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  • Databinding to the DataGridView (Enums + Collections)

    - by Ian
    I'm after a little help with the techniques to use for Databinding. It's been quite a while since I used any proper data binding and want to try and do something with the DataGridView. I'm trying to configure as much as possible so that I can simply designed the DatagridView through the form editor, and then use a custom class that exposes all my information. The sort of information I've got is as follows: public class Result { public String Name { get; set; } public Boolean PK { get; set; } public MyEnum EnumValue { get; set; } public IList<ResultInfos> { get; set; } } public class ResultInfos { get; set; } { public class Name { get; set; } public Int Value { get; set; } public override String ToString() { return Name + " : " Value.ToString(); } } I can bind to the simple information without any problem. I want to bind to the EnumValue with a DataGridViewComboBoxColumn, but when I set the DataPropertyName I get exceptions saying the enum values aren't valid. Then comes the ResultInfo collection. Currently I can't figure out how to bind to this and display my items, again really I want this to be a combobox, where the 1st Item is selected. Anyone any suggestions on what I'm doing wrong? Thanks

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  • better way of showing File Upload Errors?

    - by coure06
    Model: public class EmailAttachment { public string FileName { get; set; } public string FileType { get; set; } public int FileSize { get; set; } public Stream FileData { get; set; } } public class ContactEmail: IDataErrorInfo { public string Name { get; set; } public string Email { get; set; } public string Message { get; set; } public EmailAttachment Attachment { get; set; } public string Error { get { return null; } } public string this[string propName] { get { if (propName == "Name" && String.IsNullOrEmpty(Name)) return "Please Enter your Name"; if (propName == "Email"){ if(String.IsNullOrEmpty(Email)) return "Please Provide an Email Address"; else if(!Regex.IsMatch(Email, ".+\\@.+\\..+")) return "Please Enter a valid email Address"; } if (propName == "Message" && String.IsNullOrEmpty(Message)) return "Please Enter your Message"; return null; } }} And my controller file [AcceptVerbs(HttpVerbs.Post)] public ActionResult Con(ContactEmail ce, HttpPostedFileBase file) { return View(); } Now the Problem From the form i am getting Name,Email, Message and uploaded file. I can get validation errors automatically for Name,Email,Message using public string this[string propName]. How can i show validation errors if Attachment.FileSize 10000? If i write its code in public string this[string propName] i alwasy getting Attachment null. How can i fill Attachment Object of ContactEmail so that i can manage all errors on same place?

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  • Would like some modelling tips for dependent values

    - by orjan
    I'm working on a model for a simple fishing competition and I have some issues with my design. The main class for the fishing game is Capture and it looks like this: public class Capture : Entity { public virtual int Weight { get; set; } public virtual int Length { get; set; } public virtual DateTime DateForCapture { get; set; } public virtual User CapturedBy { get; set; } public virtual Species Species { get; set; } } So far there´s no problem but I'm not really sure how to model the game. Every Species is connected to a reference weight that changes from year to year The number of point for a capture is its Weight divided by the current reference weight for the species. One way to solve the problem is to connect a capture to SpeciesReferenceWeight instead of Species public class SpeciesReferenceWeight : Entity { public virtual Species Species { get; set; } public virtual int ReferenceWeight { get; set; } public virtual int Year { get; set; } } But in that way that Capture is connected to the implementation details of the game and from my point of view a capture is still a capture even if it's not included in a game. The result I'm aiming for is like: http://hornalen.net/fishbonkern/2007/ that I wrote a couple of years ago with brute force sql and no domain model. I would be very happy for all kinds of feeback on this issue.

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  • Entity Framework - Store parent reference on child relationship (one -> many)

    - by contactmatt
    I have a setup like this: [Table("tablename...")] public class Branch { public Branch() { Users = new List<User>(); } [Key] public int Id { get; set; } public string Name { get; set; } public List<User> Users { get; set; } } [Table("tablename...")] public class User { [Key] public int Id {get; set; } public string Username { get; set; } public string Password { get; set; } [ForeignKey("ParentBranch")] public int? ParentBranchId { get; set; } // Is this possible? public Branch ParentBranch { get; set; } // ??? } Is it possible for the User to know what parent branch it belongs to? The code above is not working. Entity Framework version 5.0 .NET 4.0 c#

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  • Separating code logic from the actual data structures. Best practices?

    - by Patrick
    I have an application that loads lots of data into memory (this is because it needs to perform some mathematical simulation on big data sets). This data comes from several database tables, that all refer to each other. The consistency rules on the data are rather complex, and looking up all the relevant data requires quite some hashes and other additional data structures on the data. Problem is that this data may also be changed interactively by the user in a dialog. When the user presses the OK button, I want to perform all the checks to see that he didn't introduce inconsistencies in the data. In practice all the data needs to be checked at once, so I cannot update my data set incrementally and perform the checks one by one. However, all the checking code work on the actual data set loaded in memory, and use the hashing and other data structures. This means I have to do the following: Take the user's changes from the dialog Apply them to the big data set Perform the checks on the big data set Undo all the changes if the checks fail I don't like this solution since other threads are also continuously using the data set, and I don't want to halt them while performing the checks. Also, the undo means that the old situation needs to be put aside, which is also not possible. An alternative is to separate the checking code from the data set (and let it work on explicitly given data, e.g. coming from the dialog) but this means that the checking code cannot use hashing and other additional data structures, because they only work on the big data set, making the checks much slower. What is a good practice to check user's changes on complex data before applying them to the 'application's' data set?

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  • Windows 7 Phone Database – Querying with Views and Filters

    - by SeanMcAlinden
    I’ve just added a feature to Rapid Repository to greatly improve how the Windows 7 Phone Database is queried for performance (This is in the trunk not in Release V1.0). The main concept behind it is to create a View Model class which would have only the minimum data you need for a page. This View Model is then stored and retrieved rather than the whole list of entities. Another feature of the views is that they can be pre-filtered to even further improve performance when querying. You can download the source from the Microsoft Codeplex site http://rapidrepository.codeplex.com/. Setting up a view Lets say you have an entity that stores lots of data about a game result for example: GameScore entity public class GameScore : IRapidEntity {     public Guid Id { get; set; }     public string GamerId {get;set;}     public string Name { get; set; }     public Double Score { get; set; }     public Byte[] ThumbnailAvatar { get; set; }     public DateTime DateAdded { get; set; } }   On your page you want to display a list of scores but you only want to display the score and the date added, you create a View Model for displaying just those properties. GameScoreView public class GameScoreView : IRapidView {     public Guid Id { get; set; }     public Double Score { get; set; }     public DateTime DateAdded { get; set; } }   Now you have the view model, the first thing to do is set up the view at application start up. This is done using the following syntax. View Setup public MainPage() {     RapidRepository<GameScore>.AddView<GameScoreView>(x => new GameScoreView { DateAdded = x.DateAdded, Score = x.Score }); } As you can see, using a little bit of lambda syntax, you put in the code for constructing a single view, this is used internally for mapping an entity to a view. *Note* you do not need to map the Id property, this is done automatically, a view model id will always be the same as it’s corresponding entity.   Adding Filters One of the cool features of the view is that you can add filters to limit the amount of data stored in the view, this will dramatically improve performance. You can add multiple filters using the fluent syntax if required. In this example, lets say that you will only ever show the scores for the last 10 days, you could add a filter like the following: Add single filter public MainPage() {     RapidRepository<GameScore>.AddView<GameScoreView>(x => new GameScoreView { DateAdded = x.DateAdded, Score = x.Score })         .AddFilter(x => x.DateAdded > DateTime.Now.AddDays(-10)); } If you wanted to further limit the data, you could also say only scores above 100: Add multiple filters public MainPage() {     RapidRepository<GameScore>.AddView<GameScoreView>(x => new GameScoreView { DateAdded = x.DateAdded, Score = x.Score })         .AddFilter(x => x.DateAdded > DateTime.Now.AddDays(-10))         .AddFilter(x => x.Score > 100); }   Querying the view model So the important part is how to query the data. This is done using the repository, there is a method called Query which accepts the type of view as a generic parameter (you can have multiple View Model types per entity type) You can either use the result of the query method directly or perform further querying on the result is required. Querying the View public void DisplayScores() {     RapidRepository<GameScore> repository = new RapidRepository<GameScore>();     List<GameScoreView> scores = repository.Query<GameScoreView>();       // display logic } Further Filtering public void TodaysScores() {     RapidRepository<GameScore> repository = new RapidRepository<GameScore>();     List<GameScoreView> todaysScores = repository.Query<GameScoreView>().Where(x => x.DateAdded > DateTime.Now.AddDays(-1)).ToList();       // display logic }   Retrieving the actual entity Retrieving the actual entity can be done easily by using the GetById method on the repository. Say for example you allow the user to click on a specific score to get further information, you can use the Id populated in the returned View Model GameScoreView and use it directly on the repository to retrieve the full entity. Get Full Entity public void GetFullEntity(Guid gameScoreViewId) {     RapidRepository<GameScore> repository = new RapidRepository<GameScore>();     GameScore fullEntity = repository.GetById(gameScoreViewId);       // display logic } Synchronising The View If you are upgrading from Rapid Repository V1.0 and are likely to have data in the repository already, you will need to perform a synchronisation to ensure the views and entities are fully in sync. You can either do this as a one off during the application upgrade or if you are a little more cautious, you could run this at each application start up. Synchronise the view public void MyUpgradeTasks() {     RapidRepository<GameScore>.SynchroniseView<GameScoreView>(); } It’s worth noting that in normal operation, the view keeps itself in sync with the entities so this is only really required if you are upgrading from V1.0 to V2.0 when it gets released shortly.   Summary I really hope you like this feature, it will be great for performance and I believe supports good practice by promoting the use of View Models for specific pages. I’m hoping to produce a beta for this over the next few days, I just want to add some more tests and hopefully iron out any bugs. I would really appreciate any thoughts on this feature and would really love to know of any bugs you find. You can download the source from the following : http://rapidrepository.codeplex.com/ Kind Regards, Sean McAlinden.

