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  • create table from another table in different database in sql server 2005

    - by Greg
    Hi, I have a database "temp" with table "A". I created new database "temp2". I want to copy table "A" from "temp" to a new table in "temp2" . I tried this statement but it says I have incorrect syntax, here is the statement: CREATE TABLE B IN 'temp2' AS (SELECT * FROM A IN 'temp'); Here is the error: Msg 156, Level 15, State 1, Line 2 Incorrect syntax near the keyword 'IN'. Msg 156, Level 15, State 1, Line 3 Incorrect syntax near the keyword 'IN'. Anyone knows whats the problem? Thanks in advance, Greg

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  • load table data on jsp using struts2 with fixed table structure

    - by Zahra
    Hi. I want to have a fixed table structure on my jsp page(3row, 4column). but I want to load data for this table from DataBase with using struts 2. I know if my table structure wasn't fixed I could have just get a List and iterate on it and add a data in every iteration, but how could I do this in this case. also if I don't have enough data to fill table, I want those places to be empty. I didn't find a good example, if you could help me or introduce me a good tutorial, it would be really appreciated. Thanks in advance.

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  • Azure - Part 4 - Table Storage Service in Windows Azure

    - by Shaun
    In Windows Azure platform there are 3 storage we can use to save our data on the cloud. They are the Table, Blob and Queue. Before the Chinese New Year Microsoft announced that Azure SDK 1.1 had been released and it supports a new type of storage – Drive, which allows us to operate NTFS files on the cloud. I will cover it in the coming few posts but now I would like to talk a bit about the Table Storage.   Concept of Table Storage Service The most common development scenario is to retrieve, create, update and remove data from the data storage. In the normal way we communicate with database. When we attempt to move our application over to the cloud the most common requirement should be have a storage service. Windows Azure provides a in-build service that allow us to storage the structured data, which is called Windows Azure Table Storage Service. The data stored in the table service are like the collection of entities. And the entities are similar to rows or records in the tradtional database. An entity should had a partition key, a row key, a timestamp and set of properties. You can treat the partition key as a group name, the row key as a primary key and the timestamp as the identifer for solving the concurrency problem. Different with a table in a database, the table service does not enforce the schema for tables, which means you can have 2 entities in the same table with different property sets. The partition key is being used for the load balance of the Azure OS and the group entity transaction. As you know in the cloud you will never know which machine is hosting your application and your data. It could be moving based on the transaction weight and the number of the requests. If the Azure OS found that there are many requests connect to your Book entities with the partition key equals “Novel” it will move them to another idle machine to increase the performance. So when choosing the partition key for your entities you need to make sure they indecate the category or gourp information so that the Azure OS can perform the load balance as you wish.   Consuming the Table Although the table service looks like a database, you cannot access it through the way you are using now, neither ADO.NET nor ODBC. The table service exposed itself by ADO.NET Data Service protocol, which allows you can consume it through the RESTful style by Http requests. The Azure SDK provides a sets of classes for us to connect it. There are 2 classes we might need: TableServiceContext and TableServiceEntity. The TableServiceContext inherited from the DataServiceContext, which represents the runtime context of the ADO.NET data service. It provides 4 methods mainly used by us: CreateQuery: It will create a IQueryable instance from a given type of entity. AddObject: Add the specified entity into Table Service. UpdateObject: Update an existing entity in the Table Service. DeleteObject: Delete an entity from the Table Service. Beofre you operate the table service you need to provide the valid account information. It’s something like the connect string of the database but with your account name and the account key when you created the storage service on the Windows Azure Development Portal. After getting the CloudStorageAccount you can create the CloudTableClient instance which provides a set of methods for using the table service. A very useful method would be CreateTableIfNotExist. It will create the table container for you if it’s not exsited. And then you can operate the eneities to that table through the methods I mentioned above. Let me explain a bit more through an exmaple. We always like code rather than sentence.   Straightforward Accessing to the Table Here I would like to build a WCF service on the Windows Azure platform, and for now just one requirement: it would allow the client to create an account entity on the table service. The WCF service would have a method named Register and accept an instance of the account which the client wants to create. After perform some validation it will add the entity into the table service. So the first thing I should do is to create a Cloud Application on my VIstial Studio 2010 RC. (The Azure SDK 1.1 only supports VS2008 and VS2010 RC.) The solution should be like this below. Then I added a configuration items for the storage account through the Settings section under the cloud project. (Double click the Services file under Roles folder and navigate to the Setting section.) This setting will be used when to retrieve my storage account information. Since for now I just in the development phase I will select “UseDevelopmentStorage=true”. And then I navigated to the WebRole.cs file under my WCF