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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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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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  • Is there a work around for slow performance of do.call(cbind.xts,...) in R 2.15.2?

    - by Petr Matousu
    I would expect cbind.xts and do.call(cbind.xts) to perform with similar elapsed time. That was true for R2.11, R2.14. For R2.15.2 and xts 0.8-8, the do.call(cbind.xts,...) variant performs drastically slower, which effectively breaks my previous codes. As Josh Ulrich notes in a comment below, the xts package maintainers are aware of this problem. In the meantime, is there a convenient work around?

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  • Flow-Design Cheat Sheet &ndash; Part I, Notation

    - by Ralf Westphal
    You want to avoid the pitfalls of object oriented design? Then this is the right place to start. Use Flow-Oriented Analysis (FOA) and –Design (FOD or just FD for Flow-Design) to understand a problem domain and design a software solution. Flow-Orientation as described here is related to Flow-Based Programming, Event-Based Programming, Business Process Modelling, and even Event-Driven Architectures. But even though “thinking in flows” is not new, I found it helpful to deviate from those precursors for several reasons. Some aim at too big systems for the average programmer, some are concerned with only asynchronous processing, some are even not very much concerned with programming at all. What I was looking for was a design method to help in software projects of any size, be they large or tiny, involing synchronous or asynchronous processing, being local or distributed, running on the web or on the desktop or on a smartphone. That´s why I took ideas from all of the above sources and some additional and came up with Event-Based Components which later got repositioned and renamed to Flow-Design. In the meantime this has generated some discussion (in the German developer community) and several teams have started to work with Flow-Design. Also I´ve conducted quite some trainings using Flow-Orientation for design. The results are very promising. Developers find it much easier to design software using Flow-Orientation than OOAD-based object orientation. Since Flow-Orientation is moving fast and is not covered completely by a single source like a book, demand has increased for at least an overview of the current state of its notation. This page is trying to answer this demand by briefly introducing/describing every notational element as well as their translation into C# source code. Take this as a cheat sheet to put next to your whiteboard when designing software. However, please do not expect any explanation as to the reasons behind Flow-Design elements. Details on why Flow-Design at all and why in this specific way you´ll find in the literature covering the topic. Here´s a resource page on Flow-Design/Event-Based Components, if you´re able to read German. Notation Connected Functional Units The basic element of any FOD are functional units (FU): Think of FUs as some kind of software code block processing data. For the moment forget about classes, methods, “components”, assemblies or whatever. See a FU as an abstract piece of code. Software then consists of just collaborating FUs. I´m using circles/ellipses to draw FUs. But if you like, use rectangles. Whatever suites your whiteboard needs best.   The purpose of FUs is to process input and produce output. FUs are transformational. However, FUs are not called and do not call other FUs. There is no dependency between FUs. Data just flows into a FU (input) and out of it (output). From where and where to is of no concern to a FU.   This way FUs can be concatenated in arbitrary ways:   Each FU can accept input from many sources and produce output for many sinks:   Flows Connected FUs form a flow with a start and an end. Data is entering a flow at a source, and it´s leaving it through a sink. Think of sources and sinks as special FUs which conntect wires to the environment of a network of FUs.   Wiring Details Data is flowing into/out of FUs through wires. This is to allude to electrical engineering which since long has been working with composable parts. Wires are attached to FUs usings pins. They are the entry/exit points for the data flowing along the wires. Input-/output pins currently need not be drawn explicitly. This is to keep designing on a whiteboard simple and quick.   Data flowing is of some type, so wires have a type attached to them. And pins have names. If there is only one input pin and output pin on a FU, though, you don´t need to mention them. The default is Process for a single input pin, and Result for a single output pin. But you´re free to give even single pins different names.   There is a shortcut in use to address a certain pin on a destination FU:   The type of the wire is put in parantheses for two reasons. 1. This way a “no-type” wire can be easily denoted, 2. this is a natural way to describe tuples of data.   To describe how much data is flowing, a star can be put next to the wire type:   Nesting – Boards and Parts If more than 5 to 10 FUs need to be put in a flow a FD starts to become hard to understand. To keep diagrams clutter free they can be nested. You can turn any FU into a flow: This leads to Flow-Designs with different levels of abstraction. A in the above illustration is a high level functional unit, A.1 and A.2 are lower level functional units. One of the purposes of Flow-Design is to be able to describe systems on different levels of abstraction and thus make it easier to understand them. Humans use abstraction/decomposition to get a grip on complexity. Flow-Design strives to support this and make levels of abstraction first class citizens for programming. You can read the above illustration like this: Functional units A.1 and A.2 detail what A is supposed to do. The whole of A´s responsibility is decomposed into smaller responsibilities A.1 and A.2. FU A thus does not do anything itself anymore! All A is responsible for is actually accomplished by the collaboration between A.1 and A.2. Since A now is not doing anything anymore except containing A.1 and A.2 functional units are devided into two categories: boards and parts. Boards are just containing other functional units; their sole responsibility is to wire them up. A is a board. Boards thus depend on the functional units nested within them. This dependency is not of a functional nature, though. Boards are not dependent on services provided by nested functional units. They are just concerned with their interface to be able to plug them together. Parts are the workhorses of flows. They contain the real domain logic. They actually transform input into output. However, they do not depend on other functional units. Please note the usage of source and sink in boards. They correspond to input-pins and output-pins of the board.   Implicit Dependencies Nesting functional units leads to a dependency tree. Boards depend on nested functional units, they are the inner nodes of the tree. Parts are independent, they are the leafs: Even though dependencies are the bane of software development, Flow-Design does not usually draw these dependencies. They are implicitly created by visually nesting functional units. And they are harmless. Boards are so simple in their functionality, they are little affected by changes in functional units they are depending on. But functional units are implicitly dependent on more than nested functional units. They are also dependent on the data types of the wires attached to them: This is also natural and thus does not need to be made explicit. And it pertains mainly to parts being dependent. Since boards don´t do anything with regard to a problem domain, they don´t care much about data types. Their infrastructural purpose just needs types of input/output-pins to match.   Explicit Dependencies You could say, Flow-Orientation is about tackling complexity at its root cause: that´s dependencies. “Natural” dependencies are depicted naturally, i.e. implicitly. And whereever possible dependencies are not even created. Functional units don´t know their collaborators within a flow. This is core to Flow-Orientation. That makes for high composability of functional units. A part is as independent of other functional units as a motor is from the rest of the car. And a board is as dependend on nested functional units as a motor is on a spark plug or a crank shaft. With Flow-Design software development moves closer to how hardware is constructed. Implicit dependencies are not enough, though. Sometimes explicit dependencies make designs easier – as counterintuitive this might sound. So FD notation needs a ways to denote explicit dependencies: Data flows along wires. But data does not flow along dependency relations. Instead dependency relations represent service calls. Functional unit C is depending on/calling services on functional unit S. If you want to be more specific, name the services next to the dependency relation: Although you should try to stay clear of explicit dependencies, they are fundamentally ok. See them as a way to add another dimension to a flow. Usually the functionality of the independent FU (“Customer repository” above) is orthogonal to the domain of the flow it is referenced by. If you like emphasize this by using different shapes for dependent and independent FUs like above. Such dependencies can be used to link in resources like databases or shared in-memory state. FUs can not only produce output but also can have side effects. A common pattern for using such explizit dependencies is to hook a GUI into a flow as the source and/or the sink of data: Which can be shortened to: Treat FUs others depend on as boards (with a special non-FD API the dependent part is connected to), but do not embed them in a flow in the diagram they are depended upon.   Attributes of Functional Units Creation and usage of functional units can be modified with attributes. So far the following have shown to be helpful: Singleton: FUs are by default multitons. FUs in the same of different flows with the same name refer to the same functionality, but to different instances. Think of functional units as objects that get instanciated anew whereever they appear in a design. Sometimes though it´s helpful to reuse the same instance of a functional unit; this is always due to valuable state it holds. Signify this by annotating the FU with a “(S)”. Multiton: FUs on which others depend are singletons by default. This is, because they usually are introduced where shared state comes into play. If you want to change them to be a singletons mark them with a “(M)”. Configurable: Some parts need to be configured before the can do they work in a flow. Annotate them with a “(C)” to have them initialized before any data items to be processed by them arrive. Do not assume any order in which FUs are configured. How such configuration is happening is an implementation detail. Entry point: In each design there needs to be a single part where “it all starts”. That´s the entry point for all processing. It´s like Program.Main() in C# programs. Mark the entry point part with an “(E)”. Quite often this will be the GUI part. How the entry point is started is an implementation detail. Just consider it the first FU to start do its job.   Patterns / Standard Parts If more than a single wire is attached to an output-pin that´s called a split (or fork). The same data is flowing on all of the wires. Remember: Flow-Designs are synchronous by default. So a split does not mean data is processed in parallel afterwards. Processing still happens synchronously and thus one branch after another. Do not assume any specific order of the processing on the different branches after the split.   It is common to do a split and let only parts of the original data flow on through the branches. This effectively means a map is needed after a split. This map can be implicit or explicit.   Although FUs can have multiple input-pins it is preferrable in most cases to combine input data from different branches using an explicit join: The default output of a join is a tuple of its input values. The default behavior of a join is to output a value whenever a new input is received. However, to produce its first output a join needs an input for all its input-pins. Other join behaviors can be: reset all inputs after an output only produce output if data arrives on certain input-pins