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  • javafx tableview get selected data from ObservableList

    - by user3717821
    i am working on a javafx project and i need your help . while i am trying to get selected data from table i can get selected data from normal cell but can't get data from ObservableList inside tableview. code for my database: -- phpMyAdmin SQL Dump -- version 4.0.4 -- http://www.phpmyadmin.net -- -- Host: localhost -- Generation Time: Jun 10, 2014 at 06:20 AM -- Server version: 5.1.33-community -- PHP Version: 5.4.12 SET SQL_MODE = "NO_AUTO_VALUE_ON_ZERO"; SET time_zone = "+00:00"; /*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */; /*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */; /*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */; /*!40101 SET NAMES utf8 */; -- -- Database: `test` -- -- -------------------------------------------------------- -- -- Table structure for table `customer` -- CREATE TABLE IF NOT EXISTS `customer` ( `col0` int(11) NOT NULL, `col1` varchar(255) DEFAULT NULL, `col2` int(11) DEFAULT NULL, PRIMARY KEY (`col0`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1; -- -- Dumping data for table `customer` -- INSERT INTO `customer` (`col0`, `col1`, `col2`) VALUES (12, 'adasdasd', 231), (22, 'adasdasd', 231), (212, 'adasdasd', 231); /*!40101 SET CHARACTER_SET_CLIENT=@OLD_CHARACTER_SET_CLIENT */; /*!40101 SET CHARACTER_SET_RESULTS=@OLD_CHARACTER_SET_RESULTS */; /*!40101 SET COLLATION_CONNECTION=@OLD_COLLATION_CONNECTION */; my javafx codes: import java.sql.Connection; import java.sql.DriverManager; import java.sql.ResultSet; import java.sql.SQLException; import java.util.Map; import javafx.application.Application; import javafx.beans.property.SimpleStringProperty; import javafx.beans.value.ChangeListener; import javafx.beans.value.ObservableValue; import javafx.collections.FXCollections; import javafx.collections.ObservableList; import javafx.event.ActionEvent; import javafx.event.EventHandler; import javafx.scene.Scene; import javafx.scene.control.Button; import javafx.scene.control.TableCell; import javafx.scene.control.TableColumn; import javafx.scene.control.TableColumn.CellDataFeatures; import javafx.scene.control.TablePosition; import javafx.scene.control.TableView; import javafx.scene.control.TableView.TableViewSelectionModel; import javafx.scene.control.cell.ChoiceBoxTableCell; import javafx.scene.control.cell.TextFieldTableCell; import javafx.scene.layout.BorderPane; import javafx.stage.Stage; import javafx.util.Callback; import javafx.util.StringConverter; class DBConnector { private static Connection conn; private static String url = "jdbc:mysql://localhost/test"; private static String user = "root"; private static String pass = "root"; public static Connection connect() throws SQLException{ try{ Class.forName("com.mysql.jdbc.Driver").newInstance(); }catch(ClassNotFoundException cnfe){ System.err.println("Error: "+cnfe.getMessage()); }catch(InstantiationException ie){ System.err.println("Error: "+ie.getMessage()); }catch(IllegalAccessException iae){ System.err.println("Error: "+iae.getMessage()); } conn = DriverManager.getConnection(url,user,pass); return conn; } public static Connection getConnection() throws SQLException, ClassNotFoundException{ if(conn !=null && !conn.isClosed()) return conn; connect(); return conn; } } public class DynamicTable extends Application{ Object newValue; //TABLE VIEW AND DATA private ObservableList<ObservableList> data; private TableView<ObservableList> tableview; //MAIN EXECUTOR public static void main(String[] args) { launch(args); } //CONNECTION DATABASE public void buildData(){ tableview.setEditable(true); Callback<TableColumn<Map, String>, TableCell<Map, String>> cellFactoryForMap = new Callback<TableColumn<Map, String>, TableCell<Map, String>>() { @Override public TableCell call(TableColumn p) { return new TextFieldTableCell(new StringConverter() { @Override public String toString(Object t) { return t.toString(); } @Override public Object fromString(String string) { return string; } }); } }; Connection c ; data = FXCollections.observableArrayList(); try{ c = DBConnector.connect(); //SQL FOR SELECTING ALL OF CUSTOMER String SQL = "SELECT * from CUSTOMer"; //ResultSet ResultSet rs = c.createStatement().executeQuery(SQL); /********************************** * TABLE COLUMN ADDED DYNAMICALLY * **********************************/ for(int i=0 ; i<rs.getMetaData().getColumnCount(); i++){ //We are using non property style for making dynamic table final int j = i; TableColumn col = new TableColumn(rs.getMetaData().getColumnName(i+1)); if(j==1){ final ObservableList<String> logLevelList = FXCollections.observableArrayList("FATAL", "ERROR", "WARN", "INFO", "INOUT", "DEBUG"); col.setCellFactory(ChoiceBoxTableCell.forTableColumn(logLevelList)); tableview.getColumns().addAll(col); } else{ col.setCellValueFactory(new Callback<CellDataFeatures<ObservableList,String>,ObservableValue<String>>(){ public ObservableValue<String> call(CellDataFeatures<ObservableList, String> param) { return new SimpleStringProperty(param.getValue().get(j).toString()); } }); tableview.getColumns().addAll(col); } if(j!=1) col.setCellFactory(cellFactoryForMap); System.out.println("Column ["+i+"] "); } /******************************** * Data added to ObservableList * ********************************/ while(rs.next()){ //Iterate Row ObservableList<String> row = FXCollections.observableArrayList(); for(int i=1 ; i<=rs.getMetaData().getColumnCount(); i++){ //Iterate Column row.add(rs.getString(i)); } System.out.println("Row [1] added "+row ); data.add(row); } //FINALLY ADDED TO TableView tableview.setItems(data); }catch(Exception e){ e.printStackTrace(); System.out.println("Error on Building Data"); } } @Override public void start(Stage stage) throws Exception { //TableView Button showDataButton = new Button("Add"); showDataButton.setOnAction(new EventHandler<ActionEvent>() { public void handle(ActionEvent event) { ObservableList<String> row = FXCollections.observableArrayList(); for(int i=1 ; i<=3; i++){ //Iterate Column row.add("asdasd"); } data.add(row); //FINALLY ADDED TO TableView tableview.setItems(data); } }); tableview = new TableView(); buildData(); //Main Scene BorderPane root = new BorderPane(); root.setCenter(tableview); root.setBottom(showDataButton); Scene scene = new Scene(root,500,500); stage.setScene(scene); stage.show(); tableview.getSelectionModel().selectedItemProperty().addListener(new ChangeListener() { @Override public void changed(ObservableValue observableValue, Object oldValue, Object newValue) { //Check whether item is selected and set value of selected item to Label if (tableview.getSelectionModel().getSelectedItem() != null) { TableViewSelectionModel selectionModel = tableview.getSelectionModel(); ObservableList selectedCells = selectionModel.getSelectedCells(); TablePosition tablePosition = (TablePosition) selectedCells.get(0); Object val = tablePosition.getTableColumn().getCellData(newValue); System.out.println("Selected Value " + val); System.out.println("Selected row " + newValue); } } }); } } please help me..

