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  • SQLite Select an id which is smaller than a number

    - by user345039
    Hi, I am trying to only select the objects where the id is smaller than an int value. e.g.: i have 3 objects - id = 1, id = 2, id = 3 Now I want to only get the objects with the id smaller than the variable i = 2; How can I manage this? sql = "SELECT id FROM table_name WHERE id <= i"; Thanks ;-)

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  • I'm maintaining a java class that's 40K lines long.. problem?

    - by Billworth Vandory
    This may be a subjective question leading to deletion but I would really like some feedback. Recently, I moved to another very large enterprise project where I work as a C++ developer. I was aghast to find most classes in the project are anywhere from 8K to 50K lines long with methods that are 1K to 8K lines long. It's mostly business logic dealing with DB tables and data management, full of conditional statements to handle the use cases. Are classes this large common in large enterprise systems? I realize without looking at the code it's hard to make a determination, but have you ever worked on a system with classes this large?

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  • object reference set in java

    - by landon9720
    I need to create a Set of objects. The concern is I do not want to base the hashing or the equality on the objects' hashCode and equals implementation. Instead, I want the hash code and equality to be based only on each object's reference identity (i.e.: the value of the reference pointer). I'm not sure how to do this in Java. The reasoning behind this is my objects do not reliably implement equals or hashCode, and in this case reference identity is good enough.

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  • Problem decrementing in Java with '-='

    - by hanesjw
    I'm making a scrolling game on Android and am having a hard time figuring out why the code below does not decrement past 0. Objects start at the end of the screen (so the x position is equal to the width of the screen) the objects move accross the screen by decrementing their x positions. I want them to scroll off of the screen, but when the x position hits 0, the objects just stay at 0, they do not move into the negatives. Here is my code to move objects on the screen private void incrementPositions(long delta) { float incrementor = (delta / 1000F) * Globals.MAP_SECTION_SPEED; for(Map.Entry<Integer, HashMap<Integer, MapSection>> column : scrollingMap.entrySet()) { for(Map.Entry<Integer, MapSection> row : column.getValue().entrySet()) { MapSection section = row.getValue(); section.x -= incrementor; } } } It works ok if I change section.x -= incrementor; to section.x = section.x - (int)incrementor; but if i do that the scrolling doesn't appear as smooth.

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  • NEED PROGRAM in visualc++ opengl [closed]

    - by HAREEFML
    I AM NEW IN OPENGL VISUALC++ PROGRAMMING.I NEEDED A PROGRAM THAT, THE ROTATING OBJECTS.WHERE THE CUBE,CYLINDER,TRIANGLE & CONE AND THESE OBJECTS ROTATE AS THE KEYBOARD ARROW KEYS ARE PRESSED AND EACH OBJECTS START ROTATION WHEN THE KEYBOARD NUMBERS 1,2,3,4 ARE PRESSED. CAN I GET PROGRAM LIKE THIS? THANKS IN ADVANCE.

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  • jQuery Templates and Data Linking (and Microsoft contributing to jQuery)

    - by ScottGu
    The jQuery library has a passionate community of developers, and it is now the most widely used JavaScript library on the web today. Two years ago I announced that Microsoft would begin offering product support for jQuery, and that we’d be including it in new versions of Visual Studio going forward. By default, when you create new ASP.NET Web Forms and ASP.NET MVC projects with VS 2010 you’ll find jQuery automatically added to your project. A few weeks ago during my second keynote at the MIX 2010 conference I announced that Microsoft would also begin contributing to the jQuery project.  During the talk, John Resig -- the creator of the jQuery library and leader of the jQuery developer team – talked a little about our participation and discussed an early prototype of a new client templating API for jQuery. In this blog post, I’m going to talk a little about how my team is starting to contribute to the jQuery project, and discuss some of the specific features that we are working on such as client-side templating and data linking (data-binding). Contributing to jQuery jQuery has a fantastic developer community, and a very open way to propose suggestions and make contributions.  Microsoft is following the same process to contribute to jQuery as any other member of the community. As an example, when working with the jQuery community to improve support for templating to jQuery my team followed the following steps: We created a proposal for templating and posted the proposal to the jQuery developer forum (http://forum.jquery.com/topic/jquery-templates-proposal and http://forum.jquery.com/topic/templating-syntax ). After receiving feedback on the forums, the jQuery team created a prototype for templating and posted the prototype at the Github code repository (http://github.com/jquery/jquery-tmpl ). We iterated on the prototype, creating a new fork on Github of the templating prototype, to suggest design improvements. Several other members of the community also provided design feedback by forking the templating code. There has been an amazing amount of participation by the jQuery community in response to the original templating proposal (over 100 posts in the jQuery forum), and the design of the templating proposal has evolved significantly based on community feedback. The jQuery team is the ultimate determiner on what happens with the templating proposal – they might include it in jQuery core, or make it an official plugin, or reject it entirely.  My team is excited to be able to participate in the open source process, and make suggestions and contributions the same way as any other member of the community. jQuery Template Support Client-side templates enable jQuery developers to easily generate and render HTML UI on the client.  Templates support a simple syntax that enables either developers or designers to declaratively specify the HTML they want to generate.  Developers can then programmatically invoke the templates on the client, and pass JavaScript objects to them to make the content rendered completely data driven.  These JavaScript objects can optionally be based on data retrieved from a server. Because the jQuery templating proposal is still evolving in response to community feedback, the final version might look very different than the version below. This blog post gives you a sense of how you can try out and use templating as it exists today (you can download the prototype by the jQuery core team at http://github.com/jquery/jquery-tmpl or the latest submission from my team at http://github.com/nje/jquery-tmpl).  jQuery Client Templates You create client-side jQuery templates by embedding content within a <script type="text/html"> tag.  For example, the HTML below contains a <div> template container, as well as a client-side jQuery “contactTemplate” template (within the <script type="text/html"> element) that can be used to dynamically display a list of contacts: The {{= name }} and {{= phone }} expressions are used within the contact template above to display the names and phone numbers of “contact” objects passed to the template. We can use the template to display either an array of JavaScript objects or a single object. The JavaScript code below demonstrates how you can render a JavaScript array of “contact” object using the above template. The render() method renders the data into a string and appends the string to the “contactContainer” DIV element: When the page is loaded, the list of contacts is rendered by the template.  All of this template rendering is happening on the client-side within the browser:   Templating Commands and Conditional Display Logic The current templating proposal supports a small set of template commands - including if, else, and each statements. The number of template commands was deliberately kept small to encourage people to place more complicated logic outside of their templates. Even this small set of template commands is very useful though. Imagine, for example, that each contact can have zero or more phone numbers. The contacts could be represented by the JavaScript array below: The template below demonstrates how you can use the if and each template commands to conditionally display and loop the phone numbers for each contact: If a contact has one or more phone numbers then each of the phone numbers is displayed by iterating through the phone numbers with the each template command: The jQuery team designed the template commands so that they are extensible. If you have a need for a new template command then you can easily add new template commands to the default set of commands. Support for Client Data-Linking The ASP.NET team recently submitted another proposal and prototype to the jQuery forums (http://forum.jquery.com/topic/proposal-for-adding-data-linking-to-jquery). This proposal describes a new feature named data linking. Data Linking enables you to link a property of one object to a property of another object - so that when one property changes the other property changes.  Data linking enables you to easily keep your UI and data objects synchronized within a page. If you are familiar with the concept of data-binding then you will be familiar with data linking (in the proposal, we call the feature data linking because jQuery already includes a bind() method that has nothing to do with data-binding). Imagine, for example, that you have a page with the following HTML <input> elements: The following JavaScript code links the two INPUT elements above to the properties of a JavaScript “contact” object that has a “name” and “phone” property: When you execute this code, the value of the first INPUT element (#name) is set to the value of the contact name property, and the value of the second INPUT element (#phone) is set to the value of the contact phone property. The properties of the contact object and the properties of the INPUT elements are also linked – so that changes to one are also reflected in the other. Because the contact object is linked to the INPUT element, when you request the page, the values of the contact properties are displayed: More interesting, the values of the linked INPUT elements will change automatically whenever you update the properties of the contact object they are linked to. For example, we could programmatically modify the properties of the “contact” object using the jQuery attr() method like below: Because our two INPUT elements are linked to the “contact” object, the INPUT element values will be updated automatically (without us having to write any code to modify the UI elements): Note that we updated the contact object above using the jQuery attr() method. In order for data linking to work, you must use jQuery methods to modify the property values. Two Way Linking The linkBoth() method enables two-way data linking. The contact object and INPUT elements are linked in both directions. When you modify the value of the INPUT element, the contact object is also updated automatically. For example, the following code adds a client-side JavaScript click handler to an HTML button element. When you click the button, the property values of the contact object are displayed using an alert() dialog: The following demonstrates what happens when you change the value of the Name INPUT element and click the Save button. Notice that the name property of the “contact” object that the INPUT element was linked to was updated automatically: The above example is obviously trivially simple.  Instead of displaying the new values of the contact object with a JavaScript alert, you can imagine instead calling a web-service to save the object to a database. The benefit of data linking is that it enables you to focus on your data and frees you from the mechanics of keeping your UI and data in sync. Converters The current data linking proposal also supports a feature called converters. A converter enables you to easily convert the value of a property during data linking. For example, imagine that you want to represent phone numbers in a standard way with the “contact” object phone property. In particular, you don’t want to include special characters such as ()- in the phone number - instead you only want digits and nothing else. In that case, you can wire-up a converter to convert the value of an INPUT element into this format using the code below: Notice above how a converter function is being passed to the linkFrom() method used to link the phone property of the “contact” object with the value of the phone INPUT element. This convertor function strips any non-numeric characters from the INPUT element before updating the phone property.  Now, if you enter the phone number (206) 555-9999 into the phone input field then the value 2065559999 is assigned to the phone property of the contact object: You can also use a converter in the opposite direction also. For example, you can apply a standard phone format string when displaying a phone number from a phone property. Combining Templating and Data Linking Our goal in submitting these two proposals for templating and data linking is to make it easier to work with data when building websites and applications with jQuery. Templating makes it easier to display a list of database records retrieved from a database through an Ajax call. Data linking makes it easier to keep the data and user interface in sync for update scenarios. Currently, we are working on an extension of the data linking proposal to support declarative data linking. We want to make it easy to take advantage of data linking when using a template to display data. For example, imagine that you are using the following template to display an array of product objects: Notice the {{link name}} and {{link price}} expressions. These expressions enable declarative data linking between the SPAN elements and properties of the product objects. The current jQuery templating prototype supports extending its syntax with custom template commands. In this case, we are extending the default templating syntax with a custom template command named “link”. The benefit of using data linking with the above template is that the SPAN elements will be automatically updated whenever the underlying “product” data is updated.  Declarative data linking also makes it easier to create edit and insert forms. For example, you could create a form for editing a product by using declarative data linking like this: Whenever you change the value of the INPUT elements in a template that uses declarative data linking, the underlying JavaScript data object is automatically updated. Instead of needing to write code to scrape the HTML form to get updated values, you can instead work with the underlying data directly – making your client-side code much cleaner and simpler. Downloading Working Code Examples of the Above Scenarios You can download this .zip file to get with working code examples of the above scenarios.  The .zip file includes 4 static HTML page: Listing1_Templating.htm – Illustrates basic templating. Listing2_TemplatingConditionals.htm – Illustrates templating with the use of the if and each template commands. Listing3_DataLinking.htm – Illustrates data linking. Listing4_Converters.htm – Illustrates using a converter with data linking. You can un-zip the file to the file-system and then run each page to see the concepts in action. Summary We are excited to be able to begin participating within the open-source jQuery project.  We’ve received lots of encouraging feedback in response to our first two proposals, and we will continue to actively contribute going forward.  These features will hopefully make it easier for all developers (including ASP.NET developers) to build great Ajax applications. Hope this helps, Scott P.S. [In addition to blogging, I am also now using Twitter for quick updates and to share links. Follow me at: twitter.com/scottgu]

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  • SQL Server and Hyper-V Dynamic Memory Part 2