project. If you have read my previous posts you would know that this file defines the process when the application start, and terminate on the cloud. What I need to do is to when the application start, set the configuration publisher to load my config file with the config name I specified. So the code would be like below. I removed the original service and contract created by the VS template and add my IAccountService contract and its implementation class - AccountService. And I add the service method Register with the parameters: email, password and it will return a boolean value to indicates the result which is very simple. At this moment if I press F5 the application will be established on my local development fabric and I can see my service runs well through the browser. Let’s implement the service method Rigister, add a new entity to the table service. As I said before the entities you want to store in the table service must have 3 properties: partition key, row key and timespan. You can create a class with these 3 properties. The Azure SDK provides us a base class for that named TableServiceEntity in Microsoft.WindowsAzure.StorageClient namespace. So what we need to do is more simply, create a class named Account and let it derived from the TableServiceEntity. And I need to add my own properties: Email, Password, DateCreated and DateDeleted. The DateDeleted is a nullable date time value to indecate whether this entity had been deleted and when. Do you notice that I missed something here? Yes it’s the partition key and row key I didn’t assigned. The TableServiceEntity base class defined 2 constructors one was a parameter-less constructor which will be used to fill values into the properties from the table service when retrieving data. The other was one with 2 parameters: partition key and row key. As I said below the partition key may affect the load balance and the row key must be unique so here I would like to use the email as the parition key and the email plus a Guid as the row key. OK now we finished the entity class we need to store onto the table service. The next step is to create a data access class for us to add it. Azure SDK gives us a base class for it named TableServiceContext as I mentioned below. So let’s create a class for operate the Account entities. The TableServiceContext need the storage account information for its constructor. It’s the combination of the storage service URI that we will create on Windows Azure platform, and the relevant account name and key. The TableServiceContext will use this information to find the related address and verify the account to operate the storage entities. Hence in my AccountDataContext class I need to override this constructor and pass the storage account into it. All entities will be saved in the table storage with one or many tables which we call them “table containers”. Before we operate an entity we need to make sure that the table container had been created on the storage. There’s a method we can use for that: CloudTableClient.CreateTableIfNotExist. So in the constructor I will perform it firstly to make sure all method will be invoked after the table had been created. Notice that I passed the storage account enpoint URI and the credentials to specify where my storage is located and who am I. Another advise is that, make your entity class name as the same as the table name when create the table. It will increase the performance when you operate it over the cloud especially querying. Since the Register WCF method will add a new account into the table service, here I will create a relevant method to add the account entity. Before implement, I should add a reference - System.Data.Services.Client to the project. This reference provides some common method within the ADO.NET Data Service which can be used in the Windows Azure Table Service. I will use its AddObject method to create my account entity. Since the table service are not fully implemented the ADO.NET Data Service, there are some methods in the System.Data.Services.Client that TableServiceContext doesn’t support, such as AddLinks, etc. Then I implemented the serivce method to add the account entity through the AccountDataContext. You can see in the service implmentation I load the storage account information through my configuration file and created the account table entity from the parameters. Then I created the AccountDataContext. If it’s my first time to invoke this method the constructor of the AccountDataContext will create a table container for me. Then I use Add method to add the account entity into the table. Next, let’s create a farely simple client application to test this service. I created a windows console application and added a service reference to my WCF service. The metadata information of the WCF service cannot be retrieved if it’s deployed on the Windows Azure even though the <serviceMetadata httpGetEnabled="true"/> had been set. If we need to get its metadata we can deploy it on the local development service and then changed the endpoint to the address which is on the cloud. In the client side app.config file I specified the endpoint to the local development fabric address. And the just implement the client to let me input an email and a password then invoke the WCF service to add my acocunt. Let’s run my application and see the result. Of course it should return TRUE to me. And in the local SQL Express I can see the data had been saved in the table.   Summary In this post I explained more about the Windows Azure Table Storage Service. I also created a small application for demostration of how to connect and consume it through the ADO.NET Data Service Managed Library provided within the Azure SDK. I only show how to create an eneity in the storage service. In the next post I would like to explain about how to query the entities with conditions thruogh LINQ. I also would like to refactor my AccountDataContext class to make it dyamic for any kinds of entities.   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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  • PHP File Downloading Questions

