Those unfamiliar with Clojure are often interested in how you manage changing state within your applications. If you've heard a few things about Clojure but haven't really looked at it, I wouldn't be surprised if you thought it was impossible to write a "real" application with Clojure since "everything is immutable". I've even heard a developer that I respect make the mistake of saying: we're not going to use Clojure because it doesn't handle state well. Clearly, state management in Clojure is greatly misunderstood. I actually had a hard time not calling this blog entry "Clojure, it's about state". I think state shapes Clojure more than any other influence; it's the core of the language (as far as I can tell). Rich Hickey has clearly spent a lot of time thinking about state - there's an essay at http://clojure.org/state which describes common problems with a traditional approach to state management and Clojure's solutions. Rich's essay does a good job of succinctly discussing his views on state; you should read it before you continue with this entry. The remainder of this entry will give examples of how you can manage state using Clojure's functions. At the end of Rich's essay he says: In the local case, since Clojure does not have mutable local variables, instead of building up values in a mutating loop, you can instead do it functionally with recur or reduce. Before we get to reduce, let's start with the simplest example. You have an array of ints and you want to double each integer. In a language with mutable state you can loop through the array and build a new array with each integer doubled. for (int i=0; i < nums.length; i++) { result.add(nums[i] * 2); } In Clojure you would build the new array by calling the map function with a function that doubles each value. (I'm using Clojure 1.2) user=> (map (fn [i] (* i 2)) [1 2 3]) (2 4 6) If you're new to Clojure there's a few things worth mentioning. "user=>" is a REPL prompt. You enter some text and hit enter and the text is evaluated. If you've completed the list (closed the parenthesis), the results of evaluating that list will be printed to the following line. I remember what I thought the first time I looked at a lisp, and I know the code might not look like readable code, so here's a version that breaks up a few of the concepts and might make it easier to digest the example. user=> (defn double-int [i] (* i 2)) #'user/double-int user=> (def the-array [1 2 3]) #'user/the-array user=> (map double-int the-array) (2 4 6) In the first Clojure example you call the fn function to create an anonymous function, that was then passed to the map function (to be applied to each element of the array). The map function is a high order function that can take an anonymous function (example 1) or a named function (double-int, example 2). In Clojure (def ...) is a special form that allows you to define a var and defn is a function that allows you to easily define a function and assign it to a var. The syntax for defn is pretty straightforward, the first argument is the name, the second argument is the argument list of the new function, and any additional forms are the body of the function you are defining. Once you get used to Clojure's syntax you can even have a bit of fun with your function naming that might result in concise and maintainable code. user=> (defn *2 [i] (* 2 i)) #'user/*2 user=> (map *2 [1 2 3]) (2 4 6) but, I digress. Similarly, you may want to sum the numbers from an array. for (int i = 0; i < nums.length; i++) { result += nums[i]; } You can achieve goal of reducing an array to a single value in Clojure using the reduce function. user=> (reduce + [1 2 3]) 6 Clojure has several functions that allow you to create new values from existing values, which should be enough to solve any problem where you would traditionally use local mutable variables. For non-local mutable state you generally have 3 options: atoms, refs, and agents. When I started programming in Clojure, atoms were my primary choice for mutable state. Atoms are very easy to use and only require that you know a few functions to interact with them. Let's assume we're building a trading application that needs to keep around the current price of Apple. Our application will call our apple-price-update function when a new price is received and we'll need to keep that price around for (possible) later usage. The example below shows how you can use an atom to track the current price of Apple. user=> (def apple-price (atom nil)) #'user/apple-price user=> (defn update-apple-price [new-price] (reset! apple-price new-price)) #'user/update-apple-price user=> @apple-price nil user=> (update-apple-price 300.00) 300.0 user=> @apple-price 300.0 user=> (update-apple-price 301.00) 301.0 user=> (update-apple-price 302.00) 302.0 user=> @apple-price 302.0 The above example demonstrates how you can create a new atom and reset its value with each price update. The reset! function sets the value of the atom synchronously and returns its new value. You can also query the price of apple at any time using @ (or deref). If you're coming from a Java background the example above should be the easiest to relate to. Each time we call the update-apple-price function our state is set to a new value. However, atoms provide much more value than simply being a variable that you can reset. You may remember the following example from Java Concurrency in Practice. @NotThreadSafe public class UnsafeSequence { private int value; /** * Returns a unique value. */ public int getNext() { return value++; } } The book explains why this could cause potential problems. The problem with UnsafeSequence is that with some unlucky timing, two threads could call getNext and receive the same value. The increment notation, nextValue++, may appear to be a single operation, but is in fact three separate operations: read the value, add one to it, and write out the new value. Since operations in multiple threads may be arbitrarily interleaved by the runtime, it is possible for two threads to read the value at the same time, both see the same value, and then both add one to it. The result is that the same sequence number is returned from multiple calls in different threads. We could write a get-next function using a Clojure atom and the same race condition would not be a concern. user=> (def uniq-id (atom 0)) #'user/uniq-id user=> (defn get-next [] (swap! uniq-id inc)) #'user/get-next user=> (get-next) 1 user=> (get-next) 2 The above code demonstrates the result of calling get-next multiple times (the inc function just adds one to the value passed in). Since we aren't in a multithreaded environment the example isn't exactly breathtaking; however, what's actually happening under the covers is described very well on clojure.org/atoms - [Y]ou change the value by applying a function to the old value. This is done in an atomic manner by swap! Internally, swap! reads the current value, applies the function to it, and attempts to compare-and-set it in. Since another thread may have changed the value in the intervening time, it may have to retry, and does so in a spin loop. The net effect is that the value will always be the result of the application of the supplied function to a current value, atomically. Also, remember that changes to atoms are synchronous, so our get-next function will never return the same value twice. (note: while Java already provides an AtomicInteger class for handling this issue - that's not the point. The point of the example is to show that an Atom is safe to use across threads.) If you're truly interested in verifying that an atom is safe across threads, The Joy of Clojure provides the following snippet of code (as well as a wonderful explanation of all things Clojure, including mutability). (import '(java.util.concurrent Executors)) (def *pool* (Executors/newFixedThreadPool (+ 2 (.availableProcessors (Runtime/getRuntime))))) (defn dothreads [f & {thread-count :threads exec-count :times :or {thread-count 1 exec-count 1}] (dotimes [t thread-count] (.submit *pool* #(dotimes [_ exec-count] (f))))) (def ticks (atom 0)) (defn tick [] (swap! ticks inc)) (dothreads tick :threads 1000 :times 100) @ticks ;=> 100000 There you have it, 1000 threads updated ticks 100 times without issue. Atoms work wonderfully when you want to insure atomic updates to an individual piece of state; however, it probably wont be long before you find yourself wanting to coordinate some type of state update. For example, if you're running an online store, when a customer cancels an order the order is either active or cancelled; however, the order should never be active and cancelled. If you were to keep a set of active orders and a set of cancelled orders, you would never want to have an order be in both sets at the same time. Clojure addresses this issue by using refs. Refs are similar to atoms, but they also participate in coordinated updates. The following example shows the cancel-order function moving an order-id from the active orders set into the cancelled orders set. user=> (def active-orders (ref #{2 3 4})) #'user/active-orders user=> (def cancelled-orders (ref #{1})) #'user/cancelled-orders user=> (defn cancel-order [id] (dosync (commute active-orders disj id) (commute cancelled-orders conj id))) #'user/cancel-order user=> (cancel-order 2) #{1 2} user=> @active-orders #{3 4} user=> @cancelled-orders #{1 2} As you can see from the example, we're moving an order id from active to cancelled. Again, our REPL session doesn't show the power of what's going on with a ref, but clojure.org/refs contains a good explanation - All changes made to Refs during a transaction (via ref-set, alter or commute) will appear to occur at a single point in the 'Ref world' timeline (its 'write point'). The above quote is actually only 1 item in a 10 point list that discusses what's actually going on. It's worth reviewing the list a few times until you feel comfortable with everything that's going on. But, you don't need to completely understand everything to get started. You can begin to experiment with refs anytime you know you need coordinated changes to more than one piece of state. When you first begin to look at refs you may wonder if you should use commute or alter. For most cases commute will provide more concurrency and is preferred; however, you may need to guarantee that the ref has not been updated during the life of the current transaction. This is generally the case where alter comes into play. The following example shows using commute to update two values. The example demonstrates that the pairs are always updated only once; however, it also shows that the function is simply applied to the current value, so the incrementing is not sequential and @uid can be dereferenced to the same value multiple times. user=> (def uid (ref 0)) #'user/uid user=> (def used-id (ref [])) #'user/used-id user=> (defn use-id [] (dosync (commute uid inc) (commute used-id conj @uid))) #'user/use-id user=> (dothreads use-id :threads 10 :times 10) nil user=> @used-id [1 2 3 4 5 6 7 8 9 10 ... 89 92 92 94 93 94 97 97 99 100] The above example shows that commute simply applies regardless of the underlying value. As a result, you may see duplicate values and gaps in your sequence (shown in the 90s in our output). If you wanted to ensure that the value didn't change during your transaction you could switch to alter. The following example shows the behavior of changing from commute to alter. user=> (def uid (ref 0)) #'user/uid user=> (def used-id (ref [])) #'user/used-id user=> (defn use-id [] (dosync (alter uid inc) (alter used-id conj @uid))) #'user/use-id user=> (dothreads use-id :threads 10 :times 10) nil user=> @used-id [1 2 3 4 5 6 7 8 9 10 ... 91 92 93 94 95 96 97 98 99 100] There are more advanced examples using refs in The Joy of Clojure for those of you looking to discuss corner case conditions. Last, but not least, agents are also available. From clojure.org/agents - Like Refs, Agents provide shared access to mutable state. Where Refs support coordinated, synchronous change of multiple locations, Agents provide independent, asynchronous change of individual locations. While I understand agents conceptually, I haven't used them much in practice. Some people love them, and the last team I was on switched to using agents heavily in one of our applications shortly after I left. But, I personally don't have enough experience to say exactly where I think they fit in. I'm sure that will be a topic for a future blog post. Between Rich's essay and the examples above I hope a few things have become clear: Clojure has plenty of support for managing state Rich's distinction between identity and value allows Clojure to benefit from immutable structures while also allowing identity reassignment. Clojure, it's about state. From http://blog.jayfields.com/2011/04/clojure-state-management.html

