATA or  SCHEMA_ONLY the former being the default. A memory-optimized table defined with DURABILITY=SCHEMA_ONLY will not persist the data to disk which means the data durability is compromised whereas DURABILITY= SCHEMA_AND_DATA ensures that data is also persisted along with the schema. Indexing Memory Optimized Table A memory-optimized table must always have an index for all tables created with DURABILITY= SCHEMA_AND_DATA and this can be achieved by declaring a PRIMARY KEY Constraint at the time of creating a table. The following example shows a PRIMARY KEY index created as a HASH index, for which a bucket count must also be specified. CREATE TABLE Mem_Table ( [Name] VARCHAR(32) NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT = 100000), [City] VARCHAR(32) NULL, [State_Province] VARCHAR(32) NULL, [LastModified] DATETIME NOT NULL, ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); Now as you can see in the above query example we have used the clause MEMORY_OPTIMIZED = ON to make sure that it is considered as a memory optimized table and not just a normal table and also used the DURABILITY Clause= SCHEMA_AND_DATA which means it will persist data along with metadata and also you can notice this table has a PRIMARY KEY mentioned upfront which is also a mandatory clause for memory-optimized tables. We will talk more about HASH Indexes and BUCKET_COUNT in later articles on this topic which will be focusing more on Row and Index storage on Memory-Optimized tables. So stay tuned for that as well. Now as we covered the basics of Memory Optimized tables and understood the key things to remember while using memory optimized tables, let’s explore more using examples to understand the Performance gains using memory-optimized tables. I will be using the database which i created earlier in this article i.e. InMemoryDB in the below Demo Exercise. USE InMemoryDB GO -- Creating a disk based table CREATE TABLE dbo.Disktable ( Id INT IDENTITY, Name CHAR(40) ) GO CREATE NONCLUSTERED INDEX IX_ID ON dbo.Disktable (Id) GO -- Creating a memory optimized table with similar structure and DURABILITY = SCHEMA_AND_DATA CREATE TABLE dbo.Memorytable_durable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA) GO -- Creating an another memory optimized table with similar structure but DURABILITY = SCHEMA_Only CREATE TABLE dbo.Memorytable_nondurable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_only) GO -- Now insert 100000 records in dbo.Disktable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Disktable(Name) VALUES('sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Do the same inserts for Memory table dbo.Memorytable_durable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_durable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Now finally do the same inserts for Memory table dbo.Memorytable_nondurable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_nondurable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END The above 3 Inserts took 1.20 minutes, 54 secs, and 2 secs respectively to insert 100000 records on my machine with 8 Gb RAM. This proves the point that memory-optimized tables can definitely help businesses achieve better performance for their highly transactional business table and memory- optimized tables with Durability SCHEMA_ONLY is even faster as it does not bother persisting its data to disk which makes it supremely fast. Koenig Solutions is one of the few organizations which offer IT training on SQL Server 2014 and all its updates. Now, I leave the decision on using memory_Optimized tables on you, I hope you like this article and it helped you understand  the fundamentals of IN-Memory OLTP . Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Koenig

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  • Scheduling thread tiles with C++ AMP

    - by Daniel Moth
    This post assumes you are totally comfortable with, what some of us call, the simple model of C++ AMP, i.e. you could write your own matrix multiplication. We are now ready to explore the tiled model, which builds on top of the non-tiled one. Tiling the extent We know that when we pass a grid (which is just an extent under the covers) to the parallel_for_each call, it determines the number of threads to schedule and their index values (including dimensionality). For the single-, two-, and three- dimensional cases you can go a step further and subdivide the threads into what we call tiles of threads (others may call them thread groups). So here is a single-dimensional example: extent<1> e(20); // 20 units in a single dimension with indices from 0-19 grid<1> g(e);      // same as extent tiled_grid<4> tg = g.tile<4>(); …on the 3rd line we subdivided the single-dimensional space into 5 single-dimensional tiles each having 4 elements, and we captured that result in a concurrency::tiled_grid (a new class in amp.h). Let's move on swiftly to another example, in pictures, this time 2-dimensional: So we start on the left with a grid of a 2-dimensional extent which has 8*6=48 threads. We then have two different examples of tiling. In the first case, in the middle, we subdivide the 48 threads into tiles where each has 4*3=12 threads, hence we have 2*2=4 tiles. In the second example, on the right, we subdivide the original input into tiles where each has 2*2=4 threads, hence we have 4*3=12 tiles. Notice how you can play with the tile size and achieve different number of tiles. The numbers you pick must be such that the original total number of threads (in our example 48), remains the same, and every tile must have the same size. Of course, you still have no clue why you would do that, but stick with me. First, we should see how we can use this tiled_grid, since the parallel_for_each function that we know expects a grid. Tiled parallel_for_each and tiled_index It turns out that we have additional overloads of parallel_for_each that accept a tiled_grid instead of a grid. However, those overloads, also expect that the lambda you pass in accepts a concurrency::tiled_index (new in amp.h), not an index<N>. So how is a tiled_index different to an index? A tiled_index object, can have only 1 or 2 or 3 dimensions (matching exactly the tiled_grid), and consists of 4 index objects that are accessible via properties: global, local, tile_origin, and tile. The global index is the same as the index we know and love: the global thread ID. The local index is the local thread ID within the tile. The tile_origin index returns the global index of the thread that is at position 0,0 of this tile, and the tile index is the position of the tile in relation to the overall grid. Confused? Here is an example accompanied by a picture that hopefully clarifies things: array_view<int, 2> data(8, 6, p_my_data); parallel_for_each(data.grid.tile<2,2>(), [=] (tiled_index<2,2> t_idx) restrict(direct3d) { /* todo */ }); Given the code above and the picture on the right, what are the values of each of the 4 index objects that the t_idx variables exposes, when the lambda is executed by T (highlighted in the picture on the right)? If you can't work it out yourselves, the solution follows: t_idx.global       = index<2> (6,3) t_idx.local          = index<2> (0,1) t_idx.tile_origin = index<2> (6,2) t_idx.tile             = index<2> (3,1) Don't move on until you are comfortable with this… the picture really helps, so use it. Tiled Matrix Multiplication Example – part 1 Let's paste here the C++ AMP matrix multiplication example, bolding the lines we are going to change (can you guess what the changes will be?) 01: void MatrixMultiplyTiled_Part1(vector<float>& vC, const vector<float>& vA, const vector<float>& vB, int M, int N, int W) 02: { 03: 04: array_view<const float,2> a(M, W, vA); 05: array_view<const float,2> b(W, N, vB); 06: array_view<writeonly<float>,2> c(M, N, vC); 07: parallel_for_each(c.grid, 08: [=](index<2> idx) restrict(direct3d) { 09: 10: int row = idx[0]; int col = idx[1]; 11: float sum = 0.0f; 12: for(int i = 0; i < W; i++) 13: sum += a(row, i) * b(i, col); 14: c[idx] = sum; 15: }); 16: } To turn this into a tiled example, first we need to decide our tile size. Let's say we want each tile to be 16*16 (which assumes that we'll have at least 256 threads to process, and that c.grid.extent.size() is divisible by 256, and moreover that c.grid.extent[0] and c.grid.extent[1] are divisible by 16). So we insert at line 03 the tile size (which must be a compile time constant). 03: static const int TS = 16; ...then we need to tile the grid to have tiles where each one has 16*16 threads, so we change line 07 to be as follows 07: parallel_for_each(c.grid.tile<TS,TS>(), ...that means that our index now has to be a tiled_index with the same characteristics as the tiled_grid, so we change line 08 08: [=](tiled_index<TS, TS> t_idx) restrict(direct3d) { ...which means, without changing our core algorithm, we need to be using the global index that the tiled_index gives us access to, so we insert line 09 as follows 09: index<2> idx = t_idx.global; ...and now this code just works and it is tiled! Closing thoughts on part 1 The process we followed just shows the mechanical transformation that can take place from the simple model to the tiled model (think of this as step 1). In fact, when we wrote the matrix multiplication example originally, the compiler was doing this mechanical transformation under the covers for us (and it has additional smarts to deal with the cases where the total number of threads scheduled cannot be divisible by the tile size). The point is that the thread scheduling is always tiled, even when you use the non-tiled model. But with this mechanical transformation, we haven't gained anything… Hint: our goal with explicitly using the tiled model is to gain even more performance. In the next post, we'll evolve this further (beyond what the compiler can automatically do for us, in this first release), so you can see the full usage of the tiled model and its benefits… Comments about this post by Daniel Moth welcome at the original blog.

