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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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  • No, iCloud Isn’t Backing Them All Up: How to Manage Photos on Your iPhone or iPad

    - by Chris Hoffman
    Are the photos you take with your iPhone or iPad backed up in case you lose your device? If you’re just relying on iCloud to manage your important memories, your photos may not be backed up at all. Apple’s iCloud has a photo-syncing feature in the form of “Photo Stream,” but Photo Stream doesn’t actually perform any long-term backups of your photos. iCloud’s Photo Backup Limitations Assuming you’ve set up iCloud on your iPhone or iPad, your device is using a feature called “Photo Stream” to automatically upload the photos you take to your iCloud storage and sync them across your devices. Unfortunately, there are some big limitations here. 1000 Photos: Photo Stream only backs up the latest 1000 photos. Do you have 1500 photos in your Camera Roll folder on your phone? If so, only the latest 1000 photos are stored in your iCloud account online. If you don’t have those photos backed up elsewhere, you’ll lose them when you lose your phone. If you have 1000 photos and take one more, the oldest photo will be removed from your iCloud Photo Stream. 30 Days: Apple also states that photos in your Photo Stream will be automatically deleted after 30 days “to give your devices plenty of time to connect and download them.” Some people report photos aren’t deleted after 30 days, but it’s clear you shouldn’t rely on iCloud for more than 30 days of storage. iCloud Storage Limits: Apple only gives you 5 GB of iCloud storage space for free, and this is shared between backups, documents, and all other iCloud data. This 5 GB can fill up pretty quickly. If your iCloud storage is full and you haven’t purchased any more storage more from Apple, your photos aren’t being backed up. Videos Aren’t Included: Photo Stream doesn’t include videos, so any videos you take aren’t automatically backed up. It’s clear that iCloud’s Photo Stream isn’t designed as a long-term way to store your photos, just a convenient way to access recent photos on all your devices before you back them up for real. iCloud’s Photo Stream is Designed for Desktop Backups If you have a Mac, you can launch iPhoto and enable the Automatic Import option under Photo Stream in its preferences pane. Assuming your Mac is on and connected to the Internet, iPhoto will automatically download photos from your photo stream and make local backups of them on your hard drive. You’ll then have to back up your photos manually so you don’t lose them if your Mac’s hard drive ever fails. If you have a Windows PC, you can install the iCloud Control Panel, which will create a Photo Stream folder on your PC. Your photos will be automatically downloaded to this folder and stored in it. You’ll want to back up your photos so you don’t lose them if your PC’s hard drive ever fails. Photo Stream is clearly designed to be used along with a desktop application. Photo Stream temporarily backs up your photos to iCloud so iPhoto or iCloud Control Panel can download them to your Mac or PC and make a local backup before they’re deleted. You could also use iTunes to sync your photos from your device to your PC or Mac, but we don’t really recommend it — you should never have to use iTunes. How to Actually Back Up All Your Photos Online So Photo Stream is actually pretty inconvenient — or, at least, it’s just a way to temporarily sync photos between your devices without storing them long-term. But what if you actually want to automatically back up your photos online without them being deleted automatically? The solution here is a third-party app that does this for you, offering the automatic photo uploads with long-term storage. There are several good services with apps in the App Store: Dropbox: Dropbox’s Camera Upload feature allows you to automatically upload the photos — and videos — you take to your Dropbox account. They’ll be easily accessible anywhere there’s a Dropbox app and you can get much more free Dropbox storage than you can iCloud storage. Dropbox will never automatically delete your old photos. Google+: Google+ offers photo and video backups with its Auto Upload feature, too. Photos will be stored in your Google+ Photos — formerly Picasa Web Albums — and will be marked as private by default so no one else can view them. Full-size photos will count against your free 15 GB of Google account storage space, but you can also choose to upload an unlimited amount of photos at a smaller resolution. Flickr: The Flickr app is no longer a mess. Flickr offers an Auto Upload feature for uploading full-size photos you take and free Flickr accounts offer a massive 1 TB of storage for you to store your photos. The massive amount of free storage alone makes Flickr worth a look. Use any of these services and you’ll get an online, automatic photo backup solution you can rely on. You’ll get a good chunk of free space, your photos will never be automatically deleted, and you can easily access them from any device. You won’t have to worry about storing local copies of your photos and backing them up manually. Apple should fix this mess and offer a better solution for long-term photo backup, especially considering the limitations aren’t immediately obvious to users. Until they do, third-party apps are ready to step in and take their place. You can also automatically back up your photos to the web on Android with Google+’s Auto Upload or Dropbox’s Camera Upload. Image Credit: Simon Yeo on Flickr     

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  • Cannot update grub with paramters on live USB

    - by Nanne
    I have booted from a live USB ("Try Ubuntu"), that also has a persistent option set (I used LiLi to create one) to do some tests for this pcie hotplug issue I'm having. I'm trying to test some boot paramaters (like in this question) by doing this sudo nano /etc/default/grub sudo update-grub The problem is that that last command gives me this: /usr/sbin/grub-probe: error: failed to get canonical path of /cow. It looks like /cow is the file-system that is mounted on /, according to: :~# df Filesystem 1K-blocks Used Available Use% Mounted on /cow 4056896 2840204 1007284 74% / udev 1525912 4 1525908 1% /dev tmpfs 613768 844 612924 1% /run .... Is there a way for me to run update-grub?

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  • SQLAuthority News – A Real Story of Book Getting ‘Out of Stock’ to A 25% Discount Story Available

    - by pinaldave
    As many of my readers may know, I have recently written a few books.  Right now I’d like to talk about SQL Server Interview Questions and Answers (http://bit.ly/sqlinterviewbook ), my newest release. What inspired me to write this book was similar to my motivations for my previous titles – I wanted to help people understand SQL Server concepts and ace interview questions so that they could get a great job they love, as much as I love my own job. If you are new to SQL Server, don’t think I left you out of my book writing efforts. If you are new to the subject or have not had to deal with SQL Server in a long time, this book is perfect for someone who wants or needs a last minute refresher. If you are facing an upcoming interview and want to impress your future bosses, this book is perfect for getting you up to speed in a short time. However, if you are already an expert, you will still find a lot to learn and many pointers and suggestions that go deep into the subject. As I said before, I wrote this book in order to help my community, and I certainly hoped that this book would become popular. However, we decided to print a very limited number of copies to begin with. We did not think that it would sell out since much of the information is available for free online. We could not have been more wrong! We incorrectly estimated what people wanted. We did not realize that there is still a need and an interest for structured learning. So, with great reservations, we printed quite a large number of copies – and it still ran out in 36 hours! We got call from the online store with a request for more copies within 12 hours. But we had printed only as many as we had sent them. There were no extra copies. We finally talked to the printer to get more copies. However, due to festivals and holidays the copies could not be shipped to the online retailer for two days. We knew for sure that they were going to be out of the book for 48 hours. 48 hours – this was very difficult as the book was very highly anticipated. Many people wanted to buy this book quickly, and receive it soon in order to meet a deadline or to study for an upcoming test of their knowledge. But now this book was out of stock on the retail store. The way the online store works is that if the Indian-priced book is not there they list the US version of the book so that buyers will not be disappointed. The problem was that the US price of the book is three times more than the Indian price – which means one has to pay three times as much to buy this book instead of the previous very low price. We received a lot of communication on this subject, here are some examples: We are now businessmen and only focusing on money Why has the price tripled in 36 hours Why we are not honest with the price If the prices will ever come down And some of the letters we cannot post here! Well, finally after 48 hours the Indian stock was finally available online. Thanks to our printer who worked day and night to get all the copies printed. He divided the complete stock in two parts. The first part they sent immediately to online retailer  and the second part they kept with them to sell. Finally, the online retailer got them online promptly as well, and the price returned to normal. Our book once again got in business and became the eighth most popular new release in 36 hours. We appreciate your love and support. Without all of your interest and love we would have never come this far and the book would not be so successful. After thinking about all your support and how patient you were with our online troubles, the online retailer has decided to give an extra 25% discount for a limited time only. I think the 48 hours when the book was out of stock were very horrible and stressful and I’d like to apologize to my loyal readers for the mishap. I hope that the 25% off is enough to sooth any remaining hurt feelings, and that everyone will continue to learn and discover things in the book. Once again thank you so much and I truly hope that you all enjoy reading the book as much as I enjoyed writing it. My book SQL Server Interview Questions and Answers is available now. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Interview Questions and Answers, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, T SQL, Technology

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  • Unit testing is… well, flawed.