Linear algebra is a commonly used area of mathematics with a wide range of applications in various engineering and scientific fields. Examples of applications include line fitting, Kalman filters, face recognition, financial software, and numerical optimization to name a few. Many computer libraries have been developed for linear algebra. One of the better known would be LAPACK, which was originally programmed in Fortran. Introducing Efficient Java Matrix Library The following article provides a brief introduction to one of the newer linear algebra libraries for Java, Efficient Java Matrix Library (EJML), a free open source library. EJML is a linear algebra library for dense real matrices. EJML's design goals are; 1) to be as computationally and memory efficient as possible for both small and large matrices, and 2) to be accessible to both novices and experts. These goals are accomplished by dynamically selecting the best algorithms to use at runtime and through a thoughtfully designed clean API. Providing good documentation, code examples, and constant benchmarking for speed, memory, and stability are also priorities. The following functionality is provided: Basic Operators (addition, multiplication, ... ) Matrix Manipulation (extract, insert, combine, ... ) Linear Solvers (linear, least squares, incremental, ... ) Decompositions (LU, QR, Cholesky, SVD, Eigenvalue, ...) Matrix Features (rank, symmetric, definitiveness, ... ) Random Matrices (covariance, orthogonal, symmetric, ... ) Different Internal Formats (row-major, block) Unit Testing EJML can be downloaded from its website at: http://ejml.org/ Application Programming Interface There are three primary ways to interact with EJML, 1) a simple to use object oriented interface, 2) procedural interface that provides greater control over memory and algorithms, 3) an expert interface that directly access specialized algorithms that is abstracted away using the first two. To see a practical comparison of these three interfaces take a look at the Kalman filter example provided at EJML's website. There each interface is used to code up a functionally identical Kalman filter: Here are three code sniplets showing the Kalman gain being computed: Simple: SimpleMatrix K = P.mult(H.transpose().mult(S.invert())); Procedural: if( !invert(S,S_inv) ) throw new RuntimeException("Invert failed"); multTransA(H,S_inv,d); mult(P,d,K); Specialized: if( !solver.setA(S) ) throw new RuntimeException("Invert failed"); solver.invert(S_inv); MatrixMatrixMult.multTransA_small(H,S_inv,d); MatrixMatrixMult.mult_small(P,d,K); The full examples are available at: http://ejml.org/wiki/index.php?title=Example_Kalman_Filter Going beyond the basics, there are easy to use yet powerful Java interfaces for solving linear systems and matrix decompositions. These provide much more control over the type of algorithm used, what it computes, how much memory is used, and remove as much of the drudgery as possible. DecompositionFactory and LinearSolverFactory are provided for creating these solvers and decompositions. By using the factory the best most update algorithm will be automatically selected. EJML offer many different decomposition algorithms, even within the same family. For instance, there are four QR decompositions provided, each of which is catered towards a different sized matrix. Different factories hide much of these detail from the end user. Who is using EJML? Even though EJML is a fairly new library it has already been picked up by several opensource projects: goGPS: http://code.google.com/p/gogps/ Set Visualiser: http://www-edc.eng.cam.ac.uk/tools/set_visualiser/ Universal Java Matrix Library (UJML): http://www.ujmp.org/ Scalalab: http://code.google.com/p/scalalab/ Java Content Based Image Retrieval (JCBIR): http://code.google.com/p/jcbir/ JquantLib (Will be added): http://www.jquantlib.org/ Performance and Benchmarks What about speed, stability, and correctness? Constant benchmarking for speed and stability is a core part of EJML's development. It has many internal benchmarks and also uses Java Matrix Benchmark (JMatBench)[1] (http://code.google.com/p/java-matrix-benchmark/) to compare its performance against other libraries. At the time of this writing there are a total of 551 unit tests that test basic correctness of almost all the functions in EJML. For runtime performance EJML is one of the fastest single threaded libraries. When compared against multi-threaded libraries on systems with several cores/CPU's it still competitive for many operations, despite its disadvantage of being single threaded. It has one of the lowest memory footprints [2]. A brief summary of EJML's performance relative to other libraries is shown below. Runtime performance is measured on a Core i7M620 system. (2 cores with 4 threads) Performance varies significantly by system. This processor was choosen because it is the most modern system benchmarked. Click here to get a explaination of the plots shown below. [1] Both EJML and JMatBench are developed by the same author. [2] Memory usage is highly dependent on the operation being used and the parameters passed to it. JMatBench only tests a few operations, but at least it shows attention is being paid to memory usage.

Sometimes the mechanics of different tests are really the same, and the only thing that changes between them are input and expected output data. As an example, consider testing mathematical calculations functions, or any kind of stateless method: you invoke it always in the same way, and then check the returned value. Another case is that of triangulation in Test-Driven Development. When an implementation is not obvious, triangulating it leads to the creation of multiple tests with different data, but usually the exact same procedure. To eliminate the duplication between these tests, we can code a method which models the whole test, and that accepts input (and possible initial state) and expected output as parameters. This method will perform the arrange, act, assert, and even teardown phases if needed. It will eliminate the need for maintaining the same test code in different places, of course. An advantage of these refactorings that we already cited in Test Utility Method is that adding a new test becomes equivalent to adding one or two lines of code. This pattern is really a specialized, standalone Test Utility Method. Implementation A simple form of Parameterized Test is a private method which is delegated from in the various test*() ones, which are called by the testing framework. The advantage of this first approach is to present more clear names to the code reader, and to support possible additional work to perform after the method is executed (it may return something which we can make further assertions on.) A second way of implementing Parameterized Test is with PHPUnit's @dataProvider annotation. Is it completely automatic, and displays data used in the test instance in case of failure in order to recognize which iteration we are in. You can also prepare input and output for @dataProvider programmatically (read: with code), so with a process as flexible and complex as you want. Variations Some older versions of Parameterized Test exist. A Loop-Driven Test uses nested loops that verify every combination of inputs/outputs. For example, it may check pairs of x and y coordinates in an image. Often exhaustive testing is an overkill and contains logic for the generation of data which may contain bugs. In a Tabular Test, you have a table, represented as code or in some configuration file, where each row corresponds to one test run and contains the necessary input/output relation. One Test Method works on everything here. This mechanism is obsolete as PHPUnit with @dataProvider executes each instance of the method into a different Testcase Object. You won't observe tests influencing each other, no problem with localization of which row is causing a failure. Example The code sample shows you how to go from a strong duplication between tests to a single copy of the code that uses @dataProvider to be executed N times. It also shows, in the third Testcase Class, how easy is to produce data for the tests with PHP code. assertEquals(0, sqrt(0)); } public function testSquareRootIsCalculatedCorrectlyByPHPFor1() { $this->assertEquals(1, sqrt(1)); } public function testSquareRootIsCalculatedCorrectlyByPHPFor4() { $this->assertEquals(2, sqrt(4)); } public function testSquareRootIsCalculatedCorrectlyByPHPFor9() { $this->assertEquals(3, sqrt(9)); } public function testSquareRootIsCalculatedCorrectlyByPHPFor16() { $this->assertEquals(4, sqrt(16)); } public function testSquareRootIsCalculatedCorrectlyByPHPForMinusOne() { $this->assertEquals('i', sqrt(-1)); } } class ParameterizedTest extends PHPUnit_Framework_TestCase { /** * This should be a static method, returning an array of arrays. * Each row of this table-like array is a set of arguments * for the Test Method. */ public static function squaresAndRoots() { return array( array(0, 0), array(1, 1), array(4, 2), array(9, 3), array(16, 4), array(-1, 'i') ); } /** * The annotation requires as unique argument the name of a public method * in this Testcase Class. * @dataProvider squaresAndRoots */ public function testSquareRootIsCalculatedCorrectlyByPHP($square, $root) { $this->assertEquals($root, sqrt($square)); } } class ProgrammaticDataProviderTest extends PHPUnit_Framework_TestCase { private static $width = 5; private static $height = 10; /** * I don't know why these methods are required to be static. It makes * no difference however as we are never going to call it directly. */ public static function everyCoupleOfCoordinates() { $testParameters = array(); for ($x = 1; $x <= self::$width; $x++) { for ($y = 1; $y <= self::$height; $y++) { $testParameters[] = array($x, $y); } } return $testParameters; } /** * I don't advise you to test an image at every pixel, but in some cases * you may need an exhaustive search. The programmatic generation of data * keeps the test code really short, but don't forget that the generation * logic may hide bugs if it becomes too complex. * @dataProvider everyCoupleOfCoordinates */ public function testImageHasCorrectTransparencyValue($x, $y) { $this->fail("This test will be executed in isolation with the x=$x and y=$y values."); } }

I recently found the Windows PowerShell and can't stop using it. I've setup a couple of helper functions to let me start CF from a simple command in the console. Before you can do anything you need to create a profile script. From within PowerShell you can run $profile to get the expected location for your profile. If you don't already have the file create it now. You will also need to tell PowerShell that it is ok to execute the new script by running Set-ExecutionPolicy Unrestricted You can, and should, read more about script execution. If you have the standard ColdFusion install you can try this script (note: you should change line 1 to point at your correct ColdFusion install, my example shows CF9): new-alias jrun "C:\ColdFusion9\runtime\bin\jrun.exe" function cf($server="coldfusion"){ jrun -start $server } Using MultiServer? Try adding this to your profile script: new-alias jrun "C:\JRun4\bin\jrun.exe" function cf($server="cfusion"){ jrun -start $server } Now, in the console, you can start your default CF servers by simply typing cf If you want to use something other than the default server, like when using multiserver, you can type cf myserver or cf -server myserver To stop the server just type Ctrl+C Happy PowerShell scripting!