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  • C# 4: The Curious ConcurrentDictionary

    - by James Michael Hare
    In my previous post (here) I did a comparison of the new ConcurrentQueue versus the old standard of a System.Collections.Generic Queue with simple locking.  The results were exactly what I would have hoped, that the ConcurrentQueue was faster with multi-threading for most all situations.  In addition, concurrent collections have the added benefit that you can enumerate them even if they're being modified. So I set out to see what the improvements would be for the ConcurrentDictionary, would it have the same performance benefits as the ConcurrentQueue did?  Well, after running some tests and multiple tweaks and tunes, I have good and bad news. But first, let's look at the tests.  Obviously there's many things we can do with a dictionary.  One of the most notable uses, of course, in a multi-threaded environment is for a small, local in-memory cache.  So I set about to do a very simple simulation of a cache where I would create a test class that I'll just call an Accessor.  This accessor will attempt to look up a key in the dictionary, and if the key exists, it stops (i.e. a cache "hit").  However, if the lookup fails, it will then try to add the key and value to the dictionary (i.e. a cache "miss").  So here's the Accessor that will run the tests: 1: internal class Accessor 2: { 3: public int Hits { get; set; } 4: public int Misses { get; set; } 5: public Func<int, string> GetDelegate { get; set; } 6: public Action<int, string> AddDelegate { get; set; } 7: public int Iterations { get; set; } 8: public int MaxRange { get; set; } 9: public int Seed { get; set; } 10:  11: public void Access() 12: { 13: var randomGenerator = new Random(Seed); 14:  15: for (int i=0; i<Iterations; i++) 16: { 17: // give a wide spread so will have some duplicates and some unique 18: var target = randomGenerator.Next(1, MaxRange); 19:  20: // attempt to grab the item from the cache 21: var result = GetDelegate(target); 22:  23: // if the item doesn't exist, add it 24: if(result == null) 25: { 26: AddDelegate(target, target.ToString()); 27: Misses++; 28: } 29: else 30: { 31: Hits++; 32: } 33: } 34: } 35: } Note that so I could test different implementations, I defined a GetDelegate and AddDelegate that will call the appropriate dictionary methods to add or retrieve items in the cache using various techniques. So let's examine the three techniques I decided to test: Dictionary with mutex - Just your standard generic Dictionary with a simple lock construct on an internal object. Dictionary with ReaderWriterLockSlim - Same Dictionary, but now using a lock designed to let multiple readers access simultaneously and then locked when a writer needs access. ConcurrentDictionary - The new ConcurrentDictionary from System.Collections.Concurrent that is supposed to be optimized to allow multiple threads to access safely. So the approach to each of these is also fairly straight-forward.  Let's look at the GetDelegate and AddDelegate implementations for the Dictionary with mutex lock: 1: var addDelegate = (key,val) => 2: { 3: lock (_mutex) 4: { 5: _dictionary[key] = val; 6: } 7: }; 8: var getDelegate = (key) => 9: { 10: lock (_mutex) 11: { 12: string val; 13: return _dictionary.TryGetValue(key, out val) ? val : null; 14: } 15: }; Nothing new or fancy here, just your basic lock on a private object and then query/insert into the Dictionary. Now, for the Dictionary with ReadWriteLockSlim it's a little more complex: 1: var addDelegate = (key,val) => 2: { 3: _readerWriterLock.EnterWriteLock(); 4: _dictionary[key] = val; 5: _readerWriterLock.ExitWriteLock(); 6: }; 7: var getDelegate = (key) => 8: { 9: string val; 10: _readerWriterLock.EnterReadLock(); 11: if(!_dictionary.TryGetValue(key, out val)) 12: { 13: val = null; 14: } 15: _readerWriterLock.ExitReadLock(); 16: return val; 17: }; And finally, the ConcurrentDictionary, which since it does all it's own concurrency control, is remarkably elegant and simple: 1: var addDelegate = (key,val) => 2: { 3: _concurrentDictionary[key] = val; 4: }; 5: var getDelegate = (key) => 6: { 7: string s; 8: return _concurrentDictionary.TryGetValue(key, out s) ? s : null; 9: };                    Then, I set up a test harness that would simply ask the user for the number of concurrent Accessors to attempt to Access the cache (as specified in Accessor.Access() above) and then let them fly and see how long it took them all to complete.  Each of these tests was run with 10,000,000 cache accesses divided among the available Accessor instances.  All times are in milliseconds. 1: Dictionary with Mutex Locking 2: --------------------------------------------------- 3: Accessors Mostly Misses Mostly Hits 4: 1 7916 3285 5: 10 8293 3481 6: 100 8799 3532 7: 1000 8815 3584 8:  9:  10: Dictionary with ReaderWriterLockSlim Locking 11: --------------------------------------------------- 12: Accessors Mostly Misses Mostly Hits 13: 1 8445 3624 14: 10 11002 4119 15: 100 11076 3992 16: 1000 14794 4861 17:  18:  19: Concurrent Dictionary 20: --------------------------------------------------- 21: Accessors Mostly Misses Mostly Hits 22: 1 17443 3726 23: 10 14181 1897 24: 100 15141 1994 25: 1000 17209 2128 The first test I did across the board is the Mostly Misses category.  The mostly misses (more adds because data requested was not in the dictionary) shows an interesting trend.  In both cases the Dictionary with the simple mutex lock is much faster, and the ConcurrentDictionary is the slowest solution.  But this got me thinking, and a little research seemed to confirm it, maybe the ConcurrentDictionary is more optimized to concurrent "gets" than "adds".  So since the ratio of misses to hits were 2 to 1, I decided to reverse that and see the results. So I tweaked the data so that the number of keys were much smaller than the number of iterations to give me about a 2 to 1 ration of hits to misses (twice as likely to already find the item in the cache than to need to add it).  And yes, indeed here we see that the ConcurrentDictionary is indeed faster than the standard Dictionary here.  I have a strong feeling that as the ration of hits-to-misses gets higher and higher these number gets even better as well.  This makes sense since the ConcurrentDictionary is read-optimized. Also note that I tried the tests with capacity and concurrency hints on the ConcurrentDictionary but saw very little improvement, I think this is largely because on the 10,000,000 hit test it quickly ramped up to the correct capacity and concurrency and thus the impact was limited to the first few milliseconds of the run. So what does this tell us?  Well, as in all things, ConcurrentDictionary is not a panacea.  It won't solve all your woes and it shouldn't be the only Dictionary you ever use.  So when should we use each? Use System.Collections.Generic.Dictionary when: You need a single-threaded Dictionary (no locking needed). You need a multi-threaded Dictionary that is loaded only once at creation and never modified (no locking needed). You need a multi-threaded Dictionary to store items where writes are far more prevalent than reads (locking needed). And use System.Collections.Concurrent.ConcurrentDictionary when: You need a multi-threaded Dictionary where the writes are far more prevalent than reads. You need to be able to iterate over the collection without locking it even if its being modified. Both Dictionaries have their strong suits, I have a feeling this is just one where you need to know from design what you hope to use it for and make your decision based on that criteria.

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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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  • Oracle Solaris Cluster 4.2 Event and its SNMP Interface

    - by user12609115
    Background The cluster event SNMP interface was first introduced in Oracle Solaris Cluster 3.2 release. The details of the SNMP interface are described in the Oracle Solaris Cluster System Administration Guide and the Cluster 3.2 SNMP blog. Prior to the Oracle Solaris Cluster 4.2 release, when the event SNMP interface was enabled, it would take effect on WARNING or higher severity events. The events with WARNING or higher severity are usually for the status change of a cluster component from ONLINE to OFFLINE. The interface worked like an alert/alarm interface when some components in the cluster were out of service (changed to OFFLINE). The consumers of this interface could not get notification for all status changes and configuration changes in the cluster. Cluster Event and its SNMP Interface in Oracle Solaris Cluster 4.2 The user model of the cluster event SNMP interface is the same as what was provided in the previous releases. The cluster event SNMP interface is not enabled by default on a freshly installed cluster; you can enable it by using the cluster event SNMP administration commands on any cluster nodes. Usually, you only need to enable it on one of the cluster nodes or a subset of the cluster nodes because all cluster nodes get the same cluster events. When it is enabled, it is responsible for two basic tasks. • Logs up to 100 most recent NOTICE or higher severity events to the MIB. • Sends SNMP traps to the hosts that are configured to receive the above events. The changes in the Oracle Solaris Cluster 4.2 release are1) Introduction of the NOTICE severity for the cluster configuration and status change events.The NOTICE severity is introduced for the cluster event in the 4.2 release. It is the severity between the INFO and WARNING severity. Now all severities for the cluster events are (from low to high) • INFO (not exposed to the SNMP interface) • NOTICE (newly introduced in the 4.2 release) • WARNING • ERROR • CRITICAL • FATAL In the 4.2 release, the cluster event system is enhanced to make sure at least one event with the NOTICE or a higher severity will be generated when there is a configuration or status change from a cluster component instance. In other words, the cluster events from a cluster with the NOTICE or higher severities will cover all status and configuration changes in the cluster (include all component instances). The cluster component instance here refers to an instance of the following cluster componentsnode, quorum, resource group, resource, network interface, device group, disk, zone cluster and geo cluster heartbeat. For example, pnode1 is an instance of the cluster node component, and oracleRG is an instance of the cluster resource group. With the introduction of the NOTICE severity event, when the cluster event SNMP interface is enabled, the consumers of the SNMP interface will get notification for all status and configuration changes in the cluster. A thrid-party system management platform with the cluster SNMP interface integration can generate alarms and clear alarms programmatically, because it can get notifications for the status change from ONLINE to OFFLINE and also from OFFLINE to ONLINE. 2) Customization for the cluster event SNMP interface • The number of events logged to the MIB is 100. When the number of events stored in the MIB reaches 100 and a new qualified event arrives, the oldest event will be removed before storing the new event to the MIB (FIFO, first in, first out). The 100 is the default and minimum value for the number of events stored in the MIB. It can be changed by setting the log_number property value using the clsnmpmib command. The maximum number that can be set for the property is 500. • The cluster event SNMP interface takes effect on the NOTICE or high severity events. The NOTICE severity is also the default and lowest event severity for the SNMP interface. The SNMP interface can be configured to take effect on other higher severity events, such as WARNING or higher severity events by setting the min_severity property to the WARNING. When the min_severity property is set to the WARNING, the cluster event SNMP interface would behave the same as the previous releases (prior to the 4.2 release). Examples, • Set the number of events stored in the MIB to 200 # clsnmpmib set -p log_number=200 event • Set the interface to take effect on WARNING or higher severity events. # clsnmpmib set -p min_severity=WARNING event Administering the Cluster Event SNMP Interface Oracle Solaris Cluster provides the following three commands to administer the SNMP interface. • clsnmpmib: administer the SNMP interface, and the MIB configuration. • clsnmphost: administer hosts for the SNMP traps • clsnmpuser: administer SNMP users (specific for SNMP v3 protocol) Only clsnmpmib is changed in the 4.2 release to support the aforementioned customization of the SNMP interface. Here are some simple examples using the commands. Examples: 1. Enable the cluster event SNMP interface on the local node # clsnmpmib enable event 2. Display the status of the cluster event SNMP interface on the local node # clsnmpmib show -v 3. Configure my_host to receive the cluster event SNMP traps. # clsnmphost add my_host Cluster Event SNMP Interface uses the common agent container SNMP adaptor, which is based on the JDMK SNMP implementation as its SNMP agent infrastructure. By default, the port number for the SNMP MIB is 11161, and the port number for the SNMP traps is 11162. The port numbers can be changed by using the cacaoadm. For example, # cacaoadm list-params Print all changeable parameters. The output includes the snmp-adaptor-port and snmp-adaptor-trap-port properties. # cacaoadm set-param snmp-adaptor-port=1161 Set the SNMP MIB port number to 1161. # cacaoadm set-param snmp-adaptor-trap-port=1162 Set the SNMP trap port number to 1162. The cluster event SNMP MIB is defined in sun-cluster-event-mib.mib, which is located in the /usr/cluster/lib/mibdirectory. Its OID is 1.3.6.1.4.1.42.2.80, that can be used to walk through the MIB data. Again, for more detail information about the cluster event SNMP interface, please see the Oracle Solaris Cluster 4.2 System Administration Guide. - Leland Chen 

