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  • Node.js Adventure - Storage Services and Service Runtime

    - by Shaun
    When I described on how to host a Node.js application on Windows Azure, one of questions might be raised about how to consume the vary Windows Azure services, such as the storage, service bus, access control, etc.. Interact with windows azure services is available in Node.js through the Windows Azure Node.js SDK, which is a module available in NPM. In this post I would like to describe on how to use Windows Azure Storage (a.k.a. WAS) as well as the service runtime.   Consume Windows Azure Storage Let’s firstly have a look on how to consume WAS through Node.js. As we know in the previous post we can host Node.js application on Windows Azure Web Site (a.k.a. WAWS) as well as Windows Azure Cloud Service (a.k.a. WACS). In theory, WAWS is also built on top of WACS worker roles with some more features. Hence in this post I will only demonstrate for hosting in WACS worker role. The Node.js code can be used when consuming WAS when hosted on WAWS. But since there’s no roles in WAWS, the code for consuming service runtime mentioned in the next section cannot be used for WAWS node application. We can use the solution that I created in my last post. Alternatively we can create a new windows azure project in Visual Studio with a worker role, add the “node.exe” and “index.js” and install “express” and “node-sqlserver” modules, make all files as “Copy always”. In order to use windows azure services we need to have Windows Azure Node.js SDK, as knows as a module named “azure” which can be installed through NPM. Once we downloaded and installed, we need to include them in our worker role project and make them as “Copy always”. You can use my “Copy all always” tool mentioned in my last post to update the currently worker role project file. You can also find the source code of this tool here. The source code of Windows Azure SDK for Node.js can be found in its GitHub page. It contains two parts. One is a CLI tool which provides a cross platform command line package for Mac and Linux to manage WAWS and Windows Azure Virtual Machines (a.k.a. WAVM). The other is a library for managing and consuming vary windows azure services includes tables, blobs, queues, service bus and the service runtime. I will not cover all of them but will only demonstrate on how to use tables and service runtime information in this post. You can find the full document of this SDK here. Back to Visual Studio and open the “index.js”, let’s continue our application from the last post, which was working against Windows Azure SQL Database (a.k.a. WASD). The code should looks like this. 1: var express = require("express"); 2: var sql = require("node-sqlserver"); 3:  4: var connectionString = "Driver={SQL Server Native Client 10.0};Server=tcp:ac6271ya9e.database.windows.net,1433;Database=synctile;Uid=shaunxu@ac6271ya9e;Pwd={PASSWORD};Encrypt=yes;Connection Timeout=30;"; 5: var port = 80; 6:  7: var app = express(); 8:  9: app.configure(function () { 10: app.use(express.bodyParser()); 11: }); 12:  13: app.get("/", function (req, res) { 14: sql.open(connectionString, function (err, conn) { 15: if (err) { 16: console.log(err); 17: res.send(500, "Cannot open connection."); 18: } 19: else { 20: conn.queryRaw("SELECT * FROM [Resource]", function (err, results) { 21: if (err) { 22: console.log(err); 23: res.send(500, "Cannot retrieve records."); 24: } 25: else { 26: res.json(results); 27: } 28: }); 29: } 30: }); 31: }); 32:  33: app.get("/text/:key/:culture", function (req, res) { 34: sql.open(connectionString, function (err, conn) { 35: if (err) { 36: console.log(err); 37: res.send(500, "Cannot open connection."); 38: } 39: else { 40: var key = req.params.key; 41: var culture = req.params.culture; 42: var command = "SELECT * FROM [Resource] WHERE [Key] = '" + key + "' AND [Culture] = '" + culture + "'"; 43: conn.queryRaw(command, function (err, results) { 44: if (err) { 45: console.log(err); 46: res.send(500, "Cannot retrieve records."); 47: } 48: else { 49: res.json(results); 50: } 51: }); 52: } 53: }); 54: }); 55:  56: app.get("/sproc/:key/:culture", function (req, res) { 57: sql.open(connectionString, function (err, conn) { 58: if (err) { 59: console.log(err); 60: res.send(500, "Cannot open connection."); 61: } 62: else { 63: var key = req.params.key; 64: var culture = req.params.culture; 65: var command = "EXEC GetItem '" + key + "', '" + culture + "'"; 66: conn.queryRaw(command, function (err, results) { 67: if (err) { 68: console.log(err); 69: res.send(500, "Cannot retrieve records."); 70: } 71: else { 72: res.json(results); 73: } 74: }); 75: } 76: }); 77: }); 78:  79: app.post("/new", function (req, res) { 80: var key = req.body.key; 81: var culture = req.body.culture; 82: var val = req.body.val; 83:  84: sql.open(connectionString, function (err, conn) { 85: if (err) { 86: console.log(err); 87: res.send(500, "Cannot open connection."); 88: } 89: else { 90: var command = "INSERT INTO [Resource] VALUES ('" + key + "', '" + culture + "', N'" + val + "')"; 91: conn.queryRaw(command, function (err, results) { 92: if (err) { 93: console.log(err); 94: res.send(500, "Cannot retrieve records."); 95: } 96: else { 97: res.send(200, "Inserted Successful"); 98: } 99: }); 100: } 101: }); 102: }); 103:  104: app.listen(port); Now let’s create a new function, copy the records from WASD to table service. 1. Delete the table named “resource”. 2. Create a new table named “resource”. These 2 steps ensures that we have an empty table. 3. Load all records from the “resource” table in WASD. 4. For each records loaded from WASD, insert them into the table one by one. 5. Prompt to user when finished. In order to use table service we need the storage account and key, which can be found from the developer portal. Just select the storage account and click the Manage Keys button. Then create two local variants in our Node.js application for the storage account name and key. Since we need to use WAS we need to import the azure module. Also I created another variant stored the table name. In order to work with table service I need to create the storage client for table service. This is very similar as the Windows Azure SDK for .NET. As the code below I created a new variant named “client” and use “createTableService”, specified my storage account name and key. 1: var azure = require("azure"); 2: var storageAccountName = "synctile"; 3: var storageAccountKey = "/cOy9L7xysXOgPYU9FjDvjrRAhaMX/5tnOpcjqloPNDJYucbgTy7MOrAW7CbUg6PjaDdmyl+6pkwUnKETsPVNw=="; 4: var tableName = "resource"; 5: var client = azure.createTableService(storageAccountName, storageAccountKey); Now create a new function for URL “/was/init” so that we can trigger it through browser. Then in this function we will firstly load all records from WASD. 1: app.get("/was/init", function (req, res) { 2: // load all records from windows azure sql database 3: sql.open(connectionString, function (err, conn) { 4: if (err) { 5: console.log(err); 6: res.send(500, "Cannot open connection."); 7: } 8: else { 9: conn.queryRaw("SELECT * FROM [Resource]", function (err, results) { 10: if (err) { 11: console.log(err); 12: res.send(500, "Cannot retrieve records."); 13: } 14: else { 15: if (results.rows.length > 0) { 16: // begin to transform the records into table service 17: } 18: } 19: }); 20: } 21: }); 22: }); When we succeed loaded all records we can start to transform them into table service. First I need to recreate the table in table service. This can be done by deleting and creating the table through table client I had just created previously. 1: app.get("/was/init", function (req, res) { 2: // load all records from windows azure sql database 3: sql.open(connectionString, function (err, conn) { 4: if (err) { 5: console.log(err); 6: res.send(500, "Cannot open connection."); 7: } 8: else { 9: conn.queryRaw("SELECT * FROM [Resource]", function (err, results) { 10: if (err) { 11: console.log(err); 12: res.send(500, "Cannot retrieve records."); 13: } 14: else { 15: if (results.rows.length > 0) { 16: // begin to transform the records into table service 17: // recreate the table named 'resource' 18: client.deleteTable(tableName, function (error) { 19: client.createTableIfNotExists(tableName, function (error) { 20: if (error) { 21: error["target"] = "createTableIfNotExists"; 22: res.send(500, error); 23: } 24: else { 25: // transform the records 26: } 27: }); 28: }); 29: } 30: } 31: }); 32: } 33: }); 34: }); As you can see, the azure SDK provide its methods in callback pattern. In fact, almost all modules in Node.js use the callback pattern. For example, when I deleted a table I invoked “deleteTable” method, provided the name of the table and a callback function which will be performed when the table had been deleted or failed. Underlying, the azure module will perform the table deletion operation in POSIX async threads pool asynchronously. And once it’s done the callback function will be performed. This is the reason we need to nest the table creation code inside the deletion function. If we perform the table creation code after the deletion code then they will be invoked in parallel. Next, for each records in WASD I created an entity and then insert into the table service. Finally I send the response to the browser. Can you find a bug in the code below? I will describe it later in this post. 1: app.get("/was/init", function (req, res) { 2: // load all records from windows azure sql database 3: sql.open(connectionString, function (err, conn) { 4: if (err) { 5: console.log(err); 6: res.send(500, "Cannot open connection."); 7: } 8: else { 9: conn.queryRaw("SELECT * FROM [Resource]", function (err, results) { 10: if (err) { 11: console.log(err); 12: res.send(500, "Cannot retrieve records."); 13: } 14: else { 15: if (results.rows.length > 0) { 16: // begin to transform the records into table service 17: // recreate the table named 'resource' 18: client.deleteTable(tableName, function (error) { 19: client.createTableIfNotExists(tableName, function (error) { 20: if (error) { 21: error["target"] = "createTableIfNotExists"; 22: res.send(500, error); 23: } 24: else { 25: // transform the records 26: for (var i = 0; i < results.rows.length; i++) { 27: var entity = { 28: "PartitionKey": results.rows[i][1], 29: "RowKey": results.rows[i][0], 30: "Value": results.rows[i][2] 31: }; 32: client.insertEntity(tableName, entity, function (error) { 33: if (error) { 34: error["target"] = "insertEntity"; 35: res.send(500, error); 36: } 37: else { 38: console.log("entity inserted"); 39: } 40: }); 41: } 42: // send the 43: console.log("all done"); 44: res.send(200, "All done!"); 45: } 46: }); 47: }); 48: } 49: } 50: }); 51: } 52: }); 53: }); Now we can publish it to the cloud and have a try. But normally we’d better test it at the local emulator first. In Node.js SDK there are three build-in properties which provides the account name, key and host address for local storage emulator. We can use them to initialize our table service client. We also need to change the SQL connection string to let it use my local database. The code will be changed as below. 1: // windows azure sql database 2: //var connectionString = "Driver={SQL Server Native Client 10.0};Server=tcp:ac6271ya9e.database.windows.net,1433;Database=synctile;Uid=shaunxu@ac6271ya9e;Pwd=eszqu94XZY;Encrypt=yes;Connection Timeout=30;"; 3: // sql server 4: var connectionString = "Driver={SQL Server Native Client 11.0};Server={.};Database={Caspar};Trusted_Connection={Yes};"; 5:  6: var azure = require("azure"); 7: var storageAccountName = "synctile"; 8: var storageAccountKey = "/cOy9L7xysXOgPYU9FjDvjrRAhaMX/5tnOpcjqloPNDJYucbgTy7MOrAW7CbUg6PjaDdmyl+6pkwUnKETsPVNw=="; 9: var tableName = "resource"; 10: // windows azure storage 11: //var client = azure.createTableService(storageAccountName, storageAccountKey); 12: // local storage emulator 13: var client = azure.createTableService(azure.ServiceClient.DEVSTORE_STORAGE_ACCOUNT, azure.ServiceClient.DEVSTORE_STORAGE_ACCESS_KEY, azure.ServiceClient.DEVSTORE_TABLE_HOST); Now let’s run the application and navigate to “localhost:12345/was/init” as I hosted it on port 12345. We can find it transformed the data from my local database to local table service. Everything looks fine. But there is a bug in my code. If we have a look on the Node.js command window we will find that it sent response before all records had been inserted, which is not what I expected. The reason is that, as I mentioned before, Node.js perform all IO operations in non-blocking model. When we inserted the records we executed the table service insert method in parallel, and the operation of sending response was also executed in parallel, even though I wrote it at the end of my logic. The correct logic should be, when all entities had been copied to table service with no error, then I will send response to the browser, otherwise I should send error message to the browser. To do so I need to import another module named “async”, which helps us to coordinate our asynchronous code. Install the module and import it at the beginning of the code. Then we can use its “forEach” method for the asynchronous code of inserting table entities. The first argument of “forEach” is the array that will be performed. The second argument is the operation for each items in the array. And the third argument will be invoked then all items had been performed or any errors occurred. Here we can send our response to browser. 1: app.get("/was/init", function (req, res) { 2: // load all records from windows azure sql database 3: sql.open(connectionString, function (err, conn) { 4: if (err) { 5: console.log(err); 6: res.send(500, "Cannot open connection."); 7: } 8: else { 9: conn.queryRaw("SELECT * FROM [Resource]", function (err, results) { 10: if (err) { 11: console.log(err); 12: res.send(500, "Cannot retrieve records."); 13: } 14: else { 15: if (results.rows.length > 0) { 16: // begin to transform the records into table service 17: // recreate the table named 'resource' 18: client.deleteTable(tableName, function (error) { 19: client.createTableIfNotExists(tableName, function (error) { 20: if (error) { 21: error["target"] = "createTableIfNotExists"; 22: res.send(500, error); 23: } 24: else { 25: async.forEach(results.rows, 26: // transform the records 27: function (row, callback) { 28: var entity = { 29: "PartitionKey": row[1], 30: "RowKey": row[0], 31: "Value": row[2] 32: }; 33: client.insertEntity(tableName, entity, function (error) { 34: if (error) { 35: callback(error); 36: } 37: else { 38: console.log("entity inserted."); 39: callback(null); 40: } 41: }); 42: }, 43: // send reponse 44: function (error) { 45: if (error) { 46: error["target"] = "insertEntity"; 47: res.send(500, error); 48: } 49: else { 50: console.log("all done"); 51: res.send(200, "All done!"); 52: } 53: } 54: ); 55: } 56: }); 57: }); 58: } 59: } 60: }); 61: } 62: }); 63: }); Run it locally and now we can find the response was sent after all entities had been inserted. Query entities against table service is simple as well. Just use the “queryEntity” method from the table service client and providing the partition key and row key. We can also provide a complex query criteria as well, for example the code here. In the code below I queried an entity by the partition key and row key, and return the proper localization value in response. 1: app.get("/was/:key/:culture", function (req, res) { 2: var key = req.params.key; 3: var culture = req.params.culture; 4: client.queryEntity(tableName, culture, key, function (error, entity) { 5: if (error) { 6: res.send(500, error); 7: } 8: else { 9: res.json(entity); 10: } 11: }); 12: }); And then tested it on local emulator. Finally if we want to publish this application to the cloud we should change the database connection string and storage account. For more information about how to consume blob and queue service, as well as the service bus please refer to the MSDN page.   Consume Service Runtime As I mentioned above, before we published our application to the cloud we need to change the connection string and account information in our code. But if you had played with WACS you should have known that the service runtime provides the ability to retrieve configuration settings, endpoints and local resource information at runtime. Which means we can have these values defined in CSCFG and CSDEF files and then the runtime should be able to retrieve the proper values. For example we can add some role settings though the property window of the role, specify the connection string and storage account for cloud and local. And the can also use the endpoint which defined in role environment to our Node.js application. In Node.js SDK we can get an object from “azure.RoleEnvironment”, which provides the functionalities to retrieve the configuration settings and endpoints, etc.. In the code below I defined the connection string variants and then use the SDK to retrieve and initialize the table client. 1: var connectionString = ""; 2: var storageAccountName = ""; 3: var storageAccountKey = ""; 4: var tableName = ""; 5: var client; 6:  7: azure.RoleEnvironment.getConfigurationSettings(function (error, settings) { 8: if (error) { 9: console.log("ERROR: getConfigurationSettings"); 10: console.log(JSON.stringify(error)); 11: } 12: else { 13: console.log(JSON.stringify(settings)); 14: connectionString = settings["SqlConnectionString"]; 15: storageAccountName = settings["StorageAccountName"]; 16: storageAccountKey = settings["StorageAccountKey"]; 17: tableName = settings["TableName"]; 18:  19: console.log("connectionString = %s", connectionString); 20: console.log("storageAccountName = %s", storageAccountName); 21: console.log("storageAccountKey = %s", storageAccountKey); 22: console.log("tableName = %s", tableName); 23:  24: client = azure.createTableService(storageAccountName, storageAccountKey); 25: } 26: }); In this way we don’t need to amend the code for the configurations between local and cloud environment since the service runtime will take care of it. At the end of the code we will listen the application on the port retrieved from SDK as well. 1: azure.RoleEnvironment.getCurrentRoleInstance(function (error, instance) { 2: if (error) { 3: console.log("ERROR: getCurrentRoleInstance"); 4: console.log(JSON.stringify(error)); 5: } 6: else { 7: console.log(JSON.stringify(instance)); 8: if (instance["endpoints"] && instance["endpoints"]["nodejs"]) { 9: var endpoint = instance["endpoints"]["nodejs"]; 10: app.listen(endpoint["port"]); 11: } 12: else { 13: app.listen(8080); 14: } 15: } 16: }); But if we tested the application right now we will find that it cannot retrieve any values from service runtime. This is because by default, the entry point of this role was defined to the worker role class. In windows azure environment the service runtime will open a named pipeline to the entry point instance, so that it can connect to the runtime and retrieve values. But in this case, since the entry point was worker role and the Node.js was opened inside the role, the named pipeline was established between our worker role class and service runtime, so our Node.js application cannot use it. To fix this problem we need to open the CSDEF file under the azure project, add a new element named Runtime. Then add an element named EntryPoint which specify the Node.js command line. So that the Node.js application will have the connection to service runtime, then it’s able to read the configurations. Start the Node.js at local emulator we can find it retrieved the connections, storage account for local. And if we publish our application to azure then it works with WASD and storage service through the configurations for cloud.   Summary In this post I demonstrated how to use Windows Azure SDK for Node.js to interact with storage service, especially the table service. I also demonstrated on how to use WACS service runtime, how to retrieve the configuration settings and the endpoint information. And in order to make the service runtime available to my Node.js application I need to create an entry point element in CSDEF file and set “node.exe” as the entry point. I used five posts to introduce and demonstrate on how to run a Node.js application on Windows platform, how to use Windows Azure Web Site and Windows Azure Cloud Service worker role to host our Node.js application. I also described how to work with other services provided by Windows Azure platform through Windows Azure SDK for Node.js. Node.js is a very new and young network application platform. But since it’s very simple and easy to learn and deploy, as well as, it utilizes single thread non-blocking IO model, Node.js became more and more popular on web application and web service development especially for those IO sensitive projects. And as Node.js is very good at scaling-out, it’s more useful on cloud computing platform. Use Node.js on Windows platform is new, too. The modules for SQL database and Windows Azure SDK are still under development and enhancement. It doesn’t support SQL parameter in “node-sqlserver”. It does support using storage connection string to create the storage client in “azure”. But Microsoft is working on make them easier to use, working on add more features and functionalities.   PS, you can download the source code here. You can download the source code of my “Copy all always” tool here.   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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  • SharePoint - Summing Calculated Columns By Groups (DVWP)

