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  • How do I access Windows Event Viewer log data from Java

    - by MatthieuF
    Is there any way to access the Windows Event Log from a java class. Has anyone written any APIs for this, and would there be any way to access the data from a remote machine? The scenario is: I run a process on a remote machine, from a controlling Java process. This remote process logs stuff to the Event Log, which I want to be able to see in the controlling process. Thanks in advance.

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  • Bind event in custom WPF control to command in ViewModel

    - by Jon Archway
    Hi, I have a custom control that has an event. I have a window using that custom control. The window is bound to a viewmodel. I would like to have the event from the custom control direct to an ICommand on my viewmodel. I am obviously being dense here as I can't figure out how to do this. Any assistance is most welcome. Thanks

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  • Why use an event cache with epoll_wait?

    - by user1827356
    Question: epoll man page has some pointers when using epoll with an 'event cache'. But, why would you need to maintain an event cahce at all - Isn't this the same as what epoll is supposed to be doing? Is it to avoid making multiple epoll_wait calls which might be slower than managing the events in user space? Is it to implement a custom 'priority' scheme over the cached events? Background: I'm trying to understand the strengths/shortcomings of epoll and its applicability to different situations

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  • 2 Controls, 1 event

    - by DTown
    I have 2 input textboxes that take a host or IP. When the user leaves an input box an event is fired that checks the input to see if it is actually a live computer. The results are then put into the appropriate label. My question is, should I be using separate events for each input box, since they update different labels? Or, can I use 1 event and check who the caller was, then update the appropriate label?

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  • Event.MOUSE_LEAVE not working in AS3

    - by TheDarkIn1978
    right, so i just tossed this super simple code example into a Flash CS4 IDE frame script, but it doesn't output anything in the console. i'm simply rolling over my mouse over the window, not clicking anything, and nothing is happening. wtf?! stage.addEventListener(Event.MOUSE_LEAVE, traceMouse); function traceMouse(Evt:Event):void { trace("Mouse Left Stage"); }

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  • Submit event from few forms

    - by Coyod
    I have a two or more forms on my page in a row. I'm trying to hook submit event like: $('form',someObj).submit(function(e){ /* Do some stuff with ajax */ return false; }); But always receive events only from a first (by code) form. Also used each() function to bind event for each object, same thing.. What's wrong? Thanks!

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  • How to track folder permission event?

    - by Sushant
    Hi, This is about folder level permissions. We have a document library with break inheritance. While adding folders, sub folders through code, again we coded for break inheritance. Now the requirement is, when a user/group is added to subfolder permission list, we need to track this event. Which sharepoint event do we use and on what level. Please help.

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  • Explicit Event add/remove, misunderstood?

    - by Hammerstein
    I've been looking into memory management a lot recently and have been looking at how events are managed, now, I'm seeing the explicit add/remove syntax for the event subscription. I think it's pretty simple, add/remove just allows me to perform other logic when I subscribe and unsubscribe? Am I getting it, or is there more to it? Also, while I'm here, any advice / best practices for cleaning up my event handles.

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  • Animating and moving a draggable shape in KineticJS's dragend event

    - by user3712941
    I would like to animate moving a draggable shape to another position after it has been dragged in KineticJS. I would like to animate the movement of the shape over a period of time (for example, over 1 second). For example, I create a draggable shape and save its initial xy coordinates. I register a "dragend" event on this shape. Then, I drag the shape to a new position. When I release the drag, the dragend event is called. In that event function, I want to animate/ease the shape back to its original position. See my JSFiddle for a complete example: DragSample. (function () { //create variables at global scope var layer; var stage; var triangle; var triangleLastX = 190; var triangleLastY = 120; var tween; function initTween() { tween = new Kinetic.Tween({ node: triangle, duration: 1, easing: Kinetic.Easings.EaseInOut, x: 400, y: 200, }); } this.init = function () { layer = new Kinetic.Layer(); stage = new Kinetic.Stage({ container: 'container', width: 800, height: 600 }); triangle = new Kinetic.RegularPolygon({ x: 190, y: 120, sides: 3, radius: 80, fill: '#00D2FF', stroke: 'black', strokeWidth: 4, draggable: true }); triangle.on('dragstart', function () { triangleLastX = triangle.attrs.x; triangleLastY = triangle.attrs.y; }); triangle.on('dragend', function () { tween.play(); stage.draw(); }); layer.add(triangle); stage.add(layer); initTween (); } window.onload = init(); })(); I have tried doing this several ways. The last way I attempted to do this was using Kinetic's Tween(), however, when I play this Tween from the dragend event handler function, it moves the shape back to its original position immediately (i.e. the position when the drag started), then applies the Tween. Is there any way to achieve animating the movement of a draggable shape to its original position (or any other position for that matter) in dragend using KineticJS?