    - by nsearle
    Hey All! I am currently running into some problems with user's downloading a file stored on my server. I have code set up to auto download a file once the user hits the download button. It is working for all files, but when the size get's larger than 30 MB it is having issues. Is there a limit on user download? Also, I have supplied my example code and am wondering if there is a better practice than using the PHP function 'file_get_contents'. Thank You all for the help! $path = $_SERVER['DOCUMENT_ROOT'] . '../path/to/file/'; $filename = 'filename.zip'; $filesize = filesize($path . $filename); @header("Content-type: application/zip"); @header("Content-Disposition: attachment; filename=$filename"); @header("Content-Length: $filesize") echo file_get_contents($path . $filename);

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  • Using Multiple File Handles for Single File

    - by Ryan Rosario
    I have an O(n^2) operation that requires me to read line i from a file, and then compare line i to every line in the file. This repeats for all i. I wrote the following code to do this with 2 file handles, but it does not yield the result I am looking for. I imagine this is a simple error on my part. IN1 = open("myfile.dat","r") IN2 = open("myfile.dat","r") for line1 in IN1: for line2 in IN2: print line1.strip(), line2.strip() IN1.close() IN2.close() The result: Hello Hello Hello World Hello This Hello is Hello an Hello Example Hello of Hello Using Hello Two Hello File Hello Pointers Hello to Hello Read Hello One Hello File The output should contain 15^2 lines.

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  • How to get table cells evenly spaced?

    - by DaveDev
    I'm trying to create a page with a number of static html tables on them. What do I need to do to get them to display each column the same size as each other column in the table? The HTML is as follows: <span class="Emphasis">Interest rates</span><br /> <table cellpadding="0px" cellspacing="0px" class="PerformanceTable"> <tr><th class="TableHeader"></th><th class="TableHeader">Current rate as at 31 March 2010</th><th class="TableHeader">31 December 2009</th><th class="TableHeader">31 March 2009</th></tr> <tr class="TableRow"><td>Index1</td><td class="PerformanceCell">1.00%</td><td>1.00%</td><td>1.50%</td></tr> <tr class="TableRow"><td>index2</td><td class="PerformanceCell">0.50%</td><td>0.50%</td><td>0.50%</td></tr> <tr class="TableRow"><td>index3</td><td class="PerformanceCell">0.25%</td><td>0.25%</td><td>0.25%</td></tr> </table> <span>Source: Bt</span><br /><br /> <span class="Emphasis">Stock markets</span><br /> <table cellpadding="0px" cellspacing="0px" class="PerformanceTable"> <tr><th class="TableHeader"></th><th class="TableHeader">As at 31 March 2010</th><th class="TableHeader">1 month change</th><th class="TableHeader">QTD change</th><th class="TableHeader">12 months change</th></tr> <tr class="TableRow"><td>index1</td><td class="PerformanceCell">1169.43</td><td class="PerformanceCell">5.88%</td><td class="PerformanceCell">4.87%</td><td class="PerformanceCell">46.57%</td></tr> <tr class="TableRow"><td>index2</td><td class="PerformanceCell">1958.34</td><td class="PerformanceCell">7.68%</td><td class="PerformanceCell">5.27%</td><td class="PerformanceCell">58.31%</td></tr> <tr class="TableRow"><td>index3</td><td class="PerformanceCell">5679.64</td><td class="PerformanceCell">6.07%</td><td class="PerformanceCell">4.93%</td><td class="PerformanceCell">44.66%</td></tr> <tr class="TableRow"><td>index4</td><td class="PerformanceCell">2943.92</td><td class="PerformanceCell">8.30%</td><td class="PerformanceCell">-0.98%</td><td class="PerformanceCell">44.52%</td></tr> <tr class="TableRow"><td>index5</td><td class="PerformanceCell">978.81</td><td class="PerformanceCell">9.47%</td><td class="PerformanceCell">7.85%</td><td class="PerformanceCell">26.52%</td></tr> <tr class="TableRow"><td>index6</td><td class="PerformanceCell">3177.77</td><td class="PerformanceCell">10.58%</td><td class="PerformanceCell">6.82%</td><td class="PerformanceCell">44.84%</td></tr> </table> <span>Source: B</span><br /><br /> I'm also open to suggestion on how to tidy this up, if there are any? :-)