    - by Mark Rackley
    I had a problem… okay.. okay.. so I have many problems… but let’s focus on one in particular or this blog post would never end… okay? Thank you…. So, I had an electronic timesheet where users entered hours for each day of the week. It also had a “Week Total” column which was a calculated column of the sum. The calculated column looked like this: Pretty easy.. nothing spectacular. So, what’s the problem? WELL……………….. There is a row in the timesheet for each task a person worked on in a given week. So, if you worked on 4 tasks, you would have 4 rows of data, and 4 week totals for that week: This is all fine and dandy, but I want to know what the total was for the entire week. Yes.. I realize the answer is 24 from my example… I mean, I know how to add! I just want SharePoint to display it for me for the executives (we all know, they have math problems).  You may be thinking, hey genius (in a sarcastic tone of course), why don’t you just go to the view and total on the “Week Total” field. What a brilliant idea! Why didn’t I think of that… let’s go to the view and do just that…. Ohhhhhh… you can’t total on a Calculated Column.. it’s not even an option…  Yeah… I had the same moment. So, what do you do? Well… what do you think I did? 1) Googled “SharePoint total calculated column” 2) Said it couldn’t be done 3) Took a nap 4) Asked the question on twitter? The correct answer of course is number 4… followed by number 3… although I may have told my boss number 2 so that I look more brilliant than I am? It’s safe to say I did NOT try to find the solution on my own doing step 1… that would be just WAY to easy… So, anyway, I posted the question on Twitter and it turns out several people had suggestions from using jQuery to using DVWPs. I tend to be a big fan of the DVWP except for the disgusting process of deploying them to another farm.. ugh… just shoot me…. so, that is the solution I went with. Laura Rogers (@WonderLaura) has a super duper easy to follow video on the subject over at EndUserSharePoint.com: SharePoint: Displaying Calculated Column SUMS in a View (Screencast) Laura’s video was very easy to follow and was ALMOST exactly what I needed. She does a great job walking you through every step of summing up a calculated field which was PART of my problem. The other part was my list is grouped by date! So, I wanted to see for a given week, the summed “Week Total” of hours. Laura got me on the right track with her video and I dug a little deeper into the DVWP to accomplish my task. So, here are the steps you follow: 1. Click on the "chevron” (I didn’t know it was actually called that until I heard Laura say it).. I always call it the “little-button-in-the-top-right-corner-with-the-greater-than-sign”.. but “chevron” is much shorter. So, click on the chevron, click on “Sort and Group”. The Add the field you want to group by, in my example it is the “Monday Date” of the timesheet entry. Make sure to check the check boxes for “Show Group Header” AND “Show Group Footer”. Click “OK”. The view now shows the count of each grouped set of data: Interesting, this looks very similar to Laura’s video… right? So, let’s take a look at the code for the Count: Count : <xsl:value-of select="count($nodeset)" /> Wow, also very similar… except in Laura’s video it looks like: Count : <xsl:value-of select="count($Rows)" /> So.. the only difference is that instead of $Rows we have $nodeset. It turns out the $nodeset will go through each Row in the group just like $Rows goes through each row in the entire view. So, using the exact same logic as in Laura’s blog except replacing $Rows with $nodeset we get the functionality of being able to sum up the values for a group. So, I want to replace “Count: #” with the total hours, this is done using the following changes to the above code: Week Total : <xsl:value-of select="sum($nodeset/@Monday)+sum($nodeset/@Tuesday) +sum($nodeset/@Wednesday)+sum($nodeset/@Thursday)+sum($nodeset/@Friday) +sum($nodeset/@Saturday)+sum($nodeset/@Sunday)" /> Our final output has the summed hours for each group! So… long story short… follow Laura’s blog, then group your list, then replace “$Rows” with “$nodeset”. One caveat, this will not work if you group by a person field. For some reason the person field does not go through each row in the group. I haven’t dug into this much yet. Maybe if I find some time… whatever that is… Anyway, Laura did all the work, I just took it one small step forward… as always, feel free to leave any additional insights you may have. We’re all learning here!

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  • How to find and fix performance problems in ORM powered applications