    - by SQLOS Team
    Part 1 of this series was an introduction and overview of Hyper-V Dynamic Memory. This part looks at SQL Server memory management and how the SQL engine responds to changing OS memory conditions.   Part 2: SQL Server Memory Management As with any Windows process, sqlserver.exe has a virtual address space (VAS) of 4GB on 32-bit and 8TB in 64-bit editions. Pages in its VAS are mapped to pages in physical memory when the memory is committed and referenced for the first time. The collection of VAS pages that have been recently referenced is known as the Working Set. How and when SQL Server allocates virtual memory and grows its working set depends on the memory model it uses. SQL Server supports three basic memory models:   1. Conventional Memory Model   The Conventional model is the default SQL Server memory model and has the following properties: - Dynamic - can grow or shrink its working set in response to load and external (operating system) memory conditions. - OS uses 4K pages – (not to be confused with SQL Server “pages” which are 8K regions of committed memory).- Pageable - Can be paged out to disk by the operating system.   2. Locked Page Model The locked page memory model is set when SQL Server is started with "Lock Pages in Memory" privilege*. It has the following characteristics: - Dynamic - can grow or shrink its working set in the same way as the Conventional model.- OS uses 4K pages - Non-Pageable – When memory is committed it is locked in memory, meaning that it will remain backed by physical memory and will not be paged out by the operating system. A common misconception is to interpret "locked" as non-dynamic. A SQL Server instance using the locked page memory model will grow and shrink (allocate memory and release memory) in response to changing workload and OS memory conditions in the same way as it does with the conventional model.   This is an important consideration when we look at Hyper-V Dynamic Memory – “locked” memory works perfectly well with “dynamic” memory.   * Note in “Denali” (Standard Edition and above), and in SQL 2008 R2 64-bit (Enterprise and above editions) the Lock Pages in Memory privilege is all that is required to set this model. In 2008 R2 64-Bit standard edition it also requires trace flag 845 to be set, in 2008 R2 32-bit editions it requires sp_configure 'awe enabled' 1.   3. Large Page Model The Large page model is set using trace flag 834 and potentially offers a small performance boost for systems that are configured with large pages. It is characterized by: - Static - memory is allocated at startup and does not change. - OS uses large (>2MB) pages - Non-Pageable The large page model is supported with Hyper-V Dynamic Memory (and Hyper-V also supports large pages), but you get no benefit from using Dynamic Memory with this model since SQL Server memory does not grow or shrink. The rest of this article will focus on the locked and conventional SQL Server memory models.   When does SQL Server grow? For “dynamic” configurations (Conventional and Locked memory models), the sqlservr.exe process grows – allocates and commits memory from the OS – in response to a workload. As much memory is allocated as is required to optimally run the query and buffer data for future queries, subject to limitations imposed by:   - SQL Server max server memory setting. If this configuration option is set, the buffer pool is not allowed to grow to more than this value. In SQL Server 2008 this value represents single page allocations, and in “Denali” it represents any size page allocations and also managed CLR procedure allocations.   - Memory signals from OS. The operating system sets a signal on memory resource notification objects to indicate whether it has memory available or whether it is low on available memory. If there is only 32MB free for every 4GB of memory a low memory signal is set, which continues until 64MB/4GB is free. If there is 96MB/4GB free the operating system sets a high memory signal. SQL Server only allocates memory when the high memory signal is set.   To summarize, for SQL Server to grow you need three conditions: a workload, max server memory setting higher than the current allocation, high memory signals from the OS.    When does SQL Server shrink caches? SQL Server as a rule does not like to return memory to the OS, but it will shrink its caches in response to memory pressure. Memory pressure can be divided into “internal” and “external”.   - External memory pressure occurs when the operating system is running low on memory and low memory signals are set. The SQL Server Resource Monitor checks for low memory signals approximately every 5 seconds and it will attempt to free memory until the signals stop.   To free memory SQL Server does the following: ·         Frees unused memory. ·         Notifies Memory Manager Clients to release memory o   Caches – Free unreferenced cache objects. o   Buffer pool - Based on oldest access times.   The freed memory is released back to the operating system. This process continues until the low memory resource notifications stop.    - Internal memory pressure occurs when the size of different caches and allocations increase but the SQL Server process needs to keep its total memory within a target value. For example if max server memory is set and certain caches are growing large, it will cause SQL to free memory for re-use internally, but not to release memory back to the OS. If you lower the value of max server memory you will generate internal memory pressure that will cause SQL to release memory back to the OS.    Memory pressure handling has not changed much since SQL 2005 and it was described in detail in a blog post by Slava Oks.   Note that SQL Server Express is an exception to the above behavior. Unlike other editions it does not assume it is the most important process running on the system but tries to be more “desktop” friendly. It will empty its working set after a period of inactivity.   How does SQL Server respond to changing OS memory?    In SQL Server 2005 support for Hot-Add memory was introduced. This feature, available in Enterprise and above editions, allows the server to make use of any extra physical memory that was added after SQL Server started. Being able to add physical memory when the system is running is limited to specialized hardware, but with the Hyper-V Dynamic Memory feature, when new memory is allocated to a guest virtual machine, it looks like hot-add physical memory to the guest. What this means is that thanks to the hot-add memory feature, SQL Server 2005 and higher can dynamically grow if more “physical” memory is granted to a guest VM by Hyper-V dynamic memory.   SQL Server checks OS memory every second and dynamically adjusts its “target” (based on available OS memory and max server memory) accordingly.   In “Denali” Standard Edition will also have sqlserver.exe support for hot-add memory when running virtualized (i.e. detecting and acting on Hyper-V Dynamic Memory allocations).   How does a SQL Server workload in a guest VM impact Hyper-V dynamic memory scheduling?   When a SQL workload causes the sqlserver.exe process to grow its working set, the Hyper-V memory scheduler will detect memory pressure in the guest VM and add memory to it. SQL Server will then detect the extra memory and grow according to workload demand. In our tests we have seen this feedback process cause a guest VM to grow quickly in response to SQL workload - we are still working on characterizing this ramp-up.    How does SQL Server respond when Hyper-V removes memory from a guest VM through ballooning?   If pressure from other VM's cause Hyper-V Dynamic Memory to take memory away from a VM through ballooning (allocating memory with a virtual device driver and returning it to the host OS), Windows Memory Manager will page out unlocked portions of memory and signal low resource notification events. When SQL Server detects these events it will shrink memory until the low memory notifications stop (see cache shrinking description above).    This raises another question. Can we make SQL Server release memory more readily and hence behave more "dynamically" without compromising performance? In certain circumstances where the application workload is predictable it may be possible to have a job which varies "max server memory" according to need, lowering it when the engine is inactive and raising it before a period of activity. This would have limited applicaability but it is something we're looking into.   What Memory Management changes are there in SQL Server “Denali”?   In SQL Server “Denali” (aka SQL11) the Memory Manager has been re-written to be more efficient. The main changes are summarized in this post. An important change with respect to Hyper-V Dynamic Memory support is that now the max server memory setting includes any size page allocations and managed CLR procedure allocations it now represents a closer approximation to total sqlserver.exe memory usage. This makes it easier to calculate a value for max server memory, which becomes important when configuring virtual machines to work well with Hyper-V Dynamic Memory Startup and Maximum RAM settings.   Another important change is no more AWE or hot-add support for 32-bit edition. This means if you're running a 32-bit edition of Denali you're limited to a 4GB address space and will not be able to take advantage of dynamically added OS memory that wasn't present when SQL Server started (though Hyper-V Dynamic Memory is still a supported configuration).   In part 3 we’ll develop some best practices for configuring and using SQL Server with Dynamic Memory. Originally posted at http://blogs.msdn.com/b/sqlosteam/

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  • Farseer tutorial for the absolute beginners

    - by Bil Simser
    This post is inspired (and somewhat a direct copy) of a couple of posts Emanuele Feronato wrote back in 2009 about Box2D (his tutorial was ActionScript 3 based for Box2D, this is C# XNA for the Farseer Physics Engine). Here’s what we’re building: What is Farseer The Farseer Physics Engine is a collision detection system with realistic physics responses to help you easily create simple hobby games or complex simulation systems. Farseer was built as a .NET version of Box2D (based on the Box2D.XNA port of Box2D). While the constructs and syntax has changed over the years, the principles remain the same. This tutorial will walk you through exactly what Emanuele create for Flash but we’ll be doing it using C#, XNA and the Windows Phone platform. The first step is to download the library from its home on CodePlex. If you have NuGet installed, you can install the library itself using the NuGet package that but we’ll also be using some code from the Samples source that can only be obtained by downloading the library. Once you download and unpacked the zip file into a folder and open the solution, this is what you will get: The Samples XNA WP7 project (and content) have all the demos for Farseer. There’s a wealth of info here and great examples to look at to learn. The Farseer Physics XNA WP7 project contains the core libraries that do all the work. DebugView XNA contains an XNA-ready class to let you view debug data and information in the game draw loop (which you can copy into your project or build the source and reference the assembly). The downloaded version has to be compiled as it’s only available in source format so you can do that now if you want (open the solution file and rebuild everything). If you’re using the NuGet package you can just install that. We only need the core library and we’ll be copying in some code from the samples later. Your first Farseer experiment Start Visual Studio and create a new project using the Windows Phone template can call it whatever you want. It’s time to edit Game1.cs 1 public class Game1 : Game 2 { 3 private readonly GraphicsDeviceManager _graphics; 4 private DebugViewXNA _debugView; 5 private Body _floor; 6 private SpriteBatch _spriteBatch; 7 private float _timer; 8 private World _world; 9 10 public Game1() 11 { 12 _graphics = new GraphicsDeviceManager(this) 13 { 14 PreferredBackBufferHeight = 800, 15 PreferredBackBufferWidth = 480, 16 IsFullScreen = true 17 }; 18 19 Content.RootDirectory = "Content"; 20 21 // Frame rate is 30 fps by default for Windows Phone. 22 TargetElapsedTime = TimeSpan.FromTicks(333333); 23 24 // Extend battery life under lock. 25 InactiveSleepTime = TimeSpan.FromSeconds(1); 26 } 27 28 protected override void LoadContent() 29 { 30 // Create a new SpriteBatch, which can be used to draw textures. 31 _spriteBatch = new SpriteBatch(_graphics.GraphicsDevice); 32 33 // Load our font (DebugViewXNA needs it for the DebugPanel) 34 Content.Load<SpriteFont>("font"); 35 36 // Create our World with a gravity of 10 vertical units 37 if (_world == null) 38 { 39 _world = new World(Vector2.UnitY*10); 40 } 41 else 42 { 43 _world.Clear(); 44 } 45 46 if (_debugView == null) 47 { 48 _debugView = new DebugViewXNA(_world); 49 50 // default is shape, controller, joints 51 // we just want shapes to display 52 _debugView.RemoveFlags(DebugViewFlags.Controllers); 53 _debugView.RemoveFlags(DebugViewFlags.Joint); 54 55 _debugView.LoadContent(GraphicsDevice, Content); 56 } 57 58 // Create and position our floor 59 _floor = BodyFactory.CreateRectangle( 60 _world, 61 ConvertUnits.ToSimUnits(480), 62 ConvertUnits.ToSimUnits(50), 63 10f); 64 _floor.Position = ConvertUnits.ToSimUnits(240, 775); 65 _floor.IsStatic = true; 66 _floor.Restitution = 0.2f; 67 _floor.Friction = 0.2f; 68 } 69 70 protected override void Update(GameTime gameTime) 71 { 72 // Allows the game to exit 73 if (GamePad.GetState(PlayerIndex.One).Buttons.Back == ButtonState.Pressed) 74 Exit(); 75 76 // Create a random box every second 77 _timer += (float) gameTime.ElapsedGameTime.TotalSeconds; 78 if (_timer >= 1.0f) 79 { 80 // Reset our timer 81 _timer = 0f; 82 83 // Determine a random size for each box 84 var random = new Random(); 85 var width = random.Next(20, 100); 86 var height = random.Next(20, 100); 87 88 // Create it and store the size in the user data 89 var box = BodyFactory.CreateRectangle( 90 _world, 91 ConvertUnits.ToSimUnits(width), 92 ConvertUnits.ToSimUnits(height), 93 10f, 94 new Point(width, height)); 95 96 box.BodyType = BodyType.Dynamic; 97 box.Restitution = 0.2f; 98 box.Friction = 0.2f; 99 100 // Randomly pick a location along the top to drop it from 101 box.Position = ConvertUnits.ToSimUnits(random.Next(50, 400), 0); 102 } 103 104 // Advance all the elements in the world 105 _world.Step(Math.Min((float) gameTime.ElapsedGameTime.TotalMilliseconds*0.001f, (1f/30f))); 106 107 // Clean up any boxes that have fallen offscreen 108 foreach (var box in from box in _world.BodyList 109 let pos = ConvertUnits.ToDisplayUnits(box.Position) 110 where pos.Y > _graphics.GraphicsDevice.Viewport.Height 111 select box) 112 { 113 _world.RemoveBody(box); 114 } 115 116 base.Update(gameTime); 117 } 118 119 protected override void Draw(GameTime gameTime) 120 { 121 GraphicsDevice.Clear(Color.FromNonPremultiplied(51, 51, 51, 255)); 122 123 _spriteBatch.Begin(); 124 125 var projection = Matrix.CreateOrthographicOffCenter( 126 0f, 127 ConvertUnits.ToSimUnits(_graphics.GraphicsDevice.Viewport.Width), 128 ConvertUnits.ToSimUnits(_graphics.GraphicsDevice.Viewport.Height), 0f, 0f, 129 1f); 130 _debugView.RenderDebugData(ref projection); 131 132 _spriteBatch.End(); 133 134 base.Draw(gameTime); 135 } 136 } 137 Lines 4: Declare the debug view we’ll use for rendering (more on that later). Lines 8: Declare _world variable of type class World. World is the main object to interact with the Farseer engine. It stores all the joints and bodies, and is responsible for stepping through the simulation. Lines 12-17: Create the graphics device we’ll be rendering on. This is an XNA component and we’re just setting it to be the same size as the phone and toggling it to be full screen (no system tray). Lines 34: We create a SpriteFont here by adding it to the project. It’s called “font” because that’s what the DebugView uses but you can name it whatever you want (and if you’re not using DebugView for your production app you might have several fonts). Lines 37-44: We create the physics environment that Farseer uses to contain all the objects by specifying it here. We’re using Vector2.UnitY*10 to represent the gravity to be used in the environment. In other words, 10 units going in a downward motion. Lines 46-56: We create the DebugViewXNA here. This is copied from the […] from the code you downloaded and provides the ability to render all entities onto the screen. In a production release you’ll be doing the rendering yourself of each object but we cheat a bit for the demo and let the DebugView do it for us. The other thing it can provide is to render out a panel of debugging information while the simulation is going on. This is useful in tracking down objects, figuring out how something works, or just keeping track of what’s in the engine. Lines 49-67: Here we create a rigid body (Farseer only supports rigid bodies) to represent the floor that we’ll drop objects onto. We create it by using one of the Farseer factories and specifying the width and height. The ConvertUnits class is copied from the samples code as-is and lets us toggle between display units (pixels) and simulation units (usually metres). We’re creating a floor that’s 480 pixels wide and 50 pixels high (converting them to SimUnits for the engine to understand). We also position it near the bottom of the screen. Values are in metres and when specifying values they refer to the centre of the body object. Lines 77-78: The game Update method fires 30 times a second, too fast to be creating objects this quickly. So we use a variable to track the elapsed seconds since the last update, accumulate that value, then create a new box to drop when 1 second has passed. Lines 89-94: We create a box the same way we created our floor (coming up with a random width and height for the box). Lines 96-101: We set the box to be Dynamic (rather than Static like the floor object) and position it somewhere along the top of the screen. And now you created the world. Gravity does the rest and the boxes fall to the ground. Here’s the result: Farseer Physics Engine Demo using XNA Lines 105: We must update the world at every frame. We do this with the Step method which takes in the time interval. [more] Lines 108-114: Body objects are added to the world but never automatically removed (because Farseer doesn’t know about the display world, it has no idea if an item is on the screen or not). Here we just loop through all the entities and anything that’s dropped off the screen (below the bottom) gets removed from the World. This keeps our entity count down (the simulation never has more than 30 or 40 objects in the world no matter how long you run it for). Too many entities and the app will grind to a halt. Lines 125-130: Farseer knows nothing about the UI so that’s entirely up to you as to how to draw things. Farseer is just tracking the objects and moving them around using the physics engine and it’s rules. You’ll still use XNA to draw items (using the SpriteBatch.Draw method) so you can load up your usual textures and draw items and pirates and dancing zombies all over the screen. Instead in this demo we’re going to cheat a little. In the sample code for Farseer you can download there’s a project called DebugView XNA. This project contains the DebugViewXNA class which just handles iterating through all the bodies in the world and drawing the shapes. So we call the RenderDebugData method here of that class to draw everything correctly. In the case of this demo, we just want to draw Shapes so take a look at the source code for the DebugViewXNA class as to how it extracts all the vertices for the shapes created (in this case simple boxes) and draws them. You’ll learn a *lot* about how Farseer works just by looking at this class. That’s it, that’s all. Simple huh? Hope you enjoy the code and library. Physics is hard and requires some math skills to really grok. The Farseer Physics Engine makes it pretty easy to get up and running and start building games. In future posts we’ll get more in-depth with things you can do with the engine so this is just the beginning. Enjoy!

