The inspection of the error logs is a common way to detect errors and bugs. We also can show errors on-screen within our developement server, or we even can use great tools like firePHP to show our PHP errors and warnings inside our firebug console. That’s cool, but we only can see our session errors/warnings. If we want to see another’s errors we need to inspect the error log. tail -f is our friend, but we need to surf against all the warnings of all sessions to see our desired ones. Because of that I want to build a tool to monitor my PHP applications in real-time. Let’s start: What’s the idea? The idea is catch all PHP’s errors and warnings at run time and send them to a node.js HTTP server. This server will work similar than a chat server but our clients will only be able to read the server’s logs. Basically the applications have three parts: the node.js server, the web client (html5) and the server part (PHP). Let me explain a bit each part: The node Server Basically it has two parts: a http server to handle the PHP errors/warnings and a websocket server to manage the realtime communications with the browser. When I say that I’m using websockets that’s means the web client will only work with a browser with websocket support like chrome. Anyway it’s pretty straightforward swap from a websocket sever to a socket.io server to use it with every browser. But websockets seems to be the future, so I will use websockets in this example. The http server: http.createServer(function (req, res) { var remoteAdrress = req.socket.remoteAddress; if (allowedIP.indexOf(remoteAdrress) >= 0) { res.writeHead(200, { 'Content-Type': 'text/plain' }); res.end('Ok\n'); try { var parsedUrl = url.parse(req.url, true); var type = parsedUrl.query.type; var logString = parsedUrl.query.logString; var ip = eval(parsedUrl.query.logString)[0]; if (inspectingUrl == "" || inspectingUrl == ip) { clients.forEach(function(client) { client.write(logString); }); } } catch(err) { console.log("500 to " + remoteAdrress); res.writeHead(500, { 'Content-Type': 'text/plain' }); res.end('System Error\n'); } } else { console.log("401 to " + remoteAdrress); res.writeHead(401, { 'Content-Type': 'text/plain' }); res.end('Not Authorized\n'); } }).listen(httpConf.port, httpConf.host); and the web socket server: var inspectingUrl = undefined; ws.createServer(function(websocket) { websocket.on('connect', function(resource) { var parsedUrl = url.parse(resource, true); inspectingUrl = parsedUrl.query.ip; clients.push(websocket); }); websocket.on('close', function() { var pos = clients.indexOf(websocket); if (pos >= 0) { clients.splice(pos, 1); } }); }).listen(wsConf.port, wsConf.host); If you want to know more about node.js and see more examples, have a look to the great site: http://nodetuts.com/. In this site Pedro Teixeira will show examples and node.js tutorials. In fact my node.js http + websoket server is a mix of two tutorials from this site. The web client. The web client is a simple websockets application. We will handle the websockets connection, reconnect if it dies and a bit more. I’s based on node.js chat demo Real time monitor Socket status: Conecting ...IP: [all]" ?>count: 0 And the javascript magic var timeout = 5000; var wsServer = '192.168.2.2:8880'; var unread = 0; var focus = false; var count = 0; function updateCount() { count++; $("#count").text(count); } function cleanString(string) { return string.replace(/&/g,"&").replace(//g,">"); } function updateUptime () { var now = new Date(); $("#uptime").text(now.toRelativeTime()); } function updateTitle(){ if (unread) { document.title = "(" + unread.toString() + ") Real time " + selectedIp + " monitor"; } else { document.title = "Real time " + selectedIp + " monitor"; } } function pad(n) { return ("0" + n).slice(-2); } function startWs(ip) { try { ws = new WebSocket("ws://" + wsServer + "?ip=" + ip); $('#toolbar').css('background', '#65A33F'); $('#socketStatus').html('Connected to ' + wsServer); //console.log("startWs:" + ip); //listen for browser events so we know to update the document title $(window).bind("blur", function() { focus = false; updateTitle(); }); $(window).bind("focus", function() { focus = true; unread = 0; updateTitle(); }); } catch (err) { //console.log(err); setTimeout(startWs, timeout); } ws.onmessage = function(event) { unread++; updateTitle(); var now = new Date(); var hh = pad(now.getHours()); var mm = pad(now.getMinutes()); var ss = pad(now.getSeconds()); var timeMark = '[' + hh + ':' + mm + ':' + ss + '] '; logString = eval(event.data); var host = logString[0]; var line = "" + timeMark + "" + host + ""; line += "" + logString[1]; + ""; if (logString[2]) { line += " " + logString[2] + ""; } $('#log').append(line); updateCount(); window.scrollBy(0, 100000000000000000); }; ws.onclose = function(){ //console.log("ws.onclose"); $('#toolbar').css('background', '#933'); $('#socketStatus').html('Disconected'); setTimeout(function() {startWs(selectedIp)}, timeout); } } $(document).ready(function() { startWs(selectedIp); }); The server part: The server part will handle silently all PHP warnings and errors and it will send them to the node server. The idea is to place a minimal PHP line of code at the beginning of the application that we want to monitor. Imagine the following piece of PHP code $a = $var[1]; $a = 1/0; class Dummy { static function err() { throw new Exception("error"); } } Dummy1::err(); it will throw: A notice: Undefined variable: var A warning: Division by zero An Uncaught exception ‘Exception’ with message ‘error’ So we will add our small library to catch those errors and send them to the node server include('client/NodeLog.php'); NodeLog::init('192.168.2.2'); $a = $var[1]; $a = 1/0; class Dummy { static function err() { throw new Exception("error"); } } Dummy1::err(); The script will work in the same way than the fist version but if we start our node.js server in a console: $ node server.js HTTP server started at 192.168.2.2::5672 Web Socket server started at 192.168.2.2::8880 We will see those errors/warnings in real-time when we start our browser Here we can see a small screencast with the working application: This is the server side library: class NodeLog { const NODE_DEF_HOST = '127.0.0.1'; const NODE_DEF_PORT = 5672; private $_host; private $_port; /** * @param String $host * @param Integer $port * @return NodeLog */ static function connect($host = null, $port = null) { return new self(is_null($host) ? self::$_defHost : $host, is_null($port) ? self::$_defPort : $port); } function __construct($host, $port) { $this->_host = $host; $this->_port = $port; } /** * @param String $log * @return Array array($status, $response) */ public function log($log) { list($status, $response) = $this->send(json_encode($log)); return array($status, $response); } private function send($log) { $url = "http://{$this->_host}:{$this->_port}?logString=" . urlencode($log); $ch = curl_init(); curl_setopt($ch, CURLOPT_URL, $url); curl_setopt($ch, CURLOPT_NOBODY, true); curl_setopt($ch, CURLOPT_RETURNTRANSFER, true); $response = curl_exec($ch); $status = curl_getinfo($ch, CURLINFO_HTTP_CODE); curl_close($ch); return array($status, $response); } static function getip() { $realip = '0.0.0.0'; if ($_SERVER) { if ( isset($_SERVER['HTTP_X_FORWARDED_FOR']) && $_SERVER['HTTP_X_FORWARDED_FOR'] ) { $realip = $_SERVER["HTTP_X_FORWARDED_FOR"]; } elseif ( isset($_SERVER['HTTP_CLIENT_IP']) && $_SERVER["HTTP_CLIENT_IP"] ) { $realip = $_SERVER["HTTP_CLIENT_IP"]; } else { $realip = $_SERVER["REMOTE_ADDR"]; } } else { if ( getenv('HTTP_X_FORWARDED_FOR') ) { $realip = getenv('HTTP_X_FORWARDED_FOR'); } elseif ( getenv('HTTP_CLIENT_IP') ) { $realip = getenv('HTTP_CLIENT_IP'); } else { $realip = getenv('REMOTE_ADDR'); } } return $realip; } public static function getErrorName($err) { $errors = array( E_ERROR => 'ERROR', E_RECOVERABLE_ERROR => 'RECOVERABLE_ERROR', E_WARNING => 'WARNING', E_PARSE => 'PARSE', E_NOTICE => 'NOTICE', E_STRICT => 'STRICT', E_DEPRECATED => 'DEPRECATED', E_CORE_ERROR => 'CORE_ERROR', E_CORE_WARNING => 'CORE_WARNING', E_COMPILE_ERROR => 'COMPILE_ERROR', E_COMPILE_WARNING => 'COMPILE_WARNING', E_USER_ERROR => 'USER_ERROR', E_USER_WARNING => 'USER_WARNING', E_USER_NOTICE => 'USER_NOTICE', E_USER_DEPRECATED => 'USER_DEPRECATED', ); return $errors[$err]; } private static function set_error_handler($nodeHost, $nodePort) { set_error_handler(function ($errno, $errstr, $errfile, $errline) use($nodeHost, $nodePort) { $err = NodeLog::getErrorName($errno); /* if (!(error_reporting() & $errno)) { // This error code is not included in error_reporting return; } */ $log = array( NodeLog::getip(), "{$err} {$errfile}:{$errline}", nl2br($errstr) ); NodeLog::connect($nodeHost, $nodePort)->log($log); return false; }); } private static function register_exceptionHandler($nodeHost, $nodePort) { set_exception_handler(function($exception) use($nodeHost, $nodePort) { $exceptionName = get_class($exception); $message = $exception->getMessage(); $file = $exception->getFile(); $line = $exception->getLine(); $trace = $exception->getTraceAsString(); $msg = count($trace) > 0 ? "Stack trace:\n{$trace}" : null; $log = array( NodeLog::getip(), nl2br("Uncaught exception '{$exceptionName}' with message '{$message}' in {$file}:{$line}"), nl2br($msg) ); NodeLog::connect($nodeHost, $nodePort)->log($log); return false; }); } private static function register_shutdown_function($nodeHost, $nodePort) { register_shutdown_function(function() use($nodeHost, $nodePort) { $error = error_get_last(); if ($error['type'] == E_ERROR) { $err = NodeLog::getErrorName($error['type']); $log = array( NodeLog::getip(), "{$err} {$error['file']}:{$error['line']}", nl2br($error['message']) ); NodeLog::connect($nodeHost, $nodePort)->log($log); } echo NodeLog::connect($nodeHost, $nodePort)->end(); }); } private static $_defHost = self::NODE_DEF_HOST; private static $_defPort = self::NODE_DEF_PORT; /** * @param String $host * @param Integer $port * @return NodeLog */ public static function init($host = self::NODE_DEF_HOST, $port = self::NODE_DEF_PORT) { self::$_defHost = $host; self::$_defPort = $port; self::register_exceptionHandler($host, $port); self::set_error_handler($host, $port); self::register_shutdown_function($host, $port); $node = self::connect($host, $port); $node->start(); return $node; } private static $time; private static $mem; public function start() { self::$time = microtime(TRUE); self::$mem = memory_get_usage(); $log = array(NodeLog::getip(), "Start >>>> {$_SERVER['REQUEST_URI']}"); $this->log($log); } public function end() { $mem = (memory_get_usage() - self::$mem) / (1024 * 1024); $time = microtime(TRUE) - self::$time; $log = array(NodeLog::getip(), "End <<<< mem: {$mem} time {$time}"); $this->log($log); } } And of course the full code on gitHub: RealTimeMonitor

Here are some Java interview questions which are un-common What is the performance effect of a large number of import statements which are not used? Answer: They are ignored if the corresponding class is not used. Give a scenario where hotspot will optimize your code? Answer: If we have defined a variable as static and then initialized this variable in a static block then the Hotspot will merge the variable and the initialization in a single statement and hence reduce the code. What will happen if an exception is thrown from the finally block? Answer: The program will exit if the exception is not catched in the finally block. How does decorator design pattern works in I/O classes? Answer: The various classes like BufferedReader , BufferedWriter workk on the underlying stream classes. Thus Buffered* class will provide a Buffer for Reader/Writer classes. If I give you an assignment to design Shopping cart web application, how will you define the architecture of this application. You are free to choose any framework, tool or server? Answer: Usually I will choose a MVC framework which will make me use other design patterns like Front Controller, Business Delegate, Service Locater, DAO, DTO, Loose Coupling etc. Struts 2 is very easy to configure and comes with other plugins like Tiles, Velocity and Validator etc. The architecture of Struts becomes the architecture of my application with various actions and corresponding JSP pages in place. What is a deadlock in Java? How will you detect and get rid of deadlocks? Answer: Deadlock exists when two threads try to get hold of a object which is already held by another object. Why is it better to use hibernate than JDBC for database interaction in various Java applications? Answer: Hibernate provides an OO view of the database by mapping the various classes to the database tables. This helps in thinking in terms of the OO language then in RDBMS terms and hence increases productivity. How can one call one constructor from another constructor in a class? Answer: Use the this() method to refer to constructors. What is the purpose of intern() method in the String class? Answer: It helps in moving the normal string objects to move to the String literal pool How will you make your web application to use the https protocol? Answer: This has more to do with the particular server being used than the application itself. Here is how it can be done on tomcat: http://tomcat.apache.org/tomcat-4.1-doc/ssl-howto.html From http://extreme-java.blogspot.com/2011/05/10-tricky-java-interview-questions.html