I’ve written a couple of posts (here and here) about Java and the JDBC Driver for SQL Server with the promise of eventually writing about how to get a Java application running on the Windows Azure platform. In this post, I’ll deliver on that promise. Specifically, I’ll show you two things: 1) how to connect to a SQL Azure Database from a Java application running locally, and 2) how to connect to a SQL Azure database from an application running in Windows Azure. You should consider these as two ordered steps in moving an application from running locally against SQL Server to running in Windows Azure against SQL Azure. In both steps, connection to SQL Azure relies on the JDBC Driver for SQL Server and SQL Azure. The instructions below assume that you already have a Windows Azure subscription. If you don’t already have one, you can create one here: http://www.microsoft.com/windowsazure/offers/. (You’ll need a Windows Live ID to sign up.) I chose the Free Trial Introductory Special, which allows me to get started for free as long as keep my usage limited. (This is a limited offer. For complete pricing details, see http://www.microsoft.com/windowsazure/pricing/.) After you purchase your subscription, you will have to activate it before you can begin using it (activation instructions will be provided in an email after signing up). Connecting to SQL Azure from an application running locally I’m going to assume you already have an application running locally and that it uses the JDBC Driver for SQL Server. If that isn’t the case, then you can start from scratch by following the steps in this post: Getting Started with the SQL Server JDBC Driver. Once you have an application running locally, then the process for running that application with a SQL Azure back-end requires two steps: 1. Migrate your database to SQL Azure. This only takes a couple of minutes (depending on the size of your database) with the SQL Azure Migration Wizard - follow the steps in the Creating a SQL Azure Server and Creating a SQL Azure Database sections of this post. 2. Change the database connection string in your application. Once you have moved your local database to SQL Azure, you only have to change the connection string in your application to use SQL Azure as your data store. In my case (using the Northwind database), this meant changing this… String connectionUrl = "jdbc:sqlserver://serverName\\sqlexpress;" + "database=Northwind;" + "user=UserName;" + "password=Password"; …to this… String connectionUrl = "jdbc:sqlserver://xxxxxxxxxx.database.windows.net;" + "database=Northwind;" + "user=UserName@xxxxxxxxxx;" + "password=Password"; (where xxxxxxxxxx is your SQL Azure server ID). Connecting to SQL Azure from an application running in Windows Azure The heading for this section might be a bit misleading. Once you have a locally running application that is using SQL Azure, then all you have to do is move your application to Windows Azure. The connecting part is easy (see above), but moving your Java application to Windows Azure takes a bit more work. Fortunately, Ben Lobaugh has written a great post that that shows how to use the Windows Azure Starter Kit for Java to get a Java application (a JSP application, actually) running in Windows Azure: Deploying a Java application to Windows Azure with Command-Line Ant. (If you are using Eclipse, see Ben’s related post: Deploying a Java application to Windows Azure with Eclipse.) I won’t repeat his work here, but I will call out the steps I took in modifying his instructions to deploy a simple JSP page that connects to SQL Azure. 1. Add the JDBC Driver for SQL Server to the Java archive. One step in Ben’s tutorial (see the Select the Java Runtime Environment section) requires that you create a .zip file from your local Java installation and add it to your Java/Azure application. Most likely, your local Java installation references the JDBC driver by setting the classpath environment variable. When you create a .zip file from your java installation, the JDBC driver will not be included and the classpath variable will not be set in the Azure environment. I found the easiest way around this was to simply add the sqljdbc4.jar file (probably located in C:\Program Files\Microsoft SQL Server JDBC Driver\sqljdbc_3.0\enu) to the \lib\ext directory of my local Java installation before creating the .zip file. Note: You can put the JDBC driver in a separate directory, include it when you create the .zip folder, and set the classpath environment variable in the startup.bat script. But, I found the above approach to be easier. 2. Modify the JSP page. Instead of the code Ben suggests for the HelloWorld.jsp file (see the Prepare your Java Application section), use code from your locally running application. In my case, I just used the code from this post after changing the connection string and making a couple minor JSP-specific changes: Northwind Customers That’s it!. To summarize the steps… Migrate your database to SQL Azure with the SQL Azure Migration Wizard. Change the database connection in your locally running application. Use the Windows Azure Starter Kit for Java to move your application to Windows Azure. (You’ll need to follow instructions in this post and instructions above.) Thanks. -Brian

This article discusses dependency injection in a tutorial format. It covers some of the features of CDI such as type safe annotations configuration, alternatives and more.

JSF and JavaScript can combine forces to develop powerful applications. For example, let's see how we can use JavaScript code with h:commandLink and h:commandButton to obtain a confirmation before getting into action. Getting ready We have developed this recipe with NetBeans 6.8, JSF 2.0, and GlassFish v3. The JSF 2.0 classes were obtained from the NetBeans JSF 2.0 bundled library. How to do it... As you know the h:commandLink takes an action after a link is clicked (on the mouse click event), while h:commandButton does the same thing, but renders a button, instead of a text link. In this case, we place a JavaScript confirmation box before the action starts its effect. This is useful in user tasks that can't be reversed, such as deleting accounts, database records, and so on. Therefore, the onclick event was implemented as shown next: How it works... Notice that we embed the JavaScript code inside the onclick event (you also may put it separately in a JS function, per example). When the user clicks the link or the button, a JS confi rmation box appear with two buttons. If you confirm the choice, the JSF action takes place, while if you deny it then nothing happens. There's more... You can use this recipe to display another JS box, such as prompt box or alert box. You can find this recipe in JSF 2.0 Cookbook from Packt From http://e-blog-java.blogspot.com/2011/03/how-to-integrate-javascript-and-jsf.html

Try with resources is a new feature in Java 7 which lets us write more elegant code by automatically closing resources like FileInputStream at the end of the try-block. Old Try Catch Finally Dealing with resources like InputStreams is painful when it comes to the try-catch-finally blocks. You need to declare the resources outside the try so that they are is accessible from finally, then you must initialize the variable to null and check for non-null when closing the resource in finally. File file = new File("input.txt"); InputStream is = null; try { is = new FileInputStream(file); // do something with this input stream // ... } catch (FileNotFoundException ex) { System.err.println("Missing file " + file.getAbsolutePath()); } finally { if (is != null) { is.close(); } } Java 7: Try with resources With Java 7, you can create one or more “resources” in the try statement. A “resources” is something that implements the java.lang.AutoCloseable interface. This resource would be automatically closed and the end of the try block. File file = new File("input.txt"); try (InputStream is = new FileInputStream(file)) { // do something with this input stream // ... } catch (FileNotFoundException ex) { System.err.println("Missing file " + file.getAbsolutePath()); } Exception handling If both the (explicit) try block and the (implicit) resource handling code throw an exception, then the try block exception is the one which will be thrown. The resource handling exception will be made available via the Throwable.getSupressed() method of the thrown exception. Throwable.getSupressed() is a new method added to the Throwable class since 1.7 specifically for this purpose. If there were no suppressed exceptions then this will return an empty array. Reference http://download.java.net/jdk7/docs/technotes/guides/language/try-with-resources.html From http://www.vineetmanohar.com/2011/03/java-7-try-with-auto-closable-resources/