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  • TRIM in centos 5.X?

    - by Frank Farmer
    I've got a bunch of centos 5 boxes with Intel X-25 drives (x25-m in dev, x25-e in prod, I think). We're seeing severely degraded disk performance on one of our dev boxes (which easily does 5+ gb of writes every day, meaning we write the full drive's worth of data several times a month). The box in question: Intel x25-m Ext3 (which doesn't support TRIM) centos 5 vmware ESXi Wikipedia mentions that newer versions of hdparm (which centos5 doesn't include) can bulk-TRIM free blocks. This utility also sounds potentially useful: http://blog.patshead.com/2009/12/a-quick-and-dirty-wipersh-fix-for-intel-x25-m.html Disk write performance has dropped to <1 MB/sec while copying a 300 meg directory on this system, as of a month or so ago -- it used to be able to perform the same copy operation at least 5 times faster. What can I do to recover performance on this system?

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  • Hypervisor for mixed client and server OSes

    - by Mark
    I need to replace three old boxes I use for development, running Linux, Win Server and Win XP. Instead of purchasing three new boxes I am thinking of purchasing a single box and virtualizing the OSes. As it is for development, absolute performance is not a problem, but I want the Linux and Win servers to run continuously, while running Win 7 as if it is a regular PC. Therefore running Linux and Win Server on top off Win 7 is not an option. Is this a viable solution? Has anyone done this? What is performance like? I'd like to get decent graphics performance with Win 7, sufficient to run the occasional game. If so, I'm looking for suggestions or recommendations on which hypervisor or virtualization option to go for.

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  • Should I install Windows 7 on a 3 years old PC?

    - by Jitendra vyas
    This is my PC configuration, Should I upgrade my Windows XP to Windows 7. Currently I'm using Windows XP SP3 32 bit. Now will I get same performance or better performance or bad performance if I install Windows 7 on this system? Or would sticking with XP be better? Memory (RAM): 1472 MB DDR RAM (not DDR 2) CPU Info: AMD Sempron(tm) Processor 2500+ CPU Speed: 1398.7 MHz Sound card: Vinyl AC'97 Audio (WAVE) Display Adapters: VIA/S3G UniChrome Pro IGP | NetMeeting driver | RDPDD Chained DD Network Adapters: Bluetooth Device (Personal Area Network) | WAN (PPP/SLIP) Interface Hard Disks: 300 GB SATA HDD Manufacturer: Phoenix Technologies, LTD Product Make: MS-7142 AC Power Status: OnLine BIOS Info: AT/AT COMPATIBLE | 01/18/06 | VIAK8M - 42302e31 Motherboard: MICRO-STAR INTERNATIONAL CO., LTD MS-7142 Modem: ZTE USB Modem FFFE CDMA :

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  • How many disks to use for eight channel RAID controller

    - by Tvrtko
    I have a 3ware 8 channel SAS controller and a back plane extender (also 8 channel) which can take 16 drives. I will be creating a single RAID 10 volume. I know that adding more drives has positive effect on performance, but I'm not sure if adding more than 8 drives on an 8 channel controller will have any positive impact at all. Am I wrong? Should I put 16 drives for best performance? Would 8 drives give me the same performance?