    - by FransBouma
    Once in a while we get requests about how to fix performance problems with our framework. As it comes down to following the same steps and looking into the same things every single time, I decided to write a blogpost about it instead, so more people can learn from this and solve performance problems in their O/R mapper powered applications. In some parts it's focused on LLBLGen Pro but it's also usable for other O/R mapping frameworks, as the vast majority of performance problems in O/R mapper powered applications are not specific for a certain O/R mapper framework. Too often, the developer looks at the wrong part of the application, trying to fix what isn't a problem in that part, and getting frustrated that 'things are so slow with <insert your favorite framework X here>'. I'm in the O/R mapper business for a long time now (almost 10 years, full time) and as it's a small world, we O/R mapper developers know almost all tricks to pull off by now: we all know what to do to make task ABC faster and what compromises (because there are almost always compromises) to deal with if we decide to make ABC faster that way. Some O/R mapper frameworks are faster in X, others in Y, but you can be sure the difference is mainly a result of a compromise some developers are willing to deal with and others aren't. That's why the O/R mapper frameworks on the market today are different in many ways, even though they all fetch and save entities from and to a database. I'm not suggesting there's no room for improvement in today's O/R mapper frameworks, there always is, but it's not a matter of 'the slowness of the application is caused by the O/R mapper' anymore. Perhaps query generation can be optimized a bit here, row materialization can be optimized a bit there, but it's mainly coming down to milliseconds. Still worth it if you're a framework developer, but it's not much compared to the time spend inside databases and in user code: if a complete fetch takes 40ms or 50ms (from call to entity object collection), it won't make a difference for your application as that 10ms difference won't be noticed. That's why it's very important to find the real locations of the problems so developers can fix them properly and don't get frustrated because their quest to get a fast, performing application failed. Performance tuning basics and rules Finding and fixing performance problems in any application is a strict procedure with four prescribed steps: isolate, analyze, interpret and fix, in that order. It's key that you don't skip a step nor make assumptions: these steps help you find the reason of a problem which seems to be there, and how to fix it or leave it as-is. Skipping a step, or when you assume things will be bad/slow without doing analysis will lead to the path of premature optimization and won't actually solve your problems, only create new ones. The most important rule of finding and fixing performance problems in software is that you have to understand what 'performance problem' actually means. Most developers will say "when a piece of software / code is slow, you have a performance problem". But is that actually the case? If I write a Linq query which will aggregate, group and sort 5 million rows from several tables to produce a resultset of 10 rows, it might take more than a couple of milliseconds before that resultset is ready to be consumed by other logic. If I solely look at the Linq query, the code consuming the resultset of the 10 rows and then look at the time it takes to complete the whole procedure, it will appear to me to be slow: all that time taken to produce and consume 10 rows? But if you look closer, if you analyze and interpret the situation, you'll see it does a tremendous amount of work, and in that light it might even be extremely fast. With every performance problem you encounter, always do realize that what you're trying to solve is perhaps not a technical problem at all, but a perception problem. The second most important rule you have to understand is based on the old saying "Penny wise, Pound Foolish": the part which takes e.g. 5% of the total time T for a given task isn't worth optimizing if you have another part which takes a much larger part of the total time T for that same given task. Optimizing parts which are relatively insignificant for the total time taken is not going to bring you better results overall, even if you totally optimize that part away. This is the core reason why analysis of the complete set of application parts which participate in a given task is key to being successful in solving performance problems: No analysis -> no problem -> no solution. One warning up front: hunting for performance will always include making compromises. Fast software can be made maintainable, but if you want to squeeze as much performance out of your software, you will inevitably be faced with the dilemma of compromising one or more from the group {readability, maintainability, features} for the extra performance you think you'll gain. It's then up to you to decide whether it's worth it. In almost all cases it's not. The reason for this is simple: the vast majority of performance problems can be solved by implementing the proper algorithms, the ones with proven Big O-characteristics so you know the performance you'll get plus you know the algorithm will work. The time taken by the algorithm implementing code is inevitable: you already implemented the best algorithm. You might find some optimizations on the technical level but in general these are minor. Let's look at the four steps to see how they guide us through the quest to find and fix performance problems. Isolate The first thing you need to do is to isolate the areas in your application which are assumed to be slow. For example, if your application is a web application and a given page is taking several seconds or even minutes to load, it's a good candidate to check out. It's important to start with the isolate step because it allows you to focus on a single code path per area with a clear begin and end and ignore the rest. The rest of the steps are taken per identified problematic area. Keep in mind that isolation focuses on tasks in an application, not code snippets. A task is something that's started in your application by either another task or the user, or another program, and has a beginning and an end. You can see a task as a piece of functionality offered by your application.  Analyze Once you've determined the problem areas, you have to perform analysis on the code paths of each area, to see where the performance problems occur and which areas are not the problem. This is a multi-layered effort: an application which uses an O/R mapper typically consists of multiple parts: there's likely some kind of interface (web, webservice, windows etc.), a part which controls the interface and business logic, the O/R mapper part and the RDBMS, all connected with either a network or inter-process connections provided by the OS or other means. Each of these parts, including the connectivity plumbing, eat up a part of the total time it takes to complete a task, e.g. load a webpage with all orders of a given customer X. To understand which parts participate in the task / area we're investigating and how much they contribute to the total time taken to complete the task, analysis of each participating task is essential. Start with the code you wrote which starts the task, analyze the code and track the path it follows through your application. What does the code do along the way, verify whether it's correct or not. Analyze whether you have implemented the right algorithms in your code for this particular area. Remember we're looking at one area at a time, which means we're ignoring all other code paths, just the code path of the current problematic area, from begin to end and back. Don't dig in and start optimizing at the code level just yet. We're just analyzing. If your analysis reveals big architectural stupidity, it's perhaps a good idea to rethink the architecture at this point. For the rest, we're analyzing which means we collect data about what could be wrong, for each participating part of the complete application. Reviewing the code you wrote is a good tool to get deeper understanding of what is going on for a given task but ultimately it lacks precision and overview what really happens: humans aren't good code interpreters, computers are. We therefore need to utilize tools to get deeper understanding about which parts contribute how much time to the total task, triggered by which other parts and for example how many times are they called. There are two different kind of tools which are necessary: .NET profilers and O/R mapper / RDBMS profilers. .NET profiling .NET profilers (e.g. dotTrace by JetBrains or Ants by Red Gate software) show exactly which pieces of code are called, how many times they're called, and the time it took to run that piece of code, at the method level and sometimes even at the line level. The .NET profilers are essential tools for understanding whether the time taken to complete a given task / area in your application is consumed by .NET code, where exactly in your code, the path to that code, how many times that code was called by other code and thus reveals where hotspots are located: the areas where a solution can be found. Importantly, they also reveal which areas can be left alone: remember our penny wise pound foolish saying: if a profiler reveals that a group of methods are fast, or don't contribute much to the total time taken for a given task, ignore them. Even if the code in them is perhaps complex and looks like a candidate for optimization: you can work all day on that, it won't matter.  As we're focusing on a single area of the application, it's best to start profiling right before you actually activate the task/area. Most .NET profilers support this by starting the application without starting the profiling procedure just yet. You navigate to the particular part which is slow, start profiling in the profiler, in your application you perform the actions which are considered slow, and afterwards you get a snapshot in the profiler. The snapshot contains the data collected by the profiler during the slow action, so most data is produced by code in the area to investigate. This is important, because it allows you to stay focused on a single area. O/R mapper and RDBMS profiling .NET profilers give you a good insight in the .NET side of things, but not in the RDBMS side of the application. As this article is about O/R mapper powered applications, we're also looking at databases, and the software making it possible to consume the database in your application: the O/R mapper. To understand which parts of the O/R mapper and database participate how much to the total time taken for task T, we need different tools. There are two kind of tools focusing on O/R mappers and database performance profiling: O/R mapper profilers and RDBMS profilers. For O/R mapper profilers, you can look at LLBLGen Prof by hibernating rhinos or the Linq to Sql/LLBLGen Pro profiler by Huagati. Hibernating rhinos also have profilers for other O/R mappers like NHibernate (NHProf) and Entity Framework (EFProf) and work the same as LLBLGen Prof. For RDBMS profilers, you have to look whether the RDBMS vendor has a profiler. For example for SQL Server, the profiler is shipped with SQL Server, for Oracle it's build into the RDBMS, however there are also 3rd party tools. Which tool you're using isn't really important, what's important is that you get insight in which queries are executed during the task / area we're currently focused on and how long they took. Here, the O/R mapper profilers have an advantage as they collect the time it took to execute the query from the application's perspective so they also collect the time it took to transport data across the network. This is important because a query which returns a massive resultset or a resultset with large blob/clob/ntext/image fields takes more time to get transported across the network than a small resultset and a database profiler doesn't take this into account most of the time. Another tool to use in this case, which is more low level and not all O/R mappers support it (though LLBLGen Pro and NHibernate as well do) is tracing: most O/R mappers offer some form of tracing or logging system which you can use to collect the SQL generated and executed and often also other activity behind the scenes. While tracing can produce a tremendous amount of data in some cases, it also gives insight in what's going on. Interpret After we've completed the analysis step it's time to look at the data we've collected. We've done code reviews to see whether we've done anything stupid and which parts actually take place and if the proper algorithms have been implemented. We've done .NET profiling to see which parts are choke points and how much time they contribute to the total time taken to complete the task we're investigating. We've performed O/R mapper profiling and RDBMS profiling to see which queries were executed during the task, how many queries were generated and executed and how long they took to complete, including network transportation. All this data reveals two things: which parts are big contributors to the total time taken and which parts are irrelevant. Both aspects are very important. The parts which are irrelevant (i.e. don't contribute significantly to the total time taken) can be ignored from now on, we won't look at them. The parts which contribute a lot to the total time taken are important to look at. We now have to first look at the .NET profiler results, to see whether the time taken is consumed in our own code, in .NET framework code, in the O/R mapper itself or somewhere else. For example if most of the time is consumed by DbCommand.ExecuteReader, the time it took to complete the task is depending on the time the data is fetched from the database. If there was just 1 query executed, according to tracing or O/R mapper profilers / RDBMS profilers, check whether that query is optimal, uses indexes or has to deal with a lot of data. Interpret means that you follow the path from begin to end through the data collected and determine where, along the path, the most time is contributed. It also means that you have to check whether this was expected or is totally unexpected. My previous example of the 10 row resultset of a query which groups millions of rows will likely reveal that a long time is spend inside the database and almost no time is spend in the .NET code, meaning the RDBMS part contributes the most to the total time taken, the rest is compared to that time, irrelevant. Considering the vastness of the source data set, it's expected this will take some time. However, does it need tweaking? Perhaps all possible tweaks are already in place. In the interpret step you then have to decide that further action in this area is necessary or not, based on what the analysis results show: if the analysis results were unexpected and in the area where the most time is contributed to the total time taken is room for improvement, action should be taken. If not, you can only accept the situation and move on. In all cases, document your decision together with the analysis you've done. If you decide that the perceived performance problem is actually expected due to the nature of the task performed, it's essential that in the future when someone else looks at the application and starts asking questions you can answer them properly and new analysis is only necessary if situations changed. Fix After interpreting the analysis results you've concluded that some areas need adjustment. This is the fix step: you're actively correcting the performance problem with proper action targeted at the real cause. In many cases related to O/R mapper powered applications it means you'll use different features of the O/R mapper to achieve the same goal, or apply optimizations at the RDBMS level. It could also mean you apply caching inside your application (compromise memory consumption over performance) to avoid unnecessary re-querying data and re-consuming the results. After applying a change, it's key you re-do the analysis and interpretation steps: compare the results and expectations with what you had before, to see whether your actions had any effect or whether it moved the problem to a different part of the application. Don't fall into the trap to do partly analysis: do the full analysis again: .NET profiling and O/R mapper / RDBMS profiling. It might very well be that the changes you've made make one part faster but another part significantly slower, in such a way that the overall problem hasn't changed at all. Performance tuning is dealing with compromises and making choices: to use one feature over the other, to accept a higher memory footprint, to go away from the strict-OO path and execute queries directly onto the RDBMS, these are choices and compromises which will cross your path if you want to fix performance problems with respect to O/R mappers or data-access and databases in general. In most cases it's not a big issue: alternatives are often good choices too and the compromises aren't that hard to deal with. What is important is that you document why you made a choice, a compromise: which analysis data, which interpretation led you to the choice made. This is key for good maintainability in the years to come. Most common performance problems with O/R mappers Below is an incomplete list of common performance problems related to data-access / O/R mappers / RDBMS code. It will help you with fixing the hotspots you found in the interpretation step. SELECT N+1: (Lazy-loading specific). Lazy loading triggered performance bottlenecks. Consider a list of Orders bound to a grid. You have a Field mapped onto a related field in Order, Customer.CompanyName. Showing this column in the grid will make the grid fetch (indirectly) for each row the Customer row. This means you'll get for the single list not 1 query (for the orders) but 1+(the number of orders shown) queries. To solve this: use eager loading using a prefetch path to fetch the customers with the orders. SELECT N+1 is easy to spot with an O/R mapper profiler or RDBMS profiler: if you see a lot of identical queries executed at once, you have this problem. Prefetch paths using many path nodes or sorting, or limiting. Eager loading problem. Prefetch paths can help with performance, but as 1 query is fetched per node, it can be the number of data fetched in a child node is bigger than you think. Also consider that data in every node is merged on the client within the parent. This is fast, but it also can take some time if you fetch massive amounts of entities. If you keep fetches small, you can use tuning parameters like the ParameterizedPrefetchPathThreshold setting to get more optimal queries. Deep inheritance hierarchies of type Target Per Entity/Type. If you use inheritance of type Target per Entity / Type (each type in the inheritance hierarchy is mapped onto its own table/view), fetches will join subtype- and supertype tables in many cases, which can lead to a lot of performance problems if the hierarchy has many types. With this problem, keep inheritance to a minimum if possible, or switch to a hierarchy of type Target Per Hierarchy, which means all entities in the inheritance hierarchy are mapped onto the same table/view. Of course this has its own set of drawbacks, but it's a compromise you might want to take. Fetching massive amounts of data by fetching large lists of entities. LLBLGen Pro supports paging (and limiting the # of rows returned), which is often key to process through large sets of data. Use paging on the RDBMS if possible (so a query is executed which returns only the rows in the page requested). When using paging in a web application, be sure that you switch server-side paging on on the datasourcecontrol used. In this case, paging on the grid alone is not enough: this can lead to fetching a lot of data which is then loaded into the grid and paged there. Keep note that analyzing queries for paging could lead to the false assumption that paging doesn't occur, e.g. when the query contains a field of type ntext/image/clob/blob and DISTINCT can't be applied while it should have (e.g. due to a join): the datareader will do DISTINCT filtering on the client. this is a little slower but it does perform paging functionality on the data-reader so it won't fetch all rows even if the query suggests it does. Fetch massive amounts of data because blob/clob/ntext/image fields aren't excluded. LLBLGen Pro supports field exclusion for queries. You can exclude fields (also in prefetch paths) per query to avoid fetching all fields of an entity, e.g. when you don't need them for the logic consuming the resultset. Excluding fields can greatly reduce the amount of time spend on data-transport across the network. Use this optimization if you see that there's a big difference between query execution time on the RDBMS and the time reported by the .NET profiler for the ExecuteReader method call. Doing client-side aggregates/scalar calculations by consuming a lot of data. If possible, try to formulate a scalar query or group by query using the projection system or GetScalar functionality of LLBLGen Pro to do data consumption on the RDBMS server. It's far more efficient to process data on the RDBMS server than to first load it all in memory, then traverse the data in-memory to calculate a value. Using .ToList() constructs inside linq queries. It might be you use .ToList() somewhere in a Linq query which makes the query be run partially in-memory. Example: var q = from c in metaData.Customers.ToList() where c.Country=="Norway" select c; This will actually fetch all customers in-memory and do an in-memory filtering, as the linq query is defined on an IEnumerable<T>, and not on the IQueryable<T>. Linq is nice, but it can often be a bit unclear where some parts of a Linq query might run. Fetching all entities to delete into memory first. To delete a set of entities it's rather inefficient to first fetch them all into memory and then delete them one by one. It's more efficient to execute a DELETE FROM ... WHERE query on the database directly to delete the entities in one go. LLBLGen Pro supports this feature, and so do some other O/R mappers. It's not always possible to do this operation in the context of an O/R mapper however: if an O/R mapper relies on a cache, these kind of operations are likely not supported because they make it impossible to track whether an entity is actually removed from the DB and thus can be removed from the cache. Fetching all entities to update with an expression into memory first. Similar to the previous point: it is more efficient to update a set of entities directly with a single UPDATE query using an expression instead of fetching the entities into memory first and then updating the entities in a loop, and afterwards saving them. It might however be a compromise you don't want to take as it is working around the idea of having an object graph in memory which is manipulated and instead makes the code fully aware there's a RDBMS somewhere. Conclusion Performance tuning is almost always about compromises and making choices. It's also about knowing where to look and how the systems in play behave and should behave. The four steps I provided should help you stay focused on the real problem and lead you towards the solution. Knowing how to optimally use the systems participating in your own code (.NET framework, O/R mapper, RDBMS, network/services) is key for success as well as knowing what's going on inside the application you built. I hope you'll find this guide useful in tracking down performance problems and dealing with them in a useful way.  