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  • Master-slave vs. peer-to-peer archictecture: benefits and problems

    - by Ashok_Ora
    Normal 0 false false false EN-US X-NONE X-NONE Almost two decades ago, I was a member of a database development team that introduced adaptive locking. Locking, the most popular concurrency control technique in database systems, is pessimistic. Locking ensures that two or more conflicting operations on the same data item don’t “trample” on each other’s toes, resulting in data corruption. In a nutshell, here’s the issue we were trying to address. In everyday life, traffic lights serve the same purpose. They ensure that traffic flows smoothly and when everyone follows the rules, there are no accidents at intersections. As I mentioned earlier, the problem with typical locking protocols is that they are pessimistic. Regardless of whether there is another conflicting operation in the system or not, you have to hold a lock! Acquiring and releasing locks can be quite expensive, depending on how many objects the transaction touches. Every transaction has to pay this penalty. To use the earlier traffic light analogy, if you have ever waited at a red light in the middle of nowhere with no one on the road, wondering why you need to wait when there’s clearly no danger of a collision, you know what I mean. The adaptive locking scheme that we invented was able to minimize the number of locks that a transaction held, by detecting whether there were one or more transactions that needed conflicting eyou could get by without holding any lock at all. In many “well-behaved” workloads, there are few conflicts, so this optimization is a huge win. If, on the other hand, there are many concurrent, conflicting requests, the algorithm gracefully degrades to the “normal” behavior with minimal cost. We were able to reduce the number of lock requests per TPC-B transaction from 178 requests down to 2! Wow! This is a dramatic improvement in concurrency as well as transaction latency. The lesson from this exercise was that if you can identify the common scenario and optimize for that case so that only the uncommon scenarios are more expensive, you can make dramatic improvements in performance without sacrificing correctness. So how does this relate to the architecture and design of some of the modern NoSQL systems? NoSQL systems can be broadly classified as master-slave sharded, or peer-to-peer sharded systems. NoSQL systems with a peer-to-peer architecture have an interesting way of handling changes. Whenever an item is changed, the client (or an intermediary) propagates the changes synchronously or asynchronously to multiple copies (for availability) of the data. Since the change can be propagated asynchronously, during some interval in time, it will be the case that some copies have received the update, and others haven’t. What happens if someone tries to read the item during this interval? The client in a peer-to-peer system will fetch the same item from multiple copies and compare them to each other. If they’re all the same, then every copy that was queried has the same (and up-to-date) value of the data item, so all’s good. If not, then the system provides a mechanism to reconcile the discrepancy and to update stale copies. So what’s the problem with this? There are two major issues: First, IT’S HORRIBLY PESSIMISTIC because, in the common case, it is unlikely that the same data item will be updated and read from different locations at around the same time! For every read operation, you have to read from multiple copies. That’s a pretty expensive, especially if the data are stored in multiple geographically separate locations and network latencies are high. Second, if the copies are not all the same, the application has to reconcile the differences and propagate the correct value to the out-dated copies. This means that the application program has to handle discrepancies in the different versions of the data item and resolve the issue (which can further add to cost and operation latency). Resolving discrepancies is only one part of the problem. What if the same data item was updated independently on two different nodes (copies)? In that case, due to the asynchronous nature of change propagation, you might land up with different versions of the data item in different copies. In this case, the application program also has to resolve conflicts and then propagate the correct value to the copies that are out-dated or have incorrect versions. This can get really complicated. My hunch is that there are many peer-to-peer-based applications that don’t handle this correctly, and worse, don’t even know it. Imagine have 100s of millions of records in your database – how can you tell whether a particular data item is incorrect or out of date? And what price are you willing to pay for ensuring that the data can be trusted? Multiple network messages per read request? Discrepancy and conflict resolution logic in the application, and potentially, additional messages? All this overhead, when all you were trying to do was to read a data item. Wouldn’t it be simpler to avoid this problem in the first place? Master-slave architectures like the Oracle NoSQL Database handles this very elegantly. A change to a data item is always sent to the master copy. Consequently, the master copy always has the most current and authoritative version of the data item. The master is also responsible for propagating the change to the other copies (for availability and read scalability). Client drivers are aware of master copies and replicas, and client drivers are also aware of the “currency” of a replica. In other words, each NoSQL Database client knows how stale a replica is. This vastly simplifies the job of the application developer. If the application needs the most current version of the data item, the client driver will automatically route the request to the master copy. If the application is willing to tolerate some staleness of data (e.g. a version that is no more than 1 second out of date), the client can easily determine which replica (or set of replicas) can satisfy the request, and route the request to the most efficient copy. This results in a dramatic simplification in application logic and also minimizes network requests (the driver will only send the request to exactl the right replica, not many). So, back to my original point. A well designed and well architected system minimizes or eliminates unnecessary overhead and avoids pessimistic algorithms wherever possible in order to deliver a highly efficient and high performance system. If you’ve every programmed an Oracle NoSQL Database application, you’ll know the difference! /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}