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  • pnp4nagios does not generate perfdata

    - by gonvaled
    I am running nagios2, pnp4nagios-0.6.16 and php 5.2.4-2ubuntu5.19. In my setup, pnp4nagios is correctly generating perfdata, which can be seen via the web interface in graphical form for lots of services. The perfdata directory contains entries of the kind: /usr/local/pnp4nagios/var/perfdata/zeus/Disk_Space_Home.rrd /usr/local/pnp4nagios/var/perfdata/zeus/Disk_Space_Home.xml I have activated performance data for a new nagios service: define serviceextinfo { host_name zeus service_description 450average action_url /pnp4nagios/index.php?host=$HOSTNAME$&srv=$SERVICEDESC$ } This service is generating monitoring data in the format: status_info|perf_data as required for performance gathering. But somehow the performance data related to this service is not being collected by pnp4nagios (no related entries in /usr/local/pnp4nagios/var/perfdata) Are there any pnp4nagios scripts or settings which I could use to debug this?

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  • Should I disable write caching on my Windows 2008 VM?

    - by javano
    I have a Windows Server 2008 x64 Standard virtual machine that runs on a machine with a hardware RAID controller, a Perc 6/i, which has a battery on-board. Doing everything I can for additional performance, I think I should disable this. Is this very dangerous though? My understand is that Battery Backed Write Caching gives a performance boost to the host OS, telling it the write was complete when they are still sitting in flash waiting to be written. However, I can't see how it would be detrimental to performance, but is there a gain (even if marginal) to enabling it / disabling it? P.s. There machine has a backup power. Here is a screen shot for clarification:

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  • File store: CouchDB vs SQL Server + file system

    - by Andrey
    I'm exploring different ways of storing user-uploaded files (all are MS Office documents or alikes) on our high load web site. It's currently designed to store documents as files and have a SQL database store all metadata for those files. I'm concerned about growing out of the storage server and SQL server performance when number of documents reaches hundreds of millions. I was reading a lot of good information about CouchDB including its built-in scalability and performance, but I'm not sure how storing files as attachments in CouchDB would compare to storing files on a file system in terms of performance. Anybody used CouchDB clusters for storing LARGE amounts of documents and in high load environment?

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  • VMWare Workstation Dev Machine Disks: one fast or four echofriendly raid?

    - by Avi
    I'm building a new dev computer. It will be running a few VMWare Worksation virtual machines - A dev machine running VS-2010, a build machine, a version-control machine, a web server for testing, a "personal" machine running office etc. I'll be connecting the computer to my stereo, so I'll also be running iTunes (possible on a dedicated VM) and I want the computer to be a silent one. I'll probably use an Antec P183 case. I was advised on Serverfault to use Raid10 for performance. Raid 10 uses 4 disks. So, my question is as follows: In terms of heat, noise, reliability, warranty, price, capacity and performance, what would you suggest: A Raid10 4 disk array using eco-friendly disks such as the $94 1TB Western Digital Caviar Green, or one high performance disk such as the 2TB Western Digital Caviar Black at $280?

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  • What can impact the throughput rate at tcp or Os level?

    - by Jimm
    I am facing a problem, where running the same application on different servers, yields unexpected performance results. For example, running the application on a particular faster server (faster cpu, more memory), with no load, yields slower performance than running on a less powerful server on the same network. I am suspecting that either OS or TCP is causing the slowness on the faster server. I cannot use IPerf , unless i modify it, because the "performance" in my application is defined as Component A sends a message to Component B. Component B sends an ACK to component A and ONLY then Component A would send the next message. So it is different from what IPerf does, which to my knowledge, simply tries to push as many messages as possible. Is there a tool that can look at OS and TCP configuration and suggest the cause of slowness?

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  • What is a proper MySql replication configuration for frequent db updates and rare selects?

    - by serg555
    We currently have 1 master db on its own server and slave db on app server. App executes very frequent but light updates (like increasing counters), and occasional (once in a few minutes) heavy selects (which is the most important part of the app). When app was connected only to master db there were no performance issues. With slave db introduction CPU load avg on app server increased to about 6-10 during that heavy select period (from 3-4 as before). When server doesn't run those frequent updates it seems like performance for selects stays within the limits. So I have a feeling that those updates is what is causing the performance drop (also these frequent updates are not critical so if slave db doesn't have them in sync with master for some time it would be ok). What would be a good db replication setup for such kind of app? What are the replication parameters we could tweak? Thanks.

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  • Visual Studio Development on Virtual Box, Boot Camp, or VMWare Fusion

    - by Eli
    I currently have a Mac, 2ghz and 2 gigs of ram, running OS X Leopard and Virtual Box with a Windows 7 Pro 32bit virtual machine. Performance on the virtual machine is fine for minor tasks but is very clunky while trying to multi-task or develop in Visual Studio 2008. What would be my best option for being able to use Visual Studio, keeping cost and time in mind? 1) Upgrade ram to 4 gigs ($100). Will this really improve my performance enough to use Visual Studio in a Windows 7 vm? Or am I just wasting time/money? 2) Reinstall/restore Windows 7 disk image as a Boot Camp partition. I assume this should improve my performance, yes? 3) Purchase VMWare fusion instead of VirtualBox. Does Fusion require less resources to run? I am open to any suggestions. Thanks in advance

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  • Iozone: sensible settings for a server with lots of RAM

    - by Frank Brenner
    I have just acquired a server with: 2x quadcore Xeons 48G ECC RAM 5x 160GB SSDs on an LSI 9260-8i Before deploying the target platform, I'd like to collect as much benchmark data as possible, testing I/O with hardware RAID in various configurations, ZFS zRAID, as well as I/O performance on vSphere and with KVM virtualization. In order to see real disk I/O performance without cache effects, I tried running Iozone with a maximum file of more than twice the physical RAM as recommended in the documentation, so: iozone -a -g100G However, as one might expect, this takes far too long to be practicable. (I stopped the run after seven hours..) I'd like to reduce the range of record and file sizes to values that might reflect realistic performance for an application server, hopefully getting the run times to under an hour or so. Any ideas? Thanks.