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  • Cancel onKey Event from onKey method

    - by user244190
    Is it possible to cancel an event from within the onKey method. I only want to allow numbers 0 through 9. If another key was pressed then I want to cancel the key press public boolean onKey(View v, int keyCode, KeyEvent ev) { // TODO Auto-generated method stub if(keyCode <30 || keyCode > 39){ //Cancel Event } return false; }

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  • Stop mouseDown event when mouseDoubleClick occured

    - by kilonet
    I have a control which is listened for both mouseDown and mouseDoubleClick events. However when mouseDoubleClick occure, I don't need mouseDown event to be handled. (Now both events fired when doubleClick happens) How can I stop handling mouseDown event when mouseDoubleClick occured?

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  • [C#] NodeMouseClick event doesn't work correctly ???

    - by Wayne
    i use a treeview to display files and folders like Windows Explorer. it has a NodeMouseClick event but sometimes when i click +, this event doesn't fire. private void treeView1_NodeMouseClick(object sender, TreeNodeMouseClickEventArgs e) { MessageBox.Show("node mouse click"); } can anyone explain for me why ? and how to know whenever i click + ? thanks in advance!

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  • question about windows controls changed event

    - by Mike
    I have several controls on my form and on changed event the logic entity properties are changed. Is it possible not to implement changed event for every control,but do it in one place and update my logic entity when user is making changes on the form?

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  • I need a row Added event for a DataGridView

    - by tizzyfoe
    What i want to do is set the background of a row based on some criteria, but the datagrid will be fairly large so i don't want to have to loop over all the rows again. The rows get created me doing something like "myDataGridView.DataSource = MyDataSource, so the only way i can think to edit rows is by using an event. there is a row*s* added event, but that gives me a list of rows that i'd have to iterate over. Thanks in advance for any help.

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  • Silverlight 4 Training Kit