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  • CodePlex Daily Summary for Friday, May 14, 2010

    CodePlex Daily Summary for Friday, May 14, 2010New ProjectsCampfire#: Campfire# is a campfire client written in .NET 4.0 using WPF, which uses the Campfire API.CHESS: Systematic Concurrency Testing: CHESS is a tool for systematic and disciplined concurrency testing. Given a concurrent test, CHESS systematically enumerates the possible thread sc...cmpp: cmppcycloid: Arcanoid gameDotNetNuke® C#: The DotNetNuke® project is developed and maintained on a Visual Basic codebase, however a C# version has always been a popular request. This is a ...EasyBuildingCMS.NET: EasyBuildingCMS is an easy use content management system.fluidCMS: Provide for flexible management of web content that is not tightly integrated with the layout and rendering of sites that consume the content.Golem: An automation tool oriented to localization engineering environmentHB Batch Encoder Mk 2: HandBrake Batch Encoder Mk II This Program was adapted from an original project downloaded from codeplex by the name of "Handbrake Batch Encoder"...Integrating Social Media Networks: This is part of my pos graduation project.Ketonic: The Ketonic project aims to improve development of websites based on the Kentico CMS. LinkSharp: LinkSharp is a short-URL provider that can be used to generate short static non changing URL's. The web interface allows you to easily add / edit /...PUC NET (C++ Network Library - PUC Minas): This is an Academic Library for an Easy Development of Applications and Games based on Network Communication.Regular Expression Tester: Small utility for testing regular expressionsSharePoint User Management WebPart: SharePoint User Management WebPartSharpBox: SharpBox makes it easier for .NET developers to interact with existing cloud storage service, e.g. DropBox or Amazon S3Snipivit: Snipivit is a snippet manager service and VS2010 plugin that allows small development teams to store all their code snippets on a central database,...Software Factories Applied: Software Factories Applied is a project collecting the companion bits for the eponymous book to be published by Wiley & Sons in 2011. The authors ...The Ping Master: A service that periodically pings network addresses and allows the running of command line type utilities in response to success or failure.Title Safe Region Checker: A simple utility for XNA developers to check screenshots from games intended for release on the LIVE Marketplace for "title safe" region compliance...Trial project: sky is blueUyghur Named Date: Generate Uyghur named date string. ئۇيغۇرچە ئاي ناملىق چىسلا ھاسىل قىلىشWildcard Search Web Part for SharePoint 2010: The Wildcard Search web part for MOSS 2007 was wildly successful. Although, SharePoint 2010 has built-in wildcard searching functionality, the out...在线Office控件 Online Offical Control: 在线Office控件软件作品发布平台: SoftwarePublishPlatform 软件作品发布平台New ReleasesDemina: Demina Binaries version 0.1: Demina binaries are now available. This release (version 0.1) is an alpha version. Please report any bugs for extermination.EasyTFS: EasyTfs 1.0 Beta 2: Added cache refreshing when contents are updated rather than just every 10 minutes. Added window title based on currently-open case. Added attachme...Extending C# editor - Outlining, classification: Initial release: Initial releaseHB Batch Encoder Mk 2: HB Batch Encoder Mk2 v1.01: Binary release files.HB Batch Encoder Mk 2: Source Code: Source CodeHobbyBrew Mobile: Beta 2: Corretti numerosi bug, data un implementazione "approssimativa" del riscaldamento per Infusione. Aggiornamento consigliato!HouseFly controls: HouseFly controls beta 1.0.2.0: HouseFly controls relase 1.0.2.0 betaHtml Reader: Beta 2: I fixed a bug in HtmlElementCollection, Which exposed an integer enumerator, instead of enumerating through HtmlElements. I added a WPF Window tha...Html to OpenXml: HtmlToOpenXml 1.2: Fix some reported bug. See change set for description. The dll library to include in your project. The dll is signed for GAC support. Compiled wi...Infection Protection: Infection Protection 0.1: This is the final version of Infection Protection that was entered into the 2010 OGPC game competition.Jobping Url Shortener: Deploy Code 0.5.1: Deployment code for Version 0.5 This version includes our Jobping style.Jobping Url Shortener: Source Code 0.5.1: Source code for the 0.5 release. This release includes our Jobping style skin.Kooboo HTML form: Kooboo HTML form module 2.1.0.1: HTML form module contributed by member aledelgo. Add SMTP user and password authentication.KooBoo Image Galery: Beta 2: This new version corrects some issues pointed by Guoqi Zheng Some schema and folders were renamed, so it's better to uninstall the module and remo...MFCMAPI: May 2010 Release: If you just want to run the tool, get the executable. If you want to debug it, get the symbol file and the source. Build: 6.0.0.1020 The 64 bit bu...MVC Turbine: Release 2.1 for MVC2: This RTM contains the same features as v2.0 RTM plus these features: Instance Registration to IServiceLocator You can now add an instance of a typ...NazTek.Extension.Clr4: NazTek.Extension.Clr4 Binary: Binary releaseNazTek.Extension.Clr4: NazTek.Extension.Clr4 Source: Cab with source codeNSIS Autorun: NSIS Autorun 0.1.8: This release includes source code, executable binaries and example materials.Ottawa IT Day: 2010 Source Code and Presentations: During the Ottawa IT Day 2010, some of the presenters shared their code (and some presentations). This release is the culmination of all those effo...PHPWord: PHPWord 0.6.1 Beta: Changelog: Fixed Error when adding a JPEG image and opening in office 2007 Issue #1 Fixed Already defined constant PHPWORD_BASE_PATH Issue #2 F...Rapid Dictionary: Rapid Dictionary Alpha 2.0: Release Notes * Try auto updatable version: http://install.rapiddict.com/index.html Rapid Dictionary Alpha 2.0 includes such functionality: ...Shake - C# Make: Shake v0.1.18: Core changes. Process wrapper class, console logger, etc.SharpBox: SharpBox-Trunk: This is the SharpBox build from the trunk source branch!SharpBox: SharpBox-Trunk-Initial-Source: The initial source code, will be updated from time to timeSpackle.NET: 4.0.0.0 Release: This new drop contains the following A CreateBigInteger() method on SecureRandom to create random BigInteger values. An extension method to prop...StreamInsight example queries, input adapters and output adapters: StreamInsight Examples for V1.0 RTM: Zipped source code.The Ping Master: v0.1.0.0: Early release of The Ping Master for test purposes. Configuration tool is unfinished and does not include an installer.Title Safe Region Checker: Title Safe Region Checker v1.0.0.1: Release 1.0 of Title Safe Region Checker. No known bugs or problems. File is a zipped directory containing the necessary installation files.TortoiseHg: TortoiseHg 1.0.3: This is a bug fix release, we recommend all users upgrade to 1.0.3Usa*Usa Libraly: Smart.Windows.Navigation 0.4: Smart.Windows.Navigation simple navigation library ver 0.4.0. Include Windows Forms & Compact Framework samples. Information - Smart.Windows.Mvc ...VCC: Latest build, v2.1.30513.0: Automatic drop of latest buildWabbitStudio Z80 Software Tools: Wabbitcode: Wabbitcode is an Z80 Assembly IDE for Windows, OS X, and Linux. Built to take full advantage of the features of SPASM and Wabbitemu, Wabbitcode has...white: Release 0.20: Source Code: https://white-project.googlecode.com/svn/tags/0.20 Add few more keyboard keys like windows button and F13-F24. Fixed bugs for keyboar...Wildcard Search Web Part for SharePoint 2010: Version 1.0 Release 1: This is the initial release of the Wildcard Search Web Part for SharePoint 2010. All queries will be issued as wildcards unless disabled with the ...Windows Azure Command-line Tools for PHP Developers: Windows Azure Command-line Tools May 2010 Update: May 2010 Update – May 13, 2010 We are pleased to announced the May 2010 update of Windows Azure Command-Line Tools. In addition to bug fixes and i...WinXmlCook: WinXmlCook 2.1: Version 2.1 released!Xrns2XMod: Xrns2XMod 1.1: some source code optimization在线Office控件 Online Offical Control: SPOffice2.0Release: 该版本在MS Office2003/2007,WPS2009,WPS2010下测试通过Most Popular ProjectsRawrWBFS ManagerAJAX Control ToolkitMicrosoft SQL Server Product Samples: DatabaseSilverlight ToolkitWindows Presentation Foundation (WPF)patterns & practices – Enterprise LibraryMicrosoft SQL Server Community & SamplesPHPExcelASP.NETMost Active Projectspatterns & practices – Enterprise LibraryMirror Testing SystemRawrBlogEngine.NETPHPExcelMicrosoft Biology FoundationwhiteWindows Azure Command-line Tools for PHP DevelopersStyleCopShake - C# Make