There was a regression in the 1.9.4 release of Gant that could not be ignored. The problem has been corrected, and a new release candidate checked by the people who found the regression (thanks due to Jeff Brown and the Grails folk). There is therefore a shiny new Gant 1.9.5 release. Everything should be in place at Codehaus already and Maven will get updated as soon as the Codehaus -> Maven sync happens. The regression whilst essentially trivial, was sufficiently fundamental that I have taken the drastic step of removing the 1.9.4 release from everywhere. This should have no effect on people using Groovy 1.7 or 1.8, they should use Gant 1.9.5. People still using Groovy 1.6 will not see a Gant 1.9.5 and will have to stay weith Gant 1.9.3. Of course people still using Groovy 1.6 should upgrade to 1.8.0 immediately and therefore not have a problem with Gant :-)

I always try to bring some thing new and useful on this blog. This time we will understand the Void.class (which in itself looks something tricky) present in rt.jar. One can consider the java.lang.Void class as a wrapper for the keyword void. Some developers draw the analogy with the primitive data types int, long, short and byte etc. which have the wrapper classes as Integer, Long, Short and Byte receptively. But it should be kept in mind that unlike those wrappers Void class doesn't store a value of type void in itself and hence is not a wrapper in true essence. Purpose: The Void class according to javadoc exists because of the fact that some time we may need to represent the void keyword as an object. But at the same point we cannot create an instance of the Void class using the new operator. This is because the constructor in Void has been declared as private. Moreover the Void class is a final class which means that there is no way we can inherit this class. So the only purpose that remains for the existence of the Void class is reflection, where we can get the return type of a method as void. The following piece of code will demonstrate this purpose: public class Test { public static void main(String[] args) throws SecurityException, NoSuchMethodException { Class c1 = Test1.class.getMethod("Testt",null).getReturnType(); System.out.println(c1 == Void.TYPE); System.out.println(c1 == Void.class); } } class Test1{ public void Testt(){} } One can also use Void class in Generics to specify that you don't care about the specific type of object being used. For example: List list1; From http://extreme-java.blogspot.com/2011/04/void-class-java.html

JavaScript does not come with support for modules. This blog post examines patterns and APIs that provide such support. It is split into the following parts: Patterns for structuring modules. APIs for loading modules asynchronously. Related reading, background and sources. 1. Patterns for structuring modules A module fulfills two purposes: First, it holds content, by mapping identifiers to values. Second, it provides a namespace for those identifiers, to prevent them from clashing with identifiers in other modules. In JavaScript, modules are implemented via objects. Namespacing: A top-level module is put into a global variable. That variable is the namespace of the module content. Holding content: Each property of the module holds a value. Nesting modules: One achieves nesting by putting a module inside another one. Filling a module with content Approach 1: Object literal. var namespace = { func: function() { ... }, value: 123 }; Approach 2: Assigning to properties. var namespace = {}; namespace.func = function() { ... }; namespace.value = 123; Accessing the content in either approach: namespace.func(); console.log(namespace.value + 44); Assessment: Object literal. Pro: Elegant syntax. Con: As a single, sometimes very long syntactic construct, it imposes constraints on its contents. One must maintain the opening brace before the content and the closing brace after the content. And one must remember to not add a comma after the last property value. This makes it harder to move content around. Assigning to properties. Con: Redundant repetitions of the namespace identifier. The Module pattern: private data and initialization In the module pattern, one uses an Immediately-Invoked Function Expression (IIFE, [1]) to attach an environment to the module data. The bindings inside that environment can be accessed from the module, but not from outside. Another advantage is that the IIFE gives you a place to perform initializations. var namespace = function() { // set up private data var arr = []; // not visible outside for(var i=0; i<4; i++) { arr.push(i); } return { // read-only access via getter get values() { return arr; } }; }(); console.log(namespace.values); // [0,1,2,3] Comments: Con: Harder to read and harder to figure out what is going on. Con: Harder to patch. Every now and then, you can reuse existing code by patching it just a little. Yes, this breaks encapsulation, but it can also be very useful for temporary solutions. The module pattern makes such patching impossible (which may be a feature, depending on your taste). Alternative for private data: use a naming convention for private properties, e.g. all properties whose names start with an underscore are private. Variation: Namespace is a function parameter. var namespace = {}; (function(ns) { // (set up private data here) ns.func = function() { ... }; ns.value = 123; }(namespace)); Variation: this as the namespace identifier (cannot accidentally be assigned to). var namespace = {}; (function() { // (set up private data here) this.func = function() { ... }; this.value = 123; }).call(namespace); // hand in implicit parameter "this" Referring to sibling properties Use this. Con: hidden if you nest functions (which includes methods in nested objects). var namespace = { _value: 123; // private via naming convention getValue: function() { return this._value; } anObject: { aMethod: function() { // "this" does not point to the module here } } } Global access. Cons: makes it harder to rename the namespace, verbose for nested namespaces. var namespace = { _value: 123; // private via naming convention getValue: function() { return namespace._value; } } Custom identifier: The module pattern (see above) enables one to use a custom local identifier to refer to the current module. Module pattern with object literal: assign the object