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  • Upload File to Windows Azure Blob in Chunks through ASP.NET MVC, JavaScript and HTML5

    - by Shaun
    Originally posted on: http://geekswithblogs.net/shaunxu/archive/2013/07/01/upload-file-to-windows-azure-blob-in-chunks-through-asp.net.aspxMany people are using Windows Azure Blob Storage to store their data in the cloud. Blob storage provides 99.9% availability with easy-to-use API through .NET SDK and HTTP REST. For example, we can store JavaScript files, images, documents in blob storage when we are building an ASP.NET web application on a Web Role in Windows Azure. Or we can store our VHD files in blob and mount it as a hard drive in our cloud service. If you are familiar with Windows Azure, you should know that there are two kinds of blob: page blob and block blob. The page blob is optimized for random read and write, which is very useful when you need to store VHD files. The block blob is optimized for sequential/chunk read and write, which has more common usage. Since we can upload block blob in blocks through BlockBlob.PutBlock, and them commit them as a whole blob with invoking the BlockBlob.PutBlockList, it is very powerful to upload large files, as we can upload blocks in parallel, and provide pause-resume feature. There are many documents, articles and blog posts described on how to upload a block blob. Most of them are focus on the server side, which means when you had received a big file, stream or binaries, how to upload them into blob storage in blocks through .NET SDK.  But the problem is, how can we upload these large files from client side, for example, a browser. This questioned to me when I was working with a Chinese customer to help them build a network disk production on top of azure. The end users upload their files from the web portal, and then the files will be stored in blob storage from the Web Role. My goal is to find the best way to transform the file from client (end user’s machine) to the server (Web Role) through browser. In this post I will demonstrate and describe what I had done, to upload large file in chunks with high speed, and save them as blocks into Windows Azure Blob Storage.   Traditional Upload, Works with Limitation The simplest way to implement this requirement is to create a web page with a form that contains a file input element and a submit button. 1: @using (Html.BeginForm("About", "Index", FormMethod.Post, new { enctype = "multipart/form-data" })) 2: { 3: <input type="file" name="file" /> 4: <input type="submit" value="upload" /> 5: } And then in the backend controller, we retrieve the whole content of this file and upload it in to the blob storage through .NET SDK. We can split the file in blocks and upload them in parallel and commit. The code had been well blogged in the community. 1: [HttpPost] 2: public ActionResult About(HttpPostedFileBase file) 3: { 4: var container = _client.GetContainerReference("test"); 5: container.CreateIfNotExists(); 6: var blob = container.GetBlockBlobReference(file.FileName); 7: var blockDataList = new Dictionary<string, byte[]>(); 8: using (var stream = file.InputStream) 9: { 10: var blockSizeInKB = 1024; 11: var offset = 0; 12: var index = 0; 13: while (offset < stream.Length) 14: { 15: var readLength = Math.Min(1024 * blockSizeInKB, (int)stream.Length - offset); 16: var blockData = new byte[readLength]; 17: offset += stream.Read(blockData, 0, readLength); 18: blockDataList.Add(Convert.ToBase64String(BitConverter.GetBytes(index)), blockData); 19:  20: index++; 21: } 22: } 23:  24: Parallel.ForEach(blockDataList, (bi) => 25: { 26: blob.PutBlock(bi.Key, new MemoryStream(bi.Value), null); 27: }); 28: blob.PutBlockList(blockDataList.Select(b => b.Key).ToArray()); 29:  30: return RedirectToAction("About"); 31: } This works perfect if we selected an image, a music or a small video to upload. But if I selected a large file, let’s say a 6GB HD-movie, after upload for about few minutes the page will be shown as below and the upload will be terminated. In ASP.NET there is a limitation of request length and the maximized request length is defined in the web.config file. It’s a number which less than about 4GB. So if we want to upload a really big file, we cannot simply implement in this way. Also, in Windows Azure, a cloud service network load balancer will terminate the connection if exceed the timeout period. From my test the timeout looks like 2 - 3 minutes. Hence, when we need to upload a large file we cannot just use the basic HTML elements. Besides the limitation mentioned above, the simple HTML file upload cannot provide rich upload experience such as chunk upload, pause and pause-resume. So we need to find a better way to upload large file from the client to the server.   Upload in Chunks through HTML5 and JavaScript In order to break those limitation mentioned above we will try to upload the large file in chunks. This takes some benefit to us such as - No request size limitation: Since we upload in chunks, we can define the request size for each chunks regardless how big the entire file is. - No timeout problem: The size of chunks are controlled by us, which means we should be able to make sure request for each chunk upload will not exceed the timeout period of both ASP.NET and Windows Azure load balancer. It was a big challenge to upload big file in chunks until we have HTML5. There are some new features and improvements introduced in HTML5 and we will use them to implement our solution.   In HTML5, the File interface had been improved with a new method called “slice”. It can be used to read part of the file by specifying the start byte index and the end byte index. For example if the entire file was 1024 bytes, file.slice(512, 768) will read the part of this file from the 512nd byte to 768th byte, and return a new object of interface called "Blob”, which you can treat as an array of bytes. In fact,  a Blob object represents a file-like object of immutable, raw data. The File interface is based on Blob, inheriting blob functionality and expanding it to support files on the user's system. For more information about the Blob please refer here. File and Blob is very useful to implement the chunk upload. We will use File interface to represent the file the user selected from the browser and then use File.slice to read the file in chunks in the size we wanted. For example, if we wanted to upload a 10MB file with 512KB chunks, then we can read it in 512KB blobs by using File.slice in a loop.   Assuming we have a web page as below. User can select a file, an input box to specify the block size in KB and a button to start upload. 1: <div> 2: <input type="file" id="upload_files" name="files[]" /><br /> 3: Block Size: <input type="number" id="block_size" value="512" name="block_size" />KB<br /> 4: <input type="button" id="upload_button_blob" name="upload" value="upload (blob)" /> 5: </div> Then we can have the JavaScript function to upload the file in chunks when user clicked the button. 1: <script type="text/javascript"> 1: 2: $(function () { 3: $("#upload_button_blob").click(function () { 4: }); 5: });</script> Firstly we need to ensure the client browser supports the interfaces we are going to use. Just try to invoke the File, Blob and FormData from the “window” object. If any of them is “undefined” the condition result will be “false” which means your browser doesn’t support these premium feature and it’s time for you to get your browser updated. FormData is another new feature we are going to use in the future. It could generate a temporary form for us. We will use this interface to create a form with chunk and associated metadata when invoked the service through ajax. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: if (window.File && window.Blob && window.FormData) { 4: alert("Your brwoser is awesome, let's rock!"); 5: } 6: else { 7: alert("Oh man plz update to a modern browser before try is cool stuff out."); 8: return; 9: } 10: }); Each browser supports these interfaces by their own implementation and currently the Blob, File and File.slice are supported by Chrome 21, FireFox 13, IE 10, Opera 12 and Safari 5.1 or higher. After that we worked on the files the user selected one by one since in HTML5, user can select multiple files in one file input box. 1: var files = $("#upload_files")[0].files; 2: for (var i = 0; i < files.length; i++) { 3: var file = files[i]; 4: var fileSize = file.size; 5: var fileName = file.name; 6: } Next, we calculated the start index and end index for each chunks based on the size the user specified from the browser. We put them into an array with the file name and the index, which will be used when we upload chunks into Windows Azure Blob Storage as blocks since we need to specify the target blob name and the block index. At the same time we will store the list of all indexes into another variant which will be used to commit blocks into blob in Azure Storage once all chunks had been uploaded successfully. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10:  11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: var blockSizeInKB = $("#block_size").val(); 14: var blockSize = blockSizeInKB * 1024; 15: var blocks = []; 16: var offset = 0; 17: var index = 0; 18: var list = ""; 19: while (offset < fileSize) { 20: var start = offset; 21: var end = Math.min(offset + blockSize, fileSize); 22:  23: blocks.push({ 24: name: fileName, 25: index: index, 26: start: start, 27: end: end 28: }); 29: list += index + ","; 30:  31: offset = end; 32: index++; 33: } 34: } 35: }); Now we have all chunks’ information ready. The next step should be upload them one by one to the server side, and at the server side when received a chunk it will upload as a block into Blob Storage, and finally commit them with the index list through BlockBlobClient.PutBlockList. But since all these invokes are ajax calling, which means not synchronized call. So we need to introduce a new JavaScript library to help us coordinate the asynchronize operation, which named “async.js”. You can download this JavaScript library here, and you can find the document here. I will not explain this library too much in this post. We will put all procedures we want to execute as a function array, and pass into the proper function defined in async.js to let it help us to control the execution sequence, in series or in parallel. Hence we will define an array and put the function for chunk upload into this array. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4:  5: // start to upload each files in chunks 6: var files = $("#upload_files")[0].files; 7: for (var i = 0; i < files.length; i++) { 8: var file = files[i]; 9: var fileSize = file.size; 10: var fileName = file.name; 11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: ... ... 14:  15: // define the function array and push all chunk upload operation into this array 16: blocks.forEach(function (block) { 17: putBlocks.push(function (callback) { 18: }); 19: }); 20: } 21: }); 22: }); As you can see, I used File.slice method to read each chunks based on the start and end byte index we calculated previously, and constructed a temporary HTML form with the file name, chunk index and chunk data through another new feature in HTML5 named FormData. Then post this form to the backend server through jQuery.ajax. This is the key part of our solution. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: blocks.forEach(function (block) { 15: putBlocks.push(function (callback) { 16: // load blob based on the start and end index for each chunks 17: var blob = file.slice(block.start, block.end); 18: // put the file name, index and blob into a temporary from 19: var fd = new FormData(); 20: fd.append("name", block.name); 21: fd.append("index", block.index); 22: fd.append("file", blob); 23: // post the form to backend service (asp.net mvc controller action) 24: $.ajax({ 25: url: "/Home/UploadInFormData", 26: data: fd, 27: processData: false, 28: contentType: "multipart/form-data", 29: type: "POST", 30: success: function (result) { 31: if (!result.success) { 32: alert(result.error); 33: } 34: callback(null, block.index); 35: } 36: }); 37: }); 38: }); 39: } 40: }); Then we will invoke these functions one by one by using the async.js. And once all functions had been executed successfully I invoked another ajax call to the backend service to commit all these chunks (blocks) as the blob in Windows Azure Storage. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.series(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); That’s all in the client side. The outline of our logic would be - Calculate the start and end byte index for each chunks based on the block size. - Defined the functions of reading the chunk form file and upload the content to the backend service through ajax. - Execute the functions defined in previous step with “async.js”. - Commit the chunks by invoking the backend service in Windows Azure Storage finally.   Save Chunks as Blocks into Blob Storage In above we finished the client size JavaScript code. It uploaded the file in chunks to the backend service which we are going to implement in this step. We will use ASP.NET MVC as our backend service, and it will receive the chunks, upload into Windows Azure Bob Storage in blocks, then finally commit as one blob. As in the client side we uploaded chunks by invoking the ajax call to the URL "/Home/UploadInFormData", I created a new action under the Index controller and it only accepts HTTP POST request. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: } 8: catch (Exception e) 9: { 10: error = e.ToString(); 11: } 12:  13: return new JsonResult() 14: { 15: Data = new 16: { 17: success = string.IsNullOrWhiteSpace(error), 18: error = error 19: } 20: }; 21: } Then I retrieved the file name, index and the chunk content from the Request.Form object, which was passed from our client side. And then, used the Windows Azure SDK to create a blob container (in this case we will use the container named “test”.) and create a blob reference with the blob name (same as the file name). Then uploaded the chunk as a block of this blob with the index, since in Blob Storage each block must have an index (ID) associated with so that finally we can put all blocks as one blob by specifying their block ID list. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var index = int.Parse(Request.Form["index"]); 9: var file = Request.Files[0]; 10: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 11:  12: var container = _client.GetContainerReference("test"); 13: container.CreateIfNotExists(); 14: var blob = container.GetBlockBlobReference(name); 15: blob.PutBlock(id, file.InputStream, null); 16: } 17: catch (Exception e) 18: { 19: error = e.ToString(); 20: } 21:  22: return new JsonResult() 23: { 24: Data = new 25: { 26: success = string.IsNullOrWhiteSpace(error), 27: error = error 28: } 29: }; 30: } Next, I created another action to commit the blocks into blob once all chunks had been uploaded. Similarly, I retrieved the blob name from the Request.Form. I also retrieved the chunks ID list, which is the block ID list from the Request.Form in a string format, split them as a list, then invoked the BlockBlob.PutBlockList method. After that our blob will be shown in the container and ready to be download. 1: [HttpPost] 2: public JsonResult Commit() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var list = Request.Form["list"]; 9: var ids = list 10: .Split(',') 11: .Where(id => !string.IsNullOrWhiteSpace(id)) 12: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 13: .ToArray(); 14:  15: var container = _client.GetContainerReference("test"); 16: container.CreateIfNotExists(); 17: var blob = container.GetBlockBlobReference(name); 18: blob.PutBlockList(ids); 19: } 20: catch (Exception e) 21: { 22: error = e.ToString(); 23: } 24:  25: return new JsonResult() 26: { 27: Data = new 28: { 29: success = string.IsNullOrWhiteSpace(error), 30: error = error 31: } 32: }; 33: } Now we finished all code we need. The whole process of uploading would be like this below. Below is the full client side JavaScript code. 1: <script type="text/javascript" src="~/Scripts/async.js"></script> 2: <script type="text/javascript"> 3: $(function () { 4: $("#upload_button_blob").click(function () { 5: // assert the browser support html5 6: if (window.File && window.Blob && window.FormData) { 7: alert("Your brwoser is awesome, let's rock!"); 8: } 9: else { 10: alert("Oh man plz update to a modern browser before try is cool stuff out."); 11: return; 12: } 13:  14: // start to upload each files in chunks 15: var files = $("#upload_files")[0].files; 16: for (var i = 0; i < files.length; i++) { 17: var file = files[i]; 18: var fileSize = file.size; 19: var fileName = file.name; 20:  21: // calculate the start and end byte index for each blocks(chunks) 22: // with the index, file name and index list for future using 23: var blockSizeInKB = $("#block_size").val(); 24: var blockSize = blockSizeInKB * 1024; 25: var blocks = []; 26: var offset = 0; 27: var index = 0; 28: var list = ""; 29: while (offset < fileSize) { 30: var start = offset; 31: var end = Math.min(offset + blockSize, fileSize); 32:  33: blocks.push({ 34: name: fileName, 35: index: index, 36: start: start, 37: end: end 38: }); 39: list += index + ","; 40:  41: offset = end; 42: index++; 43: } 44:  45: // define the function array and push all chunk upload operation into this array 46: var putBlocks = []; 47: blocks.forEach(function (block) { 48: putBlocks.push(function (callback) { 49: // load blob based on the start and end index for each chunks 50: var blob = file.slice(block.start, block.end); 51: // put the file name, index and blob into a temporary from 52: var fd = new FormData(); 53: fd.append("name", block.name); 54: fd.append("index", block.index); 55: fd.append("file", blob); 56: // post the form to backend service (asp.net mvc controller action) 57: $.ajax({ 58: url: "/Home/UploadInFormData", 59: data: fd, 60: processData: false, 61: contentType: "multipart/form-data", 62: type: "POST", 63: success: function (result) { 64: if (!result.success) { 65: alert(result.error); 66: } 67: callback(null, block.index); 68: } 69: }); 70: }); 71: }); 72:  73: // invoke the functions one by one 74: // then invoke the commit ajax call to put blocks into blob in azure storage 75: async.series(putBlocks, function (error, result) { 76: var data = { 77: name: fileName, 78: list: list 79: }; 80: $.post("/Home/Commit", data, function (result) { 81: if (!result.success) { 82: alert(result.error); 83: } 84: else { 85: alert("done!"); 86: } 87: }); 88: }); 89: } 90: }); 91: }); 92: </script> And below is the full ASP.NET MVC controller code. 1: public class HomeController : Controller 2: { 3: private CloudStorageAccount _account; 4: private CloudBlobClient _client; 5:  6: public HomeController() 7: : base() 8: { 9: _account = CloudStorageAccount.Parse(CloudConfigurationManager.GetSetting("DataConnectionString")); 10: _client = _account.CreateCloudBlobClient(); 11: } 12:  13: public ActionResult Index() 14: { 15: ViewBag.Message = "Modify this template to jump-start your ASP.NET MVC application."; 16:  17: return View(); 18: } 19:  20: [HttpPost] 21: public JsonResult UploadInFormData() 22: { 23: var error = string.Empty; 24: try 25: { 26: var name = Request.Form["name"]; 27: var index = int.Parse(Request.Form["index"]); 28: var file = Request.Files[0]; 29: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 30:  31: var container = _client.GetContainerReference("test"); 32: container.CreateIfNotExists(); 33: var blob = container.GetBlockBlobReference(name); 34: blob.PutBlock(id, file.InputStream, null); 35: } 36: catch (Exception e) 37: { 38: error = e.ToString(); 39: } 40:  41: return new JsonResult() 42: { 43: Data = new 44: { 45: success = string.IsNullOrWhiteSpace(error), 46: error = error 47: } 48: }; 49: } 50:  51: [HttpPost] 52: public JsonResult Commit() 53: { 54: var error = string.Empty; 55: try 56: { 57: var name = Request.Form["name"]; 58: var list = Request.Form["list"]; 59: var ids = list 60: .Split(',') 61: .Where(id => !string.IsNullOrWhiteSpace(id)) 62: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 63: .ToArray(); 64:  65: var container = _client.GetContainerReference("test"); 66: container.CreateIfNotExists(); 67: var blob = container.GetBlockBlobReference(name); 68: blob.PutBlockList(ids); 69: } 70: catch (Exception e) 71: { 72: error = e.ToString(); 73: } 74:  75: return new JsonResult() 76: { 77: Data = new 78: { 79: success = string.IsNullOrWhiteSpace(error), 80: error = error 81: } 82: }; 83: } 84: } And if we selected a file from the browser we will see our application will upload chunks in the size we specified to the server through ajax call in background, and then commit all chunks in one blob. Then we can find the blob in our Windows Azure Blob Storage.   Optimized by Parallel Upload In previous example we just uploaded our file in chunks. This solved the problem that ASP.NET MVC request content size limitation as well as the Windows Azure load balancer timeout. But it might introduce the performance problem since we uploaded chunks in sequence. In order to improve the upload performance we could modify our client side code a bit to make the upload operation invoked in parallel. The good news is that, “async.js” library provides the parallel execution function. If you remembered the code we invoke the service to upload chunks, it utilized “async.series” which means all functions will be executed in sequence. Now we will change this code to “async.parallel”. This will invoke all functions in parallel. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallel(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); In this way all chunks will be uploaded to the server side at the same time to maximize the bandwidth usage. This should work if the file was not very large and the chunk size was not very small. But for large file this might introduce another problem that too many ajax calls are sent to the server at the same time. So the best solution should be, upload the chunks in parallel with maximum concurrency limitation. The code below specified the concurrency limitation to 4, which means at the most only 4 ajax calls could be invoked at the same time. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallelLimit(putBlocks, 4, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: });   Summary In this post we discussed how to upload files in chunks to the backend service and then upload them into Windows Azure Blob Storage in blocks. We focused on the frontend side and leverage three new feature introduced in HTML 5 which are - File.slice: Read part of the file by specifying the start and end byte index. - Blob: File-like interface which contains the part of the file content. - FormData: Temporary form element that we can pass the chunk alone with some metadata to the backend service. Then we discussed the performance consideration of chunk uploading. Sequence upload cannot provide maximized upload speed, but the unlimited parallel upload might crash the browser and server if too many chunks. So we finally came up with the solution to upload chunks in parallel with the concurrency limitation. We also demonstrated how to utilize “async.js” JavaScript library to help us control the asynchronize call and the parallel limitation.   Regarding the chunk size and the parallel limitation value there is no “best” value. You need to test vary composition and find out the best one for your particular scenario. It depends on the local bandwidth, client machine cores and the server side (Windows Azure Cloud Service Virtual Machine) cores, memory and bandwidth. Below is one of my performance test result. The client machine was Windows 8 IE 10 with 4 cores. I was using Microsoft Cooperation Network. The web site was hosted on Windows Azure China North data center (in Beijing) with one small web role (1.7GB 1 core CPU, 1.75GB memory with 100Mbps bandwidth). The test cases were - Chunk size: 512KB, 1MB, 2MB, 4MB. - Upload Mode: Sequence, parallel (unlimited), parallel with limit (4 threads, 8 threads). - Chunk Format: base64 string, binaries. - Target file: 100MB. - Each case was tested 3 times. Below is the test result chart. Some thoughts, but not guidance or best practice: - Parallel gets better performance than series. - No significant performance improvement between parallel 4 threads and 8 threads. - Transform with binaries provides better performance than base64. - In all cases, chunk size in 1MB - 2MB gets better performance.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • Java Cloud Service Integration to REST Service