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  • FAQ: GridView Calculation with JavaScript

    - by Vincent Maverick Durano
    In my previous post I wrote a simple demo on how to Calculate Totals in GridView and Display it in the Footer. Basically what it does is it calculates the total amount by typing into the TextBox and display the grand total in the footer of the GridView and basically it was a server side implemenation.  Many users in the forums are asking how to do the same thing without postbacks and how to calculate both amount and total amount together. In this post I will demonstrate how to do this using JavaScript. To get started let's go ahead and set up the form. Just for the simplicity of this demo I just set up the form like this:   <asp:gridview ID="GridView1" runat="server" ShowFooter="true" AutoGenerateColumns="false"> <Columns> <asp:BoundField DataField="RowNumber" HeaderText="Row Number" /> <asp:BoundField DataField="Description" HeaderText="Item Description" /> <asp:TemplateField HeaderText="Item Price"> <ItemTemplate> <asp:Label ID="LBLPrice" runat="server" Text='<%# Eval("Price") %>'></asp:Label> </ItemTemplate> </asp:TemplateField> <asp:TemplateField HeaderText="Quantity"> <ItemTemplate> <asp:TextBox ID="TXTQty" runat="server"></asp:TextBox> </ItemTemplate> <FooterTemplate> <b>Total Amount:</b> </FooterTemplate> </asp:TemplateField> <asp:TemplateField HeaderText="Sub-Total"> <ItemTemplate> <asp:Label ID="LBLSubTotal" runat="server"></asp:Label> </ItemTemplate> <FooterTemplate> <asp:Label ID="LBLTotal" runat="server" ForeColor="Green"></asp:Label> </FooterTemplate> </asp:TemplateField> </Columns> </asp:gridview>   As you can see there's no fancy about the mark up above. It just a standard GridView with BoundFields and TemplateFields on it. Now just for the purpose of this demo I just use a dummy data for populating the GridView. Here's the code below:   public partial class GridCalculation : System.Web.UI.Page { private void BindDummyDataToGrid() { DataTable dt = new DataTable(); DataRow dr = null; dt.Columns.Add(new DataColumn("RowNumber", typeof(string))); dt.Columns.Add(new DataColumn("Description", typeof(string))); dt.Columns.Add(new DataColumn("Price", typeof(string))); dr = dt.NewRow(); dr["RowNumber"] = 1; dr["Description"] = "Nike"; dr["Price"] = "1000"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 2; dr["Description"] = "Converse"; dr["Price"] = "800"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 3; dr["Description"] = "Adidas"; dr["Price"] = "500"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 4; dr["Description"] = "Reebok"; dr["Price"] = "750"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 5; dr["Description"] = "Vans"; dr["Price"] = "1100"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 6; dr["Description"] = "Fila"; dr["Price"] = "200"; dt.Rows.Add(dr); //Bind the Gridview GridView1.DataSource = dt; GridView1.DataBind(); } protected void Page_Load(object sender, EventArgs e) { if (!IsPostBack) { BindDummyDataToGrid(); } } }   Now try to run the page. The output should look something like below: The Client-Side Calculation Here's the code for the GridView calculation:   <script type="text/javascript"> function CalculateTotals() { var gv = document.getElementById("<%= GridView1.ClientID %>"); var tb = gv.getElementsByTagName("input"); var lb = gv.getElementsByTagName("span"); var sub = 0; var total = 0; var indexQ = 1; var indexP = 0; for (var i = 0; i < tb.length; i++) { if (tb[i].type == "text") { sub = parseFloat(lb[indexP].innerHTML) * parseFloat(tb[i].value); if (isNaN(sub)) { lb[i + indexQ].innerHTML = ""; sub = 0; } else { lb[i + indexQ].innerHTML = sub; } indexQ++; indexP = indexP + 2; total += parseFloat(sub); } } lb[lb.length -1].innerHTML = total; } </script>   The code above calculates the sub-total by multiplying the price and the quantity and at the same time calculates the total amount  by adding the sub-total values. Now you can simply call the JavaScript function above like this:   <ItemTemplate> <asp:TextBox ID="TXTQty" runat="server" onkeyup="CalculateTotals();"></asp:TextBox> </ItemTemplate>   Running the code above will display something like below: That's it! I hope someone find this post useful! Technorati Tags: ASP.NET,JavaScript,GridView,TipsTricks

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  • FAQ: GridView Calculation with JavaScript - Formatting and Validation

    - by Vincent Maverick Durano
    In my previous post here we've talked about how to calculate the sub-totals and grand total in GridView using JavaScript. In this post I'm going take more step further and will demonstrate how are we going to format the totals into a currency and how to validate the input that would only allow you to enter a whole number in the quantity TextBox. Here are the code blocks below: ASPX Source:   <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" > <head runat="server"> <title></title> <script type="text/javascript"> function CalculateTotals() { var gv = document.getElementById("<%= GridView1.ClientID %>"); var tb = gv.getElementsByTagName("input"); var lb = gv.getElementsByTagName("span"); var sub = 0; var total = 0; var indexQ = 1; var indexP = 0; var price = 0; for (var i = 0; i < tb.length; i++) { if (tb[i].type == "text") { ValidateNumber(tb[i]); price = lb[indexP].innerHTML.replace("$", "").replace(",", ""); sub = parseFloat(price) * parseFloat(tb[i].value); if (isNaN(sub)) { lb[i + indexQ].innerHTML = "0.00"; sub = 0; } else { lb[i + indexQ].innerHTML = FormatToMoney(sub, "$", ",", "."); ; } indexQ++; indexP = indexP + 2; total += parseFloat(sub); } } lb[lb.length - 1].innerHTML = FormatToMoney(total, "$", ",", "."); } function ValidateNumber(o) { if (o.value.length > 0) { o.value = o.value.replace(/[^\d]+/g, ''); //Allow only whole numbers } } function isThousands(position) { if (Math.floor(position / 3) * 3 == position) return true; return false; }; function FormatToMoney(theNumber, theCurrency, theThousands, theDecimal) { var theDecimalDigits = Math.round((theNumber * 100) - (Math.floor(theNumber) * 100)); theDecimalDigits = "" + (theDecimalDigits + "0").substring(0, 2); theNumber = "" + Math.floor(theNumber); var theOutput = theCurrency; for (x = 0; x < theNumber.length; x++) { theOutput += theNumber.substring(x, x + 1); if (isThousands(theNumber.length - x - 1) && (theNumber.length - x - 1 != 0)) { theOutput += theThousands; }; }; theOutput += theDecimal + theDecimalDigits; return theOutput; } </script> </head> <body> <form id="form1" runat="server"> <asp:gridview ID="GridView1" runat="server" ShowFooter="true" AutoGenerateColumns="false"> <Columns> <asp:BoundField DataField="RowNumber" HeaderText="Row Number" /> <asp:BoundField DataField="Description" HeaderText="Item Description" /> <asp:TemplateField HeaderText="Item Price"> <ItemTemplate> <asp:Label ID="LBLPrice" runat="server" Text='<%# Eval("Price","{0:C}") %>'></asp:Label> </ItemTemplate> </asp:TemplateField> <asp:TemplateField HeaderText="Quantity"> <ItemTemplate> <asp:TextBox ID="TXTQty" runat="server" onkeyup="CalculateTotals();"></asp:TextBox> </ItemTemplate> <FooterTemplate> <b>Total Amount:</b> </FooterTemplate> </asp:TemplateField> <asp:TemplateField HeaderText="Sub-Total"> <ItemTemplate> <asp:Label ID="LBLSubTotal" runat="server" ForeColor="Green" Text="0.00"></asp:Label> </ItemTemplate> <FooterTemplate> <asp:Label ID="LBLTotal" runat="server" ForeColor="Green" Font-Bold="true" Text="0.00"></asp:Label> </FooterTemplate> </asp:TemplateField> </Columns> </asp:gridview> </form> </body> </html> Code Behind Source:   public partial class GridCalculation : System.Web.UI.Page { private void BindDummyDataToGrid() { DataTable dt = new DataTable(); DataRow dr = null; dt.Columns.Add(new DataColumn("RowNumber", typeof(string))); dt.Columns.Add(new DataColumn("Description", typeof(string))); dt.Columns.Add(new DataColumn("Price", typeof(decimal))); dr = dt.NewRow(); dr["RowNumber"] = 1; dr["Description"] = "Nike"; dr["Price"] = "1000"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 2; dr["Description"] = "Converse"; dr["Price"] = "800"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 3; dr["Description"] = "Adidas"; dr["Price"] = "500"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 4; dr["Description"] = "Reebok"; dr["Price"] = "750"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 5; dr["Description"] = "Vans"; dr["Price"] = "1100"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 6; dr["Description"] = "Fila"; dr["Price"] = "200"; dt.Rows.Add(dr); //Bind the Gridview GridView1.DataSource = dt; GridView1.DataBind(); } protected void Page_Load(object sender, EventArgs e) { if (!IsPostBack) { BindDummyDataToGrid(); } } } Running the code above will display something like this: On initial load After entering the quantity in the TextBox That's it! I hope someone find this post useful! Technorati Tags: ASP.NET,C#,ADO.NET,JavaScript,GridView

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  • SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS

    - by pinaldave
    Data Quality Services is a very important concept of SQL Server. I have recently started to explore the same and I am really learning some good concepts. Here are two very important blog posts which one should go over before continuing this blog post. Installing Data Quality Services (DQS) on SQL Server 2012 Connecting Error to Data Quality Services (DQS) on SQL Server 2012 This article is introduction to Data Quality Services for beginners. We will be using an Excel file Click on the image to enlarge the it. In the first article we learned to install DQS. In this article we will see how we can learn about building Knowledge Base and using it to help us identify the quality of the data as well help correct the bad quality of the data. Here are the two very important steps we will be learning in this tutorial. Building a New Knowledge Base  Creating a New Data Quality Project Let us start the building the Knowledge Base. Click on New Knowledge Base. In our project we will be using the Excel as a knowledge base. Here is the Excel which we will be using. There are two columns. One is Colors and another is Shade. They are independent columns and not related to each other. The point which I am trying to show is that in Column A there are unique data and in Column B there are duplicate records. Clicking on New Knowledge Base will bring up the following screen. Enter the name of the new knowledge base. Clicking NEXT will bring up following screen where it will allow to select the EXCE file and it will also let users select the source column. I have selected Colors and Shade both as a source column. Creating a domain is very important. Here you can create a unique domain or domain which is compositely build from Colors and Shade. As this is the first example, I will create unique domain – for Colors I will create domain Colors and for Shade I will create domain Shade. Here is the screen which will demonstrate how the screen will look after creating domains. Clicking NEXT it will bring you to following screen where you can do the data discovery. Clicking on the START will start the processing of the source data provided. Pre-processed data will show various information related to the source data. In our case it shows that Colors column have unique data whereas Shade have non-unique data and unique data rows are only two. In the next screen you can actually add more rows as well see the frequency of the data as the values are listed unique. Clicking next will publish the knowledge base which is just created. Now the knowledge base is created. We will try to take any random data and attempt to do DQS implementation over it. I am using another excel sheet here for simplicity purpose. In reality you can easily use SQL Server table for the same. Click on New Data Quality Project to see start DQS Project. In the next screen it will ask which knowledge base to use. We will be using our Colors knowledge base which we have recently created. In the Colors knowledge base we had two columns – 1) Colors and 2) Shade. In our case we will be using both of the mappings here. User can select one or multiple column mapping over here. Now the most important phase of the complete project. Click on Start and it will make the cleaning process and shows various results. In our case there were two columns to be processed and it completed the task with necessary information. It demonstrated that in Colors columns it has not corrected any value by itself but in Shade value there is a suggestion it has. We can train the DQS to correct values but let us keep that subject for future blog posts. Now click next and keep the domain Colors selected left side. It will demonstrate that there are two incorrect columns which it needs to be corrected. Here is the place where once corrected value will be auto-corrected in future. I manually corrected the value here and clicked on Approve radio buttons. As soon as I click on Approve buttons the rows will be disappeared from this tab and will move to Corrected Tab. If I had rejected tab it would have moved the rows to Invalid tab as well. In this screen you can see how the corrected 2 rows are demonstrated. You can click on Correct tab and see previously validated 6 rows which passed the DQS process. Now let us click on the Shade domain on the left side of the screen. This domain shows very interesting details as there DQS system guessed the correct answer as Dark with the confidence level of 77%. It is quite a high confidence level and manual observation also demonstrate that Dark is the correct answer. I clicked on Approve and the row moved to corrected tab. On the next screen DQS shows the summary of all the activities. It also demonstrates how the correction of the quality of the data was performed. The user can explore their data to a SQL Server Table, CSV file or Excel. The user also has an option to either explore data and all the associated cleansing info or data only. I will select Data only for demonstration purpose. Clicking explore will generate the files. Let us open the generated file. It will look as following and it looks pretty complete and corrected. Well, we have successfully completed DQS Process. The process is indeed very easy. I suggest you try this out yourself and you will find it very easy to learn. In future we will go over advanced concepts. Are you using this feature on your production server? If yes, would you please leave a comment with your environment and business need. It will be indeed interesting to see where it is implemented. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Data Warehousing, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

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  • SQL SERVER – Select and Delete Duplicate Records – SQL in Sixty Seconds #036 – Video