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  • How to Track CPU and Memory Usage Per Process

    - by Mjsk
    I have seen this question asked on here before but was unable to follow the answer which was given. I would like to monitor a processes CPU, Memory, and possibly GPU usage over a given time. The data would be useful if presented in a graph. It would be nice if I could do this using Performance Monitor, but I am open to alternative solutions as well. I have tried using Performance Monitor and my problem is that I'm not sure which performance counters to use since there are so many. I've been looking at a Process, Processor, Memory, etc. but I'm not sure which counters within those categories will be of interest to me. My OS is Windows 7.

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  • OS Isolation: Virtualization or Dual-Boot Duplication, a General How To?

    - by Mr_CryptoPrime
    I want to isolate my windows 7 operating system and I have looked into virtualization. This should work with Linux, however, I do want to still have a way to run windows 7 securely, but without significant performance loss, thus eliminating virtualization for that. I know that you can dual boot because I currently do so with my XP/Linux system. Is there a way that I can duplicate my windows 7 system so I can select one at bootup? This way I can ensure that each OS is isolated and not worry about performance loss. However, I am having a lot of trouble finding a solid method for OS duplication?! Is this even possible or must I buy two versions of win7 and somehow install them separately? Any information regarding this would be helpful, thanks! Essentially I want, Two instances of win7 (not necessarily simultaneously running) Each are isolated from one another so that a security breach in one doesn't affect the other. There is no performance loss in either from doing so

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  • KVM vs Hyper-V. Which hypervisor is best for windows guests?

    - by user198851
    I am currently testing openstack for windows guests (XP and 7). I have deployed openstack "all in one" on system with following specs Processor corei5. (4 physical cores and 8 Threads with HT Technology) RAM 8 GB. HD 500 GB. I have created 4 windows xp guests with 512MB RAM and 1VCPU. On each windows guest i have installed visual studio 2008 only. In nova.conf CPU Over-Commit ratio is 2 for better performance (as mentioned in openstack operation guide). Using KVM as hyerpvisor. I have observed poor performance when simultaneously using visual studio in four windows instances. How i can improve performance ? Should i use KVM or Hyper-V ? or any other suggestion ?

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  • Oracle Announces Oracle Big Data Appliance X3-2 and Enhanced Oracle Big Data Connectors

    - by jgelhaus
    Enables Customers to Easily Harness the Business Value of Big Data at Lower Cost Engineered System Simplifies Big Data for the Enterprise Oracle Big Data Appliance X3-2 hardware features the latest 8-core Intel® Xeon E5-2600 series of processors, and compared with previous generation, the 18 compute and storage servers with 648 TB raw storage now offer: 33 percent more processing power with 288 CPU cores; 33 percent more memory per node with 1.1 TB of main memory; and up to a 30 percent reduction in power and cooling Oracle Big Data Appliance X3-2 further simplifies implementation and management of big data by integrating all the hardware and software required to acquire, organize and analyze big data. It includes: Support for CDH4.1 including software upgrades developed collaboratively with Cloudera to simplify NameNode High Availability in Hadoop, eliminating the single point of failure in a Hadoop cluster; Oracle NoSQL Database Community Edition 2.0, the latest version that brings better Hadoop integration, elastic scaling and new APIs, including JSON and C support; The Oracle Enterprise Manager plug-in for Big Data Appliance that complements Cloudera Manager to enable users to more easily manage a Hadoop cluster; Updated distributions of Oracle Linux and Oracle Java Development Kit; An updated distribution of open source R, optimized to work with high performance multi-threaded math libraries Read More   Data sheet: Oracle Big Data Appliance X3-2 Oracle Big Data Appliance: Datacenter Network Integration Big Data and Natural Language: Extracting Insight From Text Thomson Reuters Discusses Oracle's Big Data Platform Connectors Integrate Hadoop with Oracle Big Data Ecosystem Oracle Big Data Connectors is a suite of software built by Oracle to integrate Apache Hadoop with Oracle Database, Oracle Data Integrator, and Oracle R Distribution. Enhancements to Oracle Big Data Connectors extend these data integration capabilities. With updates to every connector, this release includes: Oracle SQL Connector for Hadoop Distributed File System, for high performance SQL queries on Hadoop data from Oracle Database, enhanced with increased automation and querying of Hive tables and now supported within the Oracle Data Integrator Application Adapter for Hadoop; Transparent access to the Hive Query language from R and introduction of new analytic techniques executing natively in Hadoop, enabling R developers to be more productive by increasing access to Hadoop in the R environment. Read More Data sheet: Oracle Big Data Connectors High Performance Connectors for Load and Access of Data from Hadoop to Oracle Database

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  • .NET Weak Event Handlers – Part II

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

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  • SQL SERVER – Guest Post – Jonathan Kehayias – Wait Type – Day 16 of 28