    - by ScottGu
    We recently released a new free Silverlight 4 Training Kit that walks you through building business applications with Silverlight 4.  You can browse the training kit online or alternatively download an entire offline version of the training kit.  The training material is structured on teaching how to use the new Silverlight 4 features to build an end to end business application. The training kit includes 8 modules, 25 videos, and several hands on labs. Below is a breakdown and links to all of the content. [In addition to blogging, I am also now using Twitter for quick updates and to share links. Follow me at: twitter.com/scottgu] Module 1: Introduction Click here to watch this module. In this video John Papa and Ian Griffiths discuss the key areas that the Building Business Applications with Silverlight 4 course focuses on. This module is the overview of the course and covers many key scenarios that are faced when building business applications, and how Silverlight can help address them. Module 2: WCF RIA Services Click here to explore this module. In this lab, you will create a web site for managing conferences that will be the basis for the other labs in this course. Don’t worry if you don’t complete a particular lab in the series – all lab manual instructions are accompanied by completed solutions, so you can either build your own solution from start to finish, or dive straight in at any point using the solutions provided as a starting point. In this lab you will learn how to set up WCF RIA Services, create bindings to the domain context, filter using the domain data source, and create domain service queries. Online Link Download Source Download Lab Document Videos Module 2.1 - WCF RIA Services Ian Griffiths sets up the Entity Framework and WCF RIA Services for the sample Event Manager application for the course. He covers how to set up the services, how the Domain Services work and the role that the DomainContext plays in the sample application. He also reviews the metadata classes and integrating the navigation framework. Module 2.2 – Using WCF RIA Services to Edit Entities Ian Griffiths discusses how he adds the ability to edit and create individual entities with the features built into WCF RIA Services into the sample Event Manager application. He covers data binding fundamentals, IQueryable, LINQ, the DomainDataSource, navigation to a single entity using the navigation framework, and how to use the Visual Studio designer to do much of the work . Module 2.3 – Showing Master/Details Records Using WCF RIA Services Ian Griffiths reviews how to display master/detail records for the sample Event Manager application using WCF RIA Services. He covers how to use the Include attribute to indicate which elements to serialize back to the client. Ian also demonstrates how to use the Data Sources window in the designer to add and bind controls to specific data elements. He wraps up by showing how to create custom services to the Domain Services. Module 3 – Authentication, Validation, MVVM, Commands, Implicit Styles and RichTextBox Click here to visit this module. This lab demonstrates how to build a login screen, integrate ASP.NET authentication, and perform validation on data elements. Model-View-ViewModel (MVVM) is introduced and used in this lab as a pattern to help separate the UI and business logic. You will also learn how to use implicit styling and the new RichTextBox control. Online Link Download Source Download Lab Document Videos Module 3.1 – Authentication Ian Griffiths covers how to integrate a login screen and authentication into the sample Event Manager application. Ian shows how to use the ASP.NET authentication and integrate it into WCF RIA Services and the Silverlight presentation layer. Module 3.2 – MVVM Ian Griffiths covers how to Model-View-ViewModel (MVVM) patterns into the sample Event Manager application. He discusses why MVVM exists, what separated presentation means, and why it is important. He shows how to connect the View to the ViewModel, why data binding is important in this symbiosis, and how everything fits together in the overall application. Module 3.3 –Validation Ian Griffiths discusses how validation of user input can be integrated into the sample Event Manager application. He demonstrates how to use the DataAnnotations, the INotifyDataErrorInfo interface, binding markup extensions, and WCF RIA Services in concert to achieve great validation in the sample application. He discusses how this technique allows for property level validation, entity level validation, and asynchronous server side validation. Module 3.4 – Implicit Styles Ian Griffiths discusses how why implicit styles are important and how they can be integrated into the sample Event Manager application. He shows how implicit styles defined in a resource dictionary can be applied to all elements of a particular kind throughout the application. Module 3.5 – RichTextBox Ian Griffiths discusses how the new RichTextBox control and it can be integrated into the sample Event Manager application. He demonstrates how the RichTextBox can provide editing for the event information and how it can display the rich text for selection and copying. Module 4 – User Profiles, Drop Targets, Webcam and Clipboard Click here to visit this module. This lab builds new features into the sample application to take the user's photo. It teaches you how to use the webcam to capture an image, use Silverlight as a drop target, and take advantage of programmatic access to the clipboard. Link Download Source Download Lab Document Videos Module 4.1 – Webcam Ian Griffiths demonstrates how the webcam adds value to the sample Event Manager application by capturing an image of the attendee. He discusses the VideoCaptureDevice, the CaptureDviceConfiguration, and the CaptureSource classes and how they allow audio and video to be captured so you can grab an image from the capture device and save it. Module 4.2 - Drag and Drop in Silverlight Ian Griffiths demonstrates how to capture and handle the Drop in the sample Event Manager application so the user can drag a photo from a file and drop it into the application. Ian reviews the AllowDrop property, the Drop event, how to access the file that can be dropped, and the other drag related events. He also reviews how to make this work across browsers and the challenges for this. Module 5 – Schedule Planner and Right Mouse Click Click here to visit this module. This lab builds