    - by Jani Rautiainen
    Service (JCS) provides a platform to develop and deploy business applications in the cloud. In Fusion Applications Cloud deployments customers do not have the option to deploy custom applications developed with JDeveloper to ensure the integrity and supportability of the hosted application service. Instead the custom applications can be deployed to the JCS and integrated to the Fusion Application Cloud instance. This series of articles will go through the features of JCS, provide end-to-end examples on how to develop and deploy applications on JCS and how to integrate them with the Fusion Applications instance. In this article a custom application integrating with REST service will be implemented. We will use REST services provided by Taleo as an example; however the same approach will work with any REST service. In this example the data from the REST service is used to populate a dynamic table. Pre-requisites Access to Cloud instance In order to deploy the application access to a JCS instance is needed, a free trial JCS instance can be obtained from Oracle Cloud site. To register you will need a credit card even if the credit card will not be charged. To register simply click "Try it" and choose the "Java" option. The confirmation email will contain the connection details. See this video for example of the registration.Once the request is processed you will be assigned 2 service instances; Java and Database. Applications deployed to the JCS must use Oracle Database Cloud Service as their underlying database. So when JCS instance is created a database instance is associated with it using a JDBC data source.The cloud services can be monitored and managed through the web UI. For details refer to Getting Started with Oracle Cloud. JDeveloper JDeveloper contains Cloud specific features related to e.g. connection and deployment. To use these features download the JDeveloper from JDeveloper download site by clicking the "Download JDeveloper 11.1.1.7.1 for ADF deployment on Oracle Cloud" link, this version of JDeveloper will have the JCS integration features that will be used in this article. For versions that do not include the Cloud integration features the Oracle Java Cloud Service SDK or the JCS Java Console can be used for deployment. For details on installing and configuring the JDeveloper refer to the installation guideFor details on SDK refer to Using the Command-Line Interface to Monitor Oracle Java Cloud Service and Using the Command-Line Interface to Manage Oracle Java Cloud Service. Access to a local database The database associated with the JCS instance cannot be connected to with JDBC.  Since creating ADFbc business component requires a JDBC connection we will need access to a local database. 3rd party libraries This example will use some 3rd party libraries for implementing the REST service call and processing the input / output content. Other libraries may also be used, however these are tested to work. Jersey 1.x Jersey library will be used as a client to make the call to the REST service. JCS documentation for supported specifications states: Java API for RESTful Web Services (JAX-RS) 1.1 So Jersey 1.x will be used. Download the single-JAR Jersey bundle; in this example Jersey 1.18 JAR bundle is used. Json-simple Jjson-simple library will be used to process the json objects. Download the  JAR file; in this example json-simple-1.1.1.jar is used. Accessing data in Taleo Before implementing the application it is beneficial to familiarize oneself with the data in Taleo. Easiest way to do this is by using a RESTClient on your browser. Once added to the browser you can access the UI: The client can be used to call the REST services to test the URLs and data before adding them into the application. First derive the base URL for the service this can be done with: Method: GET URL: https://tbe.taleo.net/MANAGER/dispatcher/api/v1/serviceUrl/<company name> The response will contain the base URL to be used for the service calls for the company. Next obtain authentication token with: Method: POST URL: https://ch.tbe.taleo.net/CH07/ats/api/v1/login?orgCode=<company>&userName=<user name>&password=<password> The response includes an authentication token that can be used for few hours to authenticate with the service: {   "response": {     "authToken": "webapi26419680747505890557"   },   "status": {     "detail": {},     "success": true   } } To authenticate the service calls navigate to "Headers -> Custom Header": And add a new request header with: Name: Cookie Value: authToken=webapi26419680747505890557 Once authentication token is defined the tool can be used to invoke REST services; for example: Method: GET URL: https://ch.tbe.taleo.net/CH07/ats/api/v1/object/candidate/search.xml?status=16 This data will be used on the application to be created. For details on the Taleo REST services refer to the Taleo Business Edition REST API Guide. Create Application First Fusion Web Application is created and configured. Start JDeveloper and click "New Application": Application Name: JcsRestDemo Application Package Prefix: oracle.apps.jcs.test Application Template: Fusion Web Application (ADF) Configure Local Cloud Connection Follow the steps documented in the "Java Cloud Service ADF Web Application" article to configure a local database connection needed to create the ADFbc objects. Configure Libraries Add the 3rd party libraries into the class path. Create the following directory and copy the jar files into it: <JDEV_USER_HOME>/JcsRestDemo/lib  Select the "Model" project, navigate "Application -> Project Properties -> Libraries and Classpath -> Add JAR / Directory" and add the 2 3rd party libraries: Accessing Data from Taleo To access data from Taleo using the REST service the 3rd party libraries will be used. 2 Java classes are implemented, one representing the Candidate object and another for accessing the Taleo repository Candidate Candidate object is a POJO object used to represent the candidate data obtained from the Taleo repository. The data obtained will be used to populate the ADFbc object used to display the data on the UI. The candidate object contains simply the variables we obtain using the REST services and the getters / setters for them: Navigate "New -> General -> Java -> Java Class", enter "Candidate" as the name and create it in the package "oracle.apps.jcs.test.model".  Copy / paste the following as the content: import oracle.jbo.domain.Number; public class Candidate { private Number candId; private String firstName; private String lastName; public Candidate() { super(); } public Candidate(Number candId, String firstName, String lastName) { super(); this.candId = candId; this.firstName = firstName; this.lastName = lastName; } public void setCandId(Number candId) { this.candId = candId; } public Number getCandId() { return candId; } public void setFirstName(String firstName) { this.firstName = firstName; } public String getFirstName() { return firstName; } public void setLastName(String lastName) { this.lastName = lastName; } public String getLastName() { return lastName; } } Taleo Repository Taleo repository class will interact with the Taleo REST services. The logic will query data from Taleo and populate Candidate objects with the data. The Candidate object will then be used to populate the ADFbc object used to display data on the UI. Navigate "New -> General -> Java -> Java Class", enter "TaleoRepository" as the name and create it in the package "oracle.apps.jcs.test.model".  Copy / paste the following as the content (for details of the implementation refer to the documentation in the code): import com.sun.jersey.api.client.Client; import com.sun.jersey.api.client.ClientResponse; import com.sun.jersey.api.client.WebResource; import com.sun.jersey.core.util.MultivaluedMapImpl; import java.io.StringReader; import java.util.ArrayList; import java.util.Iterator; import java.util.List; import java.util.Map; import javax.ws.rs.core.MediaType; import javax.ws.rs.core.MultivaluedMap; import oracle.jbo.domain.Number; import org.json.simple.JSONArray; import org.json.simple.JSONObject; import org.json.simple.parser.JSONParser; /** * This class interacts with the Taleo REST services */ public class TaleoRepository { /** * Connection information needed to access the Taleo services */ String _company = null; String _userName = null; String _password = null; /** * Jersey client used to access the REST services */ Client _client = null; /** * Parser for processing the JSON objects used as * input / output for the services */ JSONParser _parser = null; /** * The base url for constructing the REST URLs. This is obtained * from Taleo with a service call */ String _baseUrl = null; /** * Authentication token obtained from Taleo using a service call. * The token can be used to authenticate on subsequent * service calls. The token will expire in 4 hours */ String _authToken = null; /** * Static url that can be used to obtain the url used to construct * service calls for a given company */ private static String _taleoUrl = "https://tbe.taleo.net/MANAGER/dispatcher/api/v1/serviceUrl/"; /** * Default constructor for the repository * Authentication details are passed as parameters and used to generate * authentication token. Note that each service call will * generate its own token. This is done to avoid dealing with the expiry * of the token. Also only 20 tokens are allowed per user simultaneously. * So instead for each call there is login / logout. * * @param company the company for which the service calls are made * @param userName the user name to authenticate with * @param password the password to authenticate with. */ public TaleoRepository(String company, String userName, String password) { super(); _company = company; _userName = userName; _password = password; _client = Client.create(); _parser = new JSONParser(); _baseUrl = getBaseUrl(); } /** * This obtains the base url for a company to be used * to construct the urls for service calls * @return base url for the service calls */ private String getBaseUrl() { String result = null; if (null != _baseUrl) { result = _baseUrl; } else { try { String company = _company; WebResource resource = _client.resource(_taleoUrl + company); ClientResponse response = resource.type(MediaType.APPLICATION_FORM_URLENCODED_TYPE).get(ClientResponse.class); String entity = response.getEntity(String.class); JSONObject jsonObject = (JSONObject)_parser.parse(new StringReader(entity)); JSONObject jsonResponse = (JSONObject)jsonObject.get("response"); result = (String)jsonResponse.get("URL"); } catch (Exception ex) { ex.printStackTrace(); } } return result; } /** * Generates authentication token, that can be used to authenticate on * subsequent service calls. Note that each service call will * generate its own token. This is done to avoid dealing with the expiry * of the token. Also only 20 tokens are allowed per user simultaneously. * So instead for each call there is login / logout. * @return authentication token that can be used to authenticate on * subsequent service calls */ private String login() { String result = null; try { MultivaluedMap<String, String> formData = new MultivaluedMapImpl(); formData.add("orgCode", _company); formData.add("userName", _userName); formData.add("password", _password); WebResource resource = _client.resource(_baseUrl + "login"); ClientResponse response = resource.type(MediaType.APPLICATION_FORM_URLENCODED_TYPE).post(ClientResponse.class, formData); String entity = response.getEntity(String.class); JSONObject jsonObject = (JSONObject)_parser.parse(new StringReader(entity)); JSONObject jsonResponse = (JSONObject)jsonObject.get("response"); result = (String)jsonResponse.get("authToken"); } catch (Exception ex) { throw new RuntimeException("Unable to login ", ex); } if (null == result) throw new RuntimeException("Unable to login "); return result; } /** * Releases a authentication token. Each call to login must be followed * by call to logout after the processing is done. This is required as * the tokens are limited to 20 per user and if not released the tokens * will only expire after 4 hours. * @param authToken */ private void logout(String authToken) { WebResource resource = _client.resource(_baseUrl + "logout"); resource.header("cookie", "authToken=" + authToken).post(ClientResponse.class); } /** * This method is used to obtain a list of candidates using a REST * service call. At this example the query is hard coded to query * based on status. The url constructed to access the service is: * <_baseUrl>/object/candidate/search.xml?status=16 * @return List of candidates obtained with the service call */ public List<Candidate> getCandidates() { List<Candidate> result = new ArrayList<Candidate>(); try { // First login, note that in finally block we must have logout _authToken = "authToken=" + login(); /** * Construct the URL, the resulting url will be: * <_baseUrl>/object/candidate/search.xml?status=16 */ MultivaluedMap<String, String> formData = new MultivaluedMapImpl(); formData.add("status", "16"); JSONArray searchResults = (JSONArray)getTaleoResource("object/candidate/search", "searchResults", formData); /** * Process the results, the resulting JSON object is something like * this (simplified for readability): * * { * "response": * { * "searchResults": * [ * { * "candidate": * { * "candId": 211, * "firstName": "Mary", * "lastName": "Stochi", * logic here will find the candidate object(s), obtain the desired * data from them, construct a Candidate object based on the data * and add it to the results. */ for (Object object : searchResults) { JSONObject temp = (JSONObject)object; JSONObject candidate = (JSONObject)findObject(temp, "candidate"); Long candIdTemp = (Long)candidate.get("candId"); Number candId = (null == candIdTemp ? null : new Number(candIdTemp)); String firstName = (String)candidate.get("firstName"); String lastName = (String)candidate.get("lastName"); result.add(new Candidate(candId, firstName, lastName)); } } catch (Exception ex) { ex.printStackTrace(); } finally { if (null != _authToken) logout(_authToken); } return result; } /** * Convenience method to construct url for the service call, invoke the * service and obtain a resource from the response * @param path the path for the service to be invoked. This is combined * with the base url to construct a url for the service * @param resource the key for the object in the response that will be * obtained * @param parameters any parameters used for the service call. The call * is slightly different depending whether parameters exist or not. * @return the resource from the response for the service call */ private Object getTaleoResource(String path, String resource, MultivaluedMap<String, String> parameters) { Object result = null; try { WebResource webResource = _client.resource(_baseUrl + path); ClientResponse response = null; if (null == parameters) response = webResource.header("cookie", _authToken).get(ClientResponse.class); else response = webResource.queryParams(parameters).header("cookie", _authToken).get(ClientResponse.class); String entity = response.getEntity(String.class); JSONObject jsonObject = (JSONObject)_parser.parse(new StringReader(entity)); result = findObject(jsonObject, resource); } catch (Exception ex) { ex.printStackTrace(); } return result; } /** * Convenience method to recursively find a object with an key * traversing down from a given root object. This will traverse a * JSONObject / JSONArray recursively to find a matching key, if found * the object with the key is returned. * @param root root object which contains the key searched for * @param key the key for the object to search for * @return the object matching the key */ private Object findObject(Object root, String key) { Object result = null; if (root instanceof JSONObject) { JSONObject rootJSON = (JSONObject)root; if (rootJSON.containsKey(key)) { result = rootJSON.get(key); } else { Iterator children = rootJSON.entrySet().iterator(); while (children.hasNext()) { Map.Entry entry = (Map.Entry)children.next(); Object child = entry.getValue(); if (child instanceof JSONObject || child instanceof JSONArray) { result = findObject(child, key); if (null != result) break; } } } } else if (root instanceof JSONArray) { JSONArray rootJSON = (JSONArray)root; for (Object child : rootJSON) { if (child instanceof JSONObject || child instanceof JSONArray) { result = findObject(child, key); if (null != result) break; } } } return result; } }   Creating Business Objects While JCS application can be created without a local database, the local database is required when using ADFbc objects even if database objects are not referred. For this example we will create a "Transient" view object that will be programmatically populated based the data obtained from Taleo REST services. Creating ADFbc objects Choose the "Model" project and navigate "New -> Business Tier : ADF Business Components : View Object". On the "Initialize Business Components Project" choose the local database connection created in previous step. On Step 1 enter "JcsRestDemoVO" on the "Name" and choose "Rows populated programmatically, not based on query": On step 2 create the following attributes: CandId Type: Number Updatable: Always Key Attribute: checked Name Type: String Updatable: Always On steps 3 and 4 accept defaults and click "Next".  On step 5 check the "Application Module" checkbox and enter "JcsRestDemoAM" as the name: Click "Finish" to generate the objects. Populating the VO To display the data on the UI the "transient VO" is populated programmatically based on the data obtained from the Taleo REST services. Open the "JcsRestDemoVOImpl.java". Copy / paste the following as the content (for details of the implementation refer to the documentation in the code): import java.sql.ResultSet; import java.util.List; import java.util.ListIterator; import oracle.jbo.server.ViewObjectImpl; import oracle.jbo.server.ViewRowImpl; import oracle.jbo.server.ViewRowSetImpl; // --------------------------------------------------------------------- // --- File generated by Oracle ADF Business Components Design Time. // --- Tue Feb 18 09:40:25 PST 2014 // --- Custom code may be added to this class. // --- Warning: Do not modify method signatures of generated methods. // --------------------------------------------------------------------- public class JcsRestDemoVOImpl extends ViewObjectImpl { /** * This is the default constructor (do not remove). */ public JcsRestDemoVOImpl() { } @Override public void executeQuery() { /** * For some reason we need to reset everything, otherwise * 2nd entry to the UI screen may fail with * "java.util.NoSuchElementException" in createRowFromResultSet * call to "candidates.next()". I am not sure why this is happening * as the Iterator is new and "hasNext" is true at the point * of the execution. My theory is that since the iterator object is * exactly the same the VO cache somehow reuses the iterator including * the pointer that has already exhausted the iterable elements on the * previous run. Working around the issue * here by cleaning out everything on the VO every time before query * is executed on the VO. */ getViewDef().setQuery(null); getViewDef().setSelectClause(null); setQuery(null); this.reset(); this.clearCache(); super.executeQuery(); } /** * executeQueryForCollection - overridden for custom java data source support. */ protected void executeQueryForCollection(Object qc, Object[] params, int noUserParams) { /** * Integrate with the Taleo REST services using TaleoRepository class. * A list of candidates matching a hard coded query is obtained. */ TaleoRepository repository = new TaleoRepository(<company>, <username>, <password>); List<Candidate> candidates = repository.getCandidates(); /** * Store iterator for the candidates as user data on the collection. * This will be used in createRowFromResultSet to create rows based on * the custom iterator. */ ListIterator<Candidate> candidatescIterator = candidates.listIterator(); setUserDataForCollection(qc, candidatescIterator); super.executeQueryForCollection(qc, params, noUserParams); } /** * hasNextForCollection - overridden for custom java data source support. */ protected boolean hasNextForCollection(Object qc) { boolean result = false; /** * Determines whether there are candidates for which to create a row */ ListIterator<Candidate> candidates = (ListIterator<Candidate>)getUserDataForCollection(qc); result = candidates.hasNext(); /** * If all candidates to be created indicate that processing is done */ if (!result) { setFetchCompleteForCollection(qc, true); } return result; } /** * createRowFromResultSet - overridden for custom java data source support. */ protected ViewRowImpl createRowFromResultSet(Object qc, ResultSet resultSet) { /** * Obtain the next candidate from the collection and create a row * for it. */ ListIterator<Candidate> candidates = (ListIterator<Candidate>)getUserDataForCollection(qc); ViewRowImpl row = createNewRowForCollection(qc); try { Candidate candidate = candidates.next(); row.setAttribute("CandId", candidate.getCandId()); row.setAttribute("Name", candidate.getFirstName() + " " + candidate.getLastName()); } catch (Exception e) { e.printStackTrace(); } return row; } /** * getQueryHitCount - overridden for custom java data source support. */ public long getQueryHitCount(ViewRowSetImpl viewRowSet) { /** * For this example this is not implemented rather we always return 0. */ return 0; } } Creating UI Choose the "ViewController" project and navigate "New -> Web Tier : JSF : JSF Page". On the "Create JSF Page" enter "JcsRestDemo" as name and ensure that the "Create as XML document (*.jspx)" is checked.  Open "JcsRestDemo.jspx" and navigate to "Data Controls -> JcsRestDemoAMDataControl -> JcsRestDemoVO1" and drag & drop the VO to the "<af:form> " as a "ADF Read-only Table": Accept the defaults in "Edit Table Columns". To execute the query navigate to to "Data Controls -> JcsRestDemoAMDataControl -> JcsRestDemoVO1 -> Operations -> Execute" and drag & drop the operation to the "<af:form> " as a "Button": Deploying to JCS Follow the same steps as documented in previous article"Java Cloud Service ADF Web Application". Once deployed the application can be accessed with URL: https://java-[identity domain].java.[data center].oraclecloudapps.com/JcsRestDemo-ViewController-context-root/faces/JcsRestDemo.jspx The UI displays a list of candidates obtained from the Taleo REST Services: Summary In this article we learned how to integrate with REST services using Jersey library in JCS. In future articles various other integration techniques will be covered.