    - by pinaldave
    Jonathan Kehayias (Blog | Twitter) is a MCITP Database Administrator and Developer, who got started in SQL Server in 2004 as a database developer and report writer in the natural gas industry. After spending two and a half years working in TSQL, in late 2006, he transitioned to the role of SQL Database Administrator. His primary passion is performance tuning, where he frequently rewrites queries for better performance and performs in depth analysis of index implementation and usage. Jonathan blogs regularly on SQLBlog, and was a coauthor of Professional SQL Server 2008 Internals and Troubleshooting. On a personal note, I think Jonathan is extremely positive person. In every conversation with him I have found that he is always eager to help and encourage. Every time he finds something needs to be approved, he has contacted me without hesitation and guided me to improve, change and learn. During all the time, he has not lost his focus to help larger community. I am honored that he has accepted to provide his views on complex subject of Wait Types and Queues. Currently I am reading his series on Extended Events. Here is the guest blog post by Jonathan: SQL Server troubleshooting is all about correlating related pieces of information together to indentify where exactly the root cause of a problem lies. In my daily work as a DBA, I generally get phone calls like, “So and so application is slow, what’s wrong with the SQL Server.” One of the funny things about the letters DBA is that they go so well with Default Blame Acceptor, and I really wish that I knew exactly who the first person was that pointed that out to me, because it really fits at times. A lot of times when I get this call, the problem isn’t related to SQL Server at all, but every now and then in my initial quick checks, something pops up that makes me start looking at things further. The SQL Server is slow, we see a number of tasks waiting on ASYNC_IO_COMPLETION, IO_COMPLETION, or PAGEIOLATCH_* waits in sys.dm_exec_requests and sys.dm_exec_waiting_tasks. These are also some of the highest wait types in sys.dm_os_wait_stats for the server, so it would appear that we have a disk I/O bottleneck on the machine. A quick check of sys.dm_io_virtual_file_stats() and tempdb shows a high write stall rate, while our user databases show high read stall rates on the data files. A quick check of some performance counters and Page Life Expectancy on the server is bouncing up and down in the 50-150 range, the Free Page counter consistently hits zero, and the Free List Stalls/sec counter keeps jumping over 10, but Buffer Cache Hit Ratio is 98-99%. Where exactly is the problem? In this case, which happens to be based on a real scenario I faced a few years back, the problem may not be a disk bottleneck at all; it may very well be a memory pressure issue on the server. A quick check of the system spec’s and it is a dual duo core server with 8GB RAM running SQL Server 2005 SP1 x64 on Windows Server 2003 R2 x64. Max Server memory is configured at 6GB and we think that this should be enough to handle the workload; or is it? This is a unique scenario because there are a couple of things happening inside of this system, and they all relate to what the root cause of the performance problem is on the system. If we were to query sys.dm_exec_query_stats for the TOP 10 queries, by max_physical_reads, max_logical_reads, and max_worker_time, we may be able to find some queries that were using excessive I/O and possibly CPU against the system in their worst single execution. We can also CROSS APPLY to sys.dm_exec_sql_text() and see the statement text, and also CROSS APPLY sys.dm_exec_query_plan() to get the execution plan stored in cache. Ok, quick check, the plans are pretty big, I see some large index seeks, that estimate 2.8GB of data movement between operators, but everything looks like it is optimized the best it can be. Nothing really stands out in the code, and the indexing looks correct, and I should have enough memory to handle this in cache, so it must be a disk I/O problem right? Not exactly! If we were to look at how much memory the plan cache is taking by querying sys.dm_os_memory_clerks for the CACHESTORE_SQLCP and CACHESTORE_OBJCP clerks we might be surprised at what we find. In SQL Server 2005 RTM and SP1, the plan cache was allowed to take up to 75% of the memory under 8GB. I’ll give you a second to go back and read that again. Yes, you read it correctly, it says 75% of the memory under 8GB, but you don’t have to take my word for it, you can validate this by reading Changes in Caching Behavior between SQL Server 2000, SQL Server 2005 RTM and SQL Server 2005 SP2. In this scenario the application uses an entirely adhoc workload against SQL Server and this leads to plan cache bloat, and up to 4.5GB of our 6GB of memory for SQL can be consumed by the plan cache in SQL Server 2005 SP1. This in turn reduces the size of the buffer cache to just 1.5GB, causing our 2.8GB of data movement in this expensive plan to cause complete flushing of the buffer cache, not just once initially, but then another time during the queries execution, resulting in excessive physical I/O from disk. Keep in mind that this is not the only query executing at the time this occurs. Remember the output of sys.dm_io_virtual_file_stats() showed high read stalls on the data files for our user databases versus higher write stalls for tempdb? The memory pressure is also forcing heavier use of tempdb to handle sorting and hashing in the environment as well. The real clue here is the Memory counters for the instance; Page Life Expectancy, Free List Pages, and Free List Stalls/sec. The fact that Page Life Expectancy is fluctuating between 50 and 150 constantly is a sign that the buffer cache is experiencing constant churn of data, once every minute to two and a half minutes. If you add to the Page Life Expectancy counter, the consistent bottoming out of Free List Pages along with Free List Stalls/sec consistently spiking over 10, and you have the perfect memory pressure scenario. All of sudden it may not be that our disk subsystem is the problem, but is instead an innocent bystander and victim. Side Note: The Page Life Expectancy counter dropping briefly and then returning to normal operating values intermittently is not necessarily a sign that the server is under memory pressure. The Books Online and a number of other references will tell you that this counter should remain on average above 300 which is the time in seconds a page will remain in cache before being flushed or aged out. This number, which equates to just five minutes, is incredibly low for modern systems and most published documents pre-date the predominance of 64 bit computing and easy availability to larger amounts of memory in SQL Servers. As food for thought, consider that my personal laptop has more memory in it than most SQL Servers did at the time those numbers were posted. I would argue that today, a system churning the buffer cache every five minutes is in need of some serious tuning or a hardware upgrade. Back to our problem and its investigation: There are two things really wrong with this server; first the plan cache is excessively consuming memory and bloated in size and we need to look at that and second we need to evaluate upgrading the memory to accommodate the workload being performed. In the case of the server I was working on there were a lot of single use plans found in sys.dm_exec_cached_plans (where usecounts=1). Single use plans waste space in the plan cache, especially when they are adhoc plans for statements that had concatenated filter criteria that is not likely to reoccur with any frequency.  SQL Server 2005 doesn’t natively have a way to evict a single plan from cache like SQL Server 2008 does, but MVP Kalen Delaney, showed a hack to evict a single plan by creating a plan guide for the statement and then dropping that plan guide in her blog post Geek City: Clearing a Single Plan from Cache. We could put that hack in place in a job to automate cleaning out all the single use plans periodically, minimizing the size of the plan cache, but a better solution would be to fix the application so that it uses proper parameterized calls to the database. You didn’t write the app, and you can’t change its design? Ok, well you could try to force parameterization to occur by creating and keeping plan guides in place, or we can try forcing parameterization at the database level by using ALTER DATABASE <dbname> SET PARAMETERIZATION FORCED and that might help. If neither of these help, we could periodically dump the plan cache for that database, as discussed as being a problem in Kalen’s blog post referenced above; not an ideal scenario. The other option is to increase the memory on the server to 16GB or 32GB, if the hardware allows it, which will increase the size of the plan cache as well as the buffer cache. In SQL Server 2005 SP1, on a system with 16GB of memory, if we set max server memory to 14GB the plan cache could use at most 9GB  [(8GB*.75)+(6GB*.5)=(6+3)=9GB], leaving 5GB for the buffer cache.  If we went to 32GB of memory and set max server memory to 28GB, the plan cache could use at most 16GB [(8*.75)+(20*.5)=(6+10)=16GB], leaving 12GB for the buffer cache. Thankfully we have SQL Server 2005 Service Pack 2, 3, and 4 these days which include the changes in plan cache sizing discussed in the Changes to Caching Behavior between SQL Server 2000, SQL Server 2005 RTM and SQL Server 2005 SP2 blog post. In real life, when I was troubleshooting this problem, I spent a week trying to chase down the cause of the disk I/O bottleneck with our Server Admin and SAN Admin, and there wasn’t much that could be done immediately there, so I finally asked if we could increase the memory on the server to 16GB, which did fix the problem. It wasn’t until I had this same problem occur on another system that I actually figured out how to really troubleshoot this down to the root cause.  I couldn’t believe the size of the plan cache on the server with 16GB of memory when I actually learned about this and went back to look at it. SQL Server is constantly telling a story to anyone that will listen. As the DBA, you have to sit back and listen to all that it’s telling you and then evaluate the big picture and how all the data you can gather from SQL about performance relate to each other. One of the greatest tools out there is actually a free in the form of Diagnostic Scripts for SQL Server 2005 and 2008, created by MVP Glenn Alan Berry. Glenn’s scripts collect a majority of the information that SQL has to offer for rapid troubleshooting of problems, and he includes a lot of notes about what the outputs of each individual query might be telling you. When I read Pinal’s blog post SQL SERVER – ASYNC_IO_COMPLETION – Wait Type – Day 11 of 28, I noticed that he referenced Checking Memory Related Performance Counters in his post, but there was no real explanation about why checking memory counters is so important when looking at an I/O related wait type. I thought I’d chat with him briefly on Google Talk/Twitter DM and point this out, and offer a couple of other points I noted, so that he could add the information to his blog post if he found it useful.  Instead he asked that I write a guest blog for this. I am honored to be a guest blogger, and to be able to share this kind of information with the community. The information contained in this blog post is a glimpse at how I do troubleshooting almost every day of the week in my own environment. SQL Server provides us with a lot of information about how it is running, and where it may be having problems, it is up to us to play detective and find out how all that information comes together to tell us what’s really the problem. This blog post is written by Jonathan Kehayias (Blog | Twitter). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: MVP, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • The C++ Standard Template Library as a BDB Database (part 1)