on the application to allow grouping in the DataGrid and implement right mouse click features to add context menu support. Link Download Source Download Lab Document Videos Module 5.1 – Grouping and Binding Ian Griffiths demonstrates how to use the grouping features for data binding in the DataGrid and how it applies to the sample Event Manager application. He reviews the role of the CollectionViewSource in grouping, customizing the templates for headers, and how to work with grouping with ItemsControls. Module 5.2 – Layout Visual States Ian Griffiths demonstrates how to use the Fluid UI animation support for visual states in the ListBox control DataGrid and how it applies to the sample Event Manager application. He reviews the 3 visual states of BeforeLoaded, AfterLoaded, and BeforeUnloaded. Module 5.3 – Right Mouse Click Ian Griffiths demonstrates how to add support for handling the right mouse button click event to display a context menu for the Event Manager application. He demonstrates how to handle the event, show a custom context menu control, and integrate it into the scheduling portion of the application. Module 6 – Printing the Schedule Click here to visit this module. This lab teaches how to use the new printing features in Silverlight 4. The lab walks through the PrintDocument class and the ViewBox control, while showing how to print multiple pages of content using them. Link Download Source Download Lab Document Videos Module 6.1 – Printing and the Viewbox Ian Griffiths demonstrates how to add the ability to print the schedule to the sample Event Manager application. He walks through the importance of the PrintDocument class and its members. He also shows how to handle printing the visual tree and how the ViewBox control can help. Module 6.2 – Multi Page Printing Ian Griffiths expands on his printing discussion by showing how to handle printing multiple pages of content for the sample Event Manager application. He shows how to paginate the content and points out various tips to keep in mind when determining the printable area. Module 7 – Running the Event Dashboard Out of Browser Click here to visit this module. This lab builds a dashboard for the sample application while explaining the fundamentals of the out of browser features, how to handle authentication, displaying notifications (toasts), and how to use native integration to use COM Interop with Silverlight. Link Download Source Download Lab Document Videos Module 7.1 – Out of Browser Ian Griffiths discusses the role of an Out of Browser application for administrators to manage the events and users in the sample Event Manager application. He discusses several reasons why out of browser applications may better suit your needs including custom chrome, toasts, window placement, cross domain access, and file access. He demonstrates the basic technique to take your application and make it work out of browser using the tools. Module 7.2 – NotificationWindow (Toasts) for Elevated Trust Out of Browser Applications Ian Griffiths discusses the how toasts can be used in the sample Event Manager application to show information that may require the user's attention. Ian covers how to create a toast using the NotificationWindow, security implications, and how to make the toast appear as needed. Module 7.3 – Out of Browser Window Placement Ian Griffiths discusses the how to manage the window positioning when building an out of browser application, handling the windows state, and controlling and handling activation of the window. Module 7.4 – Out of Browser Elevated Trust Application Overview Ian Griffiths discusses the implications of creating trusted out of browser application for the Event Manager sample application. He reviews why you might want to use elevated trust, what features is opens to you, and how to take advantage of them. Topics Ian covers include the dynamic keyword in C# 4, the AutomationFactory class, the API to check if you are in a trusted application, and communicating with Excel. Module 8 – Advanced Out of Browser and MEF Click here to visit this module. This hands-on lab walks through the creation of a trusted out of browser application and the new functionality that comes with that. You will learn to use COM Automation, handle the window closing event, set custom window chrome, digitally sign your Silverlight out of browser trusted application, create a silent install option, and take advantage of MEF. Link Download Source Download Lab Document Videos Module 8.1 – Custom Window Chrome for Elevated Trust Out of Browser Applications Ian Griffiths discusses how to replace the standard operating system window chrome with customized chrome for an elevated trusted out of browser application. He covers how it is important to handle close, resize, minimize, and maximize events. Ian mentions that the tooling was not ready when he shot this video, but the good news is that the tooling now supports setting the custom chrome directly from the property page for the Silverlight application. Module 8.2 – Window Closing Event for Out of Browser Applications Ian Griffiths discusses the WindowClosing event and how to handle and optionally cancel the event. Module 8.3 – Silent Install of Out of Browser Applications Ian Griffiths discusses how to use the SLLauncher executable to install an out of browser application. He discusses the optional command line switches that can be set including how the emulate switch can help you emulate the install process. Ian also shows how to setup a shortcut for the application and tell the application where it should look for future updates online. Module 8.4 – Digitally Signing Out of Browser Application Ian Griffiths discusses how and why to digitally sign an out of browser application using the signtool program. He covers what trusted certificates are, the implications of signing (or not signing), and the effect on the user experience. Module 8.5 – The Value of MEF with Silverlight Ian Griffiths discusses what MEF is, how your application can benefit from it, and the fundamental features it puts at your disposal. He covers the 3 step import, export and compose process as well as how to dynamically import XAP files using MEF. Summary As you can probably tell from the long list above – this series contains a ton of great content, and hopefully provides a nice end-to-end walkthrough that helps explain how to take advantage of Silverlight 4 (and all its new features).  Hope this helps, Scott