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  • javax.naming.InvalidNameException using Oracle BPM and weblogic when accessing directory

    - by alfredozn
    We are getting this exception when we start our cluster (2 managed servers, 1 admin), we have deployed only the ears corresponding to the OBPM 10.3.1 SP1 in a weblogic 10.3. When the server cluster starts, one of the managed servers (the first to start) get overloaded and ran out of connections to the directory DB because of this repeatedly error. It looks like the engine is trying to get the info from the LDAP server but I don't know why it is building a wrong query. fuego.directory.DirectoryRuntimeException: Exception [javax.naming.InvalidNameException: CN=Alvarez Guerrero Bernardo DEL:ca9ef28d-3b94-4e8f-a6bd-8c880bb3791b,CN=Deleted Objects,DC=corp: [LDAP: error code 34 - 0000208F: NameErr: DSID-031001BA, problem 2006 (BAD_NAME), data 8349, best match of: 'CN=Alvarez Guerrero Bernardo DEL:ca9ef28d-3b94-4e8f-a6bd-8c880bb3791b,CN=Deleted Objects,DC=corp,dc=televisa,dc=com,dc=mx' ^@]; remaining name 'CN=Alvarez Guerrero Bernardo DEL:ca9ef28d-3b94-4e8f-a6bd-8c880bb3791b,CN=Deleted Objects,DC=corp']. at fuego.directory.DirectoryRuntimeException.wrapException(DirectoryRuntimeException.java:85) at fuego.directory.hybrid.ldap.JNDIQueryExecutor.selectById(JNDIQueryExecutor.java:163) at fuego.directory.hybrid.ldap.JNDIQueryExecutor.selectById(JNDIQueryExecutor.java:110) at fuego.directory.hybrid.ldap.Repository.selectById(Repository.java:38) at fuego.directory.hybrid.msad.MSADGroupValueProvider.getAssignedParticipantsInternal(MSADGroupValueProvider.java:124) at fuego.directory.hybrid.msad.MSADGroupValueProvider.getAssignedParticipants(MSADGroupValueProvider.java:70) at fuego.directory.hybrid.ldap.Group$7.getValue(Group.java:149) at fuego.directory.hybrid.ldap.Group$7.getValue(Group.java:152) at fuego.directory.hybrid.ldap.LDAPResult.getValue(LDAPResult.java:76) at fuego.directory.hybrid.ldap.LDAPOrganizationGroupAccessor.setInfo(LDAPOrganizationGroupAccessor.java:352) at fuego.directory.hybrid.ldap.LDAPOrganizationGroupAccessor.build(LDAPOrganizationGroupAccessor.java:121) at fuego.directory.hybrid.ldap.LDAPOrganizationGroupAccessor.build(LDAPOrganizationGroupAccessor.java:114) at fuego.directory.hybrid.ldap.LDAPOrganizationGroupAccessor.fetchGroup(LDAPOrganizationGroupAccessor.java:94) at fuego.directory.hybrid.HybridGroupAccessor.fetchGroup(HybridGroupAccessor.java:146) at sun.reflect.GeneratedMethodAccessor66.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at fuego.directory.provider.DirectorySessionImpl$AccessorProxy.invoke(DirectorySessionImpl.java:756) at $Proxy66.fetchGroup(Unknown Source) at fuego.directory.DirOrganizationalGroup.fetch(DirOrganizationalGroup.java:275) at fuego.metadata.GroupManager.loadGroup(GroupManager.java:225) at fuego.metadata.GroupManager.find(GroupManager.java:57) at fuego.metadata.ParticipantManager.addNestedGroups(ParticipantManager.java:621) at fuego.metadata.ParticipantManager.buildCompleteRoleAssignments(ParticipantManager.java:527) at fuego.metadata.Participant$RoleTransitiveClousure.build(Participant.java:760) at fuego.metadata.Participant$RoleTransitiveClousure.access$100(Participant.java:692) at fuego.metadata.Participant.buildRoles(Participant.java:401) at fuego.metadata.Participant.updateMembers(Participant.java:372) at fuego.metadata.Participant.<init>(Participant.java:64) at fuego.metadata.Participant.createUncacheParticipant(Participant.java:84) at fuego.server.persistence.jdbc.JdbcProcessInstancePersMgr.loadItems(JdbcProcessInstancePersMgr.java:1706) at fuego.server.persistence.Persistence.loadInstanceItems(Persistence.java:838) at fuego.server.AbstractInstanceService.readInstance(AbstractInstanceService.java:791) at fuego.ejbengine.EJBInstanceService.getLockedROImpl(EJBInstanceService.java:218) at fuego.server.AbstractInstanceService.getLockedROImpl(AbstractInstanceService.java:892) at fuego.server.AbstractInstanceService.getLockedImpl(AbstractInstanceService.java:743) at fuego.server.AbstractInstanceService.getLockedImpl(AbstractInstanceService.java:730) at fuego.server.AbstractInstanceService.getLocked(AbstractInstanceService.java:144) at fuego.server.AbstractInstanceService.getLocked(AbstractInstanceService.java:162) at fuego.server.AbstractInstanceService.unselectAllItems(AbstractInstanceService.java:454) at fuego.server.execution.ToDoItemUnselect.execute(ToDoItemUnselect.java:105) at fuego.server.execution.DefaultEngineExecution$AtomicExecutionTA.runTransaction(DefaultEngineExecution.java:304) at fuego.transaction.TransactionAction.startNestedTransaction(TransactionAction.java:527) at fuego.transaction.TransactionAction.startTransaction(TransactionAction.java:548) at fuego.transaction.TransactionAction.start(TransactionAction.java:212) at fuego.server.execution.DefaultEngineExecution.executeImmediate(DefaultEngineExecution.java:123) at fuego.server.execution.DefaultEngineExecution.executeAutomaticWork(DefaultEngineExecution.java:62) at fuego.server.execution.EngineExecution.executeAutomaticWork(EngineExecution.java:42) at fuego.server.execution.ToDoItem.executeAutomaticWork(ToDoItem.java:261) at fuego.ejbengine.ItemExecutionBean$1.execute(ItemExecutionBean.java:223) at fuego.server.execution.DefaultEngineExecution$AtomicExecutionTA.runTransaction(DefaultEngineExecution.java:304) at fuego.transaction.TransactionAction.startBaseTransaction(TransactionAction.java:470) at fuego.transaction.TransactionAction.startTransaction(TransactionAction.java:551) at fuego.transaction.TransactionAction.start(TransactionAction.java:212) at fuego.server.execution.DefaultEngineExecution.executeImmediate(DefaultEngineExecution.java:123) at fuego.server.execution.EngineExecution.executeImmediate(EngineExecution.java:66) at fuego.ejbengine.ItemExecutionBean.processMessage(ItemExecutionBean.java:209) at fuego.ejbengine.ItemExecutionBean.onMessage(ItemExecutionBean.java:120) at weblogic.ejb.container.internal.MDListener.execute(MDListener.java:466) at weblogic.ejb.container.internal.MDListener.transactionalOnMessage(MDListener.java:371) at weblogic.ejb.container.internal.MDListener.onMessage(MDListener.java:327) at weblogic.jms.client.JMSSession.onMessage(JMSSession.java:4547) at weblogic.jms.client.JMSSession.execute(JMSSession.java:4233) at weblogic.jms.client.JMSSession.executeMessage(JMSSession.java:3709) at weblogic.jms.client.JMSSession.access$000(JMSSession.java:114) at weblogic.jms.client.JMSSession$UseForRunnable.run(JMSSession.java:5058) at weblogic.work.SelfTuningWorkManagerImpl$WorkAdapterImpl.run(SelfTuningWorkManagerImpl.java:516) at weblogic.work.ExecuteThread.execute(ExecuteThread.java:201) at weblogic.work.ExecuteThread.run(ExecuteThread.java:173) Caused by: javax.naming.InvalidNameException: CN=Alvarez Guerrero Bernardo DEL:ca9ef28d-3b94-4e8f-a6bd-8c880bb3791b,CN=Deleted Objects,DC=corp: [LDAP: error code 34 - 0000208F: NameErr: DSID-031001BA, problem 2006 (BAD_NAME), data 8349, best match of: 'CN=Alvarez Guerrero Bernardo DEL:ca9ef28d-3b94-4e8f-a6bd-8c880bb3791b,CN=Deleted Objects,DC=corp,dc=televisa,dc=com,dc=mx' ^@]; remaining name 'CN=Alvarez Guerrero Bernardo DEL:ca9ef28d-3b94-4e8f-a6bd-8c880bb3791b,CN=Deleted Objects,DC=corp' at com.sun.jndi.ldap.LdapCtx.processReturnCode(LdapCtx.java:2979) at com.sun.jndi.ldap.LdapCtx.processReturnCode(LdapCtx.java:2794) at com.sun.jndi.ldap.LdapCtx.searchAux(LdapCtx.java:1826) at com.sun.jndi.ldap.LdapCtx.c_search(LdapCtx.java:1749) at com.sun.jndi.toolkit.ctx.ComponentDirContext.p_search(ComponentDirContext.java:368) at com.sun.jndi.toolkit.ctx.PartialCompositeDirContext.search(PartialCompositeDirContext.java:338) at com.sun.jndi.toolkit.ctx.PartialCompositeDirContext.search(PartialCompositeDirContext.java:321) at javax.naming.directory.InitialDirContext.search(InitialDirContext.java:248) at fuego.jndi.FaultTolerantLdapContext.search(FaultTolerantLdapContext.java:612) at fuego.directory.hybrid.ldap.JNDIQueryExecutor.selectById(JNDIQueryExecutor.java:136) ... 67 more