to a local variable before returning it. Module pattern with parameter: the parameter is the custom identifier. Private data and initialization for properties An IFEE can be used to attach private data and initialization code to an object. It can do the same for a single property. var ns = { getValue: function() { var arr = []; // not visible outside for(var i=0; i<4; i++) { arr.push(i); } return function() { // actual property value return arr; }; }() }; Read on for an application of this pattern. Types in object literals Problem: A JavaScript type is defined in two steps. First, define the constructor. Second, set up the prototype of the constructor. These two steps cannot be performed in object literals. There are two solutions: Use an inheritance API where constructor and prototype can be defined simultaneously [4]. Wrap the two parts of the type in an IIFE: var ns = { Type: function() { var constructor = function() { // ... }; constructor.prototype = { // ... }; return constructor; // value of Type }() }; Managing namespaces Use the same namespace in several files: You can spread out a module definition across several files. Each file contributes features to the module. If you create the namespace variable as follows then the order in which the files are loaded does not matter. Note that this pattern does not work with object literals. var namespace = namespace || {}; Nested namespaces: With multiple modules, one can avoid a proliferation of global names by creating a single global namespace and adding sub-modules to it. Further nesting is not advisable, because it adds complexity and is slower. You can use longer names if name clashes are an issue. var topns = topns || {}; topns.module1 = { // content } topns.module2 = { // content } YUI2 uses the following pattern to create nested namespaces. YAHOO.namespace("foo.bar"); YAHOO.foo.bar.doSomething = function() { ... }; 2. APIs for loading modules asynchronously Avoiding blocking: The content of a web page is processed sequentially. When a script tag is encountered that refers to a file, two steps happen: The file is downloaded. The file is interpreted. All browsers block the processing of subsequent content until (2) is finished, because everything is single-threaded and must be processed in order. Newer browsers perform some downloads in parallel, but rendering is still blocked [2]. This unnecessarily delays the initial display of a page. Modern module APIs provide a way around this by supporting asynchronous loading of modules. There are usually two parts to using such APIs: First one specifies what modules one would like to use. Second, one provides a callback that is invoked once all modules are ready. The goal of this section is not to be a comprehensive introduction, but rather to give you an overview of what is possible in the design space of JavaScript modules. 2.1. RequireJS RequireJS has been created as a standard for modules that work both on servers and in browsers. The RequireJS website explains the relationship between RequireJS and the earlier CommonJS standard for server-side modules [3]: CommonJS defines a module format. Unfortunately, it was defined without giving browsers equal footing to other JavaScript environments. Because of that, there are CommonJS spec proposals for Transport formats and an asynchronous require. RequireJS tries to keep with the spirit of CommonJS, with using string names to refer to dependencies, and to avoid modules defining global objects, but still allow coding a module format that works well natively in the browser. RequireJS implements the Asynchronous Module Definition (formerly Transport/C) proposal. If you have modules that are in the traditional CommonJS module format, then you can easily convert them to work with RequireJS. RequireJS projects have the following file structure: project-directory/ project.html legacy.js scripts/ main.js require.js helper/ util.js project.html: My Sample Project main.js: helper/util is resolved relative to data-main. legacy.js ends with .js and is assumed to not be in module format. The consequences are that its path is resolved relative to project.html and that there isn’t a function parameter to access its (module) contents. require(["helper/util", "legacy.js"], function(util) { //This function is called when scripts/helper/util.js is loaded. require.ready(function() { //This function is called when the page is loaded //(the DOMContentLoaded event) and when all required //scripts are loaded. }); }); Other features of RequireJS: Specify and use internationalization data. Load text files (e.g. to be used for HTML templating) Use JSONP service results for initial application setup. 2.2. YUI3 Version 3 of the YUI JavaScript framework brings its own module infrastructure. YUI3 modules are loaded asynchronously. The general pattern for using them is as follows. YUI().use('dd', 'anim', function(Y) { // Y.DD is available // Y.Anim is available }); Steps: Provide IDs “dd” and “anim” of the modules you want to load. Provide a callback to be invoked once all modules have been loaded. The parameter Y of the callback is the YUI namespace. This namespace contains the sub-namespaces DD and Anim for the modules. As you can see, the ID of a module and its namespace are usually different. Method YUI.add() allows you to register your own modules. YUI.add('mymodules-mod1', function(Y) { Y.namespace('mynamespace'); Y.mynamespace.Mod1 = function() { // expose an API }; }, '0.1.1' // module version ); YUI includes a loader for retrieving modules from external files. It is configured via a parameter to the API. The following example loads two modules: The built-in YUI module dd and the external module yui_flot that is available online. YUI({ modules: { yui2_yde_datasource: { // not used below fullpath: 'http://yui.yahooapis.com/datasource-min.js' }, yui_flot: { fullpath: 'http://bluesmoon.github.com/yui-flot/yui.flot.js' } } }).use('dd', 'yui_flot', function(Y) { // do stuff }); 2.3. Script loaders Similarly to RequireJS, script loaders are replacements for script tags that allow one to load JavaScript code asynchronously and in parallel. But they are usually simpler than RequireJS. Examples: LABjs: a relatively simple script loader. Use it instead of RequireJS if you need to load scripts in a precise order and you don't need to manage module dependencies. Background: “LABjs & RequireJS: Loading JavaScript Resources the Fun Way” describes the differences between LABjs and RequireJS. yepnope: A fast script loader that allows you to make the loading of some scripts contingent on the capabilities of the web browser. 3. Related reading, background and sources Related reading: A first look at the upcoming JavaScript modules Background: JavaScript variable scoping and its pitfalls Loading Scripts Without Blocking CommonJS Modules Lightweight JavaScript inheritance APIs Main sources of this post: Namespacing in JavaScript YUI2: A JavaScript Module Pattern YUI3: YUI Global Object How to get started with RequireJS From http://www.2ality.com/2011/04/modules-and-namespaces-in-javascript.html