this tutorial will walk you through how to setup ibatis ( mybatis ) in a simple java project and will present how to work with dynamic queries (sql). pre-requisites for this tutorial i am using: ide: eclipse (you can use your favorite one) database: mysql libs/jars: mybatis , mysql conector and junit (for testing) this is how your project should look like: sample database please run the script into your database before getting started with the project implementation. you will find the script (with dummy data) inside the sql folder. 1 – article pojo i represented the pojo we are going to use in this tutorial with a uml diagram, but you can download the complete source code in the end of this article. the goal of this tutorial is to demonstrate how to retrieve the article information from database using dynamic sql to filter the data. 2 – article mapper – xml one of the most powerful features of mybatis has always been its dynamic sql capabilities. if you have any experience with jdbc or any similar framework, you understand how painful it is to conditionally concatenate strings of sql together, making sure not to forget spaces or to omit a comma at the end of a list of columns. dynamic sql can be downright painful to deal with. while working with dynamic sql will never be a party, mybatis certainly improves the situation with a powerful dynamic sql language that can be used within any mapped sql statement. the dynamic sql elements should be familiar to anyone who has used jstl or any similar xml based text processors. in previous versions of mybatis, there were a lot of elements to know and understand. mybatis 3 greatly improves upon this, and now there are less than half of those elements to work with. mybatis employs powerful ognl based expressions to eliminate most of the other elements. if choose (when, otherwise) trim (where, set) foreach let’s explain each one with examples. 1 – first scenario : we want to retrieve all the articles from database with an optional filter: title. in other words, if user specify an article title, we are going to retrieve the articles that match with the title, otherwise we are going to retrieve all the articles from database. so we are going to implement a condition (if) : select id, title, author from article where id_status = 1 and title like #{title} 2 – second scenario : now we have two optional filters: article title and author. the user can specify both, none or only one filter. so we are going to implement two conditions: select id, title, author from article where id_status = 1 and title like #{title} and author like #{author} 3 – third scenario : now we want to give the user only one option: the user will have to specify only one of the following filters: title, author or retrieve all the articles from ibatis category. so we are going to use a choose element: select id, title, author from article where id_status = 1 and title like #{title} and author like #{author} and id_category = 3 4 – fourth scenario : take a look at all three statements above. they all have a condition in common: where id_status = 1. it means we are already filtering the active articles let’s remove this condition to make it more interesting. select id, title, author from article where title like #{title} and author like #{author} what if both title and author are null? we are going to have the following statement: select id, title, authorfrom articlewhere and what if only the author is not null? we are going to have the fololwing statement: select id, title, authorfrom articlewhereand author like #{author} and both fails! how to fix it? 5 – fifth scenario : we want to retrieve all the articles with two optional filters: title and author. to avoid the 4th scenatio, we are going to use a where element: select id, title, author from article title like #{title} and author like #{author} mybatis has a simple answer that will likely work in 90% of the cases. and in cases where it doesn’t, you can customize it so that it does. the where element knows to only insert “where” if there is any content returned by the containing tags. furthermore, if that content begins with “and” or “or”, it knows to strip it off. if the where element does not behave exactly as you like, you can customize it by defining your own trim element. for example,the trim equivalent to the where element is: select id, title, author from article title like #{title} and author like #{author} the overrides attribute takes a pipe delimited list of text to override, where whitespace is relevant. the result is the removal of anything specified in the overrides attribute, and the insertion of anything in the with attribute. you can also use the trim element with set. 6 – sixth scenario : the user will choose all the categories an article can belong to. so in this case, we have a list (a collection), and we have to interate this collection and we are going to use a foreach element: select id, title, author from article title like #{title} and author like #{author} the foreach element is very powerful, and allows you to specify a collection, declare item and index variables that can be used inside the body of the element. it also allows you to specify opening and closing strings, and add a separator to place in between iterations. the element is smart in that it won’t accidentally append extra separators. note : you can pass a list instance or an array to mybatis as a parameter object. when you do, mybatis will automatically wrap it in a map, and key it by name. list instances will be keyed to the name “list” and array instances will be keyed to the name “array”. the complete article.xml file looks like this: select id, title, author from article where id_status = 1 and title like #{title} select id, title, author from article where id_status = 1 and title like #{title} and author like #{author} select id, title, author from article where id_status = 1 and title like #{title} and author like #{author} and id_category = 3 select id, title, author from article title like #{title} and author like #{author} select id, title, author from article title like #{title} and author like #{author} select id, title, author from article where id_category in #{category} download if you want to download the complete sample project, you can get it from my github account: https://github.com/loiane/ibatis-dynamic-sql if you want to download the zip file of the project, just click on download: there are more articles about ibatis to come. stay tooned! happy coding! from http://loianegroner.com/2011/03/ibatis-mybatis-working-with-dynamic-queries-sql/

One of the new features added in Java 7 is the capability to switch on a String. With Java 6, or less String color = "red"; if (color.equals("red")) { System.out.println("Color is Red"); } else if (color.equals("green")) { System.out.println("Color is Green"); } else { System.out.println("Color not found"); } String color = "red"; if (color.equals("red")) { System.out.println("Color is Red"); } else if (color.equals("green")) { System.out.println("Color is Green"); } else { System.out.println("Color not found"); } With Java 7: String color = "red"; switch (color) { case "red": System.out.println("Color is Red"); break; case "green": System.out.println("Color is Green"); break; default: System.out.println("Color not found"); } Conclusion The switch statement when used with a String uses the equals() method to compare the given expression to each value in the case statement and is therefore case-sensitive and will throw a NullPointerException if the expression is null. It is a small but useful feature which not only helps us write more readable code but the compiler will likely generate more efficient bytecode as compared to the if-then-else statement. From http://www.vineetmanohar.com/2011/03/new-java-7-feature-string-in-switch-support/

This tutorial will walk you through how to setup iBatis (MyBatis) in a simple Java project and will present an example using a discriminator column, in another words it is a inheritance mapping tutorial. Pre-Requisites For this tutorial I am using: IDE: Eclipse (you can use your favorite one) DataBase: MySQL Libs/jars: Mybatis, MySQL conector and JUnit (for testing) This is how your project should look like: Sample Database Please run the script into your database before getting started with the project implementation. You will find the script (with dummy data) inside the sql folder. 1 – POJOs – Beans I represented the beans here with a UML model, but you can download the complete source code in the end of this article. As you can see on the Data Modeling Diagram and the UML diagram above, we have a class Employee and two subclasses: Developer and Manager. The goal of this tutorial is to retrieve all the Employees from the database, but these employees can be an instance of Developer or Manager, and we are going to use a discriminator column to see which class we are going to instanciate. 2 – Employee Mapper – XML Sometimes a single database query might return result sets of many different (but hopefully somewhat related) data types. The discriminator element was designed to deal with this situation, and others, including class inheritance hierarchies. The discriminator is pretty simple to understand, as it behaves much like a switch statement in Java. A discriminator definition specifies column and javaType attributes. The column is where MyBatis will look for the value to compare. The javaType is required to ensure the proper kind of equality test is performed (although String would probably work for almost any situation). SELECT id, name, employee_type, manager_id, info, developer_id, product FROM employee E left join manager M on M.employee_id = E.id left join developer D on D.employee_id = E.id In this example, MyBatis would retrieve each record from the result set and compare its employee type value. If it matches any of the discriminator cases, then it will use the resultMap specified by the case. This is done exclusively, so in other words, the rest of the resultMap is ignored (unless it is extended, which we talk about in a second). If none of the cases match, then MyBatis simply uses the resultMap as defined outside of the discriminator block. So, if the managerResult was declared as follows: Now all of the properties from both the managerResult and developerResult will be loaded. Once again though, some may find this external definition of maps somewhat tedious. Thereforethere’s an alternative syntax for those that prefer a more concise mapping style. For example: SELECT id, name, employee_type, manager_id, info, developer_id, product FROM employee E left join manager M on M.employee_id = E.id left join developer D on D.employee_id = E.id Remember that these are all Result Maps, and if you don’t specify any results at all, then MyBatis willautomatically match up columns and properties for you. So most of these examples are more verbosethan they really need to be. That said, most databases are kind of complex and it’s unlikely that we’ll beable to depend on that for all cases. 3 - Employee Mapper – Annotations We did the configuration in XML, now let’s try to use annotations to do the same thing we did using XML. This is the code for EmployeeMapper.java: package com.loiane.data; import java.util.List; import org.apache.ibatis.annotations.Case;import org.apache.ibatis.annotations.Result;import org.apache.ibatis.annotations.Select;import org.apache.ibatis.annotations.TypeDiscriminator; import com.loiane.model.Developer;import com.loiane.model.Employee;import com.loiane.model.Manager; public interface EmployeeMapper { final String SELECT_EMPLOYEE = "SELECT id, name, employee_type, manager_id, info, developer_id, product " + "FROM employee E left join manager M on M.employee_id = E.id " + "left join developer D on D.employee_id = E.id "; /** * Returns the list of all Employee instances from the database. * @return the list of all Employee instances from the database. */ @Select(SELECT_EMPLOYEE) @TypeDiscriminator(column = "employee_type", cases = { @Case (value="1", type = Manager.class, results={ @Result(property="managerId", column="manager_id"), @Result(property="info"), }), @Case (value="2", type = Developer.class, results={ @Result(property="developerId", column="developer_id"), @Result(property="project", column="product"), }) }) List getAllEmployeesAnnotation();} If you are reading this blog lately, you are already familiar with the @Select and @Result annotations. So let’s skip it. Let’s talk about the @TypeDiscriminator and @Case annotations. @TypeDiscriminator A group of value cases that can be used to determine the result mapping to perform. Attributes: column, javaType, jdbcType, typeHandler, cases. The cases attribute is an array of Cases. @Case A single case of a value and its corresponding mappings. Attributes: value, type, results. The results attribute is an array of Results, thus this Case Annotation is similar to an actual ResultMap, specified by the Results annotation below. In this example: We set the column atribute for @TypeDiscriminator to determine which column MyBatis will look for the value to compare. And we set an array of @Case. For each @Case we set the value, so if the column matches the value, MyBatis will instanciate a object of type we set and we also set an array of @Result to match column with class atribute. Note one thing: using XML we set the id and name properties. We did not set these properties using annotations. It is not necessary, because the column matches the atribute name. But if you need to set, it is going to look like this: @Select(SELECT_EMPLOYEE)@Results(value = { @Result(property="id"), @Result(property="name")})@TypeDiscriminator(column = "employee_type", cases = { @Case (value="1", type = Manager.class, results={ @Result(property="managerId", column="manager_id"), @Result(property="info"), }), @Case (value="2", type = Developer.class, results={ @Result(property="developerId", column="developer_id"), @Result(property="project", column="product"), })})List getAllEmployeesAnnotation(); 4 – EmployeeDAO In the DAO, we have two methods: the first one will call the select statement from the XML and the second one will call the annotation method. Both returns the same result. package com.loiane.dao; import java.util.List; import org.apache.ibatis.session.SqlSession;import org.apache.ibatis.session.SqlSessionFactory; import com.loiane.data.EmployeeMapper;import com.loiane.model.Employee; public class EmployeeDAO { /** * Returns the list of all Employee instances from the database. * @return the list of all Employee instances from the database. */ @SuppressWarnings("unchecked") public List selectAll(){ SqlSessionFactory sqlSessionFactory = MyBatisConnectionFactory.getSqlSessionFactory(); SqlSession session = sqlSessionFactory.openSession(); try { List list = session.selectList("Employee.getAllEmployees"); return list; } finally { session.close(); } } /** * Returns the list of all Employee instances from the database. * @return the list of all Employee instances from the database. */ public List selectAllUsingAnnotations(){ SqlSessionFactory sqlSessionFactory = MyBatisConnectionFactory.getSqlSessionFactory(); SqlSession session = sqlSessionFactory.openSession(); try { EmployeeMapper mapper = session.getMapper(EmployeeMapper.class); List list = mapper.getAllEmployeesAnnotation(); return list; } finally { session.close(); } } The output if you call one of these methods and print: Employee ID = 1 Name = Kate Manager ID = 1 Info = info KateEmployee ID = 2 Name = Josh Developer ID = 1 Project = webEmployee ID = 3 Name = Peter Developer ID = 2 Project = desktopEmployee ID = 4 Name = James Manager ID = 2 Info = info JamesEmployee ID = 5 Name = Susan Developer ID = 3 Project = web Download If you want to download the complete sample project, you can get it from my GitHub account: https://github.com/loiane/ibatis-discriminator If you want to download the zip file of the project, just click on download: There are more articles about iBatis to come. Stay tooned! From http://loianegroner.com/2011/03/ibatis-mybatis-discriminator-column-example-inheritance-mapping-tutorial/