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  • Improving Partitioned Table Join Performance

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

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  • Class Loading Deadlocks

    - by tomas.nilsson
    Mattis follows up on his previous post with one more expose on Class Loading Deadlocks As I wrote in a previous post, the class loading mechanism in Java is very powerful. There are many advanced techniques you can use, and when used wrongly you can get into all sorts of trouble. But one of the sneakiest deadlocks you can run into when it comes to class loading doesn't require any home made class loaders or anything. All you need is classes depending on each other, and some bad luck. First of all, here are some basic facts about class loading: 1) If a thread needs to use a class that is not yet loaded, it will try to load that class 2) If another thread is already loading the class, the first thread will wait for the other thread to finish the loading 3) During the loading of a class, one thing that happens is that the <clinit method of a class is being run 4) The <clinit method initializes all static fields, and runs any static blocks in the class. Take the following class for example: class Foo { static Bar bar = new Bar(); static { System.out.println("Loading Foo"); } } The first time a thread needs to use the Foo class, the class will be initialized. The <clinit method will run, creating a new Bar object and printing "Loading Foo" But what happens if the Bar object has never been used before either? Well, then we will need to load that class as well, calling the Bar <clinit method as we go. Can you start to see the potential problem here? A hint is in fact #2 above. What if another thread is currently loading class Bar? The thread loading class Foo will have to wait for that thread to finish loading. But what happens if the <clinit method of class Bar tries to initialize a Foo object? That thread will have to wait for the first thread, and there we have the deadlock. Thread one is waiting for thread two to initialize class Bar, thread two is waiting for thread one to initialize class Foo. All that is needed for a class loading deadlock is static cross dependencies between two classes (and a multi threaded environment): class Foo { static Bar b = new Bar(); } class Bar { static Foo f = new Foo(); } If two threads cause these classes to be loaded at exactly the same time, we will have a deadlock. So, how do you avoid this? Well, one way is of course to not have these circular (static) dependencies. On the other hand, it can be very hard to detect these, and sometimes your design may depend on it. What you can do in that case is to make sure that the classes are first loaded single threadedly, for example during an initialization phase of your application. The following program shows this kind of deadlock. To help bad luck on the way, I added a one second sleep in the static block of the classes to trigger the unlucky timing. Notice that if you uncomment the "//Foo f = new Foo();" line in the main method, the class will be loaded single threadedly, and the program will terminate as it should. public class ClassLoadingDeadlock { // Start two threads. The first will instansiate a Foo object, // the second one will instansiate a Bar object. public static void main(String[] arg) { // Uncomment next line to stop the deadlock // Foo f = new Foo(); new Thread(new FooUser()).start(); new Thread(new BarUser()).start(); } } class FooUser implements Runnable { public void run() { System.out.println("FooUser causing class Foo to be loaded"); Foo f = new Foo(); System.out.println("FooUser done"); } } class BarUser implements Runnable { public void run() { System.out.println("BarUser causing class Bar to be loaded"); Bar b = new Bar(); System.out.println("BarUser done"); } } class Foo { static { // We are deadlock prone even without this sleep... // The sleep just makes us more deterministic try { Thread.sleep(1000); } catch(InterruptedException e) {} } static Bar b = new Bar(); } class Bar { static { try { Thread.sleep(1000); } catch(InterruptedException e) {} } static Foo f = new Foo(); }

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  • Using Native Drag and Drop in HTML 5 pages