i feel like writing some posts about cdi (contexts and dependency injection). so this is the first one of a series of x posts ( 0 javax.enterprise cdi-api 1.0 provided an empty beans.xml will do to enable cdi you must have a beans.xml file in your project (under the meta-inf or web-inf). that’s because cdi needs to identify the beans in your classpath (this is called bean discovery) and build its internal metamodel. with the beans.xml file cdi knows it has beans to discover. so, for all the following examples i’ll make it simple and will leave this file completely empty. java ee 6 containers let’s start with the easiest possible environment : java ee 6 containers . why is it the simplest ? well, because you don’t have to do anything : cdi is part of java ee 6 as well as the web profile 1.0 so you don’t need to manually bootstrap it. let’s see how to inject a cdi bean within an ejb 3.1 and a servlet 3.0 . ejb 3.1 since ejb 3.1 you can use the ejbcontainer api to get an in-memory embedded ejb container and you can easily unit test your ejbs. so let’s write an ejb and a test class. first let’s have a look at the code of the ejb. as you can see, with version 3.1 an ejb is just a pojo : no inheritance, no interface, just one @stateless annotation. it gets a reference of the hello bean buy using the @inject annotation and uses it in the saysomething() method. @stateless public class mainejb31 { @inject hello hello; public string saysomething() { return hello.sayhelloworld(); } } you can now package the mainejb31, hello and world classes with the empty beans.xml file into a jar, deploy it to glassfish 3.x , and it will work. but if you don’t want to bother deploying it to glassfish and just unit test it, this is what you need to do : public class mainejbtest { private static ejbcontainer ec; private static context ctx; @beforeclass public static void initcontainer() throws exception { map properties = new hashmap(); properties.put(ejbcontainer.modules, new file("target/classes")); ec = ejbcontainer.createejbcontainer(properties); ctx = ec.getcontext(); } @afterclass public static void closecontainer() throws exception { if (ec != null) ec.close(); } @test public void shoulddisplayhelloworld() throws exception { // looks up the ejb mainejb31 mainejb = (mainejb31) ctx.lookup("java:global/classes/mainejb!org.antoniogoncalves.cdi.helloworld.mainejb"); assertequals("should say hello world !!!", "hello world !!!", mainejb.saysomething()); } } in the code above the method initcontainer() initializes the ejbcontainer. the shoulddisplayhelloworld() looks up the ejb (using the new portable jndi name ), invokes it and makes sure the saysomething() method returns hello world !!!. green test. that was pretty easy too. servlet 3.0 servlet 3.0 is part of java ee 6, so again, there is no needed configuration to bootstrap cdi. let’s use the new @webservlet annotation and write a very simple one that injects a reference of hello and displays an html page with hello world !!!. this is what the servlet looks like : @webservlet(urlpatterns = "/mainservlet") public class mainservlet30 extends httpservlet { @inject hello hello; @override protected void service(httpservletrequest req, httpservletresponse resp) throws servletexception, ioexception { resp.setcontenttype("text/html"); printwriter out = resp.getwriter(); out.println(""); out.println(""); out.println(""); out.println(saysomething()); out.println(""); out.println(""); out.close(); } public string saysomething() { return hello.sayhelloworld(); } } thanks to the @webservlet i don’t need any web.xml (it’s optional in servlet 3.0) to map the mainservlet30 to the /mainservlet url. you can now package the mainservlet30, hello and world classes with the empty beans.xml and no web.xml into a war, deploy it to glassfish 3.x , go to http://localhost:8080/bootstrapping-servlet30-1.0/mainservlet and it will work. unfortunately servlet 3.0 doesn’t have an api for the container (such as ejbcontainer). there is no servletcontainer api that would let you use an embedded servlet container in a standard way and, why not, easily unit test it. application client container not many people know it, but java ee (or even older j2ee versions) comes with an application client container (acc). it’s like an ejb or servlet container but for plain pojos. for example you can develop a swing application (yes, i’m sure that some of you still use swing), run it into the acc and get some extra services given by the container (security, naming, certain annotations…). glassfish v3 has an acc that you can launch in a command line : appclient -jar . so i thought, great, i can use cdi with acc the same way i use it within ejb or servlet container, no need to bootstrap anything, it’s all out of the box. i was wrong . as per the cdi specification (section 12.1), cdi is not required to support application client bean archives. so the glassfish application client container doesn’t support it. i haven’t tried the jboss acc , maybe it works. other containers the beauty of cdi is that it doesn’t require java ee 6 . you can use cdi with simple pojos in a java se environment, as well as some servlet 2.5 containers. of course it’s not as easy to bootstrap because you need a bit of configuration. but it then works fine (not always but). java se 6 ok, so until now there was nothing to do to bootstrap cdi. it is already bundled with the ejb 3.1 and servlet 3.0 containers of java ee 6 (and web profile). so the idea here is to use cdi in a simple java se environment. coming back to our hello and world classes, we need a pojo with an entry point that will bootstrap cdi so we can use injection to get those classes. in standard java se when we say entry point , we think of a public static void main(string[] args) method. well, we need something similar… but different. weld is the reference implementation of cdi. that means it implements the specification, the standard apis (mostly found in javax.inject and javax.enterprise.context packages) but also some proprietary code (in org.jboss.weld