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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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  • CSS Table Formatting to a HTML Table

    - by Rurigok
    I am attempting to provide CSS formating to two HTML tables, but I cannot. I am setting up a webpage in HTML & CSS (with the CSS in an external sheet) and the layout of the website depends on the tables. There are 2 tables, one for the head and another for the body. They are set up whereas content is situated in one middle column of 60% width, with one column on each side of the center with 20% width each, along with other table formatting. My question is - how can I format the tables in CSS? I successfully formatted them in HTML, but this will not do. This is the CSS code for the tables - each table has the id layouttable: #layouttable{border:0px;width:100%;} #layouttable td{width:20%;vertical-align:top;} #layouttable td{width:60%;vertical-align:top;background-color:#E8E8E8;} #layouttable td{width:20%;vertical-align:top;} The tables in the html document both each have, in respective order, these elements (with content inside not shown): <table id="layouttable"><tr><td></td><td></td><td></td></tr></table> Does anyone have any idea why this CSS is not working, or can write some code to fix it? If further explanation is needed, please, ask.

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  • CSS Zebra Stripe a Specific Table tr:nth-child(even)

    - by BillR
    I want to zebra stripe only select tables using. I do not want to use jQuery for this. tbody tr:nth-child(even) td, tbody tr.even td {background:#e5ecf9;} When I put that in a css file it affects all tables on all pages that call the same stylesheet. What I would like to do is selectively apply it to specific tables. I have tried this, but it doesn't work. // in stylesheet .zebra_stripe{ tbody tr:nth-child(even) td, tbody tr.even td {background:#e5ecf9;} } // in html <table class="zebra_even"> <colgroup> <col class="width_10em" /> <col class="width_15em" /> </colgroup> <tr> <td>Odd row nice and clear.</td> <td>Some Stuff</td> </tr> <tr> <td>Even row nice and clear but it should be shaded.</td> <td>Some Stuff</td> </tr> </table> And this: <table> <colgroup> <col class="width_10em" /> <col class="width_15em" /> </colgroup> <tbody class="zebra_even"> The stylesheet works as it is properly formatting other elements of the html. Can someone help me with an answer to this problem? Thanks.

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  • add table row before or after a table row of known ID

    - by Perpetualcoder
    In a table like this: <table> <!-- Insert Row of bun here --> <tr id="meat"> <td>Hamburger</td> </tr/> <!-- Insert Row of bun here --> </table> function AddBefore(rowId){} function AddAfter(rowId){} I need to create methods without using jquery..i am familiar with append after and append before in jquery.. but i am stuck with using palin js.

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  • problem in below table:i had table inside table .my inner table contains some text.