    - by nikolaosk
    This is going to be the eighth post in a series of posts regarding HTML 5. You can find the other posts here, here , here , here, here , here and here. In this post I will show you how to implement Drag and Drop functionality in an HTML 5 page using JQuery.This is a great functionality and we do not need to resort anymore to plugins like Silverlight and Flash to achieve this great feature. This is also called a native approach on Drag and Drop.I will use some events and I will write code to respond when these events are fired.As I said earlier we need to write Javascript to implement the drag and drop functionality. I will use the very popular JQuery Library. Please download the library (minified version) from http://jquery.com/downloadI will create a simple HTML page.There will be two thumbnails pics on it. There will also be the drag and drop area where the user will drag the thumb pics into it and they will resize to their actual size. The HTML markup for the page follows<!doctype html><html lang="en"><head><title>Liverpool Legends Gallery</title><meta charset="utf-8"><link rel="stylesheet" type="text/css" href="style.css"><script type="text/javascript" charset="utf-8" src="jquery-1.8.1.min.js"></script>  <script language="JavaScript" src="drag.js"></script>   </head><body><header><h1>A page dedicated to Liverpool Legends</h1><h2>Drag and Drop the thumb image in the designated area to see the full image</h2></header><div id="main"><img src="thumbs/steven-gerrard.jpg"  big="large-images/steven-gerrard-large.jpg" alt="John Barnes"><img src="thumbs/robbie-fowler.jpg" big="large-images/robbie-fowler-large.jpg" alt="Ian Rush"><div id="drag"><p>Drop your image here</p> </div></body></html> There is nothing difficult or fancy in the HTML markup above. I have a link to the external JQuery library and another javascript file that I will implement the whole drag and drop functionality.The code for the css file (style.css) follows#main{  float: left;  width: 340px;  margin-right: 30px;}#drag{  float: left;  width: 400px;  height:300px;  background-color: #c0c0c0;}These are simple CSS rules. This post cannot be a tutorial on CSS.For all these posts I assume that you have the basic HTML,CSS,Javascript skills.Now I am going to create a javascript file (drag.js) to implement the drag and drop functionality.I will provide the whole code for the drag.js file and then I will explain what I am doing in each step.$(function() {          var players = $('#main img');          players.attr('draggable', 'true');                    players.bind('dragstart', function(event) {              var data = event.originalEvent.dataTransfer;               var src = $(this).attr("big");              data.setData("Text", src);               return true;          });          var target = $('#drag');          target.bind('drop', function(event) {            var data = event.originalEvent.dataTransfer;            var src = ( data.getData('Text') );                         var img = $("<img></img>").attr("src", src);            $(this).html(img);            if (event.preventDefault) event.preventDefault();            return(false);          });                   target.bind('dragover', function(event) {                if (event.preventDefault) event.preventDefault();            return false;          });           players.bind('dragend', function(event) {             if (event.preventDefault) event.preventDefault();             return false;           });        });   In these lines var players = $('#main img'); players.attr('draggable', 'true');We grab all the images in the #main div and store them in a variable and then make them draggable.Then in following lines I am using the dragstart event.  players.bind('dragstart', function(event) {              var data = event.originalEvent.dataTransfer;               var src = $(this).attr("big");              data.setData("Text", src);               return true;          }); In this event I am associating the custom data attribute value with the item I am dragging.Then I create a variable to get hold of the dropping area var target = $('#drag'); Then in the following lines I implement the drop event and what happens when the user drops the image in the designated area on the page. target.bind('drop', function(event) {            var data = event.originalEvent.dataTransfer;            var src = ( data.getData('Text') );                         var img = $("<img></img>").attr("src", src);            $(this).html(img);            if (event.preventDefault) event.preventDefault();            return(false);          }); The dragend  event is fired when the user has finished the drag operation        players.bind('dragend', function(event) {             if (event.preventDefault) event.preventDefault();             return false;           }); When this method event.preventDefault() is called , the default action of the event will not be triggered.Please have a look a the picture below to see how the page looks before the drag and drop takes place. Then simply I drag and drop a picture in the dropping area.Have a look at the picture below It works!!! Hope it helps!!  

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  • How many threads should an Android game use?

    - by kvance
    At minimum, an OpenGL Android game has a UI thread and a Renderer thread created by GLSurfaceView. Renderer.onDrawFrame() should be doing a minimum of work to get the higest FPS. The physics, AI, etc. don't need to run every frame, so we can put those in another thread. Now we have: Renderer thread - Update animations and draw polys Game thread - Logic & periodic physics, AI, etc. updates UI thread - Android UI interaction only Since you don't ever want to block the UI thread, I run one more thread for the game logic. Maybe that's not necessary though? Is there ever a reason to run game logic in the renderer thread?

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