    - by Dewald Galjaard
    Hey someone had to say it. I clearly recall my first IT job. I was appointed Systems Co-coordinator for a leading South African retailer at store level. Don’t get me wrong, there is absolutely nothing wrong with an honest day’s labor and in fact I highly recommend it, however I’m obliged to refer to the designation cautiously; in reality all I had to do was monitor in-store prices and two UNIX front line controllers. If anything went wrong – I only had to phone it in… Luckily that wasn’t all I did. My duties extended to some other interesting annual occurrence – stock take. Despite a bit more curious affair, it was still a tedious process that took weeks of preparation and several nights to complete.  Then also I remember that no matter how elaborate our planning was, the entire exercise would be rendered useless if we couldn’t get the basics right – that being the act of counting. Sounds simple right? We’ll with a store which could potentially carry over tens of thousands of different items… we’ll let’s just say I believe that’s when I first became a coffee addict. In those days the act of counting stock was a very humble process. Nothing like we have today. A staff member would be assigned a bin or shelve filled with items he or she had to sort then count. Thereafter they had to record their findings on a complementary piece of paper. Every night I would manage several teams. Each team was divided into two groups - counters and auditors. Both groups had the same task, only auditors followed shortly on the heels of the counters, recounting stock levels, making sure the original count correspond to their findings. It was a simple yet hugely responsible orchestration of people and thankfully there was one fundamental and golden rule I could always abide by to ensure things run smoothly – No-one was allowed to audit their own work. Nope, not even on nights when I didn’t have enough staff available. This meant I too at times had to get up there and get counting, or have the audit stand over until the next evening. The reason for this was obvious - late at night and with so much to do we were prone to make some mistakes, then on the recount, without a fresh set of eyes, you were likely to repeat the offence. Now years later this rule or guideline still holds true as we develop software (as far removed as software development from counting stock may be). For some reason it is a fundamental guideline we’re simply ignorant of. We write our code, we write our tests and thus commit the same horrendous offence. Yes, the procedure of writing unit tests as practiced in most development houses today – is flawed. Most if not all of the tests we write today exercise application logic – our logic. They are based on the way we believe an application or method should/may/will behave or function. As we write our tests, our unit tests mirror our best understanding of the inner workings of our application code. Unfortunately these tests will therefore also include (or be unaware of) any imperfections and errors on our part. If your logic is flawed as you write your initial code, chances are, without a fresh set of eyes, you will commit the same error second time around too. Not even experience seems to be a suitable solution. It certainly helps to have deeper insight, but is that really the answer we should be looking for? Is that really failsafe? What about code review? Code review is certainly an answer. You could have one developer coding away and another (or team) making sure the logic is sound. The practice however has its obvious drawbacks. Firstly and mainly it is resource intensive and from what I’ve seen in most development houses, given heavy deadlines, this guideline is seldom adhered to. Hardly ever do we have the resources, money or time readily available. So what other options are out there? A quest to find some solution revealed a project by Microsoft Research called PEX. PEX is a framework which creates several test scenarios for each method or class you write, automatically. Think of it as your own personal auditor. Within a few clicks the framework will auto generate several unit tests for a given class or method and save them to a single project. PEX help to audit your work. It lends a fresh set of eyes to any project you’re working on and best of all; it is cost effective and fast. Check them out at http://research.microsoft.com/en-us/projects/pex/ In upcoming posts we’ll dive deeper into how it works and how it can help you.   Certainly there are more similar frameworks out there and I would love to hear from you. Please share your experiences and insights.

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  • Cloud Computing Architecture Patterns: Don’t Focus on the Client

    - by BuckWoody
    Normally I try to put topics in the positive in other words "Do this" not "Don't do that". Sometimes its clearer to focus on what *not* to do. Popular development processes often start with screen mockups, or user input descriptions. In a scale-out pattern like Cloud Computing on Windows Azure, that's the wrong place to start. Start with the Data    Instead, I recommend that you start with the data that a process requires. That data might be temporary or persisted, but starting with the data and its requirements helps to define not only the storage engine you need but also drives everything from security to the integrity of the application. For instance, assume the requirements show that the user must enter their phone number, and that this datum is used in a contact management system further down the application chain. For that datum, you can determine what data type you need (U.S. only or International?) the security requirements, whether it needs ACID compliance, how it will be searched, indexed and so on. From one small data point you can extrapolate out your options for storing and processing the data. Here's the interesting part, which begins to break the patterns that we've used for decades: all of the data doesn't have the same requirements. The phone number might be best suited for a list, or an element, or a string, with either BASE or ACID requirements, based on how it is used. That means we don't have to dump everything into XML, an RDBMS, a NoSQL engine, or a flat file exclusively. In fact, one record might use all of those depending on the use-case requirements. Next Is Data Management  With the data defined, we can move on to how to store the data. Again, the requirements now dictate whether we need a full relational calculus or set-based operations, or we can choose another method based on the requirements for the data. And breaking another pattern its OK to store in more than once, in more than one location. We do this all the time for reporting systems and Business Intelligence systems, so this is a pattern we need to think about even for OLTP data. Move to Data Transport How does the data get around? We can use a connection-based method, sending the data along a transport to the storage engine, but in some cases we may want to use a cache, a queue, the Service Bus, or Complex Event Processing. Finally, Data Processing Most RDBMS engines, NoSQL, and certainly Big Data engines not only store data, but can process and manipulate it as well. Its doubtful that you'll calculate that phone number right? Well, if you're the phone company, you most certainly will. And so we see that even once we've chosen the data type, storage and engine, the same element can have different computing requirements based on how it is used. Sure, We Need A Front-End At Some Point Not all data is entered by human hands in fact most data isn't. We don't really need a Graphical User Interface (GUI) we need some way for a GUI to get data into and out of the systems listed earlier.   But when we do need to allow users to enter or examine data, that should be left to the GUI that best fits the device the user has. Ever tried to use an application designed for a web browser on a phone? Or one designed for a tablet on a phone? Its usually quite painful. The siren song of "We'll just write one interface for all devices" is strong, and has beguiled many an unsuspecting architect. But they just don't work out.   Instead, focus on the data, its transport and processing. Create API calls or a message system that allows for resilient transport to the device or interface, and let it do what it does best. References Microsoft Architecture Journal:   http://msdn.microsoft.com/en-us/architecture/bb410935.aspx Patterns and Practices:   http://msdn.microsoft.com/en-us/library/ff921345.aspx Windows Azure iOS, Android, Windows 8 Mobile Devices SDK: http://www.windowsazure.com/en-us/develop/mobile/tutorials/get-started-ios/ Windows Azure Facebook SDK: http://ntotten.com/2013/03/14/using-windows-azure-mobile-services-with-the-facebook-sdk-for-windows-phone/

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  • SPARC M7 Chip - 32 cores - Mind Blowing performance