    - by Gregory Burd
    If you've used C++ you undoubtedly have used the Standard Template Libraries. Designed for in-memory management of data and collections of data this is a core aspect of all C++ programs. Berkeley DB is a database library with a variety of APIs designed to ease development, one of those APIs extends and makes use of the STL for persistent, transactional data storage. dbstl is an STL standard compatible API for Berkeley DB. You can make use of Berkeley DB via this API as if you are using C++ STL classes, and still make full use of Berkeley DB features. Being an STL library backed by a database, there are some important and useful features that dbstl can provide, while the C++ STL library can't. The following are a few typical use cases to use the dbstl extensions to the C++ STL for data storage. When data exceeds available physical memory.Berkeley DB dbstl can vastly improve performance when managing a dataset which is larger than available memory. Performance suffers when the data can't reside in memory because the OS is forced to use virtual memory and swap pages of memory to disk. Switching to BDB's dbstl improves performance while allowing you to keep using STL containers. When you need concurrent access to C++ STL containers.Few existing C++ STL implementations support concurrent access (create/read/update/delete) within a container, at best you'll find support for accessing different containers of the same type concurrently. With the Berkeley DB dbstl implementation you can concurrently access your data from multiple threads or processes with confidence in the outcome. When your objects are your database.You want to have object persistence in your application, and store objects in a database, and use the objects across different runs of your application without having to translate them to/from SQL. The dbstl is capable of storing complicated objects, even those not located on a continous chunk of memory space, directly to disk without any unnecessary overhead. These are a few reasons why you should consider using Berkeley DB's C++ STL support for your embedded database application. In the next few blog posts I'll show you a few examples of this approach, it's easy to use and easy to learn.

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  • CodePlex Daily Summary for Thursday, December 09, 2010