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  • Integrating Flickr with ASP.Net application

    - by sreejukg
    Flickr is the popular photo management and sharing application offered by yahoo. The services from flicker allow you to store and share photos and videos online. Flicker offers strong API support for almost all services they provide. Using this API, developers can integrate photos to their public website. Since 2005, developers have collaborated on top of Flickr's APIs to build fun, creative, and gorgeous experiences around photos that extend beyond Flickr. In this article I am going to demonstrate how easily you can bring the photos stored on flicker to your website. Let me explain the scenario this article is trying to address. I have a flicker account where I upload photos and share in many ways offered by Flickr. Now I have a public website, instead of re-upload the photos again to public website, I want to show this from Flickr. Also I need complete control over what photo to display. So I went and referred the Flickr documentation and there is API support ready to address my scenario (and more… ). FlickerAPI for ASP.Net To Integrate Flicker with ASP.Net applications, there is a library available in CodePlex. You can find it here http://flickrnet.codeplex.com/ Visit the URL and download the latest version. The download includes a Zip file, when you unzip you will get a number of dlls. Since I am going to use ASP.Net application, I need FlickrNet.dll. See the screenshot of all the dlls, and there is a help file available in the download (.chm) for your reference. Once you have the dll, you need to use Flickr API from your website. I assume you have a flicker account and you are familiar with Flicker services. Arrange your photos using Sets in Flickr In flicker, you can define sets and add your uploaded photos to sets. You can compare set to photo album. A set is a logical collection of photos, which is an excellent option for you to categorize your photos. Typically you will have a number of sets each set having few photos. You can write application that brings photos from sets to your website. For the purpose of this article I already created a set Flickr and added some photos to it. Once you logged in to Flickr, you can see the Sets under the Menu. In the Sets page, you will see all the sets you have created. As you notice, you can see certain sample images I have uploaded just to test the functionality. Though I wish I couldn’t create good photos so please bear with me. I have created 2 photo sets named Blue Album and Red Album. Click on the image for the set, will take you to the corresponding set page. In the set “Red Album” there are 4 photos and the set has a unique ID (highlighted in the URL). You can simply retrieve the photos with the set id from your application. In this article I am going to retrieve the images from Red album in my ASP.Net page. For that First I need to setup FlickrAPI for my usage. Configure Flickr API Key As I mentioned, we are going to use Flickr API to retrieve the photos stored in Flickr. In order to get access to Flickr API, you need an API key. To create an API key, navigate to the URL http://www.flickr.com/services/apps/create/ Click on Request an API key link, now you need to tell Flickr whether your application in commercial or non-commercial. I have selected a non-commercial key. Now you need to enter certain information about your application. Once you enter the details, Click on the submit button. Now Flickr will create the API key for your application. Generating non-commercial API key is very easy, in couple of steps the key will be generated and you can use the key in your application immediately. ASP.Net application for retrieving photos Now we need write an ASP.Net application that display pictures from Flickr. Create an empty web application (I named this as FlickerIntegration) and add a reference to FlickerNet.dll. Add a web form page to the application where you will retrieve and display photos(I have named this as Gallery.aspx). After doing all these, the solution explorer will look similar to following. I have used the below code in the Gallery.aspx page. The output for the above code is as follows. I am going to explain the code line by line here. First it is adding a reference to the FlickrNet namespace. using FlickrNet; Then create a Flickr object by using your API key. Flickr f = new Flickr("<yourAPIKey>"); Now when you retrieve photos, you can decide what all fields you need to retrieve from Flickr. Every photo in Flickr contains lots of information. Retrieving all will affect the performance. For the demonstration purpose, I have retrieved all the available fields as follows. PhotoSearchExtras.All But if you want to specify the fields you can use logical OR operator(|). For e.g. the following statement will retrieve owner name and date taken. PhotoSearchExtras extraInfo = PhotoSearchExtras.OwnerName | PhotoSearchExtras.DateTaken; Then retrieve all the photos from a photo set using PhotoSetsGetPhotos method. I have passed the PhotoSearchExtras object created earlier. PhotosetPhotoCollection photos = f.PhotosetsGetPhotos("72157629872940852", extraInfo); The PhotoSetsGetPhotos method will return a collection of Photo objects. You can just navigate through the collection using a foreach statement. foreach (Photo p in photos) {     //access each photo properties } Photo class have lot of properties that map with the properties from Flickr. The chm documentation comes along with the CodePlex download is a great asset for you to understand the fields. In the above code I just used the following p.LargeUrl – retrieves the large image url for the photo. p.ThumbnailUrl – retrieves the thumbnail url for the photo p.Title – retrieves the Title of the photo p.DateUploaded – retrieves the date of upload Visual Studio intellisense will give you all properties, so it is easy, you can just try with Visual Studio intellisense to find the right properties you are looking for. Most of hem are self-explanatory. So you can try retrieving the required properties. In the above code, I just pushed the photos to the page. In real time you can use the retrieved photos along with JQuery libraries to create animated photo galleries, slideshows etc. Configuration and Troubleshooting If you get access denied error while executing the code, you need to disable the caching in Flickr API. FlickrNet cache the photos to your local disk when retrieved. You can specify a cache folder where the application need write permission. You can specify the Cache folder in the code as follows. Flickr.CacheLocation = Server.MapPath("./FlickerCache/"); If the application doesn’t have have write permission to the cache folder, the application will throw access denied error. If you cannot give write permission to the cache folder, then you must disable the caching. You can do this from code as follows. Flickr.CacheDisabled = true; Disabling cache will have an impact on the performance. Take care! Also you can define the Flickr settings in web.config file.You can find the documentation here. http://flickrnet.codeplex.com/wikipage?title=ExampleConfigFile&ProjectName=flickrnet Flickr is a great place for storing and sharing photos. The API access allows developers to do seamless integration with the photos uploaded on Flickr.

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  • Controlar Autentificaci&oacute;n Crystal Reports