    - by Angelo-Oracle
    The M7 Chip Oracle just announced its Next Generation Processor at the HotChips HC26 conference. As the Tech Lead in our Systems Division's Partner group, I had a front row seat to the extraordinary price performance advantage of Oracle current T5 and M6 based systems. Partner after partner tested  these systems and were impressed with it performance. Just read some of the quotes to see what our partner has been saying about our hardware. We just announced our next generation processor, the M7. This has 32 cores (up from 16-cores in T5 and 12-cores in M6). With 20 nm technology  this is our most advanced processor. The processor has more cores than anything else in the industry today. After the Sun acquisition Oracle has released 5 processors in 4 years and this is the 6th.  The S4 core  The M7 is built using the foundation of the S4 core. This is the next generation core technology. Like its predecessor, the S4 has 8 dynamic threads. It increases the frequency while maintaining the Pipeline depth. Each core has its own fine grain power estimator that keeps the core within its power envelop in 250 nano-sec granularity. Each core also includes Software in Silicon features for Application Acceleration Support. Each core includes features to improve Application Data Integrity, with almost no performance loss. The core also allows using part of the Virtual Address to store meta-data.  User-Level Synchronization Instructions are also part of the S4 core. Each core has 16 KB Instruction and 16 KB Data L1 cache. The Core Clusters  The cores on the M7 chip are organized in sets of 4-core clusters. The core clusters share  L2 cache.  All four cores in the complex share 256 KB of 4 way set associative L2 Instruction Cache, with over 1/2 TB/s of throughput. Two cores share 256 KB of 8 way set associative L2 Data Cache, with over 1/2 TB/s of throughput. With this innovative Core Cluster architecture, the M7 doubles core execution bandwidth. to maximize per-thread performance.  The Chip  Each  M7 chip has 8 sets of these core-clusters. The chip has 64 MB on-chip L3 cache. This L3 caches is shared among all the cores and is partitioned into 8 x 8 MB chunks. Each chunk is  8-way set associative cache. The aggregate bandwidth for the L3 cache on the chip is over 1.6TB/s. Each chip has 4 DDR4 memory controllers and can support upto 16 DDR4 DIMMs, allowing for 2 TB of RAM/chip. The chip also includes 4 internal links of PCIe Gen3 I/O controllers.  Each chip has 7 coherence links, allowing for 8 of these chips to be connected together gluelessly. Also 32 of these chips can be connected in an SMP configuration. A potential system with 32 chips will have 1024 cores and 8192 threads and 64 TB of RAM.  Software in Silicon The M7 chip has many built in Application Accelerators in Silicon. These features will be exposed to our Software partners using the SPARC Accelerator Program.  The M7  has built-in logic to decompress data at the speed of memory access. This means that applications can directly work on compressed data in memory increasing the data access rates. The VA Masking feature allows the use of part of the virtual address to store meta-data.  Realtime Application Data Integrity The Realtime Application Data Integrity feature helps applications safeguard against invalid, stale memory reference and buffer overflows. The first 4-bits if the Pointer can be used to store a version number and this version number is also maintained in the memory & cache lines. When a pointer accesses memory the hardware checks to make sure the two versions match. A SEGV signal is raised when there is a mismatch. This feature can be used by the Database, applications and the OS.  M7 Database In-Memory Query Accelerator The M7 chip also includes a In-Silicon Query Engines.  These accelerate tasks that work on In-Memory Columnar Vectors. Oracle In-Memory options stores data in Column Format. The M7 Query Engine can speed up In-Memory Format Conversion, Value and Range Comparisons and Set Membership lookups. This engine can work on Compressed data - this means not only are we accelerating the query performance but also increasing the memory bandwidth for queries.  SPARC Accelerated Program  At the Hotchips conference we also introduced the SPARC Accelerated Program to provide our partners and third part developers access to all the goodness of the M7's SPARC Application Acceleration features. Please get in touch with us if you are interested in knowing more about this program. 

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  • Best PHP-based web development 'stack' of 2011

    - by Jens Roland
    I have been building PHP-based web sites for many years, and lately it seems I'm discovering another interesting new tool or method once every few weeks. This begs the question - what is the current state of the art in PHP development stacks for the seasoned coder? I'm specifically interested in the following: High-performance web server Database MVC framework Build tool Revision control Continuous Integration Automated testing Non-persistent caching I'd like to optimize my stack for scalability and rapid development. I'm not looking for personal preference here, I'm looking for real, quantifiable reasons to pick this-over-that.

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  • The Oracle Retail Week Awards - in review

    - by user801960
    The Oracle Retail Week Awards 2012 were another great success, building on the legacy of previous award ceremonies. Over 1,600 of the UK's top retailers gathered at the Grosvenor House Hotel and many of Europe's top retail leaders attended the prestigious Oracle Retail VIP Reception in the Grosvenor House Hotel's Red Bar. Over the years the Oracle Retail Week Awards have become a rallying point for the morale of the retail industry, and each nominated retailer served as a demonstration that the industry is fighting fit. It was an honour to speak to so many figureheads of UK - and global - retail. All of us at Oracle Retail would like to congratulate both the winners and the nominees for the awards. Retail is a cornerstone of the economy and it was inspiring to see so many outstanding demonstrations of innovation and dedication in the entries. Winners 2012   The Market Force Customer Service Initiative of the Year Winner: Dixons Retail: Knowhow Highly Commended: Hughes Electrical: Digital Switchover     The Deloitte Employer of the Year Winner: Morrisons     Growing Retailer of the Year Winner: Hallett Retail - The Concessions People Highly Commended: Blue Inc     The TCC Marketing/Advertising Campaign of the Year Winner: Sainsbury's: Feed your Family for £50     The Brandbank Multichannel Retailer of the Year Winner: Debenhams Highly Commended: Halfords     The Ashton Partnership Product Innovation of the Year Winner: Argos: Chad Valley Highly Commended: Halfords: Private label bikes     The RR Donnelley Pure-play Online Retailer of the Year Winner: Wiggle     The Hitachi Consulting Responsible Retailer of the Year Winner: B&Q: One Planet Home     The CA Technologies Retail Technology Initiative of the Year Winner: Oasis: Argyll Street flagship launch with iPad PoS     The Premier Tax Free Speciality Retailer of the Year Winner: Holland & Barrett     Store Design of the Year Winner: Next Home and Garden, Shoreham, Sussex Highly Commended: Dixons Retail, Black concept store, Birmingham Bullring     Store Manager of the Year Winner: Ian Allcock, Homebase, Aylesford Highly Commended: Darren Parfitt, Boots UK, Melton Mowbray Health Centre     The Wates Retail Destination of the Year Winner: Westfield, Stratford     The AlixPartners Emerging Retail Leader of the Year Winner: Catriona Marshall, HobbyCraft, Chief Executive     The Wipro Retail International Retailer of the Year Winner: Apple     The Clarity Search Retail Leader of the Year Winner: Ian Cheshire, Chief Executive, Kingfisher     The Oracle Retailer of the Year Winner: Burberry     Outstanding Contribution to Retail Winner: Lord Harris of Peckham     Oracle Retail and "Your Experience Platform" Technology is the key to providing that differentiated retail experience. More specifically, it is what we at Oracle call ‘the experience platform’ - a set of integrated, cross-channel business technology solutions, selected and operated by a retail business and IT team, and deployed in accordance with that organisation’s individual strategy and processes. This business systems architecture simultaneously: Connects customer interactions across all channels and touchpoints, and every customer lifecycle phase to provide a differentiated customer experience that meets consumers’ needs and expectations. Delivers actionable insight that enables smarter decisions in planning, forecasting, merchandising, supply chain management, marketing, etc; Optimises operations to align every aspect of the retail business to gain efficiencies and economies, to align KPIs to eliminate strategic conflicts, and at the same time be working in support of customer priorities.   Working in unison, these three goals not only help retailers to successfully navigate the challenges of today but also to focus on delivering that personalised customer experience based on differentiated products, pricing, services and interactions that will help you to gain market share and grow sales.  

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  • NoSQL Java API for MySQL Cluster: Questions & Answers