Java 7 uses the underlying file system functionalities to watch the file system for changes. Now, we can watch for events like creation, deletion, modification, and get involved with our own actions. For accomplish this task, we need: • An object implementing the Watchable interface - the Path class is perfect for this job. • A set of events that we are interested in - we will use StandardWatchEventKind which implements the WatchEvent.Kind. • An event modifier that qualifies how a Watchable is registered with a WatchService. • A watcher who watch some watchable – per example, a watcher that watches the File System for changes. The abstract class is java.nio.file.WatchService but we will be using the FileSystem object to create a watcher for the File System. The below example follows the above scenario: import java.nio.file.Path; import java.nio.file.Paths; import java.nio.file.StandardWatchEventKind; import java.nio.file.WatchEvent; import java.nio.file.WatchKey; import java.nio.file.WatchService; import java.util.List; public class Main { public static void main(String[] args) { //define a folder root Path myDir = Paths.get("D:/data"); try { WatchService watcher = myDir.getFileSystem().newWatchService(); myDir.register(watcher, StandardWatchEventKind.ENTRY_CREATE, StandardWatchEventKind.ENTRY_DELETE, StandardWatchEventKind.ENTRY_MODIFY); WatchKey watckKey = watcher.take(); List> events = watckKey.pollEvents(); for (WatchEvent event : events) { if (event.kind() == StandardWatchEventKind.ENTRY_CREATE) { System.out.println("Created: " + event.context().toString()); } if (event.kind() == StandardWatchEventKind.ENTRY_DELETE) { System.out.println("Delete: " + event.context().toString()); } if (event.kind() == StandardWatchEventKind.ENTRY_MODIFY) { System.out.println("Modify: " + event.context().toString()); } } } catch (Exception e) { System.out.println("Error: " + e.toString()); } } } The FileSystem object and the WatchService can also be created like this: FileSystem fileSystem = FileSystems.getDefault(); WatchService watcher = fileSystem.newWatchService(); And the Path (watchable), what we watch, and register it with the WatchService object like this: Path myDir = fileSystem.getPath("D:/data"); myDir.register(watcher, StandardWatchEventKind.ENTRY_CREATE, StandardWatchEventKind.ENTRY_DELETE, StandardWatchEventKind.ENTRY_MODIFY);

The purpose of a Fake Object, a kind of Test Double, is to replace a collaborator with a functional copy. While Mocks prefer a specification of the behavior to check, Fake Objects are really a simplified version of the production object they substitute. A good Fake Object, however, will be very lightweight, easy to create and throw away to ensure test isolation. Usually it won't satisfy some other functional or non-functional requirements, otherwise we would use him instead of the real object. For example a UserRepository, which normally stores users in a database, may be substitued by a Fake that: does not send mails when an user is added. Doesn't really save anything in the database, but keeps a list in an array. When some complex method is called, just throws a NonImplementedException. Utility Of course performance and less brittleness of tests are the main selling points of a Fake Objects: think about testing with a real database and without one; however this is true for all Test Doubles and I don't need to repeat it. Often the Fake is used when the contract between the SUT and the collaborator is too complex to effectively build a Stub or Mock: many methods, or many calls to the same method are made. Return parameters are difficult to setup. A Fake avoids overspecifying a test for a very invasive contract, like the order of calls and precise parameters; a real implementation is more robust to changes in the contract. For example if the Fake is a collection, you can store and retrieve objects of another type, and still not change the Fake; expectations and configured return value instead would change (together). As an example, an embryonal Fake is PHPUnit $this->returnArgument() for the will() Mock expectation. It's a piece of functionality which substitutes an hard-coded expectation, in order to return one of the arguments of the call instead of adding the argument to the expectation itself. Note that many times we don't have to create new code to leverage Fakes: we can use the existing one with a different configuration (like a Cache maximumCachedItems=0, or with an adapter that use an in-memory cache instead of APC or Memcache) or something provided for us, like an sqlite in-memory database created by PDO. NullObjects are sometimes Fake, but are used in production more than in testing. Implementation Several steps are necessary for building an hand-rolled Fake: create the Fake class by hand, and define the simplest implementation that could do the job for this test. Remember, you're not testing the Fake, you're testing the SUT, so the Fake doesn't have to be perfect, but just passable for the test case at end. Instance an object from the Fake class: the constructor may be different from the real collaborator's one as it's not part of the contract. Install the Fake into the SUT, and proceed as normal. The steps for generating a Fake with PHPUnit are a little different; however, you will still have to write its businss logic: create the "mock" as always via getMock() or getMockBuilder(). Define expectations for its methods with will($this->returnCallback()) and some anonymous functions (or via self-shunting like we did for "Stubs"). Often objects passed in this closures (such as ArrayObject instances) can aid in making the Fake methods communicate with each other. Install the Fake and proceed with the test. The second approach is invaluable when you have a single method on the Fake: it's very fast. Variations Fake Database: an alternative implementation of the Database Facade that lets you test classes that depend on the database without actually using it. For exaple, a DAO which internally use an SplObjectStorage instead of the connection. In-Memory Database: sqlite3 in-memory database used with ORMs for tests that isolate you from the real database, but not from using PDO. High Return On Investment. Fake Web Service: mimics the interface of some web service like Google Analytics, when you have to interact also in write and not only in read. Fake Service Layer: simplified implementations of Service Layer classes, which avoid checking concurrency, authorization, authentication and so on in order to simplify functional testing. Example In this code sample, the test target a ForumManager class which needs to manipulate Posts. It's not a simple Facade: it moves Post around, merges threads and so on. So we inject in it a FakePostDao: ForumManager will call it instead of the database. When you I have many tests of this type, the time for writing the Fake implementation is well spent. array( new Post('Hello'), new Post('Hello!'), new Post('') ), 2 => array( new Post('Hi'), new Post('Hi!') ), 3 => array( new Post('Good morning.') ) )); $forumManager = new ForumManager($dao); $forumManager->mergeThreadsByIds(1, 2); $thread = $dao->getThread(1); $this->assertEquals(5, count($thread)); } } /** * The SUT. */ class ForumManager { private $dao; public function __construct(PostsDao $dao) { $this->dao = $dao; } public function mergeThreadsByIds($originalId, $toBeMergedId) { $original = $this->dao->getThread($originalId); $toBeMerged = $this->dao->getThread($toBeMergedId); $newOne = array_merge($original, $toBeMerged); $this->dao->removeThread($originalId); $this->dao->removeThread($toBeMergedId); $this->dao->addThread($originalId, $newOne); } } /** * Interface for the collaborator to substitute with the Test Double. */ interface PostsDao { public function getThread($id); public function removeThread($id); public function addThread($id, array $thread); } /** * Fake implementation. */ class FakePostDao implements PostsDao { private $threads; public function __construct(array $initialState) { $this->threads = $initialState; } public function getThread($id) { return $this->threads[$id]; } public function removeThread($id) { unset($this->threads[$id]); } /** * We model Thread as array of Posts for simplicity. */ public function addThread($id, array $thread) { $this->threads[$id] = $thread; } } /** * Again a Dummy object: minimal implementation, to make this test pass. */ class Post { }