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  • SRs @ Oracle: How do I License Thee?

    - by [email protected]
    With the release of the new Sun Ray product last week comes the advent of a different software licensing model. Where Sun had initially taken the approach of '1 desktop device = one license', we later changed things to be '1 concurrent connection to the server software = one license', and while there were ways to tell how many connections there were at a time, it wasn't the easiest thing to do.  And, when should you measure concurrency?  At your busiest time, of course... but when might that be?  9:00 Monday morning this week might yield a different result than 9:00 Monday morning last week.In the acquisition of this desktop virtualization product suite Oracle has changed things to be, in typical Oracle fashion, simpler.  There are now two choices for customers around licensing: Named User licenses and Per Device licenses.Here's how they work, and some examples:The Rules1) A Sun Ray device, and PC running the Desktop Access Client (DAC), are both considered unique devices.OR, 2) Any user running a session on either a Sun Ray or an DAC is still just one user.So, you have a choice of path to go down.Some Examples:Here are 6 use cases I can think of right now that will help you choose the Oracle server software licensing model that is right for your business:Case 1If I have 100 Sun Rays for 100 users, and 20 of them use DAC at home that is 100 user licenses.If I have 100 Sun Rays for 100 users, and 20 of them use DAC at home that is 120 device licenses.Two cases using the same metrics - different licensing models and therefore different results.Case 2If I have 100 Sun Rays for 200 users, and 20 of them use DAC at home that is 200 user licenses.If I have 100 Sun Rays for 200 users, and 20 of them use DAC at home that is 120 device licenses.Same metrics - very different results.Case 3If I have 100 Sun Rays for 50 users, and 20 of them use DAC at home that is 50 user licenses.If I have 100 Sun Rays for 50 users, and 20 of them use DAC at home that is 120 device licenses.Same metrics - but again - very different results.Based on the way your business operates you should be able to see which of the two licensing models is most advantageous to you.Got questions?  I'll try to help.(Thanks to Brad Lackey for the clarifications!)