    - by pinaldave
    Developers often face situations when they find their column have duplicate records and they want to delete it. A good developer will never delete any data without observing it and making sure that what is being deleted is the absolutely fine to delete. Before deleting duplicate data, one should select it and see if the data is really duplicate. In this video we are demonstrating two scripts – 1) selects duplicate records 2) deletes duplicate records. We are assuming that the table has a unique incremental id. Additionally, we are assuming that in the case of the duplicate records we would like to keep the latest record. If there is really a business need to keep unique records, one should consider to create a unique index on the column. Unique index will prevent users entering duplicate data into the table from the beginning. This should be the best solution. However, deleting duplicate data is also a very valid request. If user realizes that they need to keep only unique records in the column and if they are willing to create unique constraint, the very first requirement of creating a unique constraint is to delete the duplicate records. Let us see how to connect the values in Sixty Seconds: Here is the script which is used in the video. USE tempdb GO CREATE TABLE TestTable (ID INT, NameCol VARCHAR(100)) GO INSERT INTO TestTable (ID, NameCol) SELECT 1, 'First' UNION ALL SELECT 2, 'Second' UNION ALL SELECT 3, 'Second' UNION ALL SELECT 4, 'Second' UNION ALL SELECT 5, 'Second' UNION ALL SELECT 6, 'Third' GO -- Selecting Data SELECT * FROM TestTable GO -- Detecting Duplicate SELECT NameCol, COUNT(*) TotalCount FROM TestTable GROUP BY NameCol HAVING COUNT(*) > 1 ORDER BY COUNT(*) DESC GO -- Deleting Duplicate DELETE FROM TestTable WHERE ID NOT IN ( SELECT MAX(ID) FROM TestTable GROUP BY NameCol) GO -- Selecting Data SELECT * FROM TestTable GO DROP TABLE TestTable GO Related Tips in SQL in Sixty Seconds: SQL SERVER – Delete Duplicate Records – Rows SQL SERVER – Count Duplicate Records – Rows SQL SERVER – 2005 – 2008 – Delete Duplicate Rows Delete Duplicate Records – Rows – Readers Contribution Unique Nonclustered Index Creation with IGNORE_DUP_KEY = ON – A Transactional Behavior What would you like to see in the next SQL in Sixty Seconds video? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Query, SQL Scripts, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology, Video Tagged: Excel

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  • Working with Reporting Services Filters – Part 3: The TOP and BOTTOM Operators

    - by smisner
    Thus far in this series, I have described using the IN operator and the LIKE operator. Today, I’ll continue the series by reviewing the TOP and BOTTOM operators. Today, I happened to be working on an example of using the TOP N operator and was not successful on my first try because the behavior is just a bit different than we find when using an “equals” comparison as I described in my first post in this series. In my example, I wanted to display a list of the top 5 resellers in the United States for AdventureWorks, but I wanted it based on a filter. I started with a hard-coded filter like this: Expression Data Type Operator Value [ResellerSalesAmount] Float Top N 5 And received the following error: A filter value in the filter for tablix 'Tablix1' specifies a data type that is not supported by the 'TopN' operator. Verify that the data type for each filter value is Integer. Well, that puzzled me. Did I really have to convert ResellerSalesAmount to an integer to use the Top N operator? Just for kicks, I switched to the Top % operator like this: Expression Data Type Operator Value [ResellerSalesAmount] Float Top % 50 This time, I got exactly the results I expected – I had a total of 10 records in my dataset results, so 50% of that should yield 5 rows in my tablix. So thinking about the problem with Top N some  more, I switched the Value to an expression, like this: Expression Data Type Operator Value [ResellerSalesAmount] Float Top N =5 And it worked! So the value for Top N or Top % must reflect a number to plug into the calculation, such as Top 5 or Top 50%, and the expression is the basis for determining what’s in that group. In other words, Reporting Services will sort the rows by the expression – ResellerSalesAmount in this case – in descending order, and then filter out everything except the topmost rows based on the operator you specify. The curious thing is that, if you’re going to hard-code the value, you must enter the value for Top N with an equal sign in front of the integer, but you can omit the equal sign when entering a hard-coded value for Top %. This experience is why working with Reporting Services filters is not always intuitive! When you use a report parameter to set the value, you won’t have this problem. Just be sure that the data type of the report parameter is set to Integer. Jessica Moss has an example of using a Top N filter in a tablix which you can view here. Working with Bottom N and Bottom % works similarly. You just provide a number for N or for the percentage and Reporting Services works from the bottom up to determine which rows are kept and which are excluded.

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  • SQL SERVER – Concurrancy Problems and their Relationship with Isolation Level

    - by pinaldave
    Concurrency is simply put capability of the machine to support two or more transactions working with the same data at the same time. This usually comes up with data is being modified, as during the retrieval of the data this is not the issue. Most of the concurrency problems can be avoided by SQL Locks. There are four types of concurrency problems visible in the normal programming. 1)      Lost Update – This problem occurs when there are two transactions involved and both are unaware of each other. The transaction which occurs later overwrites the transactions created by the earlier update. 2)      Dirty Reads – This problem occurs when a transactions selects data that isn’t committed by another transaction leading to read the data which may not exists when transactions are over. Example: Transaction 1 changes the row. Transaction 2 changes the row. Transaction 1 rolls back the changes. Transaction 2 has selected the row which does not exist. 3)      Nonrepeatable Reads – This problem occurs when two SELECT statements of the same data results in different values because another transactions has updated the data between the two SELECT statements. Example: Transaction 1 selects a row, which is later on updated by Transaction 2. When Transaction A later on selects the row it gets different value. 4)      Phantom Reads – This problem occurs when UPDATE/DELETE is happening on one set of data and INSERT/UPDATE is happening on the same set of data leading inconsistent data in earlier transaction when both the transactions are over. Example: Transaction 1 is deleting 10 rows which are marked as deleting rows, during the same time Transaction 2 inserts row marked as deleted. When Transaction 1 is done deleting rows, there will be still rows marked to be deleted. When two or more transactions are updating the data, concurrency is the biggest issue. I commonly see people toying around with isolation level or locking hints (e.g. NOLOCK) etc, which can very well compromise your data integrity leading to much larger issue in future. Here is the quick mapping of the isolation level with concurrency problems: Isolation Dirty Reads Lost Update Nonrepeatable Reads Phantom Reads Read Uncommitted Yes Yes Yes Yes Read Committed No Yes Yes Yes Repeatable Read No No No Yes Snapshot No No No No Serializable No No No No I hope this 400 word small article gives some quick understanding on concurrency issues and their relation to isolation level. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • When is a Seek not a Seek?

    - by Paul White
    The following script creates a single-column clustered table containing the integers from 1 to 1,000 inclusive. IF OBJECT_ID(N'tempdb..#Test', N'U') IS NOT NULL DROP TABLE #Test ; GO CREATE TABLE #Test ( id INTEGER PRIMARY KEY CLUSTERED ); ; INSERT #Test (id) SELECT V.number FROM master.dbo.spt_values AS V WHERE V.[type] = N'P' AND V.number BETWEEN 1 AND 1000 ; Let’s say we need to find the rows with values from 100 to 170, excluding any values that divide exactly by 10.  One way to write that query would be: SELECT T.id FROM #Test AS T WHERE T.id IN ( 101,102,103,104,105,106,107,108,109, 111,112,113,114,115,116,117,118,119, 121,122,123,124,125,126,127,128,129, 131,132,133,134,135,136,137,138,139, 141,142,143,144,145,146,147,148,149, 151,152,153,154,155,156,157,158,159, 161,162,163,164,165,166,167,168,169 ) ; That query produces a pretty efficient-looking query plan: Knowing that the source column is defined as an INTEGER, we could also express the query this way: SELECT T.id FROM #Test AS T WHERE T.id >= 101 AND T.id <= 169 AND T.id % 10 > 0 ; We get a similar-looking plan: If you look closely, you might notice that the line connecting the two icons is a little thinner than before.  The first query is estimated to produce 61.9167 rows – very close to the 63 rows we know the query will return.  The second query presents a tougher challenge for SQL Server because it doesn’t know how to predict the selectivity of the modulo expression (T.id % 10 > 0).  Without that last line, the second query is estimated to produce 68.1667 rows – a slight overestimate.  Adding the opaque modulo expression results in SQL Server guessing at the selectivity.  As you may know, the selectivity guess for a greater-than operation is 30%, so the final estimate is 30% of 68.1667, which comes to 20.45 rows. The second difference is that the Clustered Index Seek is costed at 99% of the estimated total for the statement.  For some reason, the final SELECT operator is assigned a small cost of 0.0000484 units; I have absolutely no idea why this is so, or what it models.  Nevertheless, we can compare the total cost for both queries: the first one comes in at 0.0033501 units, and the second at 0.0034054.  The important point is that the second query is costed very slightly higher than the first, even though it is expected to produce many fewer rows (20.45 versus 61.9167). If you run the two queries, they produce exactly the same results, and both complete so quickly that it is impossible to measure CPU usage for a single execution.  We can, however, compare the I/O statistics for a single run by running the queries with STATISTICS IO ON: Table '#Test'. Scan count 63, logical reads 126, physical reads 0. Table '#Test'. Scan count 01, logical reads 002, physical reads 0. The query with the IN list uses 126 logical reads (and has a ‘scan count’ of 63), while the second query form completes with just 2 logical reads (and a ‘scan count’ of 1).  It is no coincidence that 126 = 63 * 2, by the way.  It is almost as if the first query is doing 63 seeks, compared to one for the second query. In fact, that is exactly what it is doing.  There is no indication of this in the graphical plan, or the tool-tip that appears when you hover your mouse over the Clustered Index Seek icon.  To see the 63 seek operations, you have click on the Seek icon and look in the Properties window (press F4, or right-click and choose from the menu): The Seek Predicates list shows a total of 63 seek operations – one for each of the values from the IN list contained in the first query.  I have expanded the first seek node to show the details; it is seeking down the clustered index to find the entry with the value 101.  Each of the other 62 nodes expands similarly, and the same information is contained (even more verbosely) in the XML form of the plan. Each of the 63 seek operations starts at the root of the clustered index B-tree and navigates down to the leaf page that contains the sought key value.  Our table is just large enough to need a separate root page, so each seek incurs 2 logical reads (one for the root, and one for the leaf).  We can see the index depth using the INDEXPROPERTY function, or by using the a DMV: SELECT S.index_type_desc, S.index_depth FROM sys.dm_db_index_physical_stats ( DB_ID(N'tempdb'), OBJECT_ID(N'tempdb..#Test', N'U'), 1, 1, DEFAULT ) AS S ; Let’s look now at the Properties window when the Clustered Index Seek from the second query is selected: There is just one seek operation, which starts at the root of the index and navigates the B-tree looking for the first key that matches the Start range condition (id >= 101).  It then continues to read records at the leaf level of the index (following links between leaf-level pages if necessary) until it finds a row that does not meet the End range condition (id <= 169).  Every row that meets the seek range condition is also tested against the Residual Predicate highlighted above (id % 10 > 0), and is only returned if it matches that as well. You will not be surprised that the single seek (with a range scan and residual predicate) is much more efficient than 63 singleton seeks.  It is not 63 times more efficient (as the logical reads comparison would suggest), but it is around three times faster.  Let’s run both query forms 10,000 times and measure the elapsed time: DECLARE @i INTEGER, @n INTEGER = 10000, @s DATETIME = GETDATE() ; SET NOCOUNT ON; SET STATISTICS XML OFF; ; WHILE @n > 0 BEGIN SELECT @i = T.id FROM #Test AS T WHERE T.id IN ( 101,102,103,104,105,106,107,108,109, 111,112,113,114,115,116,117,118,119, 121,122,123,124,125,126,127,128,129, 131,132,133,134,135,136,137,138,139, 141,142,143,144,145,146,147,148,149, 151,152,153,154,155,156,157,158,159, 161,162,163,164,165,166,167,168,169 ) ; SET @n -= 1; END ; PRINT DATEDIFF(MILLISECOND, @s, GETDATE()) ; GO DECLARE @i INTEGER, @n INTEGER = 10000, @s DATETIME = GETDATE() ; SET NOCOUNT ON ; WHILE @n > 0 BEGIN SELECT @i = T.id FROM #Test AS T WHERE T.id >= 101 AND T.id <= 169 AND T.id % 10 > 0 ; SET @n -= 1; END ; PRINT DATEDIFF(MILLISECOND, @s, GETDATE()) ; On my laptop, running SQL Server 2008 build 4272 (SP2 CU2), the IN form of the query takes around 830ms and the range query about 300ms.  The main point of this post is not performance, however – it is meant as an introduction to the next few parts in this mini-series that will continue to explore scans and seeks in detail. When is a seek not a seek?  When it is 63 seeks © Paul White 2011 email: [email protected] twitter: @SQL_kiwi

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  • how to update child records when updating the Master table using Linq [closed]

    - by user20358
    I currently use a general repositry class that can update only a single table like so public abstract class MyRepository<T> : IRepository<T> where T : class { protected IObjectSet<T> _objectSet; protected ObjectContext _context; public MyRepository(ObjectContext Context) { _objectSet = Context.CreateObjectSet<T>(); _context = Context; } public IQueryable<T> GetAll() { return _objectSet.AsQueryable(); } public IQueryable<T> Find(Expression<Func<T, bool>> filter) { return _objectSet.Where(filter); } public void Add(T entity) { _objectSet.AddObject(entity); _context.ObjectStateManager.ChangeObjectState(entity, System.Data.EntityState.Added); _context.SaveChanges(); } public void Update(T entity) { _context.ObjectStateManager.ChangeObjectState(entity, System.Data.EntityState.Modified); _context.SaveChanges(); } public void Delete(T entity) { _objectSet.Attach(entity); _context.ObjectStateManager.ChangeObjectState(entity, System.Data.EntityState.Deleted); _objectSet.DeleteObject(entity); _context.SaveChanges(); } } For every table class generated by my EDMX designer I create another class like this public class CustomerRepo : MyRepository<Customer> { public CustomerRepo (ObjectContext context) : base(context) { } } for any updates that I need to make to a particular table I do this: Customer CustomerObj = new Customer(); CustomerObj.Prop1 = ... CustomerObj.Prop2 = ... CustomerObj.Prop3 = ... CustomerRepo.Update(CustomerObj); This works perfectly well when I am updating just to the specific table called Customer. Now if I need to also update each row of another table which is a child of Customer called Orders what changes do I need to make to the class MyRepository. Orders table will have multiple records for a Customer record and multiple fields too, say for example Field1, Field2, Field3. So my questions are: 1.) If I only need to update Field1 of the Orders table for some rows based on a condition and Field2 for some other rows based on a different condition then what changes I need to do? 2.) If there is no such condition and all child rows need to be updated with the same value for all rows then what changes do I need to do? Thanks for taking the time. Look forward to your inputs...