    - by Ayyappan.Anbalagan
    Heading ## <tr style=" width:500px; float:left;"> <td style="border: thin ridge #008000; text-align:left;" align="left"; > <table class="" style=" border: 1px solid #800000; width:200px; float:left; height: 200px;"> <tr> <td>&nbsp;stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow&nbsp; </td> </tr> </table> stackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow stackoverflowstackoverflow statackoverflow sta</td> </tr> </table>

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  • jquery: remove table row while iterating through table rows

    - by deostroll
    #exceptions is a html table. I try to run the code below, but it doesn't remove the table row. $('#exceptions').find('tr').each(function(){ var flag=false; var val = 'excalibur'; $(this).find('td').each(function(){ if($(this).text().toLowerCase() == val) flag = true; }); if(flag) $(this).parent().remove($(this)); }); What is the correct way to do it?

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  • Update (ajax) only part of table without affecting whole table

    - by ile
    <table width="100%" border="0" cellspacing="0" cellpadding="0" class="list"> <tr> <th><a href="#" class="sortby">Full Name</a></th> <th><a href="#" class="sortby">City</a></th> <th><a href="#" class="sortby">Country</a></th> <th><a href="#" class="sortby">Status</a></th> <th><a href="#" class="sortby">Education</a></th> <th><a href="#" class="sortby">Tasks</a></th> </tr> <div class="dynamicData"> <tr> <td>Firstname Lastname</a></td> <td>Los Angeles</td> <td>USA</td> <td>Married</td> <td>High School</td> <td>4</td> </tr> </tr> <tr> <td>Firstname Lastname</a></td> <td>Los Angeles</td> <td>USA</td> <td>Married</td> <td>High School</td> <td>4</td> </tr> </div> </table> The idea is to update table rows when clicking on link with clasl "sortby" which is part of th table tag. I tried inserting div but this doesn't work. Separating this in two tables is not good solution because witdh in both tables cell are not following each other. Any other solution? Thanks

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  • SQL SERVER – Not Possible – Delete From Multiple Table – Update Multiple Table in Single Statement

    - by pinaldave
    There are two questions which I get every single day multiple times. In my gmail, I have created standard canned reply for them. Let us see the questions here. I want to delete from multiple table in a single statement how will I do it? I want to update multiple table in a single statement how will I do it? The answer is – No, You cannot and you should not. SQL Server does not support deleting or updating from two tables in a single update. If you want to delete or update two different tables – you may want to write two different delete or update statements for it. This method has many issues – from the consistency of the data to SQL syntax. Now here is the real reason for this blog post – yesterday I was asked this question again and I replied my canned answer saying it is not possible and it should not be any way implemented that day. In the response to my reply I was pointed out to my own blog post where user suggested that I had previously mentioned this is possible and with demo example. Let us go over my conversation – you may find it interesting. Let us call the user DJ. DJ: Pinal, can we delete multiple table in a single statement or with single delete statement? Pinal: No, you cannot and you should not. DJ: Oh okey, if that is the case, why do you suggest to do that? Pinal: (baffled) I am not suggesting that. I am rather suggesting that it is not possible and it should not be possible. DJ: Hmm… but in that case why did you blog about it earlier? Pinal: (What?) No, I did not. I am pretty confident. DJ: Well, I am confident as well. You did. Pinal: In that case, it is my word against your word. Isn’t it? DJ: I have proof. Do you want to see it that you suggest it is possible? Pinal: Yes, I will be delighted too. (After 10 Minutes) DJ: Here are not one but two of your blog posts which talks about it - SQL SERVER – Curious Case of Disappearing Rows – ON UPDATE CASCADE and ON DELETE CASCADE – Part 1 of 2 SQL SERVER – Curious Case of Disappearing Rows – ON UPDATE CASCADE and ON DELETE CASCADE – T-SQL Example – Part 2 of 2 Pinal: Oh! DJ: I know I was correct. Pinal: Well, oh man, I did not mean there what you mean here. DJ: I did not understand can you explain it further. Pinal: Here we go. The example in the other blog is the example of the cascading delete or cascading update. I think you may want to understand the concept of the foreign keys and cascading update/delete. The concept of cascading exists to maintain data integrity. If there primary keys get deleted the update or delete reflects on the foreign key table to maintain the key integrity and data consistency. SQL Server follows ANSI Entry SQL with regard to referential integrity between PrimaryKey and ForeignKey columns which requires the inserting, updating, and deleting of data in related tables to be restricted to values that preserve the integrity. This is all together different concept than deleting multiple values in a single statement. When I hear that someone wants to delete or update multiple table in a single statement what I assume is something very similar to following. DELETE/UPDATE Table 1 (cols) Table 2 (cols) VALUES … which is not valid statement/syntax as well it is not ASNI standards as well. I guess, after this discussion with DJ, I realize I need to do a blog post so I can add the link to this blog post in my canned answer. Well, it was a fun conversation with DJ and I hope it the message is very clear now. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Joins, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Display a JSON-string as a table