This short post adds to my series on connection options in the SQL Server Driver for PHP. I’ll go into a bit more detail on the Encrypt and TrustServerCertificate options than the driver documentation does. I’ll start with three important points related to these options, then I’ll go into a couple of hypothetical situations that should shed more light on what these options actually do. The first thing to note is that these two options, Encrypt and TrustServerCertificate, are often used together. The Encrypt option is used to specify whether or not the connection to the server is encrypted (the default is false). The TrustServerCertificate option is used to indicate whether the certificate on the SQL Server instance should be trusted (the default is false). Note: Setting the Encrypt option does not mean that data is stored in an encrypted form. It simply means that data is encrypted while in transport to the server. Also note that this setting does not apply to authentication credentials such as a SQL Server password – authentication credentials are always encrypted. For information about storing encrypted data, see Encryption Hierarchy in the SQL Server documentation. The second thing to note is that, by default, when SQL Server is installed it creates a self-signed certificate that it will use to encrypt connections. Of course, self-signed certificates are not ideal for secure connections (they are vulnerable to man-in-the-middle attacks), so it is best to replace this certificate with one from a certificate authority (CA). The third thing to know is how to use these options when connecting to SQL Server. If you are using the SQLSRV API, your connection code might look something like this: $serverName = "serverName"; $connectionInfo = array( "Database"=>"DbName", "UID"=>"UserName", "PWD"=>"Password", "Encrypt"=>true, "TrustServerCertificate"=>false); $conn = sqlsrv_connect( $serverName, $connectionInfo); If you are using the PDO_SQLSRV API, your connection code might look something like this: $serverName = "serverName"; $conn = new PDO("sqlsrv:Server = $serverName; Database = DBName; Encrypt = true; TrustServerCertificate = false", "UserName", "Password"); Now, with those three things in mind, let’s look at a couple of examples. First suppose the following: You did not replace the self-signed certificate created when SQL Server was installed. You set Encrypt = true. You set TrustServerCertificate = false. In this scenario, your connection will fail. When you set TrustServerCertificate = false, you are asking for some 3rd party verification of the certificate. Because this is a self-signed certificate, there is no 3rd party to do the verification. However, if you set TrustServerCertificate = true, then your connection will succeed because you are trusting the certificate. (Note that, as mentioned earlier, this connection would be vulnerable to man-in-the-middle attacks.) Now consider the following: You replaced the self-signed certificate created when SQL Server was installed with a certificate from a certificate authority (CA). You set Encrypt = true. In this case (assuming there are no problems with your certificate), regardless of your setting for TrustServerCertificate, your connection will succeed. However, for a more secure connection (one not vulnerable to man-in-the-middle attacks), you should set TrustServerCertificate = false. Doing so will force third party verification of the certificate. For information about installing a certificate on SQL Server, see How to: Enable Encrypted Connections to the Database Engine. Note that that topic references setting the Force Encryption option on the database server. Setting this option to Yes on the server does the same thing as setting Encryption = true on the client – it forces the connection to be encrypted using the server’s certificate. That’s it for today. Thanks. -Brian

There has been a lot of buzz lately on high-availability and clustering. Most developers don't care and why should they? These features should be transparent to the application architecture and not something of concern to the developers of that application. But knowledge never hurts, so I emerged myself into the world of load balancing, heartbeats and virtual IP addresses. And you know what? Next time we need a infrastructure like this, I can at least sit down with the guys from the infrastructure department and at least know what the hell they are talking about. So what exactly is a high-availability clustered infrastructure (HACI, as I'll call it from now on) ? In essence, it should be a zero-downtime infrastructure (or at least perceived as one by the end user, which means never ever returning a default browser 404 page), capable of horizontal scaling when the need for it arises and without a single point of failure. It's the SLA writer's dream. A basic HACI setup looks like this: The users enters through a virtual IP address, assigned to one of the two load balancers. Only one of the load-balancers is active (the active master, LB1), the other one is there in the event LB1 fails ((LB2, a passive slave). The two load balancers are redundant, ie. having the exact same configuration. The load balancers redirect all traffic to the real servers. This can be done through round-robin assignment or through other means like sticky sessions, where the same user is redirected to the same server each and every time within a session. Servers can be added at any moment and configured on the load balancers. Ideally, the load balancer configuration is aware of the hardware specification and balances the load accordingly, but that's beyond the scope of this article (it involves adding weights). If all servers balanced by the load balancer fail, a backup server should be used to redirect all traffic coming from the load balancer. This can be a very lightweight server, which purpose is only to provide a sensible error page to the user (something like 'Sorry, we are performing maintenance'). Again, perception and immediate feedback to the user is key. You don't want to show the user a plain 404 page. Off course, if the backup server goes down too, you're in trouble (off course, by that time, warning bells should have gone off on every level in the hierarchy). So how to achieve this with as little effort as possible? If you want to try this out, I suggest you start by installing a virtual machine like VirtualBox or VMWare. This way you can try out the configuration yourself. In this example, I'll be load-balancing 3 Tomcat servers using sticky sessions using 2 load balancers in active-passive mode. I'm assuming all 3 Tomcat servers share the same hardware configuration, so they are all able to handle the same amount of traffic each. I'm also throwing in a backup server, in case all 3 Tomcat servers go down (serving a custom 503 page kindly informing the user of a catastrophic failure, instead of dropping the standard 404 bomb). You want to start off by assigning IP addresses to the servers. This will make your life a bit easier. We'll need 7 addresses: 3 for the tomcat server, 1 for the backup server, 2 for the loadbalancers and 1 virtual IP address to be shared between the load balancers (and which will be the entry point for your users). So our assignment will be: Virtual IP 10.0.5.99 www.haci.local LB1 10.0.5.100 lb1.haci.local #MASTER LB2 10.0.5.101 lb2.haci.local #SLAVE WEB1 10.0.5.102 web1.haci.local WEB2 10.0.5.103 web2.haci.local WEB3 10.0.5.104 web3.haci.local BACKUP 10.0.5.105 backup.haci.local Setting up the web servers is easy. You just install Tomcat on each server and create a simple JSP file to be served to users (make a small change, like the background color, on each server to distinguish the servers). I won't be covering session replication between the Tomcat servers, as it'll take me too far. If you want, you can configure the appropriate session replication and storage (using multicast or JDBC for example). The backup server I'm using is a basic LAMP server that returns a simple 503 page on every request it gets. The 503 error code is important, because it reflects the current state of the system: currently unavailable. For the loadbalancers I'll be using 2 applications: HAProxy and keepalived. HAProxy is going to handle load balancing, while keepalived will handle the failover between the two load balancers. First, we're going to configure HAProxy for both LB1 and LB2. Installing HAProxy is quite easy on an ubuntu system. Just do a sudo apt-get install haproxy and you're off. After the install, backup the current HAProxy config and start editing away. cp /etc/haproxy.cfg /etc/haproxy.cfg_orig cat /dev/null > /etc/haproxy.cfg vi /etc/haproxy.cfg The content of the config to reflect our setup should become something like this (same config on LB1 and LB2): global log 127.0.0.1 local0 log 127.0.0.1 local1 notice #log loghost local0 info maxconn 4096 #debug #quiet user haproxy group haproxy defaults log global mode http option httplog option dontlognull retries 3 redispatch maxconn 2000 contimeout 5000 clitimeout 50000 srvtimeout 50000 frontend http-in bind 10.0.5.99:80 default_backend servers backend servers mode http stats enable stats auth someuser:somepassword balance roundrobin cookie JSESSIONID prefix option httpclose option forwardfor option httpchk HEAD /check.txt HTTP/1.0 server web1 10.0.5.102:80 cookie haci_web1 check server web2 10.0.5.103:80 cookie haci_web2 check server web3 10.0.5.104:80 cookie haci_web3 check server webbackup 10.0.5.105:80 backup After this, enable HAProxy on both LB1 and LB2 by editing /etc/defaults/haproxy # Set ENABLED to 1 if you want the init script to start haproxy. ENABLED=1 # Add extra flags here. #EXTRAOPTS="-de -m 16" So far for the HAProxy configuration. We can't start it up yet, as LB1 and LB2 aren't listening yet on the virtual IP address. Next we'll configure the failover of the loadbalancers using keepalived. Installing it on Ubuntu is as easy as it was for HAProxy: sudo apt-get install keepalived. But its configuration is slightly different on both load balancers. First, we need to configure the both servers to be able to listen to the shared IP address. Add the following line to /etc/sysctl.conf: net.ipv4.ip_nonlocal_bind=1 And run sysctl -p Now, we configure keepalived so that LB1 is configured as the main load balancer and binds to the shared IP address, while LB2 is on standby, ready to take over whenever LB1 goes down. The configuration for LB1 looks like this (edit /etc/keepalived/keepalived.conf): vrrp_script chk_haproxy { # Requires keepalived-1.1.13 script "killall -0 haproxy" # cheaper than pidof interval 2 # check every 2 seconds weight 2 # add 2 points of prio if OK } vrrp_instance VI_1 { interface eth0 state MASTER virtual_router_id 51 priority 101 # 101 on master, 100 on backup virtual_ipaddress { 10.0.5.99 } track_script { chk_haproxy } } Start up keepalived and check whether it is listening to the virtual IP address. /etc/init.d/keepalived start ip addr sh eth0 It should return something like this, indicating it is listening to the virtual IP address 2: eth0: mtu 1500 qdisc pfifo_fast qlen 1000 link/ether 00:0c:29:a5:5b:93 brd ff:ff:ff:ff:ff:ff inet 10.0.5.100/24 brd 10.0.5.255 scope global eth0 inet 10.0.5.99/32 scope global eth0 inet6 fe80::20c:29ff:fea5:5b93/64 scope link valid_lft forever preferred_lft forever Next, we configure LB2. The configuration is almost the same, exception for the priority. vrrp_script chk_haproxy { # Requires keepalived-1.1.13 script "killall -0 haproxy" # cheaper than pidof interval 2 # check every 2 seconds weight 2 # add 2 points of prio if OK } vrrp_instance VI_1 { interface eth0 state MASTER virtual_router_id 51 priority 100 # 101 on master, 100 on backup virtual_ipaddress { 10.0.5.99 } track_script { chk_haproxy } } Start up keepalived and check the network interface. /etc/init.d/keepalived start ip addr sh eth0 It should return something like this, indicating it is not listening to the virtual IP address 2: eth0: mtu 1500 qdisc pfifo_fast qlen 1000 link/ether 00:0c:29:a5:5b:93 brd ff:ff:ff:ff:ff:ff inet 10.0.5.101/24 brd 10.0.5.255 scope global eth0 inet6 fe80::20c:29ff:fea5:5b93/64 scope link valid_lft forever preferred_lft forever Now, start up HAProxy on both LB1 and LB2. /etc/init.d/haproxy start Now you can issue requests to 10.0.5.99 (or www.haci.local), which will go to LB1, which in turn will load-balance the request to either WEB1, WEB2 and WEB3. You can test the load balancing by turning off WEB1 (or the server you're currently on). You can also the backup server by turning all main webservers (WEB1, WEB2 and WEB3). And you can test the loadbalancer failover by turning off LB1. At that point LB2 will kick in and act as the master, loadbalancing all requests. When you turn LB1 back on, it'll take over the master role once again. HAProxy allows you to add extra servers very easily, reloading the configuration without breaking existing sessions. See the HAProxy documentation for more info or on ServerFault. (http://serverfault.com/questions/165883/is-there-a-way-to-add-more-backend-server-to-haproxy-without-restarting-haproxy). Cheap and effective. While most enterprise shops have hardware load balancers, which also have these possibilities and more, if you're on a tight budget or need to simulate a HACI environment for development purposes (a lesson here: always simulate your production environment when you're testing during development), this might be the sane option. To finish, I'll quickly explain how to set up the backup server (a simple LAMP server). Create a vhost configuration on the apache for www.haci.local or any other domain pointing to the virtual IP address and set up mod_rewrite for it: RewriteEngine On RewriteCond %{REQUEST_URI} !\.(css|gif|ico|jpg|js|png|swf|txt)$ [NC] RewriteConf %{REQUEST_URI} !/503.php RewriteRule .* /503.php [L] Then create the 503.php file and add this to the top of it: Sorry, our servers are currently undergoing maintenance. Please check back with us in a while. Thank you for your patience. You can decorate the 503.php file any way you like. You can even use CSS, JavaScript and image files in the php file. Now, back to my IDE. I'm getting withdrawal symptoms.