    - by Mat Keep
    The MySQL Cluster engineering team recently ran a live webinar, available now on-demand demonstrating the ClusterJ and ClusterJPA NoSQL APIs for MySQL Cluster, and how these can be used in building real-time, high scale Java-based services that require continuous availability. Attendees asked a number of great questions during the webinar, and I thought it would be useful to share those here, so others are also able to learn more about the Java NoSQL APIs. First, a little bit about why we developed these APIs and why they are interesting to Java developers. ClusterJ and Cluster JPA ClusterJ is a Java interface to MySQL Cluster that provides either a static or dynamic domain object model, similar to the data model used by JDO, JPA, and Hibernate. A simple API gives users extremely high performance for common operations: insert, delete, update, and query. ClusterJPA works with ClusterJ to extend functionality, including - Persistent classes - Relationships - Joins in queries - Lazy loading - Table and index creation from object model By eliminating data transformations via SQL, users get lower data access latency and higher throughput. In addition, Java developers have a more natural programming method to directly manage their data, with a complete, feature-rich solution for Object/Relational Mapping. As a result, the development of Java applications is simplified with faster development cycles resulting in accelerated time to market for new services. MySQL Cluster offers multiple NoSQL APIs alongside Java: - Memcached for a persistent, high performance, write-scalable Key/Value store, - HTTP/REST via an Apache module - C++ via the NDB API for the lowest absolute latency. Developers can use SQL as well as NoSQL APIs for access to the same data set via multiple query patterns – from simple Primary Key lookups or inserts to complex cross-shard JOINs using Adaptive Query Localization Marrying NoSQL and SQL access to an ACID-compliant database offers developers a number of benefits. MySQL Cluster’s distributed, shared-nothing architecture with auto-sharding and real time performance makes it a great fit for workloads requiring high volume OLTP. Users also get the added flexibility of being able to run real-time analytics across the same OLTP data set for real-time business insight. OK – hopefully you now have a better idea of why ClusterJ and JPA are available. Now, for the Q&A. Q & A Q. Why would I use Connector/J vs. ClusterJ? A. Partly it's a question of whether you prefer to work with SQL (Connector/J) or objects (ClusterJ). Performance of ClusterJ will be better as there is no need to pass through the MySQL Server. A ClusterJ operation can only act on a single table (e.g. no joins) - ClusterJPA extends that capability Q. Can I mix different APIs (ie ClusterJ, Connector/J) in our application for different query types? A. Yes. You can mix and match all of the API types, SQL, JDBC, ODBC, ClusterJ, Memcached, REST, C++. They all access the exact same data in the data nodes. Update through one API and new data is instantly visible to all of the others. Q. How many TCP connections would a SessionFactory instance create for a cluster of 8 data nodes? A. SessionFactory has a connection to the mgmd (management node) but otherwise is just a vehicle to create Sessions. Without using connection pooling, a SessionFactory will have one connection open with each data node. Using optional connection pooling allows multiple connections from the SessionFactory to increase throughput. Q. Can you give details of how Cluster J optimizes sharding to enhance performance of distributed query processing? A. Each data node in a cluster runs a Transaction Coordinator (TC), which begins and ends the transaction, but also serves as a resource to operate on the result rows. While an API node (such as a ClusterJ process) can send queries to any TC/data node, there are performance gains if the TC is where most of the result data is stored. ClusterJ computes the shard (partition) key to choose the data node where the row resides as the TC. Q. What happens if we perform two primary key lookups within the same transaction? Are they sent to the data node in one transaction? A. ClusterJ will send identical PK lookups to the same data node. Q. How is distributed query processing handled by MySQL Cluster ? A. If the data is split between data nodes then all of the information will be transparently combined and passed back to the application. The session will connect to a data node - typically by hashing the primary key - which then interacts with its neighboring nodes to collect the data needed to fulfil the query. Q. Can I use Foreign Keys with MySQL Cluster A. Support for Foreign Keys is included in the MySQL Cluster 7.3 Early Access release Summary The NoSQL Java APIs are packaged with MySQL Cluster, available for download here so feel free to take them for a spin today! Key Resources MySQL Cluster on-line demo  MySQL ClusterJ and JPA On-demand webinar  MySQL ClusterJ and JPA documentation MySQL ClusterJ and JPA whitepaper and tutorial

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  • texture mapping with lib3ds and SOIL help

    - by Adam West
    I'm having trouble with my project for loading a texture map onto a model. Any insight into what is going wrong with my code is fantastic. Right now the code only renders a teapot which I have assinged after creating it in 3DS Max. 3dsloader.cpp #include "3dsloader.h" Object::Object(std:: string filename) { m_TotalFaces = 0; m_model = lib3ds_file_load(filename.c_str()); // If loading the model failed, we throw an exception if(!m_model) { throw strcat("Unable to load ", filename.c_str()); } // set properties of texture coordinate generation for both x and y coordinates glTexGeni(GL_S, GL_TEXTURE_GEN_MODE, GL_EYE_LINEAR); glTexGeni(GL_T, GL_TEXTURE_GEN_MODE, GL_EYE_LINEAR); // if not already enabled, enable texture generation if(! glIsEnabled(GL_TEXTURE_GEN_S)) glEnable(GL_TEXTURE_GEN_S); if(! glIsEnabled(GL_TEXTURE_GEN_T)) glEnable(GL_TEXTURE_GEN_T); } Object::~Object() { if(m_model) // if the file isn't freed yet lib3ds_file_free(m_model); //free up memory glDisable(GL_TEXTURE_GEN_S); glDisable(GL_TEXTURE_GEN_T); } void Object::GetFaces() { m_TotalFaces = 0; Lib3dsMesh * mesh; // Loop through every mesh. for(mesh = m_model->meshes;mesh != NULL;mesh = mesh->next) { // Add the number of faces this mesh has to the total number of faces. m_TotalFaces += mesh->faces; } } void Object::CreateVBO() { assert(m_model != NULL); // Calculate the number of faces we have in total GetFaces(); // Allocate memory for our vertices and normals Lib3dsVector * vertices = new Lib3dsVector[m_TotalFaces * 3]; Lib3dsVector * normals = new Lib3dsVector[m_TotalFaces * 3]; Lib3dsTexel* texCoords = new Lib3dsTexel[m_TotalFaces * 3]; Lib3dsMesh * mesh; unsigned int FinishedFaces = 0; // Loop through all the meshes for(mesh = m_model->meshes;mesh != NULL;mesh = mesh->next) { lib3ds_mesh_calculate_normals(mesh, &normals[FinishedFaces*3]); // Loop through every face for(unsigned int cur_face = 0; cur_face < mesh->faces;cur_face++) { Lib3dsFace * face = &mesh->faceL[cur_face]; for(unsigned int i = 0;i < 3;i++) { memcpy(&texCoords[FinishedFaces*3 + i], mesh->texelL[face->points[ i ]], sizeof(Lib3dsTexel)); memcpy(&vertices[FinishedFaces*3 + i], mesh->pointL[face->points[ i ]].pos, sizeof(Lib3dsVector)); } FinishedFaces++; } } // Generate a Vertex Buffer Object and store it with our vertices glGenBuffers(1, &m_VertexVBO); glBindBuffer(GL_ARRAY_BUFFER, m_VertexVBO); glBufferData(GL_ARRAY_BUFFER, sizeof(Lib3dsVector) * 3 * m_TotalFaces, vertices, GL_STATIC_DRAW); // Generate another Vertex Buffer Object and store the normals in it glGenBuffers(1, &m_NormalVBO); glBindBuffer(GL_ARRAY_BUFFER, m_NormalVBO); glBufferData(GL_ARRAY_BUFFER, sizeof(Lib3dsVector) * 3 * m_TotalFaces, normals, GL_STATIC_DRAW); // Generate a third VBO and store the texture coordinates in it. glGenBuffers(1, &m_TexCoordVBO); glBindBuffer(GL_ARRAY_BUFFER, m_TexCoordVBO); glBufferData(GL_ARRAY_BUFFER, sizeof(Lib3dsTexel) * 3 * m_TotalFaces, texCoords, GL_STATIC_DRAW); // Clean up our allocated memory delete vertices; delete normals; delete texCoords; // We no longer need lib3ds lib3ds_file_free(m_model); m_model = NULL; } void Object::applyTexture(const char*texfilename) { float imageWidth; float imageHeight; glGenTextures(1, & textureObject); // allocate memory for one texture textureObject = SOIL_load_OGL_texture(texfilename,SOIL_LOAD_AUTO,SOIL_CREATE_NEW_ID,SOIL_FLAG_MIPMAPS); glPixelStorei(GL_UNPACK_ALIGNMENT,1); glBindTexture(GL_TEXTURE_2D, textureObject); // use our newest texture glGetTexLevelParameterfv(GL_TEXTURE_2D,0,GL_TEXTURE_WIDTH,&imageWidth); glGetTexLevelParameterfv(GL_TEXTURE_2D,0,GL_TEXTURE_HEIGHT,&imageHeight); glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR); // give the best result for texture magnification glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR); //give the best result for texture minification glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP); // don't repeat texture glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP); // don't repeat textureglTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP); // don't repeat texture glTexEnvf(GL_TEXTURE_ENV, GL_TEXTURE_ENV_MODE,GL_MODULATE); glTexImage2D(GL_TEXTURE_2D,0,GL_RGB,imageWidth,imageHeight,0,GL_RGB,GL_UNSIGNED_BYTE,& textureObject); } void Object::Draw() const { // Enable vertex, normal and texture-coordinate arrays. glEnableClientState(GL_VERTEX_ARRAY); glEnableClientState(GL_NORMAL_ARRAY); glEnableClientState(GL_TEXTURE_COORD_ARRAY); // Bind the VBO with the normals. glBindBuffer(GL_ARRAY_BUFFER, m_NormalVBO); // The pointer for the normals is NULL which means that OpenGL will use the currently bound VBO. glNormalPointer(GL_FLOAT, 0, NULL); glBindBuffer(GL_ARRAY_BUFFER, m_TexCoordVBO); glTexCoordPointer(2, GL_FLOAT, 0, NULL); glBindBuffer(GL_ARRAY_BUFFER, m_VertexVBO); glVertexPointer(3, GL_FLOAT, 0, NULL); // Render the triangles. glDrawArrays(GL_TRIANGLES, 0, m_TotalFaces * 3); glDisableClientState(GL_VERTEX_ARRAY); glDisableClientState(GL_NORMAL_ARRAY); glDisableClientState(GL_TEXTURE_COORD_ARRAY); } 3dsloader.h #include "main.h" #include "lib3ds/file.h" #include "lib3ds/mesh.h" #include "lib3ds/material.h" class Object { public: Object(std:: string filename); virtual ~Object(); virtual void Draw() const; virtual void CreateVBO(); void applyTexture(const char*texfilename); protected: void GetFaces(); unsigned int m_TotalFaces; Lib3dsFile * m_model; Lib3dsMesh* Mesh; GLuint textureObject; GLuint m_VertexVBO, m_NormalVBO, m_TexCoordVBO; }; Called in the main cpp file with: VBO,apply texture and draw (pretty simple, how ironic) and thats it, please help me forum :)