    - by Martin Aleksander
    I'm totally new to JSON, and have a json-string I need to display as a user-friendly table. I have this file, http://ish.tek.no/json_top_content.php?project_id=11&period=week, witch is showing ID-numbers for products (title) and the number of views. The Title-ID should be connected to this file; http://api.prisguide.no/export/product.php?id=158200 so I can get a table like this: ID | Product Name | Views 158200 | Samsung Galaxy SIII | 21049 How can I do this?

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  • MySQL table does not exist

    - by Phanindra
    I am getting following error in err file. 110803 6:51:26 InnoDB: Error: table `ims`.`temp_discoveryjobdetails` already exists in InnoDB internal InnoDB: data dictionary. Have you deleted the .frm file InnoDB: and not used DROP TABLE? Have you used DROP DATABASE InnoDB: for InnoDB tables in MySQL version <= 3.23.43? InnoDB: See the Restrictions section of the InnoDB manual. InnoDB: You can drop the orphaned table inside InnoDB by InnoDB: creating an InnoDB table with the same name in another InnoDB: database and copying the .frm file to the current database. InnoDB: Then MySQL thinks the table exists, and DROP TABLE will InnoDB: succeed. InnoDB: You can look for further help from InnoDB: http://dev.mysql.com/doc/refman/5.1/en/innodb-troubleshooting.html And when I do the same, like copying the frm file from other database to here and drop the table, i am getting following error, InnoDB: Error: trying to load index PRIMARY for table ims/temp_discoveryjobdetails InnoDB: but the index tree has been freed! 110803 6:50:26 InnoDB: Error: table `ims`.`temp_discoveryjobdetails` does not exist in the InnoDB internal InnoDB: data dictionary though MySQL is trying to drop it. InnoDB: Have you copied the .frm file of the table to the InnoDB: MySQL database directory from another database? InnoDB: You can look for further help from InnoDB: http://dev.mysql.com/doc/refman/5.1/en/innodb-troubleshooting.html Please any one help me out of this. Also can any one tell me why this error is coming. EDIT: The issue is occurring only when disk size is full and when we use Truncate table. Also this is occurring only in 5.1 version but not in 5.0 version.

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  • Convert table to table with autofilter/order by function [on hold]