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  • Problem with IIS 6.0 in WOW WCF 4 (.net 4.0)

    - by Kevin
    We just upgraded to WCF 4 on IIS 6 (running in WoW 32 bit mode), and all of a sudden the services started running into what appears to be concurrency problems. Upon finding out we had a problem, we changed the Behavior Configuration Changes on the WCF server to the follow: <serviceThrottling maxConcurrentCalls="1000" maxConcurrentInstances="1000" maxConcurrentSessions="1000" /> We also changed the number of worker processes from 1 to 5. Doing all of this seemed to have no effect. The service seemed to be running, but throttled by something. Is there anything else that might need to be changed to remove the "artificial" throttling? Were using the default configuration WCF which should be Per-Call (not singleton).

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  • Reminder: True WCF Asynchronous Operation

    - by Sean Feldman
    A true asynchronous service operation is not the one that returns void, but the one that is marked as IsOneWay=true using BeginX/EndX asynchronous operations (thanks Krzysztof). To support this sort of fire-and-forget invocation, Windows Communication Foundation offers one-way operations. After the client issues the call, Windows Communication Foundation generates a request message, but no correlated reply message will ever return to the client. As a result, one-way operations can't return values, and any exception thrown on the service side will not make its way to the client. One-way calls do not equate to asynchronous calls. When one-way calls reach the service, they may not be dispatched all at once and may be queued up on the service side to be dispatched one at a time, all according to the service configured concurrency mode behavior and session mode. How many messages (whether one-way or request-reply) the service is willing to queue up is a product of the configured channel and the reliability mode. If the number of queued messages has exceeded the queue's capacity, then the client will block, even when issuing a one-way call. However, once the call is queued, the client is unblocked and can continue executing while the service processes the operation in the background. This usually gives the appearance of asynchronous calls.

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  • PASS 13 Dispatches: Memory Optimized = On

    - by Tony Davis
    I'm at the PASS Summit in Charlotte for the Day 1 keynote by Quentin Clarke, Corporate VP of the data platform group at Microsoft. He's talking about how SQL Server 2014 is “pushing boundaries” and first up is SQL Server 2014's In-Memory OLTP technology (former codename “hekaton”) It is a feature that provokes a lot of interest and for good reason as, without any need for application rewrites or hardware updates, it can enable us to ensure that an application can find in memory most or all of the data it needs, and can lead to huge improvements in processing times. A good recent hekaton use cases article talks about applications that need a “Shock Absorber” when either spikes or just a high rate of incoming workload (including data in ETL scenarios) become a primary bottleneck. To get a really deep look at this technology, I would check out David DeWitt's summit keynote tomorrow (it will be live streamed). Other than that, to get started I'd recommend Kalen Delaney's whitepaper. She offers a lot of insight into how it works and how to start to define memory-optimized tables, and natively compiled stored procedures. These memory-optimized tables uses completely optimistic multi-version concurrency control – no waiting on locks! After that, Tom LaRock has compiled a useful set of links to drill deeper, and includes one to Microsoft's AMR tool to help you gauge the tables that might benefit most. Tony.

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  • VS 2010 IDE Features in a nutshell

    - by Rajesh Pillai
    Going through a VS 2010 IDE Features.  We will explore each feature in subsequent posts.  The post are documented as being reviewed by me.   Breakpoint Labeling Breakpoint Searching Breakpoint Import/Export Dynamic Data Tooling WPF Tree Visualizer Call Hierarchy Improved WPF Tooling Historical Debugging Mini-Dump Debugging Quick Search Better Multi-Monitor Support Highlight References Parallel Stacks Window Parallel Tasks Window Document Map Margin Generate from Usage Concurrency Profiler Inline Call Tree Extensible Test Runner MVC Tooling Web Deploy JQuery IntelliSense SharePoint Tooling HTML Snippets Web.config Transformation ClickOnce Enhancements for MS Office     VS is an editor as well as a platform for development and this is only more true with VS 2010.  As an editor there is improved forcus on writing code, understanding code, navigating and publishing code.   VS Shell has been completely rewritten using WPF extending huge benefits.  The start page has been rewritten using XAML, so it is easy to customize.   Support new support for Silverlight, MFC, F# , Azure and extended support for Office 2010, Sharepoint.   Has a good Extension Manager as well.   Enjoy Coding !!!

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  • C# via Java: Introduction

    - by simonc
    Originally posted on: http://geekswithblogs.net/simonc/archive/2013/11/08/c-via-java-introduction.aspxSo, I've recently changed jobs. Rather than working in .NET land, I've migrated over to Java land. But never fear! I'll continue to peer under the covers of .NET, but my next series will use my new experience in Java to explore the design decisions made in the development of the C# programming language. After all, the design of C# was based on Java 1.2, and both languages have continued to evolve since then, incorporating modern software engineering concepts and requirements. Exploring the differences and similarities between the two will (hopefully) give us a deeper understanding into why .NET is implemented the way it is, the trade-offs involved, and what choices were made when new features were designed and added to the language and framework. Among others, I'll be looking at differences in: Primitives Operators Generics Exceptions Accessibility Collections Delegates and inner classes Concurrency In my next post, I'll start off by looking at the type primitives available in each language, and how Java and C# actually incorporate two different concepts of primitive types in their fundamental language design and use. I'm also thinking of looking at the inner details of Java and the JVM in my blogs, as well as C# and the CLR. If you've got any comments or thoughts on this, please let me know.