package). bootstrapping cdi in java se is not specified so you will need to use specific weld features. you can do that in two different flavors: by observing the containerinitialized event or using the programatic bootstrap api consisting of the weld and weldcontainer classes. the following code uses the containerinitialized event. as you can see, it uses the @observes annotation that i’ll explain in a future post. but the idea is that this class is listening to the event and processes the code once the event is triggered. import org.jboss.weld.environment.se.events.containerinitialized; import javax.enterprise.event.observes; import javax.inject.inject; public class mainjavase6 { @inject hello hello; public void saysomething(@observes containerinitialized event) { system.out.println(hello.sayhelloworld()); } } but who trigers the containerinitialized event ? well, it’s the org.jboss.weld.environment.se.startmain class. i’m using maven so a nice trick is to use the exec-maven-plugin to run the startmain class. download the code , have a look at the pom.xml and give it a try. the other possibility is to programmatically bootstrap the weld container. this can be handy in unit testing. the code below initializes the weld container (with new weld().initialize()) and then looks for the hello class (using weld.instance().select(hello.class).get()). import org.jboss.weld.environment.se.weld; import org.jboss.weld.environment.se.weldcontainer; import org.junit.beforeclass; import org.junit.test; import static junit.framework.assert.assertequals; public class hellotest { @test public void shoulddisplayhelloworld() { weldcontainer weld = new weld().initialize(); hello hello = weld.instance().select(hello.class).get(); assertequals("should say hello world !!!", "hello world !!!", hello.sayhelloworld()); } } execute the test with mvn test and it should be green. as you can see, there is a bit more work using cdi in a java se environment, but it’s not that complicated. tomcat 6.x ok, and what about your legacy servlet 2.5 containers ? the first one that comes in mind is tomcat 6.x ( note that tomcat 7.x will implement servlet 3.0 but is still in beta version at the time of writing this post ). weld provides support for tomcat but you need to configure it a bit to make cdi work. first of all, this is a servlet 2.5, not a 3.0. so the code of the servlet is slightly different from the one seen before (no annotation allowed) and of course, you need your good old web.xml file : public class mainservlet25 extends httpservlet { @inject hello hello; @override protected void service(httpservletrequest req, httpservletresponse resp) throws servletexception, ioexception { resp.setcontenttype("text/html"); printwriter out = resp.getwriter(); out.println(""); out.println(""); out.println(""); out.println(saysomething()); out.println(""); out.println(""); out.close(); } public string saysomething() { return hello.sayhelloworld(); } } because we don’t have a @webservlet annotation in servlet 2.5, we need to declare and map it in the web.xml (using the servlet and servlet-mapping tags). then, you need to explicitly specify the servlet listener to boot weld and control its interaction with requests (org.jboss.weld.environment.servlet.listener). tomcat has a read-only jndi, so weld can’t automatically bind the beanmanager extension spi. to bind the beanmanager into jndi, you should populate meta-inf/context.xml and make the beanmanager available to your deployment by adding it to your web.xml: mainservlet25 org.antoniogoncalves.cdi.bootstrapping.servlet.mainservlet25 mainservlet25 /mainservlet org.jboss.weld.environment.servlet.listener beanmanager javax.enterprise.inject.spi.beanmanager the meta-inf/context.xml file is an optional file which contains a context for a single tomcat web application. this can be used to define certain behaviours for your application, jndi resources and other settings. package all the files (mainservlet25, hello, world, meta-inf/context.xml, beans.xml and web.xml) into a war and deploy it into tomcat 6.x. go to http://localhost:8080/bootstrapping-servlet25-tomcat-1.0/mainservlet and you will see your hello world page. jetty 6.x another famous servlet 2.5 containers is jetty 6.x (at codehaus) and jetty 7.x ( note that jetty 8.x will implement servlet 3.0 but it’s still in experimental stage at the time of writing this post ). if you look at the weld documentation, there is actually support for jetty 6.x and 7.x . the code is the same one as tomcat (because it’s a servlet 2.5 container), but the configuration changes. with jetty you need to add two files under web-inf : jetty-env.xml and jetty-web.xml : beanmanager javax.enterprise.inject.spi.beanmanager org.jboss.weld.resources.managerobjectfactory true package all the files (mainservlet25, hello, world, web-inf/jetty-env.xml, web-inf/jetty-web.xml, beans.xml and web.xml) into a war and deploy it into jetty 6.x. go to http://localhost:8080/bootstrapping-servlet25-jetty6/mainservlet and you will see your hello world page. there was a mistake in the weld documentation so i couldn’t make it work. i started a thread on the weld forum and thanks to dan allen , pete muir and all the weld team, this was fixed and i managed to make it work. simple as posting an email to the forum . thanks for your help guys. spring 3.x here is the tricky part. spring 3.x implements the jsr 330 : dependency injection for java , which means that @inject works out of the box. but i didn’t find a way to integrate cdi with spring 3.x . the weld documentation mentions that because of its extension points, “ integration with third-party frameworks such as spring (…) was envisaged by the designers of cdi “. i did find this blog that simulates cdi features by enabling spring ones. what i didn’t find is a clear statement or roadmap on springsource about supporting cdi or not in future releases. the last trace of this topic is a comment on a long tss flaming thread . at that time (16 december 2009), juergen huller said “ with respect to implementing cdi on top of spring (…) trying to hammer it into the semantic frame of another framework such as cdi would be an exercise that is certainly achievable (…) but ultimately pointless “. but if you have any fresh news about it, let me know. conclusion as i said, this post is not about explaining cdi, i’ll do that in future posts. i just wanted to focus on how to bootstrap it in several environments so you can try by yourself. as you saw, it’s much simpler to use cdi within an ejb 3.1 or servlet 3.0 container in java ee 6. i’ve used glassfish 3.x but it should also work with other java ee 6 or web profile containers such as jboss 6 or resin . when you don’t use java ee 6, there is a bit more work to do. depending on your environment or servlet container you need some configuration to bootstrap weld. by the way, i’ve used weld because it’s the reference implementation, the one bunddled with glassfish and jboss. but you could also use openwebbeans , another cdi implementation. download the code , give it a try, and give me some feedback. from http://agoncal.wordpress.com/2011/01/12/bootstrapping-cdi-in-several-environments/