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  • Online Password Security Tactics

    - by BuckWoody
    Recently two more large databases were attacked and compromised, one at the popular Gawker Media sites and the other at McDonald’s. Every time this kind of thing happens (which is FAR too often) it should remind the technical professional to ensure that they secure their systems correctly. If you write software that stores passwords, it should be heavily encrypted, and not human-readable in any storage. I advocate a different store for the login and password, so that if one is compromised, the other is not. I also advocate that you set a bit flag when a user changes their password, and send out a reminder to change passwords if that bit isn’t changed every three or six months.    But this post is about the *other* side – what to do to secure your own passwords, especially those you use online, either in a cloud service or at a provider. While you’re not in control of these breaches, there are some things you can do to help protect yourself. Most of these are obvious, but they contain a few little twists that make the process easier.   Use Complex Passwords This is easily stated, and probably one of the most un-heeded piece of advice. There are three main concepts here: ·         Don’t use a dictionary-based word ·         Use mixed case ·         Use punctuation, special characters and so on   So this: password Isn’t nearly as safe as this: P@ssw03d   Of course, this only helps if the site that stores your password encrypts it. Gawker does, so theoretically if you had the second password you’re in better shape, at least, than the first. Dictionary words are quickly broken, regardless of the encryption, so the more unusual characters you use, and the farther away from the dictionary words you get, the better.   Of course, this doesn’t help, not even a little, if the site stores the passwords in clear text, or the key to their encryption is broken. In that case…   Use a Different Password at Every Site What? I have hundreds of sites! Are you kidding me? Nope – I’m not. If you use the same password at every site, when a site gets attacked, the attacker will store your name and password value for attacks at other sites. So the only safe thing to do is to use different names or passwords (or both) at each site. Of course, most sites use your e-mail as a username, so you’re kind of hosed there. So even though you have hundreds of sites you visit, you need to have at least a different password at each site.   But it’s easier than you think – if you use an algorithm.   What I’m describing is to pick a “root” password, and then modify that based on the site or purpose. That way, if the site is compromised, you can still use that root password for the other sites.   Let’s take that second password: P@ssw03d   And now you can append, prepend or intersperse that password with other characters to make it unique to the site. That way you can easily remember the root password, but make it unique to the site. For instance, perhaps you read a lot of information on Gawker – how about these:   P@ssw03dRead ReadP@ssw03d PR@esasdw03d   If you have lots of sites, tracking even this can be difficult, so I recommend you use password software such as Password Safe or some other tool to have a secure database of your passwords at each site. DO NOT store this on the web. DO NOT use an Office document (Microsoft or otherwise) that is “encrypted” – the encryption office automation packages use is very trivial, and easily broken. A quick web search for tools to do that should show you how bad a choice this is.   Change Your Password on a Schedule I know. It’s a real pain. And it doesn’t seem worth it…until your account gets hacked. A quick note here – whenever a site gets hacked (and I find out about it) I change the password at that site immediately (or quit doing business with them) and then change the root password on every site, as quickly as I can.   If you follow the tip above, it’s not as hard. Just add another number, year, month, day, something like that into the mix. It’s not unlike making a Primary Key in an RDBMS.   P@ssw03dRead10242010   Change the site, and then update your password database. I do this about once a month, on the first or last day, during staff meetings. (J)   If you have other tips, post them here. We can all learn from each other on this.

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  • It is possible to hibernate an LXC container?

    - by Jo-Erlend Schinstad
    I know about lxc-freeze and lxc-unfreeze, but as I understand it, these simply pauses the container, similar to sending SIGSTOP and SIGCONT to a process. If I reboot the host, then the containers will cease to exist, right? I would really like a way to save state to persistent storage so that I could resume them at some later time, even the host is rebooted or something like that. I can achieve exactly what I want using VirtualBox by using the "Save machine state" mechanism, but if I could do it with LXC, it would be completely awesome.

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  • ISACA Webcast follow up: Managing High Risk Access and Compliance with a Platform Approach to Privileged Account Management

    - by Darin Pendergraft
    Last week we presented how Oracle Privileged Account Manager (OPAM) could be used to manage high risk, privileged accounts.  If you missed the webcast, here is a link to the replay: ISACA replay archive (NOTE: you will need to use Internet Explorer to view the archive) For those of you that did join us on the call, you will know that I only had a little bit of time for Q&A, and was only able to answer a few of the questions that came in.  So I wanted to devote this blog to answering the outstanding questions.  Here they are. 1. Can OPAM track admin or DBA activity details during a password check-out session? Oracle Audit Vault is monitoring these activities which can be correlated to check-out events. 2. How would OPAM handle simultaneous requests? OPAM can be configured to allow for shared passwords.  By default sharing is turned off. 3. How long are the passwords valid?  Are the admins required to manually check them in? Password expiration can be configured and set in the password policy according to your corporate standards.  You can specify if you want forced check-in or not. 4. Can 2-factor authentication be used with OPAM? Yes - 2-factor integration with OPAM is provided by integration with Oracle Access Manager, and Oracle Adaptive Access Manager. 5. How do you control access to OPAM to ensure that OPAM admins don't override the functionality to access privileged accounts? OPAM provides separation of duties by using Admin Roles to manage access to targets and privileged accounts and to control which operations admins can perform. 6. How and where are the passwords stored in OPAM? OPAM uses Oracle Platform Security Services (OPSS) Credential Store Framework (CSF) to securely store passwords.  This is the same system used by Oracle Applications. 7. Does OPAM support hierarchical/level based privileges?  Is the log maintained for independent review/audit? Yes. OPAM uses the Fusion Middleware (FMW) Audit Framework to store all OPAM related events in a dedicated audit database.  8. Does OPAM support emergency access in the case where approvers are not available until later? Yes.  OPAM can be configured to release a password under a "break-glass" emergency scenario. 9. Does OPAM work with AIX? Yes supported UNIX version are listed in the "certified component section" of the UNIX connector guide at:http://docs.oracle.com/cd/E22999_01/doc.111/e17694/intro.htm#autoId0 10. Does OPAM integrate with Sun Identity Manager? Yes.  OPAM can be integrated with SIM using the REST  APIs.  OPAM has direct integration with Oracle Identity Manager 11gR2. 11. Is OPAM available today and what does it cost? Yes.  OPAM is available now.  Ask your Oracle Account Manager for pricing. 12. Can OPAM be used in SAP environments? Yes, supported SAP version are listed in the "certified component section" of the SAP  connector guide here: http://docs.oracle.com/cd/E22999_01/doc.111/e25327/intro.htm#autoId0 13. How would this product integrate, if at all, with access to a particular field in the DB that need additional security such as SSN's? OPAM can work with DB Vault and DB Firewall to provide the fine grained access control for databases. 14. Is VM supported? As a deployment platform Oracle VM is supported. For further details about supported Virtualization Technologies see Oracle Fusion Middleware Supported System configurations here: http://www.oracle.com/technetwork/middleware/ias/downloads/fusion-certification-100350.html 15. Where did this (OPAM) technology come from? OPAM was built by Oracle Engineering. 16. Are all Linux flavors supported?  How about BSD? BSD is not supported. For supported UNIX version see the "certified component section" of the UNIX connector guide http://docs.oracle.com/cd/E22999_01/doc.111/e17694/intro.htm#autoId0 17. What happens if users don't check passwords in at the end of a work task? In OPAM a time frame can be defined how long a password can be checked out. The security admin can force a check-in at any given time. 18. is MySQL supported? Yes, supported DB version are listed in the "certified component section" of the DB connector guide here: http://docs.oracle.com/cd/E22999_01/doc.111/e28315/intro.htm#BABGJJHA 19. What happens when OPAM crashes and you need to use the password? OPAM can be configured for high availability, but if required, OPAM data can be backed up/recovered.  See the OPAM admin guide. 20. Is OPAM Standalone product or does it leverage other components from IDM? OPAM can be run stand-alone, but will also leverage other IDM components