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  • C# via Java: Introduction

    - by Simon Cooper
    So, I’ve recently changed jobs. Rather than working in .NET land, I’ve migrated over to Java land. But never fear! I’ll continue to peer under the covers of .NET, but my next series will use my new experience in Java to explore the design decisions made in the development of the C# programming language. After all, the design of C# was based on Java 1.2, and both languages have continued to evolve since then, incorporating modern software engineering concepts and requirements. Exploring the differences and similarities between the two will (hopefully) give us a deeper understanding into why .NET is implemented the way it is, the trade-offs involved, and what choices were made when new features were designed and added to the language and framework. Among others, I’ll be looking at differences in: Primitives Operators Generics Exceptions Accessibility Collections Delegates and inner classes Concurrency In my next post, I’ll start off by looking at the type primitives available in each language, and how Java and C# actually incorporate two different concepts of primitive types in their fundamental language design and use. I’m also thinking of looking at the inner details of Java and the JVM in my blogs, as well as C# and the CLR. If you’ve got any comments or thoughts on this, please let me know.

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  • Configuring jdbc-pool (tomcat 7)

    - by john
    i'm having some problems with tomcat 7 for configuring jdbc-pool : i`ve tried to follow this example: http://www.tomcatexpert.com/blog/2010/04/01/configuring-jdbc-pool-high-concurrency so i have: conf/server.xml <GlobalNamingResources> <Resource type="javax.sql.DataSource" name="jdbc/DB" factory="org.apache.tomcat.jdbc.pool.DataSourceFactory" driverClassName="com.mysql.jdbc.Driver" url="jdbc:mysql://localhost:3306/mydb" username="user" password="password" /> </GlobalNamingResources> conf/context.xml <Context> <ResourceLink type="javax.sql.DataSource" name="jdbc/LocalDB" global="jdbc/DB" /> <Context> and when i try to do this: Context initContext = new InitialContext(); Context envContext = (Context)initContext.lookup("java:/comp/env"); DataSource datasource = (DataSource)envContext.lookup("jdbc/LocalDB"); Connection con = datasource.getConnection(); i keep getting this error: javax.naming.NameNotFoundException: Name jdbc is not bound in this Context at org.apache.naming.NamingContext.lookup(NamingContext.java:803) at org.apache.naming.NamingContext.lookup(NamingContext.java:159) pls help tnx

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  • Announcing Berkeley DB Java Edition Major Release

    - by Eric Jensen
    Berkeley DB Java Edition 5.0 was just released. There are a number of new features, enhancements, and options in there that our users have been asking for. Chief among them is a new class called DiskOrderedCursor, which greatly increases performance of systems using spinning platter magnetic hard drives. A number of users expressed interest in this feature, including Alex Feinberg of LinkedIn. Berkeley DB Java Edition is part of Project Voldemort, a distributed key/value database used by LinkedIn. There have been many other improvements and optimizations. Concurrency is significantly improved, as is the performance of update and delete operations. New and interesting methods include Environment.preload, which allows multiple databases to be preloaded simultaneously. New Cursor methods enable for more effective searching through the database. We continue to enhance Berkeley DB Java Edition’s High Availability as well. One new feature is the ability to open a replicated node read-only when the master is unavailable. This can allow critical systems to continue offering some functionality, even during a network or master node failure. There’s a lot more in release 5.0. I encourage you to take a look at the extensive changelog yourself. As always, you can download the new release and try it out here: http://www.oracle.com/technetwork/database/berkeleydb/downloads/index.html

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  • Parallel Computing Platform Developer Lab

    - by Daniel Moth
    This is an exciting announcement that I must share: "Microsoft Developer & Platform Evangelism, in collaboration with the Microsoft Parallel Computing Platform product team, is hosting a developer lab at the Platform Adoption Center on April 12-15, 2010.  This event is for Microsoft Partners and Customers seeking to incorporate either .NET Framework 4 or Visual C++ 2010 parallelism features into their new or existing applications, and to gain expertise with new Visual Studio 2010 tools including the Parallel Tasks and Parallel Stacks debugger toolwindows, and the Concurrency Visualizer in the profiler. Opportunities for attendees include: Gain expert design assistance with your Parallel Computing Platform based solution. Develop a solution prototype in collaboration with Microsoft Software Engineers. Attend topical presentations and “chalk-talk” sessions. Your team will be assigned private, secure offices for confidential collaboration activities. The event has limited capacity, thus enrollment is based on an application process.   Please download and complete the application form then return it to the event management team per instructions included within the form.  Applications will be evaluated based upon the technical solution scenario along with indicated project readiness timelines.  Microsoft event management team members may contact you directly for additional clarification and discussion of your project scenario during the nomination process." Comments about this post welcome at the original blog.

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  • AIOUG TechDay @ Lovely Professional University, Jalandhar, India

    - by Tori Wieldt
    by guest blogger Jitendra Chittoda, co-leader, Delhi and NCR JUG On 30 August 2013, Lovely Professional University (LPU) Jalandhar organized an All India Oracle User Group (AIOUG) TechDay event on Oracle and Java. This was a full day event with various sessions on J2EE 6, Java Concurrency, NoSQL, MongoDB, Oracle 12c, Oracle ADF etc. It was an overwhelming response from students, auditorium was jam packed with 600+ LPU energetic students of B.Tech and MCA stream. Navein Juneja Sr. Director LPU gave the keynote and introduced the speakers of AIOUG and Delhi & NCR Java User Group (DaNJUG). Mr. Juneja explained about the LPU and its students. He explained how Oracle and Java is most used and accepted technologies in world. Rohit Dhand Additional Dean LPU came on stage and share about how his career started with Oracle databases. He encouraged students to learn these technologies and build their career. Satyendra Kumar vice-president AIOUG thanked LPU and their stuff for organizing such a good technical event and students for their overwhelming response.  He talked about the India Oracle group and its events at various geographical locations all over India. Jitendra Chittoda Co-Leader DaNJUG explained how to make a new Java User Groups (JUG), what are its benefits and how to promote it. He explained how the Indian JUGs are contributing to the different initiatives like Adopt-a-JSR and Adopt-OpenJDK. After the inaugural address event started with two different tracks one for Oracle Database and another for Java and its related technologies. Speakers: Satyendra Kumar Pasalapudi (Co-founder and Vice President of AIOUG) Aman Sharma (Oracle Database Consultant and Instructor) Shekhar Gulati (OpenShift Developer Evangelist at RedHat) Rohan Walia (Oracle ADF Consultant at Oracle) Jitendra Chittoda (Co-leader Delhi & NCR JUG and Senior Developer at ION Trading)