    - by Jason Ulloa
    Para todos los que hemos trabajamos con Crystal Reports, no es un secreto que cuando tratamos de conectar nuestro reporte directamente a la base de datos, se nos viene encima el problema de autenticación. Es decir nuestro reporte al momento de iniciar la carga nos solicita autentificarnos en el servidor y sino lo hacemos, simplemente no veremos el reporte. Esto, además de ser tedioso para los usuarios se convierte en un problema de seguridad bastante grande, de ahí que en la mayoría de los casos se recomienda utilizar dataset. Sin embargo, para todos los que aún sabiendo esto no desean utilizar datasets, sino que, quieren conectar su crystal directamente veremos como implementar una pequeña clase que nos ayudará con esa tarea. Generalmente, cuando trabajamos con una aplicación web, nuestra cadena de conexión esta incluida en el web.config y también en muchas ocasiones contiene los datos como el usuario y password para acceder a la base de datos.  De esta cadena de conexión y estos datos es de los que nos ayudaremos para implementar la autentificación en el reporte. Generalmente, la cadena de conexión se vería así <connectionStrings> <remove name="LocalSqlServer"/> <add name="xxx" connectionString="Data Source=.\SqlExpress;Integrated Security=False;Initial Catalog=xxx;user id=myuser;password=mypass" providerName="System.Data.SqlClient"/> </connectionStrings>   Para nuestro ejemplo, nombraremos a nuestra clase CrystalRules (es solo algo que pensé de momento) 1. Primer Paso Creamos una variable de tipo SqlConnectionStringBuilder, a la cual le asignaremos la cadena de conexión que definimos en el web.config, y que luego utilizaremos para obtener los datos del usuario y el password para el crystal report. SqlConnectionStringBuilder builder = new SqlConnectionStringBuilder(ConfigurationManager.ConnectionStrings["xxx"].ConnectionString); 2. Implementación de propiedad Para ser más ordenados crearemos varias propiedad de tipo Privado, que se encargarán de recibir los datos de:   La Base de datos, el password, el usuario y el servidor private string _dbName; private string _serverName; private string _userID; private string _passWord;   private string dataBase { get { return _dbName; } set { _dbName = value; } }   private string serverName { get { return _serverName; } set { _serverName = value; } }   private string userName { get { return _userID; } set { _userID = value; } }   private string dataBasePassword { get { return _passWord; } set { _passWord = value; } } 3. Creación del Método para aplicar los datos de conexión Una vez que ya tenemos las propiedades, asignaremos a las variables los valores que se han recogido en el SqlConnectionStringBuilder. Y crearemos una variable de tipo ConnectionInfo para aplicar los datos de conexión. internal void ApplyInfo(ReportDocument _oRpt) { dataBase = builder.InitialCatalog; serverName = builder.DataSource; userName = builder.UserID; dataBasePassword = builder.Password;   Database oCRDb = _oRpt.Database; Tables oCRTables = oCRDb.Tables; //Table oCRTable = default(Table); TableLogOnInfo oCRTableLogonInfo = default(TableLogOnInfo); ConnectionInfo oCRConnectionInfo = new ConnectionInfo();   oCRConnectionInfo.DatabaseName = _dbName; oCRConnectionInfo.ServerName = _serverName; oCRConnectionInfo.UserID = _userID; oCRConnectionInfo.Password = _passWord;   foreach (Table oCRTable in oCRTables) { oCRTableLogonInfo = oCRTable.LogOnInfo; oCRTableLogonInfo.ConnectionInfo = oCRConnectionInfo; oCRTable.ApplyLogOnInfo(oCRTableLogonInfo);     }   }   4. Creación del report document y aplicación de la seguridad Una vez recogidos los datos y asignados, crearemos un elemento report document al cual le asignaremos el CrystalReportViewer y le aplicaremos los datos de acceso que obtuvimos anteriormente public void loadReport(string repName, CrystalReportViewer viewer) {   // attached our report to viewer and set database login. ReportDocument report = new ReportDocument(); report.Load(HttpContext.Current.Server.MapPath("~/Reports/" + repName)); ApplyInfo(report); viewer.ReportSource = report; } Al final, nuestra clase completa ser vería así public class CrystalRules { SqlConnectionStringBuilder builder = new SqlConnectionStringBuilder(ConfigurationManager.ConnectionStrings["Fatchoy.Data.Properties.Settings.FatchoyConnectionString"].ConnectionString);   private string _dbName; private string _serverName; private string _userID; private string _passWord;   private string dataBase { get { return _dbName; } set { _dbName = value; } }   private string serverName { get { return _serverName; } set { _serverName = value; } }   private string userName { get { return _userID; } set { _userID = value; } }   private string dataBasePassword { get { return _passWord; } set { _passWord = value; } }   internal void ApplyInfo(ReportDocument _oRpt) { dataBase = builder.InitialCatalog; serverName = builder.DataSource; userName = builder.UserID; dataBasePassword = builder.Password;   Database oCRDb = _oRpt.Database; Tables oCRTables = oCRDb.Tables; //Table oCRTable = default(Table); TableLogOnInfo oCRTableLogonInfo = default(TableLogOnInfo); ConnectionInfo oCRConnectionInfo = new ConnectionInfo();   oCRConnectionInfo.DatabaseName = _dbName; oCRConnectionInfo.ServerName = _serverName; oCRConnectionInfo.UserID = _userID; oCRConnectionInfo.Password = _passWord;   foreach (Table oCRTable in oCRTables) { oCRTableLogonInfo = oCRTable.LogOnInfo; oCRTableLogonInfo.ConnectionInfo = oCRConnectionInfo; oCRTable.ApplyLogOnInfo(oCRTableLogonInfo);     }   }   public void loadReport(string repName, CrystalReportViewer viewer) {   // attached our report to viewer and set database login. ReportDocument report = new ReportDocument(); report.Load(HttpContext.Current.Server.MapPath("~/Reports/" + repName)); ApplyInfo(report); viewer.ReportSource = report; }       #region instance   private static CrystalRules m_instance;   // Properties public static CrystalRules Instance { get { if (m_instance == null) { m_instance = new CrystalRules(); } return m_instance; } }   public DataDataContext m_DataContext { get { return DataDataContext.Instance; } }     #endregion instance   }   Si bien, la solución no es robusta y no es la mas segura. En casos de uso como una intranet y cuando estamos contra tiempo, podría ser de gran ayuda.

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  • OBIEE 11.1.1 - How to configure HTTP compression / caching on Oracle BI Mobile app

    - by Ahmed Awan
     Applies to: OBIEE 11.1.1.5 Supported Physical Devices and OS: The Oracle BI Mobile application with HTTP compression / caching configurations is tested on following devices: iPhone 4S, 4, 3GS. iPad 2 and 1. Note these devices must be running the latest version of the iOS version, i.e. iOS 4.2.1 / iOS 5 is also supported. Configuring Pre-requisites: Prior to configuration, the Oracle Web tier software must be installed on server, as described in product documentation i.e. Enterprise Deployment Guide for Oracle Business Intelligence in Section 3.2, "Installing Oracle HTTP Server." The steps for configuring the compression and caching on Oracle HTTP Server are described in this PA blog at http://blogs.oracle.com/pa/entry/obiee_11g_user_interface_ui and in support Doc ID 1312299.1. Configuration Steps in Oracle BI Mobile application: 1. Download the BI Mobile app from the Apple iTunes App Store. The link is http://itunes.apple.com/us/app/oracle-business-intelligence/id434559909?mt=8 . 2. Add Server for example http://pew801.us.oracle.com:7777/analytics/ , here is how your “Server Setting” screen should look like on your OBI Mobile app:                                 Performance Gain Test (using Oracle® HTTP Server with OBIEE) The test with/without HTTP compression / caching was conducted on iPhone 4S / iPad 2 to measure the throughput (i.e. total bytes received) for Oracle® Business Intelligence Enterprise Edition. Below table shows the throughput comparison before and after using HTTP compression / caching for SampleApp using “QuickStart” dashboard accessing reports i.e. Overview, Details, Published Reporting and Scorecard. Testing shows that total bytes received were reduced from 2.3 MB to 723 KB. a. Test Results > Without HTTP Compression / Caching setting - Total Throughput (in Bytes) captured below: Total Bytes Statistics:        b. Test Results > With HTTP Compression / Caching settings - Total Throughput (in Bytes) captured below: Total Bytes Statistics:      

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  • Running Windows Phone Developers Tools CTP under VMWare Player - Yes you can! - But do you want to?

    - by Liam Westley
    This blog is the result of a quick investigation of running the Windows Phone Developer Tools CTP under VMWare Player.  In the release notes for Windows Phone Developer Tools CTP it mentions that it is not supported under VirtualPC or Hyper-V.  Some developers have policies where ‘no non-production code’ can be installed on their development workstation and so the only way they can use a CTP like this is in a virtual machine. The dilemma here is that the emulator for Windows Phone itself is a virtual machine and running a virtual machine within another virtual machine is normally frowned upon.  Even worse, previous Windows Mobile emulators detected they were in a virtual machine and refused to run.  Why VMWare? I selected VMWare as a possible solution as it is possible to run VMWare ESXi under VMWare Workstation by manually setting configuration options in the VMX configuration file so that it does not detect the presence of a virtual environment. I actually found that I could use VMWare Player (the free version, that can now create VM images) and that there was no need for any editing of the configuration file (I tried various switches, none of which made any difference to performance). So you can run the CTP under VMWare Player, that’s the good news. The bad news is that it is incredibly slow, bordering on unusable.  However, if it’s the only way you can use the CTP, at least this is an option. VMWare Player configuration I used the latest VMWare Player, 3.0, running under Windows x64 on my HP 6910p laptop with an Intel T7500 Dual Core CPU running at 2.2GHz, 4Gb of memory and using a separate drive for the virtual machines. I created a machine in VMWare Player with a single CPU, 1536 Mb memory and installed Windows 7 x64 from an ISO image.  I then performed a Windows Update, installed VMWare Tools, and finally the Windows Phone Developer Tools CTP After a few warnings about performance, I configured Windows 7 to run with Windows 7 Basic theme rather than use Aero (which is available under VMWare Player as it has a WDDM driver). Timings As a test I first launched Microsoft Visual Studio 2010 Express for Windows Phone, and created a default Windows Phone Application project.  I then clicked the run button, which starts the emulator and then loads the default application onto the emulator. For the second test I left the emulator running, stopped the default application, added a single button to change the page title and redeployed to the already running emulator by clicking the run button.   Test 1 (1st run) Test 2 (emulator already running)   VMWare Player 10 minutes  1 minute   Windows x64 native 1 minute  < 10 seconds   Conclusion You can run the Windows Phone Developer Tools CTP under VMWare Player, but it’s really, really slow and you would have to have very good reasons to try this approach. If you need to keep a development system free of non production code, and the two systems aren’t required to run simultaneously, then I’d consider a boot from VHD option.  Then you can completely isolate the Windows Phone Developer Tools CTP and development environment into a single VHD separate from your main development system.