If you start off trying to get to grips with the in-built Java Secure Sockets Extension you're gonna be stunned by the complexity of it all.

The concept of behavior verification consists in verifying not only the output of the System Under Test, but the calls to other components. These method calls are an output normally not visible to a caller like the test; unless he injects, instead of the real collaborator, a Test Double which can be accessed also by him. Today we will explore the first pattern for behavior verification: the Test Spy. It is a Test Double which records calls so that assertions can be made on them later in the test (after the exercise part). Spies gives us the ability to observe side-effects of the SUT, expressed as interactions with other objects. Usually behavior verification is taught with Mocks, which also verify the calls that are made on them but with specifications provided before the exercise phase. Use cases Why using a Spy instead a Mock? I bet you have heard already about Mocks at this point in the series. Here are some possible use cases on when to define Test Spies. When we cannot predict what the collaborator will be called with (if we can, we may use a Mock instead). When an assertion on calls is complex to define beforehand, or needs all (or more than one of) the calls to be completed before taking place. The matchers used for Mocks are not sufficient for verification. When a Mock that immediately threw an exception would only result in the SUT swallowing it. So a Spy is better in this case as it leaves verification for later. PHPUnit matchers in general do not always throw an exception or always wait for the end of test (it depends on the particular constraint.) The behavior involves more than one collaborator (it happens sometimes). In this case, you can make an assertion using the data recorded by more than one Spy. Implementation 1. Like with all Test Doubles, provide an alternate implementation or subclass. Probably the calls to the Spy won't return anything (or will return something canned to avoid the SUT's failure, like in a Stub). The methods implementations will just record all calls, or some property of the calls like the first parameter or their total number. 2. Inject the Test Double and exercise the SUT. 3. Make assertions on the Test Double data gathered by the Spy. Variations The variations of the pattern are mainly on how to retrieve the data from the Test Spy. Retrieval Interface: the Test Spy is a class with additional methods, or public properties (eek) that expose the recorded data. This variation cannot be coded with PHPUnit generation of Test Doubles, only by hand-rolling them. Self Shunt: the Test Spy and Test Case Object are a single object. This means we inject $this as the Test Double and we have the maximum freedom of defining new methods and access the recording. The caveat is that it can only be done with interfaces in PHPUnit, because Test Case Classes must extend PHPUnit_Framework_TestCase. That's a good reason to extract an interface, though. Inner Test Double: we inject a private class (not existent in PHP) or a closure which records everything. A closure for example can access $this public properties or methods, or some other local ArrayObject passed by handler (or variable passed by reference) where the data can be kept. Indirect Output Registry: same as Inner Test Double, but the target for recordings is a full-fledged object. Almost always an overkill. Examples The sample code shows you how to implement a Spy, in its different variations and in use cases where it actually make sense. Most of the times, Mock are used instead and the pattern is equivalent. Test Spies are a little more difficult to write, but they are invaluable in the case where your verification logic does not fit the framework of Mocks (predefined, single-object expectations). createUser(array('mail' => '[email protected]', 'nickname' => 'johndoe')); $this->assertEquals(array('executeQuery', 'mail'), $this->order); } private $order = array(); private $queries = array(); public function executeQuery($query, array $params) { $this->order[] = 'executeQuery'; $this->queries[] = $query; } private $mails = array(); public function mail($to, $subject, $object) { $this->order[] = 'mail'; $this->mails[] = array('to' => $to, 'subject' => $subject, 'object' => $object); } public function testInnerTestDoubleArrayObject() { $parts = new ArrayObject(); $receiver = $this->getMock('Receiver'); $receiver->expects($this->any()) ->method('definePart') ->will($this->returnCallback(function($amount) use ($parts) { $parts[] = $amount; })); $sut = new RandomDivider($receiver); $sut->divide(10); $this->assertEquals(10, array_sum($parts->getArrayCopy())); } public function testInnerTestDoubleArrayPassedByReference() { $parts = array(); // an array would not be passed by handler by default $receiver = $this->getMock('Receiver'); $receiver->expects($this->any()) ->method('definePart') ->will($this->returnCallback(function($amount) use (&$parts) { // but we can pass it by reference $parts[] = $amount; })); $sut = new RandomDivider($receiver); $sut->divide(10); $this->assertEquals(10, array_sum($parts)); } } interface Mailer { public function mail($to, $subject, $object); } interface Db { public function executeQuery($query, array $params); } class UserDao { private $db; private $mailer; public function __construct(DB $db, Mailer $mailer) { $this->db = $db; $this->mailer = $mailer; } public function createUser(array $userDetails) { // internally it would use PDO $this->db->executeQuery("INSERT INTO users ...", $userDetails); $this->mailer->mail($userDetails['mail'], 'You have been registered on example.com', '...'); } } interface Receiver { public function definePart($amount); } class RandomDivider { private $receiver; public function __construct(Receiver $receiver) { $this->receiver = $receiver; } public function divide($total) { $part = ceil(rand() * $total); $this->receiver->definePart($part); $this->receiver->definePart($total - $part); } }

As you may know, one of new features of upcoming Java 7 will be the diamond operator. Purpose of the diamond operator is to simplify instantiation of generic classes. For example, instead of List p = new ArrayList(); with the diamond operator we can write only List p = new ArrayList<>(); and let compiler infer the value of type argument. Nice simplification. But do we really need to write <>? Isn't new ArrayList() enough? In this article, I will describe the arguments of the <> proponents and explain why I think that these arguments are not very strong. However, I also describe arguments why we need <>. In Java 1.4, we had raw types only: List p = new ArrayList(); Java 5 introduced generics: List p = new ArrayList(); Many types in Java API were generified and even though we can still use generic types as raw types, there is no reason for this in Java 5 or newer. When generics were introduced, raw types were allowed for backward compatibility so that we could gradually and smoothly adopt generics. For example, code in Java 1.4 can be combined with new generic code because raw and generic types are allowed together. This is also expressed in the JLS (4.8 Raw Types): "The use of raw types is allowed only as a concession to compatibility of legacy code. The use of raw types in code written after the introduction of genericity into the Java programming language is strongly discouraged. It is possible that future versions of the Java programming language will disallow the use of raw types." Now let's go back to the diamond operator and ask again: "Do we really need <>?". The proponents of the <> syntax say that we need <> to preserve backward compatibility. Let's look at an example from the coin-dev conference: class Foo { Foo(X x) { } Foo get(X x) { return this; } } class Test { void test() { Foo f1 = new Foo(1).get(""); //ok - can pass String where Object is expected Foo f2 = new Foo<>(1).get(""); //fail - cannot pass String where Integer is expected } } This shows the difference between new Foo(1) and new Foo<>(1). Clearly, these two are different and if we changed the semantics of new Foo(1), it would break backward compatibility. But wait. Backward compatibility with what? Isn't line Foo f1 = new Foo(1).get(""); a little suspicious? It uses generic type in the left part and raw type in the right part. Although it is legal, it is probably either omission or malpractice. And its legality is probably only a side effect of "a concession to compatibility of legacy code". Let's go further and look at another example from the coin-dev conference. It shows the difference between raw type and parameterized type with the diamond: public class X { public X(T t) { } public T get() { return null; } public static int f(String s) { return 1; } public static int f(Object o) { return 2; } public static void main(String[] args) { System.out.println(f(new X<>("").get())); System.out.println(f(new X("").get())); } } Let's play with the code a bit. Let's assume that there was a library with the X class: public class X { public X(Object o) { } public Object get() { return null; } } and some code that compiled against this library: public class Client { static int f(String s) { return 1; } static int f(Object o) { return 2; } public static void main(String[] args) { System.out.println(f(new X("").get())); } } Then, the library was generified: public class X { public X(T t) { } public T get() { return null; } } and we compiled the client project against the generified version. Now, if we changed the semantics of new X("") to new X("") (or new X<>("") with the diamond syntax), the code would behave differently. So, the answer to the title question is 'yes'. If we want to stay backward compatible, we need <> and we cannot put new X("") semantically equal to new X<>(""). Another questions are how long can Java evolve and remain compatible with concessions to compatibility and whether newcomers to Java will appreciate this. From http://tronicek.blogspot.com/2011/03/do-we-really-need-in-diamond-operator.html