    - by evachristine
    How can I make any normal HTML table: <table border=1 style='border:2px solid black;border-collapse:collapse;'><tr><td>foo1</td><td>foo2</td><td>foo3</td><td>foo3</td><td>foo4</td><td>foo5</td><td>foo6</td></tr> <tr><td><a href="https://foo.com/adsf">adsf</a></td><td>ksjdajsfljdsaljfxycaqrf</td><td><a href="mailto:[email protected]?Subject=adsf - ksjdajsfljdsaljfxycaqrf">[email protected]</a></td><td>nmasdfdsadfafd</td><td>INPROG</td><td>3</td><td>2014-03-04 10:37</td> <tr><td><a href="https://foo.com/adsflkjsadlf">adsflkjsadlf</a></td><td>alksjdlsadjfyxcvyx</td><td><a href="mailto:[email protected]?Subject=adsflkjsadlf - alksjdlsadjfyxcvyx">[email protected]</a></td><td>nmasdfdsadfafd</td><td>INPROG</td><td>3</td><td>2014-04-24 00:00</td> <tr><td><a href="https://foo.com/asdfasdfsadf">asdfasdfsadf</a></td><td>jdsalajslkfjyxcgrearafs</td><td><a href="mailto:[email protected]?Subject=asdfasdfsadf - jdsalajslkfjyxcgrearafs">[email protected]</a></td><td>nmasdfdsadfafd</td><td>INPROG</td><td>3</td><td>2014-04-24 00:00</td> </table> to a table what's first row (ex.: foo1; foo2; foo3, etc..) is clickable in a way that it will make the columns in order, ex.: order by foo2, etc. Just like an order by in an XLS. (extra: how in the hell can I put autofilter too?:D )

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  • Multi-statement Table Valued Function vs Inline Table Valued Function

    - by AndyC
    ie: CREATE FUNCTION MyNS.GetUnshippedOrders() RETURNS TABLE AS RETURN SELECT a.SaleId, a.CustomerID, b.Qty FROM Sales.Sales a INNER JOIN Sales.SaleDetail b ON a.SaleId = b.SaleId INNER JOIN Production.Product c ON b.ProductID = c.ProductID WHERE a.ShipDate IS NULL GO versus: CREATE FUNCTION MyNS.GetLastShipped(@CustomerID INT) RETURNS @CustomerOrder TABLE (SaleOrderID INT NOT NULL, CustomerID INT NOT NULL, OrderDate DATETIME NOT NULL, OrderQty INT NOT NULL) AS BEGIN DECLARE @MaxDate DATETIME SELECT @MaxDate = MAX(OrderDate) FROM Sales.SalesOrderHeader WHERE CustomerID = @CustomerID INSERT @CustomerOrder SELECT a.SalesOrderID, a.CustomerID, a.OrderDate, b.OrderQty FROM Sales.SalesOrderHeader a INNER JOIN Sales.SalesOrderHeader b ON a.SalesOrderID = b.SalesOrderID INNER JOIN Production.Product c ON b.ProductID = c.ProductID WHERE a.OrderDate = @MaxDate AND a.CustomerID = @CustomerID RETURN END GO Is there an advantage to using one over the other? Is there certain scenarios when one is better than the other or are the differences purely syntactical? I realise the 2 example queries are doing different things but is there a reason I would write them in that way? Reading about them and the advantages/differences haven't really been explained. Thanks

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  • PHP File Upload second file does not upload, first file does without error

    - by Curtis
    So I have a script I have been using and it generally works well with multiple files... When I upload a very large file in a multiple file upload, only the first file is uploaded. I am not seeing an errors as to why. I figure this is related to a timeout setting but can not figure it out - Any ideas? I have foloowing set in my htaccess file php_value post_max_size 1024M php_value upload_max_filesize 1024M php_value memory_limit 600M php_value output_buffering on php_value max_execution_time 259200 php_value max_input_time 259200 php_value session.cookie_lifetime 0 php_value session.gc_maxlifetime 259200 php_value default_socket_timeout 259200

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  • scp No such file or directory

    - by Joe
    I've a confusing question for which superuser doesn't seem to have a good answer, and neither google. I'm trying to scp a file from a remote server to my local machine. The command is this scp user@server:/path/to/source/file.gz /path/to/destination The error I get is: scp: /path/to/source/file.gz: No such file or directory user is my username on the server. The command syntax appears fine to me. ssh works fine and I can cd to the file and it doesn't seem to be an access control issue? Thanks; Edit: Thank you John. I spotted the issue. ls returned this: -r--r--r-- 1 nobody users 168967171 Mar 10 2009 /path/to/source/file.gz So, the file was on a read-only file system and user is able to read it but not scp. I just copied the file to a different directory and chown it and worked fine. It would be good if someone can explain why this is the case though.