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  • Parallelism in .NET – Part 6, Declarative Data Parallelism

    - by Reed
    When working with a problem that can be decomposed by data, we have a collection, and some operation being performed upon the collection.  I’ve demonstrated how this can be parallelized using the Task Parallel Library and imperative programming using imperative data parallelism via the Parallel class.  While this provides a huge step forward in terms of power and capabilities, in many cases, special care must still be given for relative common scenarios. C# 3.0 and Visual Basic 9.0 introduced a new, declarative programming model to .NET via the LINQ Project.  When working with collections, we can now write software that describes what we want to occur without having to explicitly state how the program should accomplish the task.  By taking advantage of LINQ, many operations become much shorter, more elegant, and easier to understand and maintain.  Version 4.0 of the .NET framework extends this concept into the parallel computation space by introducing Parallel LINQ. Before we delve into PLINQ, let’s begin with a short discussion of LINQ.  LINQ, the extensions to the .NET Framework which implement language integrated query, set, and transform operations, is implemented in many flavors.  For our purposes, we are interested in LINQ to Objects.  When dealing with parallelizing a routine, we typically are dealing with in-memory data storage.  More data-access oriented LINQ variants, such as LINQ to SQL and LINQ to Entities in the Entity Framework fall outside of our concern, since the parallelism there is the concern of the data base engine processing the query itself. LINQ (LINQ to Objects in particular) works by implementing a series of extension methods, most of which work on IEnumerable<T>.  The language enhancements use these extension methods to create a very concise, readable alternative to using traditional foreach statement.  For example, let’s revisit our minimum aggregation routine we wrote in Part 4: double min = double.MaxValue; foreach(var item in collection) { double value = item.PerformComputation(); min = System.Math.Min(min, value); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here, we’re doing a very simple computation, but writing this in an imperative style.  This can be loosely translated to English as: Create a very large number, and save it in min Loop through each item in the collection. For every item: Perform some computation, and save the result If the computation is less than min, set min to the computation Although this is fairly easy to follow, it’s quite a few lines of code, and it requires us to read through the code, step by step, line by line, in order to understand the intention of the developer. We can rework this same statement, using LINQ: double min = collection.Min(item => item.PerformComputation()); Here, we’re after the same information.  However, this is written using a declarative programming style.  When we see this code, we’d naturally translate this to English as: Save the Min value of collection, determined via calling item.PerformComputation() That’s it – instead of multiple logical steps, we have one single, declarative request.  This makes the developer’s intentions very clear, and very easy to follow.  The system is free to implement this using whatever method required. Parallel LINQ (PLINQ) extends LINQ to Objects to support parallel operations.  This is a perfect fit in many cases when you have a problem that can be decomposed by data.  To show this, let’s again refer to our minimum aggregation routine from Part 4, but this time, let’s review our final, parallelized version: // Safe, and fast! double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach( collection, // First, we provide a local state initialization delegate. () => double.MaxValue, // Next, we supply the body, which takes the original item, loop state, // and local state, and returns a new local state (item, loopState, localState) => { double value = item.PerformComputation(); return System.Math.Min(localState, value); }, // Finally, we provide an Action<TLocal>, to "merge" results together localState => { // This requires locking, but it's only once per used thread lock(syncObj) min = System.Math.Min(min, localState); } ); Here, we’re doing the same computation as above, but fully parallelized.  Describing this in English becomes quite a feat: Create a very large number, and save it in min Create a temporary object we can use for locking Call Parallel.ForEach, specifying three delegates For the first delegate: Initialize a local variable to hold the local state to a very large number For the second delegate: For each item in the collection, perform some computation, save the result If the result is less than our local state, save the result in local state For the final delegate: Take a lock on our temporary object to protect our min variable Save the min of our min and local state variables Although this solves our problem, and does it in a very efficient way, we’ve created a set of code that is quite a bit more difficult to understand and maintain. PLINQ provides us with a very nice alternative.  In order to use PLINQ, we need to learn one new extension method that works on IEnumerable<T> – ParallelEnumerable.AsParallel(). That’s all we need to learn in order to use PLINQ: one single method.  We can write our minimum aggregation in PLINQ very simply: double min = collection.AsParallel().Min(item => item.PerformComputation()); By simply adding “.AsParallel()” to our LINQ to Objects query, we converted this to using PLINQ and running this computation in parallel!  This can be loosely translated into English easily, as well: Process the collection in parallel Get the Minimum value, determined by calling PerformComputation on each item Here, our intention is very clear and easy to understand.  We just want to perform the same operation we did in serial, but run it “as parallel”.  PLINQ completely extends LINQ to Objects: the entire functionality of LINQ to Objects is available.  By simply adding a call to AsParallel(), we can specify that a collection should be processed in parallel.  This is simple, safe, and incredibly useful.

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  • Where did I hide my TSQL mojo?

    - by fatherjack
    LiveJournal Tags: How To,SQL Server,Tips and Tricks,TSQL,Reporting Services A little while ago I wrote a piece about finding database objects that rely on other objects that no longer exist - OK, I have my database ready, now what's missing? . This is linked to that sort of process. Many SQL Server installations are associated in some way with a Reporting Services installation, it's a very logical way to distribute your database contents to system users so they can work effectively. Databases,...(read more)

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  • WCF client hell (2 replies)

    I've a remote service available via tcp://. When I add a service reference on my client project, VS doesn't create all proxy objects! I miss every xxxClient class, and I have only types used as parameters in my methods. I tried to start a new empty project, add the same service reference, and in this project I can see al proxy objects! It's an hell, what can I do? thanks

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  • WCF client hell (2 replies)

    I've a remote service available via tcp://. When I add a service reference on my client project, VS doesn't create all proxy objects! I miss every xxxClient class, and I have only types used as parameters in my methods. I tried to start a new empty project, add the same service reference, and in this project I can see al proxy objects! It's an hell, what can I do? thanks

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  • Advanced TSQL Tuning: Why Internals Knowledge Matters