    CodePlex Daily Summary for Thursday, December 09, 2010Popular ReleasesAutoLoL: AutoLoL v1.4.3: AutoLoL now supports importing the build pages from Mobafire.com as well! Just insert the url to the build and voila. (For example: http://www.mobafire.com/league-of-legends/build/unforgivens-guide-how-to-build-a-successful-mordekaiser-24061) Stable release of AutoChat (It is still recommended to use with caution and to read the documentation) It is now possible to associate *.lolm files with AutoLoL to quickly open them The selected spells are now displayed in the masteries tab for qu...SubtitleTools: SubtitleTools 1.2: - Added auto insertion of RLE (RIGHT-TO-LEFT EMBEDDING) Unicode character for the RTL languages. - Fixed delete rows issue.PHP Manager for IIS: PHP Manager 1.1 for IIS 7: This is a final stable release of PHP Manager 1.1 for IIS 7. This is a minor incremental release that contains all the functionality available in 53121 plus additional features listed below: Improved detection logic for existing PHP installations. Now PHP Manager detects the location to php.ini file in accordance to the PHP specifications Configuring date.timezone. PHP Manager can automatically set the date.timezone directive which is required to be set starting from PHP 5.3 Ability to ...Algorithmia: Algorithmia 1.1: Algorithmia v1.1, released on December 8th, 2010.SuperSocket, an extensible socket application framework: SuperSocket 1.0 SP1: Fixed bugs: fixed a potential bug that the running state hadn't been updated after socket server stopped fixed a synchronization issue when clearing timeout session fixed a bug in ArraySegmentList fixed a bug on getting configuration valueCslaGenFork: CslaGenFork 4.0 CTP 2: The version is 4.0.1 CTP2 and was released 2010 December 7 and includes the following files: CslaGenFork 4.0.1-2010-12-07 Setup.msi Templates-2010-10-07.zip For getting started instructions, refer to How to section. Overview of the changes Since CTP1 there were 53 work items closed (28 features, 24 issues and 1 task). During this 60 days a lot of work has been done on several areas. First the stereotypes: EditableRoot is OK EditableChild is OK EditableRootCollection is OK Editable...Windows Workflow Foundation on Codeplex: WF AppFabric Caching Activity Pack 0.1: This release includes a set of AppFabric Caching Activities that allow you to use Windows Server AppFabric Caching with WF4. Video endpoint.tv - New WF4 Caching Activities for Windows Server AppFabric ActivitiesDataCacheAdd DataCacheGet DataCachePut DataCacheGet DataCacheRemove WaitForCacheBulkNotification WaitForCacheNotification WaitForFailureNotification WaitForItemNotification WaitForRegionNotification Unit TestsUnit tests are included in the source. Be sure to star...My Web Pages Starter Kit: 1.3.1 Production Release (Security HOTFIX): Due to a critical security issue, it's strongly advised to update the My Web Pages Starter Kit to this version. Possible attackers could misuse the image upload to transmit any type of file to the website. If you already have a running version of My Web Pages Starter Kit 1.3.0, you can just replace the ftb.imagegallery.aspx file in the root directory with the one attached to this release.EnhSim: EnhSim 2.2.0 ALPHA: 2.2.0 ALPHAThis release adds in the changes for 4.03a. at level 85 To use this release, you must have the Microsoft Visual C++ 2010 Redistributable Package installed. This can be downloaded from http://www.microsoft.com/downloads/en/details.aspx?FamilyID=A7B7A05E-6DE6-4D3A-A423-37BF0912DB84 To use the GUI you must have the .NET 4.0 Framework installed. This can be downloaded from http://www.microsoft.com/downloads/en/details.aspx?FamilyID=9cfb2d51-5ff4-4491-b0e5-b386f32c0992 - Updated En...ASP.NET MVC Project Awesome (jQuery Ajax helpers): 1.4: A rich set of helpers (controls) that you can use to build highly responsive and interactive Ajax-enabled Web applications. These helpers include Autocomplete, AjaxDropdown, Lookup, Confirm Dialog, Popup Form, Popup and Pager new stuff: popup WhiteSpaceFilterAttribute tested on mozilla, safari, chrome, opera, ie 9b/8/7/6nopCommerce. ASP.NET open source shopping cart: nopCommerce 1.90: To see the full list of fixes and changes please visit the release notes page (http://www.nopCommerce.com/releasenotes.aspx).TweetSharp: TweetSharp v2.0.0.0 - Preview 4: Documentation for this release may be found at http://tweetsharp.codeplex.com/wikipage?title=UserGuide&referringTitle=Documentation. Note: This code is currently preview quality. Preview 4 ChangesReintroduced fluent interface support via satellite assembly Added entities support, entity segmentation, and ITweetable/ITweeter interfaces for client development Numerous fixes reported by preview users Preview 3 ChangesNumerous fixes and improvements to core engine Twitter API coverage: a...Aura: Aura Preview 1: Rewritten from scratch. This release supports getting color only from icon of foreground window.MBG Extensions Library: MBG.Extensions_v1.3: MBG.Extensions Collections.CollectionExtensions - AddIfNew - RemoveRange (Moved From ListExtensions to here, where it should have been) Collections.EnumerableExtensions - ToCommaSeparatedList has been replaced by: Join() and ToValueSeparatedList Join is for a single line of values. ToValueSeparatedList is generally for collection and will separate each entity in the collection by a new line character - ToQueue - ToStack Core.ByteExtensions - TripleDESDecrypt Core.DateTimeExtension...myCollections: Version 1.2: New in version 1.2: Big performance improvement. New Design (Added Outlook style View, New detail view, New Groub By...) Added Sort by Media Added Manage Movie Studio Zoom preference is now saved. Media name are now editable. Added Portuguese version You can now Hide details panel Add support for FLAC tags You can now imports books from BibTex Xml file BugFixingmytrip.mvc (CMS & e-Commerce): mytrip.mvc 1.0.49.0 beta: mytrip.mvc 1.0.49.0 beta web Web for install hosting System Requirements: NET 4.0, MSSQL 2008 or MySql (auto creation table to database) if .\SQLEXPRESS auto creation database (App_Data folder) mytrip.mvc 1.0.49.0 beta src System Requirements: Visual Studio 2010 or Web Deweloper 2010 MSSQL 2008 or MySql (auto creation table to database) if .\SQLEXPRESS auto creation database (App_Data folder) Connector/Net 6.3.4, MVC3 RC WARNING For run and debug mytrip.mvc 1.0.49.0 beta src download and ...Menu and Context Menu for Silverlight 4.0: Silverlight Menu and Context Menu v2.3 Beta: - Added keyboard navigation support with access keys - Shortcuts like Ctrl-Alt-A are now supported(where the browser permits it) - The PopupMenuSeparator is now completely based on the PopupMenuItem class - Moved item manipulation code to a partial class in PopupMenuItemsControl.cs - Moved menu management and keyboard navigation code to the new PopupMenuManager class - Simplified the layout by removing the RootGrid element(all content is now placed in OverlayCanvas and is accessed by the new ...MiniTwitter: 1.62: MiniTwitter 1.62 ???? ?? ??????????????????????????????????????? 140 ?????????????????????????? ???????????????????????????????? ?? ??????????????????????????????????Phalanger - The PHP Language Compiler for the .NET Framework: 2.0 (December 2010): The release is targetted for stable daily use. With improved performance and enhanced compatibility with several latest PHP open source applications; it makes this release perfect replacement of your old PHP runtime. Changes made within this release include following and much more: Performance improvements based on real-world applications experience. We determined biggest bottlenecks and we found and removed overheads causing performance problems in many PHP applications. Reimplemented nat...Chronos WPF: Chronos v2.0 Beta 3: Release notes: Updated introduction document. Updated Visual Studio 2010 Extension (vsix) package. Added horizontal scrolling to the main window TaskBar. Added new styles for ListView, ListViewItem, GridViewColumnHeader, ... Added a new WindowViewModel class (allowing to fetch data). Added a new Navigate method (with several overloads) to the NavigationViewModel class (protected). Reimplemented Task usage for the WorkspaceViewModel.OnDelete method. Removed the reflection effect...New Projects:WinK: WinK Project1000 bornes: This project is the adaptation of the famous French card game 1000 bornes (http://en.wikipedia.org/wiki/Mille_Bornes) There will be 3 types of clients: - Windows Application (WPF) - Internet Application (ASP.NET, Ajax) - Silverlight Application It's developed in C#.AutomaTones: BDSA Project 2010. Team Anders is developing an application that uses automatons to generate music.EIRENE: UnknownFinal: TDD driven analize of avalable tdd frameworks ect.HomeGrown Database Project tools: A set of tools that can be used to deploy Visual Studio SQL Databse and Server Projects. Developed using Visual Basic .Net 4.0mcssolution: no summaryMobile-enabled ASP.NET Web Forms / MVC application samples: Code samples for the whitepaper "Add mobile pages to your ASP.NET Web Forms / MVC application" linked from http://asp.net/mobileMSDI Projects: www.msdi.cnObject TreeView Visualizer: This is a Helper Library for easy Access to Visual a Object to an treeview. Nice feature to display data, if an error happen. One Place To Rule Them All: Desktop system to manage basics system functions in 3d environmant. Optra also provide community support and easy transfer data and setups between varius devices using xmpp protocol and OpenFire jabber server.Performance Data Suite: The Performance Data Suite will help you to monitor, analyze and optimize your server infrastructure. There will be predefined sets of data collections(e.g. MySQL, Apache, IIS) but it will also help you to create collections on your own.Secure Group Communication in AdHoc Networks: Secure Group Communication in AdHoc Networksimweb: simweb - is a research project which own by GCR and all its copyright belong to GCR. You can download the code for reference only but not able to be commercial without a fees.starLiGHT.Engine: starLiGHT.Engine is a set of libraries for indie game developers using XNA. It is in development for some years now as a closed source project. Now I will release some (most) parts as Open Source (dual licensing).UMC? ???? .NET ??? ?? ???? ???: ???(Junil, Um)? ???? .NET ???? ?? ?? ???? ??? ???.University of Ottawa tour for WP7: This is a Windows Phone 7 tour guide app for the University of Ottawa. vutpp for VS2010: C++ UnitTest Gui Addin????: ????