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  • Multiple foreign keys in one table to 1 other table in mysql

    - by djerry
    Hey guys, I got 2 tables in my database: user and call. User exists of 3 fields: id, name, number and call : id, 'source', 'destination', 'referred', date. I need to monitor calls in my app. The 3 ' ' fields above are actually userid numbers. now i'm wondering, can i make those 3 field foreign key elements of the id-field in table user? Thanks in advance...

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  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • Designing status management for a file processing module

    - by bot
    The background One of the functionality of a product that I am currently working on is to process a set of compressed files ( containing XML files ) that will be made available at a fixed location periodically (local or remote location - doesn't really matter for now) and dump the contents of each XML file in a database. I have taken care of the design for a generic parsing module that should be able to accommodate the parsing of any file type as I have explained in my question linked below. There is no need to take a look at the following link to answer my question but it would definitely provide a better context to the problem Generic file parser design in Java using the Strategy pattern The Goal I want to be able to keep a track of the status of each XML file and the status of each compressed file containing the XML files. I can probably have different statuses defined for the XML files such as NEW, PROCESSING, LOADING, COMPLETE or FAILED. I can derive the status of a compressed file based on the status of the XML files within the compressed file. e.g status of the compressed file is COMPLETE if no XML file inside the compressed file is in a FAILED state or status of the compressed file is FAILED if the status of at-least one XML file inside the compressed file is FAILED. A possible solution The Model I need to maintain the status of each XML file and the compressed file. I will have to define some POJOs for holding the information about an XML file as shown below. Note that there is no need to store the status of a compressed file as the status of a compressed file can be derived from the status of its XML files. public class FileInformation { private String compressedFileName; private String xmlFileName; private long lastModifiedDate; private int status; public FileInformation(final String compressedFileName, final String xmlFileName, final long lastModified, final int status) { this.compressedFileName = compressedFileName; this.xmlFileName = xmlFileName; this.lastModifiedDate = lastModified; this.status = status; } } I can then have a class called StatusManager that aggregates a Map of FileInformation instances and provides me the status of a given file at any given time in the lifetime of the appliciation as shown below : public class StatusManager { private Map<String,FileInformation> processingMap = new HashMap<String,FileInformation>(); public void add(FileInformation fileInformation) { fileInformation.setStatus(0); // 0 will indicates that the file is in NEW state. 1 will indicate that the file is in process and so on.. processingMap.put(fileInformation.getXmlFileName(),fileInformation); } public void update(String filename,int status) { FileInformation fileInformation = processingMap.get(filename); fileInformation.setStatus(status); } } That takes care of the model for the sake of explanation. So whats my question? Edited after comments from Loki and answer from Eric : - I would like to know if there are any existing design patterns that I can refer to while coming up with a design. I would also like to know how I should go about designing the status management classes. I am more interested in understanding how I can model the status management classes. I am not interested in how other components are going to be updated about a change in status at the moment as suggested by Eric.

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  • multiple pivot table consolidation to another pivot table

    - by phill
    I have to SQL Server views being drawn to 2 seperate worksheets as pivot tables in an excel 2007 file. the results on worksheet1 include example data: - company_name, tickets, month, year company1, 3, 1,2009 company2, 4, 1,2009 company3, 5, 1,2009 company3, 2, 2,2009 results from worksheet2 include example data: company_name, month, year , fee company1, 1 , 2009 , 2.00 company2, 1 , 2009 , 3.00 company3, 1 , 2009 , 4.00 company3, 2 , 2009 , 2.00 I would like the results of one worksheet to be reflected onto the pivot table of another with their corresponding companies. for example in this case: - company_name, tickets, month, year, fee company1, 3, 1,2009 , 2 company2, 4, 1,2009 , 3 company3, 5, 1,2009 , 4 company3, 2, 2,2009 , 2 Is there a way to do this without vba? thanks in advance

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