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  • how to improve concepts for interview

    - by Rahul Mehta
    Hi, I had given the interview , and interviewer tell me to improve the concepts , e.g. he ask me type of array ,and i answered two types of array simple array and associative array . e.g. 2 he ask me why you use pdo , and i answered we can use any database e.g. oracle , mysql and it helps in sql injection , then he ask me how it helps in sql injection then i was not having correct answer. e.g. 3 he ask me about persistent connection , i just use the mysql_pconnect i dont where it will be used and how . is there is any standard way to follow to improve concepts. Please suggest . Thanks

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  • Master Data Management Implementation Styles

    - by david.butler(at)oracle.com
    In any Master Data Management solution deployment, one of the key decisions to be made is the choice of the MDM architecture. Gartner and other analysts describe some different Hub deployment styles, which must be supported by a best of breed MDM solution in order to guarantee the success of the deployment project.   Registry Style: In a Registry Style MDM Hub, the various source systems publish their data and a subscribing Hub stores only the source system IDs, the Foreign Keys (record IDs on source systems) and the key data values needed for matching. The Hub runs the cleansing and matching algorithms and assigns unique global identifiers to the matched records, but does not send any data back to the source systems. The Registry Style MDM Hub uses data federation capabilities to build the "virtual" golden view of the master entity from the connected systems.   Consolidation Style: The Consolidation Style MDM Hub has a physically instantiated, "golden" record stored in the central Hub. The authoring of the data remains distributed across the spoke systems and the master data can be updated based on events, but is not guaranteed to be up to date. The master data in this case is usually not used for transactions, but rather supports reporting; however, it can also be used for reference operationally.   Coexistence Style: The Coexistence Style MDM Hub involves master data that's authored and stored in numerous spoke systems, but includes a physically instantiated golden record in the central Hub and harmonized master data across the application portfolio. The golden record is constructed in the same manner as in the consolidation style, and, in the operational world, Consolidation Style MDM Hubs often evolve into the Coexistence Style. The key difference is that in this architectural style the master data stored in the central MDM system is selectively published out to the subscribing spoke systems.   Transaction Style: In this architecture, the Hub stores, enhances and maintains all the relevant (master) data attributes. It becomes the authoritative source of truth and publishes this valuable information back to the respective source systems. The Hub publishes and writes back the various data elements to the source systems after the linking, cleansing, matching and enriching algorithms have done their work. Upstream, transactional applications can read master data from the MDM Hub, and, potentially, all spoke systems subscribe to updates published from the central system in a form of harmonization. The Hub needs to support merging of master records. Security and visibility policies at the data attribute level need to be supported by the Transaction Style hub, as well.   Adaptive Transaction Style: This is similar to the Transaction Style, but additionally provides the capability to respond to diverse information and process requests across the enterprise. This style emerged most recently to address the limitations of the above approaches. With the Adaptive Transaction Style, the Hub is built as a platform for consolidating data from disparate third party and internal sources and for serving unified master entity views to operational applications, analytical systems or both. This approach delivers a real-time Hub that has a reliable, persistent foundation of master reference and relationship data, along with all the history and lineage of data changes needed for audit and compliance tracking. On top of this persistent master data foundation, the Hub can dynamically aggregate transaction data on demand from different source systems to deliver the unified golden view to downstream systems. Data can also be accessed through batch interfaces, published to a message bus or served through a real-time services layer. New data sources can be readily added in this approach by extending the data model and by configuring the new source mappings and the survivorship rules, meaning that all legacy data hubs can be leveraged to contribute their records/rules into the new transaction hub. Finally, through rich user interfaces for data stewardship, it allows exception handling by business analysts to keep it current with business rules/practices while maintaining the reliability of best-of-breed master records.   Confederation Style: In this architectural style, several Hubs are maintained at departmental and/or agency and/or territorial level, and each of them are connected to the other Hubs either directly or via a central Super-Hub. Each Domain level Hub can be implemented using any of the previously described styles, but normally the Central Super-Hub is a Registry Style one. This is particularly important for Public Sector organizations, where most of the time it is practically or legally impossible to store in a single central hub all the relevant constituent information from all departments.   Oracle MDM Solutions can be deployed according to any of the above MDM architectural styles, and have been specifically designed to fully support the Transaction and Adaptive Transaction styles. Oracle MDM Solutions provide strong data federation and integration capabilities which are key to enabling the use of the Confederated Hub as a possible architectural style approach. Don't lock yourself into a solution that cannot evolve with your needs. With Oracle's support for any type of deployment architecture, its ability to leverage the outstanding capabilities of the Oracle technology stack, and its open interfaces for non-Oracle technology stacks, Oracle MDM Solutions provide a low TCO and a quick ROI by enabling a phased implementation strategy.

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  • Cloud to On-Premise Connectivity Patterns