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  • New Release of Oracle Berkeley DB

    - by Eric Jensen
    We are pleased to announce that a new release of Oracle Berkeley DB, version 11.2.5.2.28, is available today. Our latest release includes yet more value added features for SQLite users, as well as several performance enhancements and new customer-requested features to the key-value pair API.  We continue to provide technology leadership, features and performance for SQLite applications.  This release introduces additional features that are not available in native SQLite, and adds functionality allowing customers to create richer, more scalable, more concurrent applications using the Berkeley DB SQL API. This release is compelling to Oracle’s customers and partners because it: delivers a complete, embeddable SQL92 database as a library under 1MB size drop-in API compatible with SQLite version 3 no-oversight, zero-touch database administration industrial quality, battle tested Berkeley DB B-TREE for concurrent transactional data storage New Features Include: MVCC support for even higher concurrency direct SQL support for HA/replication transactionally protected Sequence number generation functions lower memory requirements, shared memory regions and faster/smaller memory on startup easier B-TREE page size configuration with new ''db_tuner" utility New Key-Value API Features Include: HEAP access method for constrained disk-space applications (key-value API) faster QUEUE access method operations for highly concurrent applications -- up 2-3X faster! (key-value API) new X/open compliant XA resource manager, easily integrated with Oracle Tuxedo (key-value API) additional HA/replication management and communication options (key-value API) and a lot more! BDB is hands-down the best edge, mobile, and embedded database available to developers. Downloads available today on the Berkeley DB download pageProduct Documentation

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  • New Packt Books: APEX & JRockit

    - by [email protected]
      I have received these 2 ebooks from Packt Publkishing and I am currently reviewing them. Both of them look great so far.   Oracle Application Express 3.2 - The Essentials and More First of all, I have to mention that I am new to APEX. I was interested on this product which is a development tool for Web applications on the Oracle Database. As I support JDeveloper and ADF, which are products that work very closely with the Oracle Database and are a rapid development tool as well, it is always interesting and useful to know complementary tools. APEX looks very useful and the book includes many working examples. A more complete review of this book is coming soon. Further information about this book can be seen at Packt.   Oracle JRockit: The Definitive Guide Many of our Oracle Coherence customers run their caches and clusters using JRockit. This JVM has helped us to solve lots of Service Requests. It is a really reliable, fast and stable JVM. It works great on both development and production environments with big amounts of data, concurrency, multi-threading and many other factors that can make a JVM crash. I must also mention JRockit Mission Control (JRMC), which is a great tool for management and monitoring. I really recommend it. As a matter of fact, some months ago, I created a document entitled "How to Monitor Coherence-Based Applications using JRockit Mission Control" (Doc Id 961617.1) on My Oracle Support. Also, the JRockit Runtime Analyzer (JRA) and it successor of newer versions, the JRockit Flight Recorder (JFR) are deeply reviewed. This book contains very clear and complete information about all this and more. I will post an entry with a more complete review soon (and will probably post an entry about Coherence monitoring with JRMC soon too). Further information about this book can be seen at Packt.  

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  • Tab Sweep - Upgrade to Java EE 6, Groovy NetBeans, JSR310, JCache interview, OEPE, and more

    - by alexismp
    Recent Tips and News on Java, Java EE 6, GlassFish & more : • Implementing JSR 310 (New Date/Time API) in Java 8 Is Very Strongly Favored by Developers (java.net) • Upgrading To The Java EE 6 Web Profile (Roger) • NetBeans for Groovy (blogs.oracle.com) • Client Side MOXy JSON Binding Explained (Blaise) • Control CDI Containers in SE and EE (Strub) • Java EE on Google App Engine: CDI to the Rescue - Aleš Justin (jaxenter) • The Java EE 6 Example - Testing Galleria - Part 4 (Markus) • Why is OpenWebBeans so fast? (Strub) • Welcome to the new Oracle Enterprise Pack for Eclipse Blog (blogs.oracle.com) • Java Spotlight Episode 75: Greg Luck on JSR 107 Java Temporary Caching API (Spotlight Podcast) • Glassfish cluster installation and administration on top of SSH + public key (Paulo) • Jfokus 2012 on Parleys.com (Parleys) • Java Tuning in a Nutshell - Part 1 (Rupesh) • New Features in Fork/Join from Java Concurrency Master, Doug Lea (DZone) • A Java7 Grammar for VisualLangLab (Sanjay) • Glassfish version 3.1.2: Secure Admin must be enabled to access the DAS remotely (Charlee) • Oracle Announces the Certification of the Oracle Database on Oracle Linux 6 and Red Hat Enterprise Linux 6

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  • The Java Specialist: An Interview with Java Champion Heinz Kabutz