Preface This document explains the working of a JPA application purely based on XML configurations. This explains how to create entity classes without using any annotations in Java classes. Entire configuration is done using xml files. Introduction Persistence is one of the major challenges for enterprise applications. JPA is the persistence standard from Sun Microsystems. JPA supports two ways of configuring persistence information- through annotations and XML files. This article discusses the XML based approach towards embedding persistence information for the entity classes. Setting up the environment JPA is light-weight persistence model. It works for both JavaSE and JavaEE applications. All the required class files are present in the JavaEE.jar file which should be added to the classpath. Along with the JavaEE.jar file, we need to add the Persistence Provider jar file as well to the classpath. The provider can be Toplink/ Hibernate/ App server specific persistence provider. In this application, we’ve used Toplink as the persistence provider. So we need to add toplink-essentials.jar file as well in the class path. To summarize, the jar files required are: 1.JavaEE.jar 2.ToplinkEssentials.jar (Replace with the jar file for Persistence Provider, in case you want to use other persistence provider) 3.derbyClient.jar (For JavaDB (Derby) Database, replace this with the jar file of your database) Instead of adding the configuration details in the entity class in the form of annotations, we’ll add the configuration information in the orm.xml file. Before we look into the code, let us discuss the orm.xml file in detail. orm.xml orm.xml file contains all the configuration details required for mapping a class to a relational database table. These details include the primary key of the entity class and the various constraints/ rules to be applied for the primary key. Other details about the entity class include the various attributes of the entity class and columns to which the attributes should be mapped. We can also specify multiple constraints for the same attribute. Other configurable things include information about the various relationships the entity may have with other entities (one-to-one, many-to-one, many-to-many or one-to-many), embedded attributes, information about the version and transient attributes. One application can have multiple entities. The configuration details about all these entity classes, embeddable classes go inside the same orm.xml file. Name of this configuration file can be anything, not mandatorily orm.xml. It should be placed in META-INF subdirectory along with the persistence.xml file. Persistence.xml file should include the orm.xml file. Please find below sample orm.xml and persistence.xml file. The xml schema definitions are highlighted for both. Also, please note the entry required in persistence.xml for including the orm.xml file. PFB the entries made in a sample orm.xml, which’ll persist employee instances to the database. My First JPA XML Application entity Different properties in orm.xml file The various properties in the orm.xml in the order defined in xml schema definition are discussed below: Root tag is It contains the following four types of elements: The persistence-unit-metadata element contains metadata for the entire persistence unit. It is undefined if this element occurs in multiple mapping files within the same persistence unit. The package, schema, catalog and access elements apply to all the entity, mapped-superclass and embeddable elements defined in the same file in which they occur. The sequence-generator, table-generator, named-query, named-native-query and sql-result-set-mapping elements are global to the persistence unit. The entity, mapped-superclass and embeddable elements each define the mapping information for a managed persistent class. The mapping information contained in these elements may be complete or it may be partial. One line string information about the entity classes in the application. specifies the package of the classes listed within the sub elements and attributes of the same mapping file only. classAttribute defines the fully qualified class name of the entity class name: Attribute defines the name of the entity entity: tag can be repeated to embed mapping information for all the entity classes in the application. The Sub-Tags of entity tag are maps the database table to which the entity class shall be persisted overrides or creates a new id-class setting overrides or creates a new inheritance setting overrides or creates a new discriminator value setting, useful in Single_Table Inheritance strategy. overrides or creates a new discriminator column setting, used while configuring Super class in Single_Table inheritance strategy. A sequence-generator is unique by name A table-generator is unique by name used to define named-query for the entity class. Can be repeated to define multiple named queries for the entity class. used to define native named query for the entity class. creates or overrides a pre-persist setting i.e. the entity listener method to be invoked before persisting the entity instance. creates or overrides a post-persist setting i.e. the entity listener method to be invoked after persisting the entity instance. creates or overrides a pre-remove setting i.e. the entity listener method to be invoked before removing the entity instance. creates or overrides a post-remove setting i.e. the entity listener method to be invoked after removing the entity instance. creates or overrides a pre-update setting i.e. the entity listener method to be invoked before updating the entity instance. creates or overrides a post-update setting i.e. the entity listener method to be invoked before updating the entity instance. creates or overrides a post-load setting i.e. the entity listener method to be invoked after loading the entity instance state from database defines the attributes of the entity class which shall be persisted in the database table. The sub tags of the attributes are: :- this tag defines the id column of the entity class. Cannot be repeated for an entity. An entity class can have only one Id attribute. :-this is used to define the ID generation strategy to be used for the primary key column. :-this tag is used to map the entity columns to the columns in the database table. Should be repeated to provide configurations for all the attributes of the entity class. :- This tag is used to add the various column-level constraints on the entity attributes. E.g. unique, insertable, updatable, length, precision etc. :- maps the Version attribute of the entity class. Development Environment We’ve used NetBeans IDE 6.5.1 for creating this JavaSE application for persistence through XML files. NetBeans has persistence support for JPA as well as Hibernate. So, we can choose either Hibernate or Toplink Essentials as the Persistence Provider. Let’s name the application as “JPAEntity”. The name of the entity class is “Employee” and is placed in the package “entity”. “EmpClient.java” is the client class. The xml configuration files are put in META-INF sub directory. The directory structure of the application is as follows:- First JPA META-INF orm.xml persistence.xml entity Employee.java EmpClient.java Developing the Application Step1:Start the NetBeans IDE. Create a new Java Application Step 2:Add the jar files for the Persistence Provider (Toplink Essentials in our case), JavaEE.jar and the database driver(DerbyClient.jar in our case) to the classpath. Step 3: Add the entity class “Employee.java” and client class “EmpClient.java” in the package "jpaentity" This is the code of Employee.java package jpaentity; public class Employee { private int empId; private String empName; private double empSalary; public Employee() { } public Employee(int empId, String empName, double empSalary) { this.empId = empId; this.empName = empName; this.empSalary = empSalary; } public int getEmpId() { return empId; } public void setEmpId(int empId) { this.empId = empId; } public String getEmpName() { return empName; } public void setEmpName(String empName) { this.empName = empName; } public double getEmpSalary() { return empSalary; } public void setEmpSalary(double empSalary) { this.empSalary = empSalary; } @Override public String toString() { return "Employee Id:="+empId+ +" Employee Name:="+empName+" Employee Salary:="+empSalary; } }//End of Employee.java This is the code of EmpClient.java package jpaentity; import java.util.List; import javax.persistence.EntityManager; import javax.persistence.EntityManagerFactory; import javax.persistence.Persistence; public class EmpClient { private EntityManager em; private EntityManagerFactory emf; public void initEmfAndEm() { emf=Persistence.createEntityManagerFactory("JPAEntityPU"); em=emf.createEntityManager(); } public void cleanup() { em.close(); } public void insertAndRetrieve() { System.out.println("-------------------Creating the Objects---------------------"); Employee empObj1=new Employee(1, "Anu", 1000.0); Employee empObj2=new Employee(2, "Rahul", 1500.0); System.out.println("-------------------Starting the transaction---------------------"); em.getTransaction().begin(); em.persist(empObj1); em.persist(empObj2); System.out.println("-------------------Committing the transaction---------------------"); em.getTransaction().commit(); System.out.println("-------------------Objects saved successfully--------------------"); System.out.println("*******************************************************************"); System.out.println("------------------- Reading Objects--------------------"); List emps=em.createQuery("select p from Employee p").getResultList(); for (Employee current:emps) System.out.println(current); System.out.println("-------------------Finished Reading Objects--------------------"); } public static void main(String args[]) { EmpClient myClient=new EmpClient(); System.out.println("-------------------Starting the Client---------------------"); myClient.initEmfAndEm(); myClient.insertAndRetrieve(); myClient.cleanup(); System.out.println("---------------Shutting down the Client---------------------"); } }//End of EmpClient.java Step 4: Set up the database connection. Go to Services Tab in the NetBeans IDE and expand the Databases node. Right click on JavaDB node and select Create Database. Give the database name, username and password and click on OK. The database is created and ready to accept connections. Step 5: Add the persistence unit to the application. Also, add the orm.xml file. The name of the orm.xml file can be changed. We have named it mapping.xml. Following is the code of mapping.xml file My First JPA XML Application entity Add the following code to the persistence.xml file oracle.toplink.essentials.PersistenceProvider \META-INF\orm.xml jpaentity.Employee Steps for execution Compile the source files Build the project Start the Database Server Execute the “EmpClient” class. Connect to the database & verify the table has been created & data is also added in the table. Advantages and Disadvantages of Using XML for Configuration Advantages No coupling between the metadata and the source code Compatible with pre EJB3.0 development process Support from IDEs like NetBeans, Eclipse etc Easy to modify with the help of good editors. Disadvantages Complexity Difficulty in debugging in absence of editors Wrap Up This article helps in understanding entity configuration with XML files as an alternative to embedding annotations in Java code for configuring persistence details.

This tutorial will present examples using advanced result mappings, how to handle mappings with association, collections, the n+1 problem, and more.