A code kata is an exercise where you focus on your technique instead of on the final product of your mind and fingers. But a kata can also be used as a constant parameter, while other variables change, like in scientific experiments. For example, when learning a new programming language or framework, you can execute an old kata in order to explore it. I decided to perform a small and famous Kata that we used also during interviews to separate programmers from not programmers: the FizzBuzz kata. My goal was to learn how to setup a platform for Test-Driven Development in JavaScript, following the advice of the Test-Driven JavaScript Development book. The parameters that change from my habits are the tools for running tests and the programming language, but my IDE (Unix&Vim) remained fixed along with the Kata: Write a function that returns its numerical argument. But for multiples of three return Fizz instead of the number and for the multiples of five return Buzz. For numbers which are multiples of both three and five return FizzBuzz. Additional requirement: when passed a multiple of 7, return Bang; when passed a multiple of 5 and 7, return BuzzBang; and so on for all the combinations. As my tools for running the tests, I used JsTestDriver and Firefox, as suggested by the book Test-Driven JavaScript Development which I'm currently reading. JsTestDriver JsTestDriver will make you feel the joy of a green bar again. Download its jar, put it somewhere and add an alias in your .bashrc: export JSTESTDRIVER_HOME=~/bin alias jstestdriver="java -jar $JSTESTDRIVER_HOME/JsTestDriver-1.3.2.jar" Start the server: jstestdriver --port 4224 Point an open browser (I used Firefox) to localhost:4224. The browser will ping it via Ajax requests undefinitely to gather tests to run. Now we can use the command line to run tests, like you'll do with PHPUnit if you are a PHPer: jstestdriver --tests all The Kata I started with a simple function, fizzbuzz(), and a single test case. I never wrote a test with JsTestDriver before so I needed to gain some confidence and be sure the configuration file was correct. server: http://localhost:4224 load: - src/*.js - test/*.js In JsTestDriver, a Test case is created by passing to TestCase (global function provided by JsTestDriver) a map containing anonymous functions. TestCase("FizzBuzzTest", { "test should return Fizz when passed 3" : function () { assertEquals("Fizz", fizzbuzz(3)); } }); The functions whose names start with test will be executed; there are some reserved keywords like setUp which are used as hooks for fixture creation. Running the test with the alias command is really simple: jstestdriver --tests all I made the first test pass with fizzbuzz.js, a file containing a first version of the function (with a fake implementation): function fizzbuzz() { return 'Fizz'; } The result? A green bar (metaphorically green; all tests pass.) . Total 1 tests (Passed: 1; Fails: 0; Errors: 0) (0,00 ms) Firefox 4.0 Linux: Run 1 tests (Passed: 1; Fails: 0; Errors 0) (0,00 ms) You can capture more than one browser if you want to run test simultaneously in all of them, but it will probably slow down the TDD basic cycle. You can leave cross-browser testing for later. Going on After this first test, I went on adding new ones and making them pass, until I even converted the function to an object, for the sake of easy configuration (a function returning a function would be the same). Since I also needed to create the object in just one place, I started using setUp for the fixture creation: TestCase("FizzBuzzTest", { setUp : function () { this.fizzbuzz = new FizzBuzz({ 3 : 'Fizz', 5 : 'Buzz', 7 : 'Bang' }); }, "test should return the number when passed 1 or 2" : function () { assertEquals(1, this.fizzbuzz.accept(1)); assertEquals(2, this.fizzbuzz.accept(2)); }, "test should return Fizz when passed 3 or a multiple" : function () { assertEquals("Fizz", this.fizzbuzz.accept(3)); assertEquals("Fizz", this.fizzbuzz.accept(6)); }, "test should return Buzz when passed 5 or a multiple" : function () { assertEquals("Buzz", this.fizzbuzz.accept(5)); assertEquals("Buzz", this.fizzbuzz.accept(10)); }, "test should return FizzBuzz when passed a multiple of both 3 and 5" : function () { assertEquals("FizzBuzz", this.fizzbuzz.accept(15)); assertEquals("FizzBuzz", this.fizzbuzz.accept(30)); }, "test should return Bang when passed a multiple of 7" : function () { assertEquals("Bang", this.fizzbuzz.accept(7)); assertEquals("Bang", this.fizzbuzz.accept(14)); }, "test should return FizzBuzzBang when it is the case" : function () { assertEquals("FizzBuzzBang", this.fizzbuzz.accept(3*5*7)); } }); You can use this to share fixtures between the setUp and the different test methods: the test does not look different from JUnit and PHPUnit ones. Like in all xUnit testing frameworks, the setUp is executed on a brand new object for each test, to preserve isolation. I like a bit the way in which in JavaScript you can tear and put together objects: after all, it's called object-oriented programming, not class-oriented programming. I decided to use a small function constructor as you may infer from the test: function FizzBuzz(correspondences) { this.correspondences = correspondences; this.accept = function (number) { var result = ''; for (var divisor in this.correspondences) { if (number % divisor == 0) { result = result + this.correspondences[divisor]; } } if (result) { return result; } else { return number; } } } All the code is on Github, to see the intermediate steps of the Kata if you need them. You can also use the repository to try out your installation of JsTestDriver: a git pull followed by running the tests will confirm that it's working. Sometimes we don't test code in alien environments like JavaScript console or database queries because we don't know how; but a Kata which takes just two Pomodoros can solve the issue and let you enjoy a green bar even when working with a browser's interpreter.