    - by Janice J. Heiss
    Dr. Heinz Kabutz is well known for his Java Specialists’ Newsletter, initiated in November 2000, where he displays his acute grasp of the intricacies of the Java platform for an estimated 70,000 readers; for his work as a consultant; and for his workshops and trainings at his home on the Island of Crete where he has lived since 2006 -- where he is known to curl up on the beach with his laptop to hack away, in between dips in the Mediterranean. Kabutz was born of German parents and raised in Cape Town, South Africa, where he developed a love of programming in junior high school through his explorations on a ZX Spectrum computer. He received a B.S. from the University of Cape Town, and at 25, a Ph.D., both in computer science. He will be leading a two-hour hands-on lab session, HOL6500 – “Finding and Solving Java Deadlocks,” at this year’s JavaOne that will explore what causes deadlocks and how to solve them. Q: Tell us about your JavaOne plans.A: I am arriving on Sunday evening and have just one hands-on-lab to do on Monday morning. This is the first time that a non-Oracle team is doing a HOL at JavaOne under Oracle's stewardship and we are all a bit nervous about how it will turn out. Oracle has been immensely helpful in getting us set up. I have a great team helping me: Kirk Pepperdine, Dario Laverde, Benjamin Evans and Martijn Verburg from jClarity, Nathan Reynolds from Oracle, Henri Tremblay of OCTO Technology and Jeff Genender of Savoir Technologies. Monday will be hard work, but after that, I will hopefully get to network with fellow Java experts, attend interesting sessions and just enjoy San Francisco. Oh, and my kids have already given me a shopping list of things to get, like a GoPro Hero 2 dive housing for shooting those nice videos of Crete. (That's me at the beginning diving down.) Q: What sessions are you attending that we should know about?A: Sometimes the most unusual sessions are the best. I avoid the "big names". They often are spread too thin with all their sessions, which makes it difficult for them to deliver what I would consider deep content. I also avoid entertainers who might be good at presenting but who do not say that much.In 2010, I attended a session by Vladimir Yaroslavskiy where he talked about sorting. Although he struggled to speak English, what he had to say was spectacular. There was hardly anybody in the room, having not heard of Vladimir before. To me that was the highlight of 2010. Funnily enough, he was supposed to speak with Joshua Bloch, but if you remember, Google cancelled. If Bloch has been there, the room would have been packed to capacity.Q: Give us an update on the Java Specialists’ Newsletter.A: The Java Specialists' Newsletter continues being read by an elite audience around the world. The apostrophe in the name is significant.  It is a newsletter for Java specialists. When I started it twelve years ago, I was trying to find non-obvious things in Java to write about. Things that would be interesting to an advanced audience.As an April Fool's joke, I told my readers in Issue 44 that subscribing would remain free, but that they would have to pay US$5 to US$7 depending on their geographical location. I received quite a few angry emails from that one. I would have not earned that much from unsubscriptions. Most readers stay for a very long time.After Oracle bought Sun, the Java community held its breath for about two years whilst Oracle was figuring out what to do with Java. For a while, we were quite concerned that there was not much progress shown by Oracle. My newsletter still continued, but it was quite difficult finding new things to write about. We have probably about 70,000 readers, which is quite a small number for a Java publication. However, our readers are the top in the Java industry. So I don't mind having "only" 70000 readers, as long as they are the top 0.7%.Java concurrency is a very important topic that programmers think they should know about, but often neglect to fully understand. I continued writing about that and made some interesting discoveries. For example, in Issue 165, I showed how we can get thread starvation with the ReadWriteLock. This was a bug in Java 5, which was corrected in Java 6, but perhaps a bit too much. Whereas we could get starvation of writers in Java 5, in Java 6 we could now get starvation of readers. All of these interesting findings make their way into my courseware to help companies avoid these pitfalls.Another interesting discovery was how polymorphism works in the Server HotSpot compiler in Issue 157 and Issue 158. HotSpot can inline methods from interfaces that have only one implementation class in the JVM. When a new subclass is instantiated and called for the first time, the JVM will undo the previous optimization and re-optimize differently.Here is a little memory puzzle for your readers: public class JavaMemoryPuzzle {  private final int dataSize =      (int) (Runtime.getRuntime().maxMemory() * 0.6);  public void f() {    {      byte[] data = new byte[dataSize];    }    byte[] data2 = new byte[dataSize];  }  public static void main(String[] args) {    JavaMemoryPuzzle jmp = new JavaMemoryPuzzle();    jmp.f();  }}When you run this you will always get an OutOfMemoryError, even though the local variable data is no longer visible outside of the code block.So here comes the puzzle, that I'd like you to ponder a bit. If you very politely ask the VM to release memory, then you don't get an OutOfMemoryError: public class JavaMemoryPuzzlePolite {  private final int dataSize =      (int) (Runtime.getRuntime().maxMemory() * 0.6);  public void f() {    {      byte[] data = new byte[dataSize];    }    for(int i=0; i<10; i++) {      System.out.println("Please be so kind and release memory");    }    byte[] data2 = new byte[dataSize];  }  public static void main(String[] args) {    JavaMemoryPuzzlePolite jmp = new JavaMemoryPuzzlePolite();    jmp.f();    System.out.println("No OutOfMemoryError");  }}Why does this work? When I published this in my newsletter, I received over 400 emails from excited readers around the world, most of whom sent me the wrong explanation. After the 300th wrong answer, my replies became unfortunately a bit curt. Have a look at Issue 174 for a detailed explanation, but before you do, put on your thinking caps and try to figure it out yourself. Q: What do you think Java developers should know that they currently do not know?A: They should definitely get to know more about concurrency. It is a tough subject that most programmers try to avoid. Unfortunately we do come in contact with it. And when we do, we need to know how to protect ourselves and how to solve tricky system errors.Knowing your IDE is also useful. Most IDEs have a ton of shortcuts, which can make you a lot more productive in moving code around. Another thing that is useful is being able to read GC logs. Kirk Pepperdine has a great talk at JavaOne that I can recommend if you want to learn more. It's this: CON5405 – “Are Your Garbage Collection Logs Speaking to You?” Q: What are you looking forward to in Java 8?A: I'm quite excited about lambdas, though I must confess that I have not studied them in detail yet. Maurice Naftalin's Lambda FAQ is quite a good start to document what you can do with them. I'm looking forward to finding all the interesting bugs that we will now get due to lambdas obscuring what is really going on underneath, just like we had with generics.I am quite impressed with what the team at Oracle did with OpenJDK's performance. A lot of the benchmarks now run faster.Hopefully Java 8 will come with JSR 310, the Date and Time API. It still boggles my mind that such an important API has been left out in the cold for so long.What I am not looking forward to is losing perm space. Even though some systems run out of perm space, at least the problem is contained and they usually manage to work around it. In most cases, this is due to a memory leak in that region of memory. Once they bundle perm space with the old generation, I predict that memory leaks in perm space will be harder to find. More contracts for us, but also more pain for our customers. Originally published on blogs.oracle.com/javaone.

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