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  • Have you used the ExecutionValue and ExecValueVariable properties?

    The ExecutionValue execution value property and it’s friend ExecValueVariable are a much undervalued feature of SSIS, and many people I talk to are not even aware of their existence, so I thought I’d try and raise their profile a bit. The ExecutionValue property is defined on the base object Task, so all tasks have it available, but it is up to the task developer to do something useful with it. The basic idea behind it is that it allows the task to return something useful and interesting about what it has performed, in addition to the standard success or failure result. The best example perhaps is the Execute SQL Task which uses the ExecutionValue property to return the number of rows affected by the SQL statement(s). This is a very useful feature, something people often want to capture into a variable, and start using the result set options to do. Unfortunately we cannot read the value of a task property at runtime from within a SSIS package, so the ExecutionValue property on its own is a bit of a let down, but enter the ExecValueVariable and we have the perfect marriage. The ExecValueVariable is another property exposed through the task (TaskHost), which lets us select a SSIS package variable. What happens now is that when the task sets the ExecutionValue, the interesting value is copied into the variable we set on the ExecValueVariable property, and a variable is something we can access and do something with. So put simply if the ExecutionValue property value is of interest, make sure you create yourself a package variable and set the name as the ExecValueVariable. Have  look at the 3 step guide below: 1 Configure your task as normal, for example the Execute SQL Task, which here calls a stored procedure to do some updates. 2 Create variable of a suitable type to match the ExecutionValue, an integer is used to match the result we want to capture, the number of rows. 3 Set the ExecValueVariable for the task, just select the variable we created in step 2. You need to do this in Properties grid for the task (Short-cut key, select the task and press F4) Now when we execute the sample task above, our variable UpdateQueueRowCount will get the number of rows we updated in our Execute SQL Task. I’ve tried to collate a list of tasks that return something useful via the ExecutionValue and ExecValueVariable mechanism, but the documentation isn’t always great. Task ExecutionValue Description Execute SQL Task Returns the number of rows affected by the SQL statement or statements. File System Task Returns the number of successful operations performed by the task. File Watcher Task Returns the full path of the file found Transfer Error Messages Task Returns the number of error messages that have been transferred Transfer Jobs Task Returns the number of jobs that are transferred Transfer Logins Task Returns the number of logins transferred Transfer Master Stored Procedures Task Returns the number of stored procedures transferred Transfer SQL Server Objects Task Returns the number of objects transferred WMI Data Reader Task Returns an object that contains the results of the task. Not exactly clear, but I assume it depends on the WMI query used.

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  • How do large blobs affect SQL delete performance, and how can I mitigate the impact?

    - by Max Pollack
    I'm currently experiencing a strange issue that my understanding of SQL Server doesn't quite mesh with. We use SQL as our file storage for our internal storage service, and our database has about half a million rows in it. Most of the files (86%) are 1mb or under, but even on fresh copies of our database where we simply populate the table with data for the purposes of a test, it appears that rows with large amounts of data stored in a BLOB frequently cause timeouts when our SQL Server is under load. My understanding of how SQL Server deletes rows is that it's a garbage collection process, i.e. the row is marked as a ghost and the row is later deleted by the ghost cleanup process after the changes are copied to the transaction log. This suggests to me that regardless of the size of the data in the blob, row deletion should be close to instantaneous. However when deleting these rows we are definitely experiencing large numbers of timeouts and astoundingly low performance. In our test data set, its files over 30mb that cause this issue. This is an edge case, we don't frequently encounter these, and even though we're looking into SQL filestream as a solution to some of our problems, we're trying to narrow down where these issues are originating from. We ARE performing our deletes inside of a transaction. We're also performing updates to metadata such as file size stats, but these exist in a separate table away from the file data itself. Hierarchy data is stored in the table that contains the file information. Really, in the end it's not so much what we're doing around the deletes that matters, we just can't find any references to low delete performance on rows that contain a large amount of data in a BLOB. We are trying to determine if this is even an avenue worth exploring, or if it has to be one of our processes around the delete that's causing the issue. Are there any situations in which this could occur? Is it common for a database server to come to the point of complete timeouts when many of these deletes are occurring simultaneously? Is there a way to combat this issue if it exists? (cross-posted from StackOverflow )

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  • Deletes that Split Pages and Forwarded Ghosts

    - by Paul White
    Can DELETE operations cause pages to split? Yes. It sounds counter-intuitive on the face of it; deleting rows frees up space on a page, and page splitting occurs when a page needs additional space. Nevertheless, there are circumstances when deleting rows causes them to expand before they can be deleted. The mechanism at work here is row versioning (extract from Books Online below): Isolation Levels The relationship between row-versioning isolation levels (the first bullet point) and page splits is...(read more)

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  • TPC-H Benchmarks on SQL Server 2014 with Columnstore

    - by jchang
    Three TPC-H benchmark results were published in April of this year at SQL Server 2014 launch, where the new updateable columnstore feature was used. SQL Server 2012 had non-updateable columnstore that required the base table to exist in rowstore form. This was not used in the one published TPC-H benchmark result on SQL Server 2012, which includes two refresh stored procedures, one inserting rows, the second deleting rows. It is possible that the TPC-H rules do not allow a view to union two tables?...(read more)

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  • Accessing Controls Within A Gridview

    - by Bunch
    Sometimes you need to access a control within a GridView, but it isn’t quite as straight forward as just using FindControl to grab the control like you can in a FormView. Since the GridView builds multiple rows the key is to specify the row. In this example there is a GridView with a control for a player’s errors. If the errors is greater than 9 the GridView should display the control (lblErrors) in red so it stands out. Here is the GridView: <asp:GridView ID="gvFielding" runat="server" DataSourceID="sqlFielding" DataKeyNames="PlayerID" AutoGenerateColumns="false" >     <Columns>         <asp:BoundField DataField="PlayerName" HeaderText="Player Name" />         <asp:BoundField DataField="PlayerNumber" HeaderText="Player Number" />         <asp:TemplateField HeaderText="Errors">             <ItemTemplate>                 <asp:Label ID="lblErrors" runat="server" Text='<%# EVAL("Errors") %>'  />             </ItemTemplate>         </asp:TemplateField>     </Columns> </asp:GridView> In the code behind you can add the code to change the label’s ForeColor property to red based on the amount of errors. In this case 10 or more errors triggers the color change. Protected Sub gvFielding_DataBound(ByVal sender As Object, ByVal e As System.EventArgs) Handles gvFielding.DataBound     Dim errorLabel As Label     Dim errors As Integer     Dim i As Integer = 0     For Each row As GridViewRow In gvFielding.Rows         errorLabel = gvFielding.Rows(i).FindControl("lblErrors")         If Not errorLabel.Text = Nothing Then             Integer.TryParse(errorLabel.Text, errors)             If errors > 9 Then                 errorLabel.ForeColor = Drawing.Color.Red             End If         End If         i += 1     Next End Sub The main points in the DataBound sub is use a For Each statement to loop through the rows and to increment the variable i so you loop through every row. That way you check each one and if the value is greater than 9 the label changes to red. The If Not errorLabel.Text = Nothing line is there as a check in case no data comes back at all for Errors. Technorati Tags: GridView,ASP.Net,VB.Net

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  • PyGtk: Scrolllist with Entry, can I set an Id

    - by max246
    I have a scroll list on my window that I am going to insert 2 entry for each row, I am trying to understand how I can catch the entry that has been changed and update my array with this value. I will explain what is my code: I have an array that has 2 fields: Name and Description Each row has 2 entry, Name and Description When I am going to modify the row number 2 I want to update my object on my array: rows[1].name = XXX rows[1].description = YYY

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  • PMDB Block Size Choice

    - by Brian Diehl
    Choosing a block size for the P6 PMDB database is not a difficult task. In fact, taking the default of 8k is going to be just fine. Block size is one of those things that is always hotly debated. Everyone has their personal preference and can sight plenty of good reasons for their choice. To add to the confusion, Oracle supports multiple block sizes withing the same instance. So how to decide and what is the justification? Like most OLTP systems, Oracle Primavera P6 has a wide variety of data. A typical table's average row size may be less than 50 bytes or upwards of 500 bytes. There are also several tables with BLOB types but the LOB data tends not to be very large. It is likely that no single block size would be perfect for every table. So how to choose? My preference is for the 8k (8192 bytes) block size. It is a good compromise that is not too small for the wider rows, yet not to big for the thin rows. It is also important to remember that database blocks are the smallest unit of change and caching. I prefer to have more, individual "working units" in my database. For an instance with 4gb of buffer cache, an 8k block will provide 524,288 blocks of cache. The following SQL*Plus script returns the average, median, min, and max rows per block. column "AVG(CNT)" format 999.99 set verify off select avg(cnt), median(cnt), min(cnt), max(cnt), count(*) from ( select dbms_rowid.ROWID_RELATIVE_FNO(rowid) , dbms_rowid.ROWID_BLOCK_NUMBER(rowid) , count(*) cnt from &tab group by dbms_rowid.ROWID_RELATIVE_FNO(rowid) , dbms_rowid.ROWID_BLOCK_NUMBER(rowid) ) Running this for the TASK table, I get this result on a database with an 8k block size. Each activity, on average, has about 19 rows per block. Enter value for tab: task AVG(CNT) MEDIAN(CNT) MIN(CNT) MAX(CNT) COUNT(*) -------- ----------- ---------- ---------- ---------- 18.72 19 3 28 415917 I recommend an 8k block size for the P6 transactional database. All of our internal performance and scalability test are done with this block size. This does not mean that other block sizes will not work. Instead, like many other parameters, this is the safest choice.

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  • 12c - flashforward, flashback or see it as of now...