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  • Oracle Expands Sun Blade Portfolio for Cloud and Highly Virtualized Environments

    - by Ferhat Hatay
    Oracle announced the expansion of Sun Blade Portfolio for cloud and highly virtualized environments that deliver powerful performance and simplified management as tightly integrated systems.  Along with the SPARC T3-1B blade server, Oracle VM blade cluster reference configuration and Oracle's optimized solution for Oracle WebLogic Suite, Oracle introduced the dual-node Sun Blade X6275 M2 server module with some impressive benchmark results.   Benchmarks on the Sun Blade X6275 M2 server module demonstrate the outstanding performance characteristics critical for running varied commercial applications used in cloud and highly virtualized environments.  These include best-in-class SPEC CPU2006 results with the Intel Xeon processor 5600 series, six Fluent world records and 1.8 times the price-performance of the IBM Power 755 running NAMD, a prominent bio-informatics workload.   Benchmarks for Sun Blade X6275 M2 server module  SPEC CPU2006  The Sun Blade X6275 M2 server module demonstrated best in class SPECint_rate2006 results for all published results using the Intel Xeon processor 5600 series, with a result of 679.  This result is 97% better than the HP BL460c G7 blade, 80% better than the IBM HS22V blade, and 79% better than the Dell M710 blade.  This result demonstrates the density advantage of the new Oracle's server module for space-constrained data centers.     Sun Blade X6275M2 (2 Nodes, Intel Xeon X5670 2.93GHz) - 679 SPECint_rate2006; HP ProLiant BL460c G7 (2.93 GHz, Intel Xeon X5670) - 347 SPECint_rate2006; IBM BladeCenter HS22V (Intel Xeon X5680)  - 377 SPECint_rate2006; Dell PowerEdge M710 (Intel Xeon X5680, 3.33 GHz) - 380 SPECint_rate2006.  SPEC, SPECint, SPECfp reg tm of Standard Performance Evaluation Corporation. Results from www.spec.org as of 11/24/2010 and this report.    For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   Fluent The Sun Fire X6275 M2 server module produced world-record results on each of the six standard cases in the current "FLUENT 12" benchmark test suite at 8-, 12-, 24-, 32-, 64- and 96-core configurations. These results beat the most recent QLogic score with IBM DX 360 M series platforms and QLogic "Truescale" interconnects.  Results on sedan_4m test case on the Sun Blade X6275 M2 server module are 23% better than the HP C7000 system, and 20% better than the IBM DX 360 M2; Dell has not posted a result for this test case.  Results can be found at the FLUENT website.   ANSYS's FLUENT software solves fluid flow problems, and is based on a numerical technique called computational fluid dynamics (CFD), which is used in the automotive, aerospace, and consumer products industries. The FLUENT 12 benchmark test suite consists of seven models that are well suited for multi-node clustered environments and representative of modern engineering CFD clusters. Vendors benchmark their systems with the principal objective of providing comparative performance information for FLUENT software that, among other things, depends on compilers, optimization, interconnect, and the performance characteristics of the hardware.   FLUENT application performance is representative of other commercial applications that require memory and CPU resources to be available in a scalable cluster-ready format.  FLUENT benchmark has six conventional test cases (eddy_417k, turbo_500k, aircraft_2m, sedan_4m, truck_14m, truck_poly_14m) at various core counts.   All information on the FLUENT website (http://www.fluent.com) is Copyrighted1995-2010 by ANSYS Inc. Results as of November 24, 2010. For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   NAMD Results on the Sun Blade X6275 M2 server module running NAMD (a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems) show up to a 1.8X better price/performance than IBM's Power 7-based system.  For space-constrained environments, the ultra-dense Sun Blade X6275 M2 server module provides a 1.7X better price/performance per rack unit than IBM's system.     IBM Power 755 4-way Cluster (16U). Total price for cluster: $324,212. See IBM United States Hardware Announcement 110-008, dated February 9, 2010, pp. 4, 21 and 39-46.  Sun Blade X6275 M2 8-Blade Cluster (10U). Total price for cluster:  $193,939. Price/performance and performance/RU comparisons based on f1ATPase molecule test results. Sun Blade X6275 M2 cluster: $3,568/step/sec, 5.435 step/sec/RU. IBM Power 755 cluster: $6,355/step/sec, 3.189 step/sec/U. See http://www-03.ibm.com/systems/power/hardware/reports/system_perf.html. See http://www.ks.uiuc.edu/Research/namd/performance.html for more information, results as of 11/24/10.   For more specifics about these results, please go to see http://blogs.sun.com/BestPerf   Reverse Time Migration The Reverse Time Migration is heavily used in geophysical imaging and modeling for Oil & Gas Exploration.  The Sun Blade X6275 M2 server module showed up to a 40% performance improvement over the previous generation server module with super-linear scalability to 16 nodes for the 9-Point Stencil used in this Reverse Time Migration computational kernel.  The balanced combination of Oracle's Sun Storage 7410 system with the Sun Blade X6275 M2 server module cluster showed linear scalability for the total application throughput, including the I/O and MPI communication, to produce a final 3-D seismic depth imaged cube for interpretation. The final image write time from the Sun Blade X6275 M2 server module nodes to Oracle's Sun Storage 7410 system achieved 10GbE line speed of 1.25 GBytes/second or better performance. Between subsequent runs, the effects of I/O buffer caching on the Sun Blade X6275 M2 server module nodes and write optimized caching on the Sun Storage 7410 system gave up to 1.8 GBytes/second effective write performance. The performance results and characterization of this Reverse Time Migration benchmark could serve as a useful measure for many other I/O intensive commercial applications. 3D VTI Reverse Time Migration Seismic Depth Imaging, see http://blogs.sun.com/BestPerf/entry/3d_vti_reverse_time_migration for more information, results as of 11/14/2010.                            

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  • IE9

    - by Kit Ong
    Yep Internet Explorer 9 is in the works even though IE8 is still relatively new. IE8 totally failed the infamous Acid3 Test, things have improved even with the early preview version of IE9, here's a link to test drive Internet Explorer 9 http://ie.microsoft.com/testdrive/

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  • How does a website latency simulator work

    - by nighthawk457
    Sites like webpagetest allow users to enter a website url and a test location, to run a speed test on the site from multiple locations using real browsers. Can anyone give me a basic idea of how sites like this work? You also have plugin's like Aptimize latency simulator or charles web debugging proxy app, that simulate the delay while accessing a site from different locations. I am assuming since these are plugin's these function in a different way. How do these plugin's work ?

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  • Disk errors on tty and syslog/dmesg