    - by Paul White
    There is much more to query tuning than reducing logical reads and adding covering nonclustered indexes.  Query tuning is not complete as soon as the query returns results quickly in the development or test environments.  In production, your query will compete for memory, CPU, locks, I/O and other resources on the server.  Today’s entry looks at some tuning considerations that are often overlooked, and shows how deep internals knowledge can help you write better TSQL. As always, we’ll need some example data.  In fact, we are going to use three tables today, each of which is structured like this: Each table has 50,000 rows made up of an INTEGER id column and a padding column containing 3,999 characters in every row.  The only difference between the three tables is in the type of the padding column: the first table uses CHAR(3999), the second uses VARCHAR(MAX), and the third uses the deprecated TEXT type.  A script to create a database with the three tables and load the sample data follows: USE master; GO IF DB_ID('SortTest') IS NOT NULL DROP DATABASE SortTest; GO CREATE DATABASE SortTest COLLATE LATIN1_GENERAL_BIN; GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest', SIZE = 3GB, MAXSIZE = 3GB ); GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest_log', SIZE = 256MB, MAXSIZE = 1GB, FILEGROWTH = 128MB ); GO ALTER DATABASE SortTest SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE SortTest SET AUTO_CLOSE OFF ; ALTER DATABASE SortTest SET AUTO_CREATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_SHRINK OFF ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS_ASYNC ON ; ALTER DATABASE SortTest SET PARAMETERIZATION SIMPLE ; ALTER DATABASE SortTest SET READ_COMMITTED_SNAPSHOT OFF ; ALTER DATABASE SortTest SET MULTI_USER ; ALTER DATABASE SortTest SET RECOVERY SIMPLE ; USE SortTest; GO CREATE TABLE dbo.TestCHAR ( id INTEGER IDENTITY (1,1) NOT NULL, padding CHAR(3999) NOT NULL,   CONSTRAINT [PK dbo.TestCHAR (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestMAX ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAX (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestTEXT ( id INTEGER IDENTITY (1,1) NOT NULL, padding TEXT NOT NULL,   CONSTRAINT [PK dbo.TestTEXT (id)] PRIMARY KEY CLUSTERED (id), ) ; -- ============= -- Load TestCHAR (about 3s) -- ============= INSERT INTO dbo.TestCHAR WITH (TABLOCKX) ( padding ) SELECT padding = REPLICATE(CHAR(65 + (Data.n % 26)), 3999) FROM ( SELECT TOP (50000) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) - 1 FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) AS Data ORDER BY Data.n ASC ; -- ============ -- Load TestMAX (about 3s) -- ============ INSERT INTO dbo.TestMAX WITH (TABLOCKX) ( padding ) SELECT CONVERT(VARCHAR(MAX), padding) FROM dbo.TestCHAR ORDER BY id ; -- ============= -- Load TestTEXT (about 5s) -- ============= INSERT INTO dbo.TestTEXT WITH (TABLOCKX) ( padding ) SELECT CONVERT(TEXT, padding) FROM dbo.TestCHAR ORDER BY id ; -- ========== -- Space used -- ========== -- EXECUTE sys.sp_spaceused @objname = 'dbo.TestCHAR'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAX'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestTEXT'; ; CHECKPOINT ; That takes around 15 seconds to run, and shows the space allocated to each table in its output: To illustrate the points I want to make today, the example task we are going to set ourselves is to return a random set of 150 rows from each table.  The basic shape of the test query is the same for each of the three test tables: SELECT TOP (150) T.id, T.padding FROM dbo.Test AS T ORDER BY NEWID() OPTION (MAXDOP 1) ; Test 1 – CHAR(3999) Running the template query shown above using the TestCHAR table as the target, we find that the query takes around 5 seconds to return its results.  This seems slow, considering that the table only has 50,000 rows.  Working on the assumption that generating a GUID for each row is a CPU-intensive operation, we might try enabling parallelism to see if that speeds up the response time.  Running the query again (but without the MAXDOP 1 hint) on a machine with eight logical processors, the query now takes 10 seconds to execute – twice as long as when run serially. Rather than attempting further guesses at the cause of the slowness, let’s go back to serial execution and add some monitoring.  The script below monitors STATISTICS IO output and the amount of tempdb used by the test query.  We will also run a Profiler trace to capture any warnings generated during query execution. DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TC.id, TC.padding FROM dbo.TestCHAR AS TC ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; Let’s take a closer look at the statistics and query plan generated from this: Following the flow of the data from right to left, we see the expected 50,000 rows emerging from the Clustered Index Scan, with a total estimated size of around 191MB.  The Compute Scalar adds a column containing a random GUID (generated from the NEWID() function call) for each row.  With this extra column in place, the size of the data arriving at the Sort operator is estimated to be 192MB. Sort is a blocking operator – it has to examine all of the rows on its input before it can produce its first row of output (the last row received might sort first).  This characteristic means that Sort requires a memory grant – memory allocated for the query’s use by SQL Server just before execution starts.  In this case, the Sort is the only memory-consuming operator in the plan, so it has access to the full 243MB (248,696KB) of memory reserved by SQL Server for this query execution. Notice that the memory grant is significantly larger than the expected size of the data to be sorted.  SQL Server uses a number of techniques to speed up sorting, some of which sacrifice size for comparison speed.  Sorts typically require a very large number of comparisons, so this is usually a very effective optimization.  One of the drawbacks is that it is not possible to exactly predict the sort space needed, as it depends on the data itself.  SQL Server takes an educated guess based on data types, sizes, and the number of rows expected, but the algorithm is not perfect. In spite of the large memory grant, the Profiler trace shows a Sort Warning event (indicating that the sort ran out of memory), and the tempdb usage monitor shows that 195MB of tempdb space was used – all of that for system use.  The 195MB represents physical write activity on tempdb, because SQL Server strictly enforces memory grants – a query cannot ‘cheat’ and effectively gain extra memory by spilling to tempdb pages that reside in memory.  Anyway, the key point here is that it takes a while to write 195MB to disk, and this is the main reason that the query takes 5 seconds overall. If you are wondering why using parallelism made the problem worse, consider that eight threads of execution result in eight concurrent partial sorts, each receiving one eighth of the memory grant.  The eight sorts all spilled to tempdb, resulting in inefficiencies as the spilled sorts competed for disk resources.  More importantly, there are specific problems at the point where the eight partial results are combined, but I’ll cover that in a future post. CHAR(3999) Performance Summary: 5 seconds elapsed time 243MB memory grant 195MB tempdb usage 192MB estimated sort set 25,043 logical reads Sort Warning Test 2 – VARCHAR(MAX) We’ll now run exactly the same test (with the additional monitoring) on the table using a VARCHAR(MAX) padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TM.id, TM.padding FROM dbo.TestMAX AS TM ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query takes around 8 seconds to complete (3 seconds longer than Test 1).  Notice that the estimated row and data sizes are very slightly larger, and the overall memory grant has also increased very slightly to 245MB.  The most marked difference is in the amount of tempdb space used – this query wrote almost 391MB of sort run data to the physical tempdb file.  Don’t draw any general conclusions about VARCHAR(MAX) versus CHAR from this – I chose the length of the data specifically to expose this edge case.  In most cases, VARCHAR(MAX) performs very similarly to CHAR – I just wanted to make test 2 a bit more exciting. MAX Performance Summary: 8 seconds elapsed time 245MB memory grant 391MB tempdb usage 193MB estimated sort set 25,043 logical reads Sort warning Test 3 – TEXT The same test again, but using the deprecated TEXT data type for the padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TT.id, TT.padding FROM dbo.TestTEXT AS TT ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query runs in 500ms.  If you look at the metrics we have been checking so far, it’s not hard to understand why: TEXT Performance Summary: 0.5 seconds elapsed time 9MB memory grant 5MB tempdb usage 5MB estimated sort set 207 logical reads 596 LOB logical reads Sort warning SQL Server’s memory grant algorithm still underestimates the memory needed to perform the sorting operation, but the size of the data to sort is so much smaller (5MB versus 193MB previously) that the spilled sort doesn’t matter very much.  Why is the data size so much smaller?  The query still produces the correct results – including the large amount of data held in the padding column – so what magic is being performed here? TEXT versus MAX Storage The answer lies in how columns of the TEXT data type are stored.  By default, TEXT data is stored off-row in separate LOB pages – which explains why this is the first query we have seen that records LOB logical reads in its STATISTICS IO output.  You may recall from my last post that LOB data leaves an in-row pointer to the separate storage structure holding the LOB data. SQL Server can see that the full LOB value is not required by the query plan until results are returned, so instead of passing the full LOB value down the plan from the Clustered Index Scan, it passes the small in-row structure instead.  SQL Server estimates that each row coming from the scan will be 79 bytes long – 11 bytes for row overhead, 4 bytes for the integer id column, and 64 bytes for the LOB pointer (in fact the pointer is rather smaller – usually 16 bytes – but the details of that don’t really matter right now). OK, so this query is much more efficient because it is sorting a very much smaller data set – SQL Server delays retrieving the LOB data itself until after the Sort starts producing its 150 rows.  The question that normally arises at this point is: Why doesn’t SQL Server use the same trick when the padding column is defined as VARCHAR(MAX)? The answer is connected with the fact that if the actual size of the VARCHAR(MAX) data is 8000 bytes or less, it is usually stored in-row in exactly the same way as for a VARCHAR(8000) column – MAX data only moves off-row into LOB storage when it exceeds 8000 bytes.  The default behaviour of the TEXT type is to be stored off-row by default, unless the ‘text in row’ table option is set suitably and there is room on the page.  There is an analogous (but opposite) setting to control the storage of MAX data – the ‘large value types out of row’ table option.  By enabling this option for a table, MAX data will be stored off-row (in a LOB structure) instead of in-row.  SQL Server Books Online has good coverage of both options in the topic In Row Data. The MAXOOR Table The essential difference, then, is that MAX defaults to in-row storage, and TEXT defaults to off-row (LOB) storage.  You might be thinking that we could get the same benefits seen for the TEXT data type by storing the VARCHAR(MAX) values off row – so let’s look at that option now.  This script creates a fourth table, with the VARCHAR(MAX) data stored off-row in LOB pages: CREATE TABLE dbo.TestMAXOOR ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAXOOR (id)] PRIMARY KEY CLUSTERED (id), ) ; EXECUTE sys.sp_tableoption @TableNamePattern = N'dbo.TestMAXOOR', @OptionName = 'large value types out of row', @OptionValue = 'true' ; SELECT large_value_types_out_of_row FROM sys.tables WHERE [schema_id] = SCHEMA_ID(N'dbo') AND name = N'TestMAXOOR' ; INSERT INTO dbo.TestMAXOOR WITH (TABLOCKX) ( padding ) SELECT SPACE(0) FROM dbo.TestCHAR ORDER BY id ; UPDATE TM WITH (TABLOCK) SET padding.WRITE (TC.padding, NULL, NULL) FROM dbo.TestMAXOOR AS TM JOIN dbo.TestCHAR AS TC ON TC.id = TM.id ; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAXOOR' ; CHECKPOINT ; Test 4 – MAXOOR We can now re-run our test on the MAXOOR (MAX out of row) table: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) MO.id, MO.padding FROM dbo.TestMAXOOR AS MO ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; TEXT Performance Summary: 0.3 seconds elapsed time 245MB memory grant 0MB tempdb usage 193MB estimated sort set 207 logical reads 446 LOB logical reads No sort warning The query runs very quickly – slightly faster than Test 3, and without spilling the sort to tempdb (there is no sort warning in the trace, and the monitoring query shows zero tempdb usage by this query).  SQL Server is passing the in-row pointer structure down the plan and only looking up the LOB value on the output side of the sort. The Hidden Problem There is still a huge problem with this query though – it requires a 245MB memory grant.  No wonder the sort doesn’t spill to tempdb now – 245MB is about 20 times more memory than this query actually requires to sort 50,000 records containing LOB data pointers.  Notice that the estimated row and data sizes in the plan are the same as in test 2 (where the MAX data was stored in-row). The optimizer assumes that MAX data is stored in-row, regardless of the sp_tableoption setting ‘large value types out of row’.  Why?  Because this option is dynamic – changing it does not immediately force all MAX data in the table in-row or off-row, only when data is added or actually changed.  SQL Server does not keep statistics to show how much MAX or TEXT data is currently in-row, and how much is stored in LOB pages.  This is an annoying limitation, and one which I hope will be addressed in a future version of the product. So why should we worry about this?  Excessive memory grants reduce concurrency and may result in queries waiting on the RESOURCE_SEMAPHORE wait type while they wait for memory they do not need.  245MB is an awful lot of memory, especially on 32-bit versions where memory grants cannot use AWE-mapped memory.  Even on a 64-bit server with plenty of memory, do you really want a single query to consume 0.25GB of memory unnecessarily?  That’s 32,000 8KB pages that might be put to much better use. The Solution The answer is not to use the TEXT data type for the padding column.  That solution happens to have better performance characteristics for this specific query, but it still results in a spilled sort, and it is hard to recommend the use of a data type which is scheduled for removal.  I hope it is clear to you that the fundamental problem here is that SQL Server sorts the whole set arriving at a Sort operator.  Clearly, it is not efficient to sort the whole table in memory just to return 150 rows in a random order. The TEXT example was more efficient because it dramatically reduced the size of the set that needed to be sorted.  We can do the same thing by selecting 150 unique keys from the table at random (sorting by NEWID() for example) and only then retrieving the large padding column values for just the 150 rows we need.  The following script implements that idea for all four tables: SET STATISTICS IO ON ; WITH TestTable AS ( SELECT * FROM dbo.TestCHAR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id = ANY (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAX ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestTEXT ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAXOOR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; All four queries now return results in much less than a second, with memory grants between 6 and 12MB, and without spilling to tempdb.  The small remaining inefficiency is in reading the id column values from the clustered primary key index.  As a clustered index, it contains all the in-row data at its leaf.  The CHAR and VARCHAR(MAX) tables store the padding column in-row, so id values are separated by a 3999-character column, plus row overhead.  The TEXT and MAXOOR tables store the padding values off-row, so id values in the clustered index leaf are separated by the much-smaller off-row pointer structure.  This difference is reflected in the number of logical page reads performed by the four queries: Table 'TestCHAR' logical reads 25511 lob logical reads 000 Table 'TestMAX'. logical reads 25511 lob logical reads 000 Table 'TestTEXT' logical reads 00412 lob logical reads 597 Table 'TestMAXOOR' logical reads 00413 lob logical reads 446 We can increase the density of the id values by creating a separate nonclustered index on the id column only.  This is the same key as the clustered index, of course, but the nonclustered index will not include the rest of the in-row column data. CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestCHAR (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAX (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestTEXT (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAXOOR (id); The four queries can now use the very dense nonclustered index to quickly scan the id values, sort them by NEWID(), select the 150 ids we want, and then look up the padding data.  The logical reads with the new indexes in place are: Table 'TestCHAR' logical reads 835 lob logical reads 0 Table 'TestMAX' logical reads 835 lob logical reads 0 Table 'TestTEXT' logical reads 686 lob logical reads 597 Table 'TestMAXOOR' logical reads 686 lob logical reads 448 With the new index, all four queries use the same query plan (click to enlarge): Performance Summary: 0.3 seconds elapsed time 6MB memory grant 0MB tempdb usage 1MB sort set 835 logical reads (CHAR, MAX) 686 logical reads (TEXT, MAXOOR) 597 LOB logical reads (TEXT) 448 LOB logical reads (MAXOOR) No sort warning I’ll leave it as an exercise for the reader to work out why trying to eliminate the Key Lookup by adding the padding column to the new nonclustered indexes would be a daft idea Conclusion This post is not about tuning queries that access columns containing big strings.  It isn’t about the internal differences between TEXT and MAX data types either.  It isn’t even about the cool use of UPDATE .WRITE used in the MAXOOR table load.  No, this post is about something else: Many developers might not have tuned our starting example query at all – 5 seconds isn’t that bad, and the original query plan looks reasonable at first glance.  Perhaps the NEWID() function would have been blamed for ‘just being slow’ – who knows.  5 seconds isn’t awful – unless your users expect sub-second responses – but using 250MB of memory and writing 200MB to tempdb certainly is!  If ten sessions ran that query at the same time in production that’s 2.5GB of memory usage and 2GB hitting tempdb.  Of course, not all queries can be rewritten to avoid large memory grants and sort spills using the key-lookup technique in this post, but that’s not the point either. The point of this post is that a basic understanding of execution plans is not enough.  Tuning for logical reads and adding covering indexes is not enough.  If you want to produce high-quality, scalable TSQL that won’t get you paged as soon as it hits production, you need a deep understanding of execution plans, and as much accurate, deep knowledge about SQL Server as you can lay your hands on.  The advanced database developer has a wide range of tools to use in writing queries that perform well in a range of circumstances. By the way, the examples in this post were written for SQL Server 2008.  They will run on 2005 and demonstrate the same principles, but you won’t get the same figures I did because 2005 had a rather nasty bug in the Top N Sort operator.  Fair warning: if you do decide to run the scripts on a 2005 instance (particularly the parallel query) do it before you head out for lunch… This post is dedicated to the people of Christchurch, New Zealand. © 2011 Paul White email: @[email protected] twitter: @SQL_Kiwi

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  • Learning resources for working with POCO entities in EF 4.0

    - by boghydan
    Here are some links that can help you start working with POCO entities in EF 4.0: ADO.NET Team blog: - Working with POCO objects: http://blogs.msdn.com/adonet/archive/2009/05/21/poco-in-the-entity-framework-part-1-the-experience.aspx - Proxy objects for POCO entities: http://blogs.msdn.com/adonet/archive/2009/12/22/poco-proxies-part-1.aspx MSDN Library: - Working with POCO  http://msdn.microsoft.com/en-us/library/dd456853.aspx - T4 editor for POCO generator can be downloaded from here: http://visualstudiogallery.msdn.microsoft.com/en-us/60297607-5fd4-4da4-97e1-3715e90c1a23

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  • Duplication in parallel inheritance hierarchies

    - by flamingpenguin
    Using an OO language with static typing (like Java), what are good ways to represent the following model invariant without large amounts of duplication. I have two (actually multiple) flavours of the same structure. Each flavour requires its own (unique to that flavour data) on each of the objects within that structure as well as some shared data. But within each instance of the aggregation only objects of one (the same) flavour are allowed. FooContainer can contain FooSources and FooDestinations and associations between the "Foo" objects BarContainer can contain BarSources and BarDestinations and associations between the "Bar" objects interface Container() { List<? extends Source> sources(); List<? extends Destination> destinations(); List<? extends Associations> associations(); } interface FooContainer() extends Container { List<? extends FooSource> sources(); List<? extends FooDestination> destinations(); List<? extends FooAssociations> associations(); } interface BarContainer() extends Container { List<? extends BarSource> sources(); List<? extends BarDestination> destinations(); List<? extends BarAssociations> associations(); } interface Source { String getSourceDetail1(); } interface FooSource extends Source { String getSourceDetail2(); } interface BarSource extends Source { String getSourceDetail3(); } interface Destination { String getDestinationDetail1(); } interface FooDestination extends Destination { String getDestinationDetail2(); } interface BarDestination extends Destination { String getDestinationDetail3(); } interface Association { Source getSource(); Destination getDestination(); } interface FooAssociation extends Association { FooSource getSource(); FooDestination getDestination(); String getFooAssociationDetail(); } interface BarAssociation extends Association { BarSource getSource(); BarDestination getDestination(); String getBarAssociationDetail(); }

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  • ROA on top of SOA

    - by Vaibhav Pujari
    I already have a stable Service Oriented Architecture for my application which exposes services as API calls. (the verbs) Now, I need to build a Resource Oriented Architecture to expose a RESTful API to interact with the application objects. (the nouns) What are the best practices to reuse the existing services: - without any persistence inside my new code. - without putting unnecessary logic into the REST layer i.e. it should ideally just leverage the services provided by SOA API. I want this layer to be as thin as possible. - without modifying the existing SOA API - allow easy extension of the REST API i.e. it should be easy to add more resources without changing the (yet to be written) core code. (I want to make resource names and their associated actions configurable so more contributors can easily add resources without a need to understand my module) Any advices/suggestions how to achieve this? Edit: Adding more info My Stack: My existing stacks is in Java. But since I plan to just use the services, I don't think that should affect the design of new REST code. I am planning to implement the new REST code in PHP. How well the services map to resources? Some services are mapped well i.e. there are services for creating, updating application objects. But for other application objects, there are no direct services available. More importantly, there are actions beyond just create, update etc. that apply to application objects. And I would like to provide some way for these actions to be exposed through REST. Since these are verbs, how do I deal with them? Where exactly I need help? I would appreciate any help towards high level design to accomplish the task along-with making the framework extendible. For instance, tomorrow there are some new services added to my SOA layer, I want to make sure it is easy for a fresh developer to write a REST call by simply registering a new resource (in a config file/db) and write code for connecting it with SOA calls. Just like plugin.