    - by noreply(at)blogger.com (Thomas Kyte)
    Oracle 9i exposed flashback query to developers for the first time.  The ability to flashback query dates back to version 4 however (it just wasn't exposed).  Every time you run a query in Oracle it is in fact a flashback query - it is what multi-versioning is all about.However, there was never a flashforward query (well, ok, the workspace manager has this capability - but with lots of extra baggage).  We've never been able to ask a table "what will you look like tomorrow" - but now we do.The capability is called Temporal Validity.  If you have a table with data that is effective dated - has a "start date" and "end date" column in it - we can now query it using flashback query like syntax.  The twist is - the date we "flashback" to can be in the future.  It works by rewriting the query to transparently the necessary where clause and filter out the right rows for the right period of time - and since you can have records whose start date is in the future - you can query a table and see what it would look like at some future time.Here is a quick example, we'll start with a table:ops$tkyte%ORA12CR1> create table addresses  2  ( empno       number,  3    addr_data   varchar2(30),  4    start_date  date,  5    end_date    date,  6    period for valid(start_date,end_date)  7  )  8  /Table created.the new bit is on line 6 (it can be altered into an existing table - so any table  you have with a start/end date column will be a candidate).  The keyword is PERIOD, valid is an identifier I chose - it could have been foobar, valid just sounds nice in the query later.  You identify the columns in your table - or we can create them for you if they don't exist.  Then you just create some data:ops$tkyte%ORA12CR1> insert into addresses (empno, addr_data, start_date, end_date )  2  values ( 1234, '123 Main Street', trunc(sysdate-5), trunc(sysdate-2) );1 row created.ops$tkyte%ORA12CR1>ops$tkyte%ORA12CR1> insert into addresses (empno, addr_data, start_date, end_date )  2  values ( 1234, '456 Fleet Street', trunc(sysdate-1), trunc(sysdate+1) );1 row created.ops$tkyte%ORA12CR1>ops$tkyte%ORA12CR1> insert into addresses (empno, addr_data, start_date, end_date )  2  values ( 1234, '789 1st Ave', trunc(sysdate+2), null );1 row created.and you can either see all of the data:ops$tkyte%ORA12CR1> select * from addresses;     EMPNO ADDR_DATA                      START_DAT END_DATE---------- ------------------------------ --------- ---------      1234 123 Main Street                27-JUN-13 30-JUN-13      1234 456 Fleet Street               01-JUL-13 03-JUL-13      1234 789 1st Ave                    04-JUL-13or query "as of" some point in time - as  you can see in the predicate section - it is just doing a query rewrite to automate the "where" filters:ops$tkyte%ORA12CR1> select * from addresses as of period for valid sysdate-3;     EMPNO ADDR_DATA                      START_DAT END_DATE---------- ------------------------------ --------- ---------      1234 123 Main Street                27-JUN-13 30-JUN-13ops$tkyte%ORA12CR1> @planops$tkyte%ORA12CR1> select * from table(dbms_xplan.display_cursor);PLAN_TABLE_OUTPUT-------------------------------------------------------------------------------SQL_ID  cthtvvm0dxvva, child number 0-------------------------------------select * from addresses as of period for valid sysdate-3Plan hash value: 3184888728-------------------------------------------------------------------------------| Id  | Operation         | Name      | Rows  | Bytes | Cost (%CPU)| Time     |-------------------------------------------------------------------------------|   0 | SELECT STATEMENT  |           |       |       |     3 (100)|          ||*  1 |  TABLE ACCESS FULL| ADDRESSES |     1 |    48 |     3   (0)| 00:00:01 |-------------------------------------------------------------------------------Predicate Information (identified by operation id):---------------------------------------------------   1 - filter((("T"."START_DATE" IS NULL OR              "T"."START_DATE"<=SYSDATE@!-3) AND ("T"."END_DATE" IS NULL OR              "T"."END_DATE">SYSDATE@!-3)))Note-----   - dynamic statistics used: dynamic sampling (level=2)24 rows selected.ops$tkyte%ORA12CR1> select * from addresses as of period for valid sysdate;     EMPNO ADDR_DATA                      START_DAT END_DATE---------- ------------------------------ --------- ---------      1234 456 Fleet Street               01-JUL-13 03-JUL-13ops$tkyte%ORA12CR1> @planops$tkyte%ORA12CR1> select * from table(dbms_xplan.display_cursor);PLAN_TABLE_OUTPUT-------------------------------------------------------------------------------SQL_ID  26ubyhw9hgk7z, child number 0-------------------------------------select * from addresses as of period for valid sysdatePlan hash value: 3184888728-------------------------------------------------------------------------------| Id  | Operation         | Name      | Rows  | Bytes | Cost (%CPU)| Time     |-------------------------------------------------------------------------------|   0 | SELECT STATEMENT  |           |       |       |     3 (100)|          ||*  1 |  TABLE ACCESS FULL| ADDRESSES |     1 |    48 |     3   (0)| 00:00:01 |-------------------------------------------------------------------------------Predicate Information (identified by operation id):---------------------------------------------------   1 - filter((("T"."START_DATE" IS NULL OR              "T"."START_DATE"<=SYSDATE@!) AND ("T"."END_DATE" IS NULL OR              "T"."END_DATE">SYSDATE@!)))Note-----   - dynamic statistics used: dynamic sampling (level=2)24 rows selected.ops$tkyte%ORA12CR1> select * from addresses as of period for valid sysdate+3;     EMPNO ADDR_DATA                      START_DAT END_DATE---------- ------------------------------ --------- ---------      1234 789 1st Ave                    04-JUL-13ops$tkyte%ORA12CR1> @planops$tkyte%ORA12CR1> select * from table(dbms_xplan.display_cursor);PLAN_TABLE_OUTPUT-------------------------------------------------------------------------------SQL_ID  36bq7shnhc888, child number 0-------------------------------------select * from addresses as of period for valid sysdate+3Plan hash value: 3184888728-------------------------------------------------------------------------------| Id  | Operation         | Name      | Rows  | Bytes | Cost (%CPU)| Time     |-------------------------------------------------------------------------------|   0 | SELECT STATEMENT  |           |       |       |     3 (100)|          ||*  1 |  TABLE ACCESS FULL| ADDRESSES |     1 |    48 |     3   (0)| 00:00:01 |-------------------------------------------------------------------------------Predicate Information (identified by operation id):---------------------------------------------------   1 - filter((("T"."START_DATE" IS NULL OR              "T"."START_DATE"<=SYSDATE@!+3) AND ("T"."END_DATE" IS NULL OR              "T"."END_DATE">SYSDATE@!+3)))Note-----   - dynamic statistics used: dynamic sampling (level=2)24 rows selected.All in all a nice, easy way to query effective dated information as of a point in time without a complex where clause.  You need to maintain the data - it isn't that a delete will turn into an update the end dates a record or anything - but if you have tables with start/end dates, this will make it much easier to query them.

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  • CBO????????

    - by Liu Maclean(???)
    ???Itpub????????CBO??????????, ????????: SQL> create table maclean1 as select * from dba_objects; Table created. SQL> update maclean1 set status='INVALID' where owner='MACLEAN'; 2 rows updated. SQL> commit; Commit complete. SQL> create index ind_maclean1 on maclean1(status); Index created. SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN1',cascade=>true); PL/SQL procedure successfully completed. SQL> explain plan for select * from maclean1 where status='INVALID'; Explained. SQL> set linesize 140 pagesize 1400 SQL> select * from table(dbms_xplan.display()); PLAN_TABLE_OUTPUT --------------------------------------------------------------------------- Plan hash value: 987568083 ------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | 11320 | 1028K| 85 (0)| 00:00:02 | |* 1 | TABLE ACCESS FULL| MACLEAN1 | 11320 | 1028K| 85 (0)| 00:00:02 | ------------------------------------------------------------------------------ Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter("STATUS"='INVALID') 13 rows selected. 10053 trace Access path analysis for MACLEAN1 *************************************** SINGLE TABLE ACCESS PATH   Single Table Cardinality Estimation for MACLEAN1[MACLEAN1]   Column (#10): STATUS(     AvgLen: 7 NDV: 2 Nulls: 0 Density: 0.500000   Table: MACLEAN1  Alias: MACLEAN1     Card: Original: 22639.000000  Rounded: 11320  Computed: 11319.50  Non Adjusted: 11319.50   Access Path: TableScan     Cost:  85.33  Resp: 85.33  Degree: 0       Cost_io: 85.00  Cost_cpu: 11935345       Resp_io: 85.00  Resp_cpu: 11935345   Access Path: index (AllEqRange)     Index: IND_MACLEAN1     resc_io: 185.00  resc_cpu: 8449916     ix_sel: 0.500000  ix_sel_with_filters: 0.500000     Cost: 185.24  Resp: 185.24  Degree: 1   Best:: AccessPath: TableScan          Cost: 85.33  Degree: 1  Resp: 85.33  Card: 11319.50  Bytes: 0 ?????10053????????????,?????Density = 0.5 ?? 1/ NDV ??? ??????????????STATUS='INVALID"???????????, ????????????????? ????”STATUS”=’INVALID’ condition???2?,?status??????,??????dbms_stats?????????????,???CBO????INDEX Range ind_maclean1,???????,??????opitimizer?????? ?????????????????????????,????????,??????????status=’INVALID’???????card??,????????: [oracle@vrh4 ~]$ sqlplus / as sysdba SQL*Plus: Release 11.2.0.2.0 Production on Mon Oct 17 19:15:45 2011 Copyright (c) 1982, 2010, Oracle. All rights reserved. Connected to: Oracle Database 11g Enterprise Edition Release 11.2.0.2.0 - 64bit Production With the Partitioning, OLAP, Data Mining and Real Application Testing options SQL> select * from v$version; BANNER -------------------------------------------------------------------------------- Oracle Database 11g Enterprise Edition Release 11.2.0.2.0 - 64bit Production PL/SQL Release 11.2.0.2.0 - Production CORE 11.2.0.2.0 Production TNS for Linux: Version 11.2.0.2.0 - Production NLSRTL Version 11.2.0.2.0 - Production SQL> show parameter optimizer_fea NAME TYPE VALUE ------------------------------------ ----------- ------------------------------ optimizer_features_enable string 11.2.0.2 SQL> select * from global_name; GLOBAL_NAME -------------------------------------------------------------------------------- www.oracledatabase12g.com & www.askmaclean.com SQL> drop table maclean; Table dropped. SQL> create table maclean as select * from dba_objects; Table created. SQL> update maclean set status='INVALID' where owner='MACLEAN'; 2 rows updated. SQL> commit; Commit complete. SQL> create index ind_maclean on maclean(status); Index created. SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN',cascade=>true, method_opt=>'FOR ALL COLUMNS SIZE 2'); PL/SQL procedure successfully completed. ???????2?bucket????, ??????????????? ???Quest???Guy Harrison???????FREQUENCY????????,??????: rem rem Generate a histogram of data distribution in a column as recorded rem in dba_tab_histograms rem rem Guy Harrison Jan 2010 : www.guyharrison.net rem rem hexstr function is from From http://asktom.oracle.com/pls/asktom/f?p=100:11:0::::P11_QUESTION_ID:707586567563 set pagesize 10000 set lines 120 set verify off col char_value format a10 heading "Endpoint|value" col bucket_count format 99,999,999 heading "bucket|count" col pct format 999.99 heading "Pct" col pct_of_max format a62 heading "Pct of|Max value" rem col endpoint_value format 9999999999999 heading "endpoint|value" CREATE OR REPLACE FUNCTION hexstr (p_number IN NUMBER) RETURN VARCHAR2 AS l_str LONG := TO_CHAR (p_number, 'fm' || RPAD ('x', 50, 'x')); l_return VARCHAR2 (4000); BEGIN WHILE (l_str IS NOT NULL) LOOP l_return := l_return || CHR (TO_NUMBER (SUBSTR (l_str, 1, 2), 'xx')); l_str := SUBSTR (l_str, 3); END LOOP; RETURN (SUBSTR (l_return, 1, 6)); END; / WITH hist_data AS ( SELECT endpoint_value,endpoint_actual_value, NVL(LAG (endpoint_value) OVER (ORDER BY endpoint_value),' ') prev_value, endpoint_number, endpoint_number, endpoint_number - NVL (LAG (endpoint_number) OVER (ORDER BY endpoint_value), 0) bucket_count FROM dba_tab_histograms JOIN dba_tab_col_statistics USING (owner, table_name,column_name) WHERE owner = '&owner' AND table_name = '&table' AND column_name = '&column' AND histogram='FREQUENCY') SELECT nvl(endpoint_actual_value,endpoint_value) endpoint_value , bucket_count, ROUND(bucket_count*100/SUM(bucket_count) OVER(),2) PCT, RPAD(' ',ROUND(bucket_count*50/MAX(bucket_count) OVER()),'*') pct_of_max FROM hist_data; WITH hist_data AS ( SELECT endpoint_value,endpoint_actual_value, NVL(LAG (endpoint_value) OVER (ORDER BY endpoint_value),' ') prev_value, endpoint_number, endpoint_number, endpoint_number - NVL (LAG (endpoint_number) OVER (ORDER BY endpoint_value), 0) bucket_count FROM dba_tab_histograms JOIN dba_tab_col_statistics USING (owner, table_name,column_name) WHERE owner = '&owner' AND table_name = '&table' AND column_name = '&column' AND histogram='FREQUENCY') SELECT hexstr(endpoint_value) char_value, bucket_count, ROUND(bucket_count*100/SUM(bucket_count) OVER(),2) PCT, RPAD(' ',ROUND(bucket_count*50/MAX(bucket_count) OVER()),'*') pct_of_max FROM hist_data ORDER BY endpoint_value; ?????,??????????FREQUENCY?????: ??dbms_stats ?????STATUS=’INVALID’ bucket count=9 percent = 0.04 ,??????10053 trace????????: SQL> explain plan for select * from maclean where status='INVALID'; Explained. SQL>  select * from table(dbms_xplan.display()); PLAN_TABLE_OUTPUT ------------------------------------- Plan hash value: 3087014066 ------------------------------------------------------------------------------------------- | Id  | Operation                   | Name        | Rows  | Bytes | Cost (%CPU)| Time     | ------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT            |             |     9 |   837 |     2   (0)| 00:00:01 | |   1 |  TABLE ACCESS BY INDEX ROWID| MACLEAN     |     9 |   837 |     2   (0)| 00:00:01 | |*  2 |   INDEX RANGE SCAN          | IND_MACLEAN |     9 |       |     1   (0)| 00:00:01 | ------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    2 - access("STATUS"='INVALID') ??????????????CBO???????STATUS=’INVALID’?cardnality?? , ??????????? ,??index range scan??Full table scan? ????????????????10053 trace: SQL> alter system flush shared_pool; System altered. SQL> oradebug setmypid; Statement processed. SQL> oradebug event 10053 trace name context forever ,level 1; Statement processed. SQL> explain plan for select * from maclean where status='INVALID'; Explained. SINGLE TABLE ACCESS PATH Single Table Cardinality Estimation for MACLEAN[MACLEAN] Column (#10): NewDensity:0.000199, OldDensity:0.000022 BktCnt:22640, PopBktCnt:22640, PopValCnt:2, NDV:2 ???NewDensity= bucket_count / SUM(bucket_count) /2 Column (#10): STATUS( AvgLen: 7 NDV: 2 Nulls: 0 Density: 0.000199 Histogram: Freq #Bkts: 2 UncompBkts: 22640 EndPtVals: 2 Table: MACLEAN Alias: MACLEAN Card: Original: 22640.000000 Rounded: 9 Computed: 9.00 Non Adjusted: 9.00 Access Path: TableScan Cost: 85.30 Resp: 85.30 Degree: 0 Cost_io: 85.00 Cost_cpu: 10804625 Resp_io: 85.00 Resp_cpu: 10804625 Access Path: index (AllEqRange) Index: IND_MACLEAN resc_io: 2.00 resc_cpu: 20763 ix_sel: 0.000398 ix_sel_with_filters: 0.000398 Cost: 2.00 Resp: 2.00 Degree: 1 Best:: AccessPath: IndexRange Index: IND_MACLEAN Cost: 2.00 Degree: 1 Resp: 2.00 Card: 9.00 Bytes: 0 ???????????2 bucket?????CBO????????????,???????????????????,???dbms_stats.DEFAULT_METHOD_OPT????????????????????? ???dbms_stats?????????????????????col_usage$??????predicate???????,??col_usage$??<????????SMON??(?):??col_usage$????>? ??????????dbms_stats????????,col_usage$????????????predicate???,??dbms_stats??????????????????, ?: SQL> drop table maclean; Table dropped. SQL> create table maclean as select * from dba_objects; Table created. SQL> update maclean set status='INVALID' where owner='MACLEAN'; 2 rows updated. SQL> commit; Commit complete. SQL> create index ind_maclean on maclean(status); Index created. ??dbms_stats??method_opt??maclean? SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN'); PL/SQL procedure successfully completed. @histogram.sql Enter value for owner: SYS old  12:    WHERE owner = '&owner' new  12:    WHERE owner = 'SYS' Enter value for table: MACLEAN old  13:      AND table_name = '&table' new  13:      AND table_name = 'MACLEAN' Enter value for column: STATUS old  14:      AND column_name = '&column' new  14:      AND column_name = 'STATUS' no rows selected ????col_usage$?????,????????status????? declare begin for i in 1..500 loop execute immediate ' alter system flush shared_pool'; DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO; execute immediate 'select count(*) from maclean where status=''INVALID'' ' ; end loop; end; / PL/SQL procedure successfully completed. SQL> select obj# from obj$ where name='MACLEAN';       OBJ# ----------      97215 SQL> select * from  col_usage$ where  OBJ#=97215;       OBJ#    INTCOL# EQUALITY_PREDS EQUIJOIN_PREDS NONEQUIJOIN_PREDS RANGE_PREDS LIKE_PREDS NULL_PREDS TIMESTAMP ---------- ---------- -------------- -------------- ----------------- ----------- ---------- ---------- ---------      97215          1              1              0                 0           0          0          0 17-OCT-11      97215         10            499              0                 0           0          0          0 17-OCT-11 SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN'); PL/SQL procedure successfully completed. @histogram.sql Enter value for owner: SYS Enter value for table: MACLEAN Enter value for column: STATUS Endpoint        bucket         Pct of value            count     Pct Max value ---------- ----------- ------- -------------------------------------------------------------- INVALI               2     .04 VALIC3           5,453   99.96  *************************************************