    - by Shoaibi
    Recently I have started to get a lot of these errors: Jun 18 08:57:42 abacus kernel: [ 401.554292] ata5: SError: { HostInt 10B8B } Jun 18 08:57:42 abacus kernel: [ 401.559346] sr 4:0:0:0: CDB: Test Unit Ready: 00 00 00 00 00 00 Jun 18 08:57:42 abacus kernel: [ 401.560191] ata5.00: cmd a0/00:00:00:00:00/00:00:00:00:00/a0 tag 0 Jun 18 08:57:42 abacus kernel: [ 401.560231] res 51/20:03:00:00:00/00:00:00:00:00/a0 Emask 0x40 (internal error) Jun 18 08:57:42 abacus kernel: [ 401.575310] ata5.00: status: { DRDY ERR } Jun 18 08:57:42 abacus kernel: [ 401.579801] ata5: hard resetting link Jun 18 08:57:42 abacus kernel: [ 401.929320] ata5: SATA link up 1.5 Gbps (SStatus 113 SControl 300) Jun 18 08:57:42 abacus kernel: [ 401.941936] ata5.00: configured for UDMA/100 Jun 18 08:57:42 abacus kernel: [ 401.969426] ata5: EH complete Jun 18 08:57:54 abacus kernel: [ 413.527699] ata5.00: exception Emask 0x40 SAct 0x0 SErr 0x80800 action 0x6 Jun 18 08:57:54 abacus kernel: [ 413.527779] ata5.00: irq_stat 0x40000001 Jun 18 08:57:54 abacus kernel: [ 413.527822] ata5: SError: { HostInt 10B8B } Jun 18 08:57:54 abacus kernel: [ 413.527901] sr 4:0:0:0: CDB: Test Unit Ready: 00 00 00 00 00 00 Jun 18 08:57:54 abacus kernel: [ 413.528103] ata5.00: cmd a0/00:00:00:00:00/00:00:00:00:00/a0 tag 0 Jun 18 08:57:54 abacus kernel: [ 413.528142] res 51/20:03:00:00:00/00:00:00:00:00/a0 Emask 0x40 (internal error) Jun 18 08:57:54 abacus kernel: [ 413.528184] ata5.00: status: { DRDY ERR } Jun 18 08:57:54 abacus kernel: [ 413.528303] ata5: hard resetting link Jun 18 08:57:54 abacus kernel: [ 413.875894] ata5: SATA link up 1.5 Gbps (SStatus 113 SControl 300) Jun 18 08:57:54 abacus kernel: [ 413.888267] ata5.00: configured for UDMA/100 Jun 18 08:57:54 abacus kernel: [ 413.916365] ata5: EH complete Jun 18 08:57:56 abacus kernel: [ 415.537834] ata5.00: exception Emask 0x40 SAct 0x0 SErr 0x80800 action 0x6 Jun 18 08:57:56 abacus kernel: [ 415.545253] ata5.00: irq_stat 0x40000001 Jun 18 08:57:56 abacus kernel: [ 415.549788] ata5: SError: { HostInt 10B8B } Jun 18 08:57:56 abacus kernel: [ 415.554840] sr 4:0:0:0: CDB: Test Unit Ready: 00 00 00 00 00 00 Jun 18 08:57:56 abacus kernel: [ 415.555201] ata5.00: cmd a0/00:00:00:00:00/00:00:00:00:00/a0 tag 0 Jun 18 08:57:56 abacus kernel: [ 415.555242] res 51/20:03:00:00:00/00:00:00:00:00/a0 Emask 0x40 (internal error) Jun 18 08:57:56 abacus kernel: [ 415.570483] ata5.00: status: { DRDY ERR } Jun 18 08:57:56 abacus kernel: [ 415.574695] ata5: hard resetting link Jun 18 08:57:56 abacus kernel: [ 415.924954] ata5: SATA link up 1.5 Gbps (SStatus 113 SControl 300) Jun 18 08:57:56 abacus kernel: [ 415.936831] ata5.00: configured for UDMA/100 Jun 18 08:57:56 abacus kernel: [ 415.965001] ata5: EH complete Jun 18 08:58:02 abacus kernel: [ 421.529784] ata5.00: exception Emask 0x40 SAct 0x0 SErr 0x80800 action 0x6 Jun 18 08:58:02 abacus kernel: [ 421.529904] ata5.00: irq_stat 0x40000001 Jun 18 08:58:02 abacus kernel: [ 421.530023] ata5: SError: { HostInt 10B8B } Jun 18 08:58:02 abacus kernel: [ 421.530104] sr 4:0:0:0: CDB: Test Unit Ready: 00 00 00 00 00 00 Jun 18 08:58:02 abacus kernel: [ 421.530425] ata5.00: cmd a0/00:00:00:00:00/00:00:00:00:00/a0 tag 0 Jun 18 08:58:02 abacus kernel: [ 421.530466] res 51/20:03:00:00:00/00:00:00:00:00/a0 Emask 0x40 (internal error) Jun 18 08:58:02 abacus kernel: [ 421.530583] ata5.00: status: { DRDY ERR } Jun 18 08:58:02 abacus kernel: [ 421.530705] ata5: hard resetting link Jun 18 08:58:02 abacus kernel: [ 421.873218] ata5: SATA link up 1.5 Gbps (SStatus 113 SControl 300) Jun 18 08:58:02 abacus kernel: [ 421.885040] ata5.00: configured for UDMA/100 Jun 18 08:58:02 abacus kernel: [ 421.913404] ata5: EH complete Are these critical error messages? What would be the cause and remedy?

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  • MSDN za svakoga

    - by panjkov
    Visual Studio 2010 objavljen je 12. aprila 2010. godine, a može se kupiti kroz programe kolicinskog licenciranja ili kroz maloprodajni (retail) kanal. U maloprodajnom kanalu mogu se kupiti Professional, Premium, Ultimate i Test Professional edicije Visual Studija, i to Microsoft Visual Studio 2010 Ultimate with MSDN Microsoft Visual Studio 2010 Premium with MSDN Microsoft Visual Studio 2010 Professional with MSDN Microsoft Visual Studio Test Professional 2010 with MSDN Microsoft Visual Studio 2010...(read more)

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  • Unit testing code paths

    - by Michael
    When unit testing using expectations, you define a set of method calls and corresponding results for those calls. These define the path through the method that you want to test. I have read that unit tests should not duplicate the code. But when you define these expectations, isn't that duplicating the code, or at least the process? How do you know when you're duplicating functionality under test?

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  • Integration Patterns with Azure Service Bus Relay, Part 1: Exposing the on-premise service

    - by Elton Stoneman
    We're in the process of delivering an enabling project to expose on-premise WCF services securely to Internet consumers. The Azure Service Bus Relay is doing the clever stuff, we register our on-premise service with Azure, consumers call into our .servicebus.windows.net namespace, and their requests are relayed and serviced on-premise. In theory it's all wonderfully simple; by using the relay we get lots of protocol options, free HTTPS and load balancing, and by integrating to ACS we get plenty of security options. Part of our delivery is a suite of sample consumers for the service - .NET, jQuery, PHP - and this set of posts will cover setting up the service and the consumers. Part 1: Exposing the on-premise service In theory, this is ultra-straightforward. In practice, and on a dev laptop it is - but in a corporate network with firewalls and proxies, it isn't, so we'll walkthrough some of the pitfalls. Note that I'm using the "old" Azure portal which will soon be out of date, but the new shiny portal should have the same steps available and be easier to use. We start with a simple WCF service which takes a string as input, reverses the string and returns it. The Part 1 version of the code is on GitHub here: on GitHub here: IPASBR Part 1. Configuring Azure Service Bus Start by logging into the Azure portal and registering a Service Bus namespace which will be our endpoint in the cloud. Give it a globally unique name, set it up somewhere near you (if you’re in Europe, remember Europe (North) is Ireland, and Europe (West) is the Netherlands), and  enable ACS integration by ticking "Access Control" as a service: Authenticating and authorizing to ACS When we try to register our on-premise service as a listener for the Service Bus endpoint, we need to supply credentials, which means only trusted service providers can act as listeners. We can use the default "owner" credentials, but that has admin permissions so a dedicated service account is better (Neil Mackenzie has a good post On Not Using owner with the Azure AppFabric Service Bus with lots of permission details). Click on "Access Control Service" for the namespace, navigate to Service Identities and add a new one. Give the new account a sensible name and description: Let ACS generate a symmetric key for you (this will be the shared secret we use in the on-premise service to authenticate as a listener), but be sure to set the expiration date to something usable. The portal defaults to expiring new identities after 1 year - but when your year is up *your identity will expire without warning* and everything will stop working. In production, you'll need governance to manage identity expiration and a process to make sure you renew identities and roll new keys regularly. The new service identity needs to be authorized to listen on the service bus endpoint. This is done through claim mapping in ACS - we'll set up a rule that says if the nameidentifier in the input claims has the value serviceProvider, in the output we'll have an action claim with the value Listen. In the ACS portal you'll see that there is already a Relying Party Application set up for ServiceBus, which has a Default rule group. Edit the rule group and click Add to add this new rule: The values to use are: Issuer: Access Control Service Input claim type: http://schemas.xmlsoap.org/ws/2005/05/identity/claims/nameidentifier Input claim value: serviceProvider Output claim type: net.windows.servicebus.action Output claim value: Listen When your service namespace and identity are set up, open the Part 1 solution and put your own namespace, service identity name and secret key into the file AzureConnectionDetails.xml in Solution Items, e.g: <azure namespace="sixeyed-ipasbr">    <!-- ACS credentials for the listening service (Part1):-->   <service identityName="serviceProvider"            symmetricKey="nuR2tHhlrTCqf4YwjT2RA2BZ/+xa23euaRJNLh1a/V4="/>  </azure> Build the solution, and the T4 template will generate the Web.config for the service project with your Azure details in the transportClientEndpointBehavior:           <behavior name="SharedSecret">             <transportClientEndpointBehavior credentialType="SharedSecret">               <clientCredentials>                 <sharedSecret issuerName="serviceProvider"                               issuerSecret="nuR2tHhlrTCqf4YwjT2RA2BZ/+xa23euaRJNLh1a/V4="/>               </clientCredentials>             </transportClientEndpointBehavior>           </behavior> , and your service namespace in the Azure endpoint:         <!-- Azure Service Bus endpoints -->          <endpoint address="sb://sixeyed-ipasbr.servicebus.windows.net/net"                   binding="netTcpRelayBinding"                   contract="Sixeyed.Ipasbr.Services.IFormatService"                   behaviorConfiguration="SharedSecret">         </endpoint> The sample project is hosted in IIS, but it won't register with Azure until the service is activated. Typically you'd install AppFabric 1.1 for Widnows Server and set the service to auto-start in IIS, but for dev just navigate to the local REST URL, which will activate the service and register it with Azure. Testing the service locally As well as an Azure endpoint, the service has a WebHttpBinding for local REST access:         <!-- local REST endpoint for internal use -->         <endpoint address="rest"                   binding="webHttpBinding"                   behaviorConfiguration="RESTBehavior"                   contract="Sixeyed.Ipasbr.Services.IFormatService" /> Build the service, then navigate to: http://localhost/Sixeyed.Ipasbr.Services/FormatService.svc/rest/reverse?string=abc123 - and you should see the reversed string response: If your network allows it, you'll get the expected response as before, but in the background your service will also be listening in the cloud. Good stuff! Who needs network security? Onto the next post for consuming the service with the netTcpRelayBinding.  Setting up network access to Azure But, if you get an error, it's because your network is secured and it's doing something to stop the relay working. The Service Bus relay bindings try to use direct TCP connections to Azure, so if ports 9350-9354 are available *outbound*, then the relay will run through them. If not, the binding steps down to standard HTTP, and issues a CONNECT across port 443 or 80 to set up a tunnel for the relay. If your network security guys are doing their job, the first option will be blocked by the firewall, and the second option will be blocked by the proxy, so you'll get this error: System.ServiceModel.CommunicationException: Unable to reach sixeyed-ipasbr.servicebus.windows.net via TCP (9351, 9352) or HTTP (80, 443) - and that will probably be the start of lots of discussions. Network guys don't really like giving servers special permissions for the web proxy, and they really don't like opening ports, so they'll need to be convinced about this. The resolution in our case was to put up a dedicated box in a DMZ, tinker with the firewall and the proxy until we got a relay connection working, then run some traffic which the the network guys monitored to do a security assessment afterwards. Along the way we hit a few more issues, diagnosed mainly with Fiddler and Wireshark: System.Net.ProtocolViolationException: Chunked encoding upload is not supported on the HTTP/1.0 protocol - this means the TCP ports are not available, so Azure tries to relay messaging traffic across HTTP. The service can access the endpoint, but the proxy is downgrading traffic to HTTP 1.0, which does not support tunneling, so Azure can’t make its connection. We were using the Squid proxy, version 2.6. The Squid project is incrementally adding HTTP 1.1 support, but there's no definitive list of what's supported in what version (here are some hints). System.ServiceModel.Security.SecurityNegotiationException: The X.509 certificate CN=servicebus.windows.net chain building failed. The certificate that was used has a trust chain that cannot be verified. Replace the certificate or change the certificateValidationMode. The evocation function was unable to check revocation because the revocation server was offline. - by this point we'd given up on the HTTP proxy and opened the TCP ports. We got this error when the relay binding does it's authentication hop to ACS. The messaging traffic is TCP, but the control traffic still goes over HTTP, and as part of the ACS authentication the process checks with a revocation server to see if Microsoft’s ACS cert is still valid, so the proxy still needs some clearance. The service account (the IIS app pool identity) needs access to: www.public-trust.com mscrl.microsoft.com We still got this error periodically with different accounts running the app pool. We fixed that by ensuring the machine-wide proxy settings are set up, so every account uses the correct proxy: netsh winhttp set proxy proxy-server="http://proxy.x.y.z" - and you might need to run this to clear out your credential cache: certutil -urlcache * delete If your network guys end up grudgingly opening ports, they can restrict connections to the IP address range for your chosen Azure datacentre, which might make them happier - see Windows Azure Datacenter IP Ranges. After all that you've hopefully got an on-premise service listening in the cloud, which you can consume from pretty much any technology.