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  • Is INT_MIN-1 an underflow or overflow?

    - by Johannes Schaub - litb
    I seem to remember that I was reading that underflow means you have a too small magnitude that cannot be presented anymore in a type overflow means you have a too large magnitude that cannot be presented anymore in a type However, in practice I perceive that the terms are used such that underflow means you have a too small value that cannot be presented anymore in a type overflow means you have a too large value that cannot be presented anymore in a type What is the correct meaning to use here? Are the terms defined differently for integer and floating point types?

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  • Be Careful When Referencing SPList.Items

    - by Brian Jackett
    Be very careful how you reference your SPListItem objects through the SharePoint API.  I’ll say it again.  Be very careful how you reference your SPListItem objects through the SharePoint API.  Ok, now that you get the point that this will be a “learn from my mistakes and don’t do unsmart things like I did” post, let’s dig into what it was that I did poorly. Scenario     For the past year I’ve been building custom .Net applications that are hosted through SharePoint.  These application involve a number of SharePoint lists, external databases, custom web parts, and other SharePoint elements to provide functionality.  About two weeks ago I received a message from one of our end users that a custom application was performing slowly.  Specifically performance was slow when users were performing actions that interacted with the primary SharePoint list storing data for that app. The Problem     I took a copy of the production site into a dev environment to investigate the code that was executing.  After attaching the debugger and running through the code I quickly found pieces of code referencing SPListItem objects (like below) that were performing very poorly: SPListItem myItem = SPContext.Current.Web.Lists["List Name"].Items.GetItemById(value); // do updates on SPListItem retrieved     As it turns out the SPList I was referencing was fairly large at ~1000 items and weighing in over 150 MB.  You see the problem with my above code is that I retrieved the SPListItem by first (unnecessarily) going through the Items member of the list.  As I understand it, when doing so the executing code will attempt to resolve that entity and pull it from the database and into RAM (all 150 MB.)  This causes the equivalent of a 50 car pile up in terms of performance with a single update taking more than 15 seconds. The Solution     The solution is actually quite simple and I wish I had realized this during development.  Instead of going through the Items member it is possible to call GetItemById(…) directly on the SPList as in the example below: SPListItem myItem = SPContext.Current.Web.Lists["List Name"].GetItemById(value); // do updates on SPListItem retrieved     After making this simple change performance skyrocketed and updates were back to less than a second.   Conclusion     When given the option between two solutions, usually the simplest is the best solution.  In my scenario I was adding extra complexity going through the API the long way around to get to the objects I needed and it ended up hurting performance greatly.  Luckily we were able to find and resolve the performance issue in a relatively short amount of time.  Like I said at the beginning of the post, learn from my mistakes and hope it helps you.         -Frog Out   Image linked from http://www.freespirit.com/files/IMAGE/COVER/LARGE/BeCarefulSafe.jpg

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  • Music Notation Editor - Refactoring view creation logic elsewhere

    - by Cyril Silverman
    Let me preface by saying that knowing some elementary music theory and music notation may be helpful in grasping the problem at hand. I'm currently building a Music Notation and Tablature Editor (in Javascript). But I've come to a point where the core parts of the program are more or less there. All functionality I plan to add at this point will really build off the foundation that I've created. As a result, I want to refactor to really solidify my code. I'm using an API called VexFlow to render notation. Basically I pass the parts of the editor's state to VexFlow to build the graphical representation of the score. Here is a rough and stripped down UML diagram showing you the outline of my program: In essence, a Part has many Measures which has many Notes which has many NoteItems (yes, this is semantically weird, as a chord is represented as a Note with multiple NoteItems, individual pitches or fret positions). All of the relationships are bi-directional. There are a few problems with my design because my Measure class contains the majority of the entire application view logic. The class holds the data about all VexFlow objects (the graphical representation of the score). It contains the graphical Staff object and the graphical notes. (Shouldn't these be placed somewhere else in the program?) While VexFlowFactory deals with actual creation (and some processing) of most of the VexFlow objects, Measure still "directs" the creation of all the objects and what order they are supposed to be created in for both the VexFlowStaff and VexFlowNotes. I'm not looking for a specific answer as you'd need a much deeper understanding of my code. Just a general direction to go in. Here's a thought I had, create an MeasureView/NoteView/PartView classes that contains the basic VexFlow objects for each class in addition to any extraneous logic for it's creation? but where would these views be contained? Do I create a ScoreView that is a parallel graphical representation of everything? So that ScoreView.render() would cascade down PartView and call render for each PartView and casade down into each MeasureView, etc. Again, I just have no idea what direction to go in. The more I think about it, the more ways to go seem to pop into my head. I tried to be as concise and simplistic as possible while still getting my problem across. Please feel free to ask me any questions if anything is unclear. It's quite a struggle trying to dumb down a complicated problem to its core parts.

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  • SQL SERVER – SSIS Look Up Component – Cache Mode – Notes from the Field #028

    - by Pinal Dave
    [Notes from Pinal]: Lots of people think that SSIS is all about arranging various operations together in one logical flow. Well, the understanding is absolutely correct, but the implementation of the same is not as easy as it seems. Similarly most of the people think lookup component is just component which does look up for additional information and does not pay much attention to it. Due to the same reason they do not pay attention to the same and eventually get very bad performance. Linchpin People are database coaches and wellness experts for a data driven world. In this 28th episode of the Notes from the Fields series database expert Tim Mitchell (partner at Linchpin People) shares very interesting conversation related to how to write a good lookup component with Cache Mode. In SQL Server Integration Services, the lookup component is one of the most frequently used tools for data validation and completion.  The lookup component is provided as a means to virtually join one set of data to another to validate and/or retrieve missing values.  Properly configured, it is reliable and reasonably fast. Among the many settings available on the lookup component, one of the most critical is the cache mode.  This selection will determine whether and how the distinct lookup values are cached during package execution.  It is critical to know how cache modes affect the result of the lookup and the performance of the package, as choosing the wrong setting can lead to poorly performing packages, and in some cases, incorrect results. Full Cache The full cache mode setting is the default cache mode selection in the SSIS lookup transformation.  Like the name implies, full cache mode will cause the lookup transformation to retrieve and store in SSIS cache the entire set of data from the specified lookup location.  As a result, the data flow in which the lookup transformation resides will not start processing any data buffers until all of the rows from the lookup query have been cached in SSIS. The most commonly used cache mode is the full cache setting, and for good reason.  The full cache setting has the most practical applications, and should be considered the go-to cache setting when dealing with an untested set of data. With a moderately sized set of reference data, a lookup transformation using full cache mode usually performs well.  Full cache mode does not require multiple round trips to the database, since the entire reference result set is cached prior to data flow execution. There are a few potential gotchas to be aware of when using full cache mode.  First, you can see some performance issues – memory pressure in particular – when using full cache mode against large sets of reference data.  If the table you use for the lookup is very large (either deep or wide, or perhaps both), there’s going to be a performance cost associated with retrieving and caching all of that data.  Also, keep in mind that when doing a lookup on character data, full cache mode will always do a case-sensitive (and in some cases, space-sensitive) string comparison even if your database is set to a case-insensitive collation.  This is because the in-memory lookup uses a .NET string comparison (which is case- and space-sensitive) as opposed to a database string comparison (which may be case sensitive, depending on collation).  There’s a relatively easy workaround in which you can use the UPPER() or LOWER() function in the pipeline data and the reference data to ensure that case differences do not impact the success of your lookup operation.  Again, neither of these present a reason to avoid full cache mode, but should be used to determine whether full cache mode should be used in a given situation. Full cache mode is ideally useful when one or all of the following conditions exist: The size of the reference data set is small to moderately sized The size of the pipeline data set (the data you are comparing to the lookup table) is large, is unknown at design time, or is unpredictable Each distinct key value(s) in the pipeline data set is expected to be found multiple times in that set of data Partial Cache When using the partial cache setting, lookup values will still be cached, but only as each distinct value is encountered in the data flow.  Initially, each distinct value will be retrieved individually from the specified source, and then cached.  To be clear, this is a row-by-row lookup for each distinct key value(s). This is a less frequently used cache setting because it addresses a narrower set of scenarios.  Because each distinct key value(s) combination requires a relational round trip to the lookup source, performance can be an issue, especially with a large pipeline data set to be compared to the lookup data set.  If you have, for example, a million records from your pipeline data source, you have the potential for doing a million lookup queries against your lookup data source (depending on the number of distinct values in the key column(s)).  Therefore, one has to be keenly aware of the expected row count and value distribution of the pipeline data to safely use partial cache mode. Using partial cache mode is ideally suited for the conditions below: The size of the data in the pipeline (more specifically, the number of distinct key column) is relatively small The size of the lookup data is too large to effectively store in cache The lookup source is well indexed to allow for fast retrieval of row-by-row values No Cache As you might guess, selecting no cache mode will not add any values to the lookup cache in SSIS.  As a result, every single row in the pipeline data set will require a query against the lookup source.  Since no data is cached, it is possible to save a small amount of overhead in SSIS memory in cases where key values are not reused.  In the real world, I don’t see a lot of use of the no cache setting, but I can imagine some edge cases where it might be useful. As such, it’s critical to know your data before choosing this option.  Obviously, performance will be an issue with anything other than small sets of data, as the no cache setting requires row-by-row processing of all of the data in the pipeline. I would recommend considering the no cache mode only when all of the below conditions are true: The reference data set is too large to reasonably be loaded into SSIS memory The pipeline data set is small and is not expected to grow There are expected to be very few or no duplicates of the key values(s) in the pipeline data set (i.e., there would be no benefit from caching these values) Conclusion The cache mode, an often-overlooked setting on the SSIS lookup component, represents an important design decision in your SSIS data flow.  Choosing the right lookup cache mode directly impacts the fidelity of your results and the performance of package execution.  Know how this selection impacts your ETL loads, and you’ll end up with more reliable, faster packages. If you want me to take a look at your server and its settings, or if your server is facing any issue we can Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SSIS

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  • Part 1: What are EBS Customizations?

    - by volker.eckardt(at)oracle.com
    Everything what is not shipped as Oracle standard may be called customization. And very often we differentiate between setup and customization, although setup can also be required when working with customizations.This highlights one of the first challenges, because someone needs to track setup brought over with customizations and this needs to be synchronized with the (standard) setup done manually. This is not only a tracking issue, but also a documentation issue. I will cover this in one of the following blogs in more detail.But back to the topic itself. Mainly our code pieces (java, pl/sql, sql, shell scripts), custom objects (tables, views, packages etc.) and application objects (concurrent programs, lookups, forms, reports, OAF pages etc.) are treated as customizations. In general we define two types: customization by extension and customization by modification. For sure we like to minimize standard code modifications, but sometimes it is just not possible to provide a certain functionality without doing it.Keep in mind that the EBS provides a number of alternatives for modifications, just to mention some:Files in file system    add your custom top before the standard top to the pathBI Publisher Report    add a custom layout and disable the standard layout, automatically yours will be taken.Form /OAF Change    use personalization or substitutionUsing such techniques you are on the safe site regarding standard patches, but for sure a retest is always required!Many customizations are growing over the time, initially it was just one file, but in between we have 5, 10 or 15 files in our customization pack. The more files you have, the more important is the installation order.Last but not least also personalization's are treated as customizations, although you may not use any deployment pack to transfer such personalisation's (but you can). For OAF personalization's you can use iSetup, I have also enabled iSetup to allow Forms personalizations to transport.Interfaces and conversion objects are quite often also categorized as customizations and I promote this decision. Your development standards are related to all these kinds of custom code whether we are exchanging data with users (via form or report) or with other systems (via inbound or outbound interface).To cover all these types of customizations two acronyms have been defined: RICE and CEMLI.RICE = Reports, Interfaces, Conversions, and ExtensionsCEMLI = Customization, Extension, Modification, Localization, IntegrationThe word CEMLI has been introduced by Oracle On Demand and is used within Oracle projects quite often, but also RICE is well known as acronym.It doesn't matter which acronym you are using, the main task here is to classify and categorize your customizations to allow everyone to understand when you talk about RICE- 211, CEMLI XXFI_BAST or XXOM_RPT_030.Side note: Such references are not automatically objects prefixes, but they are often used as such. I plan also to address this point in one other blog.Thank you!Volker

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  • Hype and LINQ

    - by Tony Davis
    "Tired of querying in antiquated SQL?" I blinked in astonishment when I saw this headline on the LinqPad site. Warming to its theme, the site suggests that what we need is to "kiss goodbye to SSMS", and instead use LINQ, a modern query language! Elsewhere, there is an article entitled "Why LINQ beats SQL". The designers of LINQ, along with many DBAs, would, I'm sure, cringe with embarrassment at the suggestion that LINQ and SQL are, in any sense, competitive ways of doing the same thing. In fact what LINQ really is, at last, is an efficient, declarative language for C# and VB programmers to access or manipulate data in objects, local data stores, ORMs, web services, data repositories, and, yes, even relational databases. The fact is that LINQ is essentially declarative programming in a .NET language, and so in many ways encourages developers into a "SQL-like" mindset, even though they are not directly writing SQL. In place of imperative logic and loops, it uses various expressions, operators and declarative logic to build up an "expression tree" describing only what data is required, not the operations to be performed to get it. This expression tree is then parsed by the language compiler, and the result, when used against a relational database, is a SQL string that, while perhaps not always perfect, is often correctly parameterized and certainly no less "optimal" than what is achieved when a developer applies blunt, imperative logic to the SQL language. From a developer standpoint, it is a mistake to consider LINQ simply as a substitute means of querying SQL Server. The strength of LINQ is that that can be used to access any data source, for which a LINQ provider exists. Microsoft supplies built-in providers to access not just SQL Server, but also XML documents, .NET objects, ADO.NET datasets, and Entity Framework elements. LINQ-to-Objects is particularly interesting in that it allows a declarative means to access and manipulate arrays, collections and so on. Furthermore, as Michael Sorens points out in his excellent article on LINQ, there a whole host of third-party LINQ providers, that offers a simple way to get at data in Excel, Google, Flickr and much more, without having to learn a new interface or language. Of course, the need to be generic enough to deal with a range of data sources, from something as mundane as a text file to as esoteric as a relational database, means that LINQ is a compromise and so has inherent limitations. However, it is a powerful and beautifully compact language and one that, at least in its "query syntax" guise, is accessible to developers and DBAs alike. Perhaps there is still hope that LINQ can fulfill Phil Factor's lobster-induced fantasy of a language that will allow us to "treat all data objects, whether Word files, Excel files, XML, relational databases, text files, HTML files, registry files, LDAPs, Outlook and so on, in the same logical way, as linked databases, and extract the metadata, create the entities and relationships in the same way, and use the same SQL syntax to interrogate, create, read, write and update them." Cheers, Tony.

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