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  • SQL SERVER – 2008 – Missing Index Script – Download

    - by pinaldave
    Download Missing Index Script with Unused Index Script Performance Tuning is quite interesting and Index plays a vital role in it. A proper index can improve the performance and a bad index can hamper the performance. Here is the script from my script bank which I use to identify missing indexes on any database. Please note, if you should not create all the missing indexes this script suggest. This is just for guidance. You should not create more than 5-10 indexes per table. Additionally, this script sometime does not give accurate information so use your common sense. Any way, the scripts is good starting point. You should pay attention to Avg_Estimated_Impact when you are going to create index. The index creation script is also provided in the last column. Download Missing Index Script with Unused Index Script -- Missing Index Script -- Original Author: Pinal Dave (C) 2011 SELECT TOP 25 dm_mid.database_id AS DatabaseID, dm_migs.avg_user_impact*(dm_migs.user_seeks+dm_migs.user_scans) Avg_Estimated_Impact, dm_migs.last_user_seek AS Last_User_Seek, OBJECT_NAME(dm_mid.OBJECT_ID,dm_mid.database_id) AS [TableName], 'CREATE INDEX [IX_' + OBJECT_NAME(dm_mid.OBJECT_ID,dm_mid.database_id) + '_' + REPLACE(REPLACE(REPLACE(ISNULL(dm_mid.equality_columns,''),', ','_'),'[',''),']','') + CASE WHEN dm_mid.equality_columns IS NOT NULL AND dm_mid.inequality_columns IS NOT NULL THEN '_' ELSE '' END + REPLACE(REPLACE(REPLACE(ISNULL(dm_mid.inequality_columns,''),', ','_'),'[',''),']','') + ']' + ' ON ' + dm_mid.statement + ' (' + ISNULL (dm_mid.equality_columns,'') + CASE WHEN dm_mid.equality_columns IS NOT NULL AND dm_mid.inequality_columns IS NOT NULL THEN ',' ELSE '' END + ISNULL (dm_mid.inequality_columns, '') + ')' + ISNULL (' INCLUDE (' + dm_mid.included_columns + ')', '') AS Create_Statement FROM sys.dm_db_missing_index_groups dm_mig INNER JOIN sys.dm_db_missing_index_group_stats dm_migs ON dm_migs.group_handle = dm_mig.index_group_handle INNER JOIN sys.dm_db_missing_index_details dm_mid ON dm_mig.index_handle = dm_mid.index_handle WHERE dm_mid.database_ID = DB_ID() ORDER BY Avg_Estimated_Impact DESC GO Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Download, SQL Index, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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