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  • Transfer websites and domains to new server

    - by Albert
    We have currently around 40 websites and 80+ domains/sub-domains in a shared 1&1 hosting package, and we just acquired a managed dedicated server with 1&1 as well. Now it's time to start transferring everything over to the new server. Transferring just the websites and databases wouldn't be a problem, it would take time but it's pretty straight forward. The problem comes when transferring the domains, let me explain why. Many of the websites we have are accessible via sub-domains of a parent domain. Ideally, we would like to transfer the sites one by one, in order to check for each one that everything works fine in the new server. However, since we also need to transfer the domain so it's managed in the new server, once we do that means that all the websites using that domain need to be already in the new server before transferring that domain, thus not allowing the "one by one" philosophy. Another issue is the downtime when transferring the domain, from the moment it stops working in the hosting package and becomes active in the new server. I believe there's nothing we can do here. So my question is if there's any way we can do the "one by one" transferring of the websites (and their corresponding sub-domains) in the circumstances described above. One idea I had would be: 1. Let's say we have website A, which is accessible using subdomain.mydomain.com (and there are many other websites accessible via other sub-domains of mydomain.com) 2. Transfer the files of website A to the new server 3. Point a test domain in the new server to the website A's folder (the new server comes with a "test" domain) 4. Test if website A works with that "test" domain 5. In the old hosting, somehow point the real sub-domain (subdomain.mydomain.com) to the new location of website A, in a way that user always see the same URL as always 6. Repeat 2-5 for every website belonging to the same domain 7. Once all are working in the new server, do the actual transfer of the domain to the new server, and then re-create all the sub-domains and point them to their corresponding website That way, users wouldn't notice that there's been a change (except for a small down time of the websites when doing the domain transfer). The part I'm not sure about is point 5 of the above. Is there any way to do that? I mean do it in a way that users see the original domain all the time in their browser, even for internal pages (so not only for the "home page", which would be sub-domain.mydomain.com, but also for example for the contact page, which would be sub-domain.mydomain.com/contact.php). Is there any way to do this? Or are we SOL and we're going to have to transfer all at the same time?

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  • Tips On Using The Service Contracts Import Program

    - by LuciaC
    Prior to release 12.1 there was no supported way to import contracts into the EBS Service Contracts application - there were no public APIs nor contract load programs provided.  From release 12.1 onwards the 'Service Contracts Import Program' is provided to load service contracts into the application. The Service Contracts Import functionality is explained in How to Use the Service Contracts Import Program - Scope and Limitations (Doc ID 1057242.1).  This note includes an attached document which explains the program architecture, shows the Entity Relationship Diagram and details the interface table definitions. The Import program takes data from the interface tables listed below and populates the contracts schema tables:  OKS_USAGE_COUNTERS_INTERFACE OKS_SALES_CREDITS_INTERFACEOKS_NOTES_INTERFACEOKS_LINES_INTERFACEOKS_HEADERS_INTERFACEOKS_COVERED_LEVELS_INTERFACEThese interface tables must be loaded via a custom load program.The Service Contracts Import concurrent request is then submitted to create contracts from this legacy data. The parameters to run the Import program are:  Parameter Description  Mode Validate only, Import  Batch Number Batch_Id (unique id populated into the OKS_HEADERS_INTERFACE table)  Number of Workers Number of workers required (these are spawned as separate sub-requests)  Commit size Represents number of successfully processed contracts commited to database The program spawns sub-requests for the import worker(s) and the 'Service Contracts Import Report'.  The data is validated prior to import and into the Contracts tables and will report errors in the Service Contracts Import Report program output file (Import Execution Report).  Troubleshooting tips are provided in R12.1 - Common Service Contract Import Errors (Doc ID 762545.1); this document lists some, but not all, import errors.  The document will be updated over time.  Additional help is given in Debugging Tip for Service Contracts Import Errors (Doc ID 971426.1).After you successfully import contracts, you can purge the records from the interface tables by running the Service Contracts Import Purge concurrent program. Note that there is no supported way to mass delete data from the Contracts schema tables once they are populated, so data loaded by the Import program must be fully tested and verified before the program is run to load data into a Production system.A Service Contracts Import Test program has been provided which will take an existing contract in the application and load the interface tables using the data from that contract.  This can be used as an example for guidance on how to load the interface tables.  The Test program functionality is explained in How to Use the Service Contracts Test Import Program Provided in Release 12.1 (Doc ID 761209.1).  Note that the Test program has some limitations which do not apply to the full Import program and is not a supported program, it is simply a testing tool.  

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  • DNS client configuration steps in Oracle Solaris 11

    - by Gurubalan
    This guide covers Quick how to configure DNS client on Solaris 11. DNS client configuration in Solaris 11 is based on SMF service rather than file based. When you configure a system as DNS client, you will be performing the following two configurations. I. DNS client setup II. Configure Name service switch to use DNS I. DNS client setup 1. Configure using SMF service network/dns/client # svccfg -s network/dns/clientsvc:/network/dns/client> setprop config/search = astring: ("test.com" "service.test.com")svc:/network/dns/client> setprop config/nameserver = net_address: (192.168.10.10 192.168.10.11)svc:/network/dns/client> exit 2.  Enable the DNS client service (when you configure it for the first time) #svccfg enable -r dns/client 3. Restart/Refresh DNS client service (It is done when there is any update to the configuration) #svccfg refresh dns/client #svccfg restart dns/client 4. Verify /etc/resolv.conf if it is updated with the changes. # more /etc/resolv.conf ## _AUTOGENERATED_FROM_SMF_V1_## WARNING: THIS FILE GENERATED FROM SMF DATA.#   DO NOT EDIT THIS FILE.  EDITS WILL BE LOST.# See resolv.conf(4) for details.search               test.com service.test.comnameserver      192.168.10.10nameserver      192.168.10.11 --- II.  Configuring Name service switch to use DNS 1. Configure using SMF service  system/name-service/switch # svccfg -s system/name-service/switchsvc:/system/name-service/switch> setprop config/host = astring: "files dns"svc:/system/name-service/switch>exit 2.  Restart/Refresh name-service/switch service #svccfg refresh name-service/switch #svccfg restart  name-service/switch 3. Verfiy host entry in /etc/nsswitch.conf  is updated with dns. # more /etc/nsswitch.conf## _AUTOGENERATED_FROM_SMF_V1_## WARNING: THIS FILE GENERATED FROM SMF DATA.#   DO NOT EDIT THIS FILE.  EDITS WILL BE LOST.# See nsswitch.conf(4) for details.passwd: filesgroup:  fileshosts:  files dnsipnodes:        files dns . --- PS: Thank you ollasi for your motivation behind the screen.

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