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  • Random Sampling in Excel

    - by bonsvr
    I have an Excel sheet as follows: NO NAME AMOUNT 1 A 50 1 B 50 2 A 100 2 C 100 3 D 70 3 B 70 4 A 30 4 F 30 5 C 150 5 G 150 . . . . There are let's say 10,000 rows. I want to get a random sample from rows. There are 2 conditions: 1. Sampling must be based on "NO" column. 2. Size of the sample is determined by the user: it can be %5, %10 or %20. For example, one decides to randomly choose %20 of total rows in the above example: The result is like: NO NAME AMOUNT 2 A 100 2 C 100 90 Z 500 90 E 500 . . . . There should be 2,000 rows. I don't know whether my question is too specific. I am new to Excel VBA, and I faced a situation like this. Above process is about getting a random sample from an account ledger for auditing purposes.

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  • MySQL /G output

    - by conandor
    I ran mysql query as below on a non-partition table mysql> use test31 Reading table information for completion of table and column names You can turn off this feature to get a quicker startup with -A Database changed mysql> explain partitions SELECT * FROM my_friends WHERE (requestor = '1234567890' OR contact = '1234567890') AND status = 1 ORDER BY request_id DESC LIMIT 0,100\G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: my_friends partitions: NULL type: index_merge possible_keys: friend_index,requestor,contact key: friend_index,contact key_len: 17,17 ref: NULL rows: 2 Extra: Using sort_union(friend_index,contact); Using where; Using filesort 1 row in set (0.00 sec) on a partition table mysql> explain partitions SELECT * FROM my_friends WHERE (requestor = '1234567890' OR contact = '1234567890') AND status = 1 ORDER BY request_id DESC LIMIT 0,100\G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: my_friends partitions: p1_p1sp0,p1_p1sp1,p1_p1sp2,p1_p1sp3,p1_p1sp4,p1_p1sp5,p1_p1sp6,p1_p1sp7,p1_p1sp8,p1_p1sp9,p1_p1sp10,p1_p1sp11,p1_p1sp12,p1_p1sp13,p1_p1sp14,p1_p1sp15,p1_p1sp16,p1_p1sp17,p1_p1sp18,p1_p1sp19,p1_p1sp20,p1_p1sp21,p1_p1sp22,p1_p1sp23,p1_p1sp24,p1_p1sp25,p1_p1sp26,p1_p1sp27,p1_p1sp28,p1_p1sp29 type: index_merge possible_keys: friend_index,requestor,contact key: friend_index,contact key_len: 17,17 ref: NULL rows: 60 Extra: Using sort_union(friend_index,contact); Using where; Using filesort 1 row in set (0.01 sec) What does the "rows" mean? less rows is more faster query?

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  • Spreadsheet application that can handle big data OS X

    - by Peter
    I've been working with Excel for quite a while for some statistical analysis that I do regularly. The size of the data that I'm working with has gotten much larger as of late, however. The layout of the databases in question is quite simple, usually just three rows which includes a UNIX timestamp, and EST value, a proprietary numeric value and finally an average of the rows that have a timestamp +/- 1000 that row's timestamp (little AVERAGEIFS() formula). That formula and the EST conversion are the only formulas in the sheet. I'm beginning to work with files with 500,000+ rows. Running the average formula down the entire row takes forever. The end result is the production of print-worthy graphs. I'm looking for either a UNIX CL utility or separate spreadsheet/database application that can handle this amount of data without melting my CPU or making me wait an hour. Is there anything out there? TL;DR: Simple excel sheet with over half a million rows is getting too slow to work with. OS X alternatives?

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  • Delete cell content in Libre (Open) Office based on the cell value

    - by take2
    I have a huge csv file (tens of thousands of rows) that I need to filter based on different criteria. After trying to find a proper CSV editor, I decided to use LibreOffice Calc. CSVed is great, but it doesn't support neither UTF-8 nor macros for advanced filtering. So, there are 4 columns, 3 of which contain numbers (with decimal numbers) and 1 of which contains text. I'm trying to find a way to delete rows with a macro code. I can achieve the desired behavior with filters too, but it's annoying to type all of the filtering values over and over again and there doesn't seem to be a way to export the filter and us it repeatedly. These rows should be deleted: The ones that don't contain certain words in textual column (column A). There are a few thousand different words used in that column and I want to keep only the rows that contain one of about 30 words in that column. Additionally, the number is the other columns should be bigger than 3.8 (column B), 4.5 (column C) and smaller than 20 (column C). The row-deletion type is "Shift up". Hopefully I have explained it well. Thanks a lot in advance for your help!

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  • PHP/MySQL allowing current user to edit there account information

    - by user1837896
    i have created 2 pages update.php edit.php we start on edit.php so here is edit.php's script <?php session_start(); $id = $_SESSION["id"]; $username = $_POST["username"]; $fname = $_POST["fname"]; $password = $_POST["password"]; $email = $_POST["email"]; mysql_connect('mysql13.000webhost.com', 'a2670376_Users', 'Password') or die(mysql_error()); echo "MySQL Connection Established! <br>"; mysql_select_db("a2670376_Pass") or die(mysql_error()); echo "Database Found! <br>"; $query = "UPDATE members SET username = '$username', fname = '$fname', password = '$password' WHERE id = '$id'"; $res = mysql_query($query); if ($res) echo "<p>Record Updated<p>"; else echo "Problem updating record. MySQL Error: " . mysql_error(); ?> <form action="update.php" method="post"> <input type="hidden" name="id" value="<?=$id;?>"> ScreenName:<br> <input type='text' name='username' id='username' maxlength='25' style='width:247px' name="username" value="<?=$username;?>"/><br> FullName:<br> <input type='text' name='fname' id='fname' maxlength='20' style='width:248px' name="ud_img" value="<?=$fname;?>"/><br> Email:<br> <input type='text' name='email' id='email' maxlength='50' style='width:250px' name="ud_img" value="<?=$email;?>"/><br> Password:<br> <input type='text' name='password' id='password' maxlength='25' style='width:251px' value="<?=$password;?>"/><br> <input type="Submit"> </form> now here is the update.php page where i am having the MAJOR problem <?php session_start(); mysql_connect('mysql13.000webhost.com', 'a2670376_Users', 'Password') or die(mysql_error()); mysql_select_db("a2670376_Pass") or die(mysql_error()); $id = (int)$_SESSION["id"]; $username = mysql_real_escape_string($_POST["username"]); $fname = mysql_real_escape_string($_POST["fname"]); $email = mysql_real_escape_string($_POST["email"]); $password = mysql_real_escape_string($_POST["password"]); $query="UPDATE members SET username = '$username', fname = '$fname', email = '$email', password = '$password' WHERE id='$id'"; mysql_query($query)or die(mysql_error()); if(mysql_affected_rows()>=1){ echo "<p>($id) Record Updated<p>"; }else{ echo "<p>($id) Not Updated<p>"; } ?> now on edit.php i fill out the form to edit the account "test" while i am logged into it now once the form if filled out i click on |Submit!| button and it takes me to update.php and it returns this (0) Not Updated (0) <= id of user logged in Not Updated <= MySql Error from mysql_query($query)or die(mysql_error()); if(mysql_affected_rows()>=1){ i want it to update the user logged in and if i am not mistaken in this script it says $id = (int)$_SESSION["id"]; witch updates the user with the id of the person who is logged in but it isnt updating its saying that no tables were effected if it helps heres my MySql Database picture just click here http://i50.tinypic.com/21juqfq.png if this could possibly be any help to find the solution i have 2 more files delete.php and delete_ac.php they have can remove users from my sql database and they show the user id and it works there are no bugs in this script at all PLEASE DO NOT MAKE SUGGESTIONS FOR THE SCRIPTS BELOW delete.php first <?php $host="mysql13.000webhost.com"; // Host name $username="a2670376_Users"; // Mysql username $password="PASSWORD"; // Mysql password $db_name="a2670376_Pass"; // Database name $tbl_name="members"; // Table name // Connect to server and select database. mysql_connect("$host", "$username", "$password")or die("cannot connect"); mysql_select_db("$db_name")or die("cannot select DB"); // select record from mysql $sql="SELECT * FROM $tbl_name"; $result=mysql_query($sql); ?> <table border="0" cellpadding="3" cellspacing="1" bgcolor="#CCCCCC"> <tr> <td colspan="8" style="bgcolor: #FFFFFF"><strong><img src="http://i47.tinypic.com/u6ihk.png" height="30" widht="30">Delete data in mysql</strong> </td> </tr> <tr> <td align="center" bgcolor="#FFFFFF"><strong>Id</strong></td> <td align="center" bgcolor="#FFFFFF"><strong>UserName</strong></td> <td align="center" bgcolor="#FFFFFF"><strong>FullName</strong></td> <td align="center" bgcolor="#FFFFFF"><strong>Password</strong></td> <td align="center" bgcolor="#FFFFFF"><strong>Email</strong></td> <td align="center" bgcolor="#FFFFFF"><strong>Date</strong></td> <td align="center" bgcolor="#FFFFFF"><strong>Ip</strong></td> <td align="center" bgcolor="#FFFFFF">&nbsp;</td> </tr> <?php while($rows=mysql_fetch_array($result)){ ?> <tr> <td bgcolor="#FFFFFF"><? echo $rows['id']; ?></td> <td bgcolor="#FFFFFF"><? echo $rows['username']; ?></td> <td bgcolor="#FFFFFF"><? echo $rows['fname']; ?></td> <td bgcolor="#FFFFFF"><? echo $rows['password']; ?></td> <td bgcolor="#FFFFFF"><? echo $rows['email']; ?></td> <td bgcolor="#FFFFFF"><? echo $rows['date']; ?></td> <td bgcolor="#FFFFFF"><? echo $rows['ip']; ?></td> <td bgcolor="#FFFFFF"><a href="delete_ac.php?id=<? echo $rows['id']; ?>">delete</a></td> </tr> <?php // close while loop } ?> </table> <?php // close connection; sql_close(); ?> and now delete_ac.php <table width="500" border="0" cellpadding="3" cellspacing="1" bgcolor="#CCCCCC"> <tr> <td colspan="8" bgcolor="#FFFFFF"><strong><img src="http://t2.gstatic.com/images? q=tbn:ANd9GcS_kwpNSSt3UuBHxq5zhkJQAlPnaXyePaw07R652f4StmvIQAAf6g" height="30" widht="30">Removal Of Account</strong> </td> </tr> <tr> <td align="center" bgcolor="#FFFFFF"> <?php $host="mysql13.000webhost.com"; // Host name $username="a2670376_Users"; // Mysql username $password="javascript00"; // Mysql password $db_name="a2670376_Pass"; // Database name $tbl_name="members"; // Table name // Connect to server and select databse. mysql_connect("$host", "$username", "$password")or die("cannot connect"); mysql_select_db("$db_name")or die("cannot select DB"); // get value of id that sent from address bar $id=$_GET['id']; // Delete data in mysql from row that has this id $sql="DELETE FROM $tbl_name WHERE id='$id'"; $result=mysql_query($sql); // if successfully deleted if($result){ echo "Deleted Successfully"; echo "<BR>"; echo "<a href='delete.php'>Back to main page</a>"; } else { echo "ERROR"; } ?> <?php // close connection mysql_close(); ?> </td> </tr> </table>

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