    - by Rajesh Raheja
    Do you have a requirement to convert an Opportunity in Salesforce.com to an Order/Quote in Oracle E-Business Suite? Or maybe you want the creation of an Oracle RightNow Incident to trigger an on-premise Oracle E-Business Suite Service Request creation for RMA and Field Scheduling? If so, read on. In a previous blog post, I discussed integrating TO cloud applications, however the use cases above are the reverse i.e. receiving data FROM cloud applications (SaaS) TO on-premise applications/databases that sit behind a firewall. Oracle SOA Suite is assumed to be on-premise with with Oracle Service Bus as the mediation and virtualization layer. The main considerations for the patterns are are security i.e. shielding enterprise resources; and scalability i.e. minimizing firewall latency. Let me use an analogy to help visualize the patterns: the on-premise system is your home - with your most valuable possessions - and the SaaS app is your favorite on-line store which regularly ships (inbound calls) various types of parcels/items (message types/service operations). You need the items at home (on-premise) but want to safe guard against misguided elements of society (internet threats) who may masquerade as postal workers and vandalize property (denial of service?). Let's look at the patterns. Pattern: Pull from Cloud The on-premise system polls from the SaaS apps and picks up the message instead of having it delivered. This may be done using Oracle RightNow Object Query Language or SOAP APIs. This is particularly suited for certain integration approaches wherein messages are trickling in, can be centralized and batched e.g. retrieving event notifications on an hourly schedule from the Oracle Messaging Service. To compare this pattern with the home analogy, you are avoiding any deliveries to your home and instead go to the post office/UPS/Fedex store to pick up your parcel. Every time. Pros: On-premise assets not exposed to the Internet, firewall issues avoided by only initiating outbound connections Cons: Polling mechanisms may affect performance, may not satisfy near real-time requirements Pattern: Open Firewall Ports The on-premise system exposes the web services that needs to be invoked by the cloud application. This requires opening up firewall ports, routing calls to the appropriate internal services behind the firewall. Fusion Applications uses this pattern, and auto-provisions the services on the various virtual hosts to secure the topology. This works well for service integration, but may not suffice for large volume data integration. Using the home analogy, you have now decided to receive parcels instead of going to the post office every time. A door mail slot cut out allows the postman can drop small parcels, but there is still concern about cutting new holes for larger packages. Pros: optimal pattern for near real-time needs, simpler administration once the service is provisioned Cons: Needs firewall ports to be opened up for new services, may not suffice for batch integration requiring direct database access Pattern: Virtual Private Networking The on-premise network is "extended" to the cloud (or an intermediary on-demand / managed service offering) using Virtual Private Networking (VPN) so that messages are delivered to the on-premise system in a trusted channel. Using the home analogy, you entrust a set of keys with a neighbor or property manager who receives the packages, and then drops it inside your home. Pros: Individual firewall ports don't need to be opened, more suited for high scalability needs, can support large volume data integration, easier management of one connection vs a multitude of open ports Cons: VPN setup, specific hardware support, requires cloud provider to support virtual private computing Pattern: Reverse Proxy / API Gateway The on-premise system uses a reverse proxy "API gateway" software on the DMZ to receive messages. The reverse proxy can be implemented using various mechanisms e.g. Oracle API Gateway provides firewall and proxy services along with comprehensive security, auditing, throttling benefits. If a firewall already exists, then Oracle Service Bus or Oracle HTTP Server virtual hosts can provide reverse proxy implementations on the DMZ. Custom built implementations are also possible if specific functionality (such as message store-n-forward) is needed. In the home analogy, this pattern sits in between cutting mail slots and handing over keys. Instead, you install (and maintain) a mailbox in your home premises outside your door. The post office delivers the parcels in your mailbox, from where you can securely retrieve it. Pros: Very secure, very flexible Cons: Introduces a new software component, needs DMZ deployment and management Pattern: On-Premise Agent (Tunneling) A light weight "agent" software sits behind the firewall and initiates the communication with the cloud, thereby avoiding firewall issues. It then maintains a bi-directional connection either with pull or push based approaches using (or abusing, depending on your viewpoint) the HTTP protocol. Programming protocols such as Comet, WebSockets, HTTP CONNECT, HTTP SSH Tunneling etc. are possible implementation options. In the home analogy, a resident receives the parcel from the postal worker by opening the door, however you still take precautions with chain locks and package inspections. Pros: Light weight software, IT doesn't need to setup anything Cons: May bypass critical firewall checks e.g. virus scans, separate software download, proliferation of non-IT managed software Conclusion The patterns above are some of the most commonly encountered ones for cloud to on-premise integration. Selecting the right pattern for your project involves looking at your scalability needs, security restrictions, sync vs asynchronous implementation, near real-time vs batch expectations, cloud provider capabilities, budget, and more. In some cases, the basic "Pull from Cloud" may be acceptable, whereas in others, an extensive VPN topology may be well justified. For more details on the Oracle cloud integration strategy, download this white paper.

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  • Cloud to On-Premise Connectivity Patterns

    - by Rajesh Raheja
    Do you have a requirement to convert an Opportunity in Salesforce.com to an Order/Quote in Oracle E-Business Suite? Or maybe you want the creation of an Oracle RightNow Incident to trigger an on-premise Oracle E-Business Suite Service Request creation for RMA and Field Scheduling? If so, read on. In a previous blog post, I discussed integrating TO cloud applications, however the use cases above are the reverse i.e. receiving data FROM cloud applications (SaaS) TO on-premise applications/databases that sit behind a firewall. Oracle SOA Suite is assumed to be on-premise with with Oracle Service Bus as the mediation and virtualization layer. The main considerations for the patterns are are security i.e. shielding enterprise resources; and scalability i.e. minimizing firewall latency. Let me use an analogy to help visualize the patterns: the on-premise system is your home - with your most valuable possessions - and the SaaS app is your favorite on-line store which regularly ships (inbound calls) various types of parcels/items (message types/service operations). You need the items at home (on-premise) but want to safe guard against misguided elements of society (internet threats) who may masquerade as postal workers and vandalize property (denial of service?). Let's look at the patterns. Pattern: Pull from Cloud The on-premise system polls from the SaaS apps and picks up the message instead of having it delivered. This may be done using Oracle RightNow Object Query Language or SOAP APIs. This is particularly suited for certain integration approaches wherein messages are trickling in, can be centralized and batched e.g. retrieving event notifications on an hourly schedule from the Oracle Messaging Service. To compare this pattern with the home analogy, you are avoiding any deliveries to your home and instead go to the post office/UPS/Fedex store to pick up your parcel. Every time. Pros: On-premise assets not exposed to the Internet, firewall issues avoided by only initiating outbound connections Cons: Polling mechanisms may affect performance, may not satisfy near real-time requirements Pattern: Open Firewall Ports The on-premise system exposes the web services that needs to be invoked by the cloud application. This requires opening up firewall ports, routing calls to the appropriate internal services behind the firewall. Fusion Applications uses this pattern, and auto-provisions the services on the various virtual hosts to secure the topology. This works well for service integration, but may not suffice for large volume data integration. Using the home analogy, you have now decided to receive parcels instead of going to the post office every time. A door mail slot cut out allows the postman can drop small parcels, but there is still concern about cutting new holes for larger packages. Pros: optimal pattern for near real-time needs, simpler administration once the service is provisioned Cons: Needs firewall ports to be opened up for new services, may not suffice for batch integration requiring direct database access Pattern: Virtual Private Networking The on-premise network is "extended" to the cloud (or an intermediary on-demand / managed service offering) using Virtual Private Networking (VPN) so that messages are delivered to the on-premise system in a trusted channel. Using the home analogy, you entrust a set of keys with a neighbor or property manager who receives the packages, and then drops it inside your home. Pros: Individual firewall ports don't need to be opened, more suited for high scalability needs, can support large volume data integration, easier management of one connection vs a multitude of open ports Cons: VPN setup, specific hardware support, requires cloud provider to support virtual private computing Pattern: Reverse Proxy / API Gateway The on-premise system uses a reverse proxy "API gateway" software on the DMZ to receive messages. The reverse proxy can be implemented using various mechanisms e.g. Oracle API Gateway provides firewall and proxy services along with comprehensive security, auditing, throttling benefits. If a firewall already exists, then Oracle Service Bus or Oracle HTTP Server virtual hosts can provide reverse proxy implementations on the DMZ. Custom built implementations are also possible if specific functionality (such as message store-n-forward) is needed. In the home analogy, this pattern sits in between cutting mail slots and handing over keys. Instead, you install (and maintain) a mailbox in your home premises outside your door. The post office delivers the parcels in your mailbox, from where you can securely retrieve it. Pros: Very secure, very flexible Cons: Introduces a new software component, needs DMZ deployment and management Pattern: On-Premise Agent (Tunneling) A light weight "agent" software sits behind the firewall and initiates the communication with the cloud, thereby avoiding firewall issues. It then maintains a bi-directional connection either with pull or push based approaches using (or abusing, depending on your viewpoint) the HTTP protocol. Programming protocols such as Comet, WebSockets, HTTP CONNECT, HTTP SSH Tunneling etc. are possible implementation options. In the home analogy, a resident receives the parcel from the postal worker by opening the door, however you still take precautions with chain locks and package inspections. Pros: Light weight software, IT doesn't need to setup anything Cons: May bypass critical firewall checks e.g. virus scans, separate software download, proliferation of non-IT managed software Conclusion The patterns above are some of the most commonly encountered ones for cloud to on-premise integration. Selecting the right pattern for your project involves looking at your scalability needs, security restrictions, sync vs asynchronous implementation, near real-time vs batch expectations, cloud provider capabilities, budget, and more. In some cases, the basic "Pull from Cloud" may be acceptable, whereas in others, an extensive VPN topology may be well justified. For more details on the Oracle cloud integration strategy, download this white paper.

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  • How to use RDP protocol in Remote Desktop Viewer?

    - by drgrog
    I am using a Natty Live-USB, and the Remote Desktop Viewer application (Applications - Internet - Remote Desktop Viewer) only shows SSH & VNC protocols. How can I include RDP in the list, in order to connect to existing Windows Remote Desktop sessions on Windows XP machines. I am aware that I can use the gnome-rdp application, or even rdesktop hostname from a terminal, but I would like to create a persistent LiveUSB that uses Remote Desktop Viewer to connect to Windows RDP (port 3389). I do not want to set up VNC, TeamViewer or any others, as these do not solve my problem due to the fact they need additional software on the remote hosts.

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  • Using Transaction Logging to Recover Post-Archived Essbase data

    - by Keith Rosenthal
    Data recovery is typically performed by restoring data from an archive.  Data added or removed since the last archive took place can also be recovered by enabling transaction logging in Essbase.  Transaction logging works by writing transactions to a log store.  The information in the log store can then be recovered by replaying the log store entries in sequence since the last archive took place.  The following information is recorded within a transaction log entry: Sequence ID Username Start Time End Time Request Type A request type can be one of the following categories: Calculations, including the default calculation as well as both server and client side calculations Data loads, including data imports as well as data loaded using a load rule Data clears as well as outline resets Locking and sending data from SmartView and the Spreadsheet Add-In.  Changes from Planning web forms are also tracked since a lock and send operation occurs during this process. You can use the Display Transactions command in the EAS console or the query database MAXL command to view the transaction log entries. Enabling Transaction Logging Transaction logging can be enabled at the Essbase server, application or database level by adding the TRANSACTIONLOGLOCATION essbase.cfg setting.  The following is the TRANSACTIONLOGLOCATION syntax: TRANSACTIONLOGLOCATION [appname [dbname]] LOGLOCATION NATIVE ENABLE | DISABLE Note that you can have multiple TRANSACTIONLOGLOCATION entries in the essbase.cfg file.  For example: TRANSACTIONLOGLOCATION Hyperion/trlog NATIVE ENABLE TRANSACTIONLOGLOCATION Sample Hyperion/trlog NATIVE DISABLE The first statement will enable transaction logging for all Essbase applications, and the second statement will disable transaction logging for the Sample application.  As a result, transaction logging will be enabled for all applications except the Sample application. A location on a physical disk other than the disk where ARBORPATH or the disk files reside is recommended to optimize overall Essbase performance. Configuring Transaction Log Replay Although transaction log entries are stored based on the LOGLOCATION parameter of the TRANSACTIONLOGLOCATION essbase.cfg setting, copies of data load and rules files are stored in the ARBORPATH/app/appname/dbname/Replay directory to optimize the performance of replaying logged transactions.  The default is to archive client data loads, but this configuration setting can be used to archive server data loads (including SQL server data loads) or both client and server data loads. To change the type of data to be archived, add the TRANSACTIONLOGDATALOADARCHIVE configuration setting to the essbase.cfg file.  Note that you can have multiple TRANSACTIONLOGDATALOADARCHIVE entries in the essbase.cfg file to adjust settings for individual applications and databases. Replaying the Transaction Log and Transaction Log Security Considerations To replay the transactions, use either the Replay Transactions command in the EAS console or the alter database MAXL command using the replay transactions grammar.  Transactions can be replayed either after a specified log time or using a range of transaction sequence IDs. The default when replaying transactions is to use the security settings of the user who originally performed the transaction.  However, if that user no longer exists or that user's username was changed, the replay operation will fail. Instead of using the default security setting, add the REPLAYSECURITYOPTION essbase.cfg setting to use the security settings of the administrator who performs the replay operation.  REPLAYSECURITYOPTION 2 will explicitly use the security settings of the administrator performing the replay operation.  REPLAYSECURITYOPTION 3 will use the administrator security settings if the original user’s security settings cannot be used. Removing Transaction Logs and Archived Replay Data Load and Rules Files Transaction logs and archived replay data load and rules files are not automatically removed and are only removed manually.  Since these files can consume a considerable amount of space, the files should be removed on a periodic basis. The transaction logs should be removed one database at a time instead of all databases simultaneously.  The data load and rules files associated with the replayed transactions should be removed in chronological order from earliest to latest.  In addition, do not remove any data load and rules files with a timestamp later than the timestamp of the most recent archive file. Partitioned Database Considerations For partitioned databases, partition commands such as synchronization commands cannot be replayed.  When recovering data, the partition changes must be replayed manually and logged transactions must be replayed in the correct chronological order. If the partitioned database includes any @XREF commands in the calc script, the logged transactions must be selectively replayed in the correct chronological order between the source and target databases. References For additional information, please see the Oracle EPM System Backup and Recovery Guide.  For EPM 11.1.2.2, the link is http://docs.oracle.com/cd/E17236_01/epm.1112/epm_backup_recovery_1112200.pdf

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  • Examples of Liskov Substitution

    - by james lewis
    I'm facilitating a session next week on the Liskov Substitution Principle and I was wondering if anyone had any examples of violations 'from the trenches'? I'm looking for something other than uncle Bob's rectangle - square problem and the persistent set problem he talks about in A-PPP (although that is a great example). So far I'm using the example of a (very simple) List and an IndexedList as the 'correct' use of inheritance. And the addition of a Set to this hierarchy as a violation (as a Set is distinct; strengthening the pre condition of the Add method). I've also taken this great example and it's solution from this question Both those examples are great but I'm looking for something more subtle and harder to spot. So far I've come up with nothing so if you've got a great, subtle example post it up. Also, any metaphors you've come across that helped you understand LSP would be really useful too.

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  • Sound unavailable every other session

    - by Oxwivi
    On my desktop running Oneiric, sometimes there's no sound at all, but it would work normally at other times. My setup is built ground-up from minimal Ubuntu, but since sounds work fine, at times, I don't think it is a backend dependency issue. When it works, it will play anything from regular audio files and movies to YouTube Flash players. For the record, I installed LXDE with the alsa-base and alsa-utils packages which are the only audio-related dependencies for the lubuntu-desktop. For a while, I also used persistent Oneiric live USB, and do not recall any sound issues. It's one thing to not play sound entirely, but playing sound only under some very unclear circumstances is something else. Please help me diagnose it.

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  • Include Method Extension for IObjectSet What about the mocks?

    Eager loading with Entity Framework depends on the special ObjectQuery.Include method. We’ve had that from Day 1 (first version of ef). Now we use ObjectSets in EF4 which inherit from ObjectQuery (thereby inheriting Include) and also implement IObjectSet. IObjectSet allows us to break the queries apart from ObjectQuery and ObjectContext so we can write persistent ignorant, testable code. But IObjectSet doesn’t come with the Include method and you have to create an extension method to...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • WCF Keep Alive: Whether to disable keepAliveEnabled

    - by Lijo
    I have a WCF web service hosted in a load balanced environment. I do not need any WCF session related functionality in the service. QUESTION What are the scenarios in which performances will be best if keepAliveEnabled = false keepAliveEnabled = true Reference From Load Balancing By default, the BasicHttpBinding sends a connection HTTP header in messages with a Keep-Alive value, which enables clients to establish persistent connections to the services that support them. This configuration offers enhanced throughput because previously established connections can be reused to send subsequent messages to the same server. However, connection reuse may cause clients to become strongly associated to a specific server within the load-balanced farm, which reduces the effectiveness of round-robin load balancing. If this behavior is undesirable, HTTP Keep-Alive can be disabled on the server using the KeepAliveEnabled property with a CustomBinding or user-defined Binding.

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  • Level and Player objects - which should contain which?

    - by Thane Brimhall
    I've been working on a several simple games, and I've always come to a decision point where I have to choose whether to have the Level object as an attribute of the Player class or the Player as an attribute of the Level class. I can see arguments for both: The Level should contain the player because it also contains every other entity. In fact it just makes sense this way: "John is in the room." It makes it a bit more difficult to move the player to a new level, however, because then each level has to pass its player object to an upcoming level. On the other hand, it makes programming sense to me to leave the player as the top-level object that is persistent between levels, and the environment changes because the player decides to change his level and location. It becomes very easy to change levels, because all I have to do is replace the level variable on the player. What's the most common practice here? Or better yet, is there a "right" way to architecture this relationship?

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