JmsTemplate is easy for simple message sending. What if we want to add headers, intercept or transform the message? Then we have to write more code. So, how do we solve this common task with more configurability in lieu of more code? First, lets review JMS in Spring. Spring JMS Options JmsTemplate – either to send and receive messages inline Use send()/convertAndSend() methods to send messages Use receive()/receiveAndConvert() methods to receive messages. BEWARE: these are blocking methods! If there is no message on the Destination, it will wait until a message is received or times out. MessageListenerContainer – Async JMS message receipt by polling JMS Destinations and directing messages to service methods or MDBs Both JmsTemplate and MessageListenerContainer have been successfully implemented in Spring applications, if we have to do something a little different, we introduce new code. What could possibly go wrong? Future Extensibility? On many projects new use-cases arise, such as: Route messages to different destinations, based on header values or contents? Log the message contents? Add header values? Buffer the messages? Improved response and error handling? Make configuration changes without having to recompile? and more… Now we have to refactor code and introduce new code and test cases, run it through QA, etc. etc. A More Configurable Solution! It is time to graduate Spring JmsTemplate and play with the big kids. We can easily do this with a Spring Integration flow. How it is done with Spring Integration Here we have a diagram illustrating the 3 simple components to Spring Integration replacing the JmsTemplate send. Create a Gateway interface – an interface defining method(s) that accept the type of data you wish to send and any optional header values. Define a Channel – the pipe connecting our endpoints Define an Outbound JMS Adapter – sends the message to your JMS provider (ActiveMQ, RabbitMQ, etc.) Simply inject this into our service classes and invoke the methods. Immediate Gains Add header & header values via the methods defined in the interface Simple invokation of Gateway methods from our service classes Multiple Gateway methods Configure method level or class level destinations Future Gains Change the JMS Adapter (one-way) to a JMS Gateway (two-way) to processes responses from JMS We can change the channel to a queue (buffered) channel We can wire in a transformer for message transformation We can wire in additional destinations, and wire in a “header (key), header value, or content based” router and add another adapter We can wire in other inbound adapters receiving data from another source, such as SMTP, FTP, File, etc. Wiretap the channel to send a copy of the message elsewhere Change the channel to a logging adapter channel which would provide us with logging of the messages coming through Add the “message-history” option to our SI configuration to track the message along its route and more… Optimal JMS Send Solution The Spring Integration Gateway Interface Gateway provides a one or two way communication with Spring Integration. If the method returns void, it is inherently one-way. The interface MyJmsGateway, has one Gateway method declared sendMyMessage(). When this method is invoked by your service class, the first argument will go into a message header field named “myHeaderKey”, the second argument goes into the payload. package com.gordondickens.sijms; import org.springframework.integration.annotation.Gateway;import org.springframework.integration.annotation.Header; public interface MyJmsGateway { @Gateway public void sendMyMessage(@Header("myHeaderKey") String s, Object o);} Spring Integration Configuration Because the interface is proxied at runtime, we need to configure in the Gateway via XML. Sending the Message package com.gordondickens.sijms; import org.junit.Test;import org.junit.runner.RunWith;import org.springframework.beans.factory.annotation.Autowired;import org.springframework.test.context.ContextConfiguration;import org.springframework.test.context.junit4.SpringJUnit4ClassRunner; @ContextConfiguration("classpath:/com/gordondickens/sijms/JmsSenderTests-context.xml")@RunWith(SpringJUnit4ClassRunner.class)public class JmsSenderTests { @Autowired MyJmsGateway myJmsGateway; @Test public void testJmsSend() { myJmsGateway.sendMyMessage("myHeaderValue", "MY PayLoad"); } Summary Simple implementation Invoke a method to send a message to JMS – Very SOA eh? Flexible configuration Reconfigure & restart WITHOUT recompiling – SWEET!

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.

Bixo Labs shows how to use Solr as a NoSQL solution for big data Many people use the Hadoop open source project to process large data sets because it’s a great solution for scalable, reliable data processing workflows. Hadoop is by far the most popular system for handling big data, with companies using massive clusters to store and process petabytes of data on thousands of servers. Since it emerged from the Nutch open source web crawler project in 2006, Hadoop has grown in every way imaginable – users, developers, associated projects (aka the “Hadoop ecosystem”). Starting at roughly the same time, the Solr open source project has become the most widely used search solution on planet Earth. Solr wraps the API-level indexing and search functionality of Lucene with a RESTful API, GUI, and lots of useful administrative and data integration functionality. The interesting thing about combining these two open source projects is that you can use Hadoop to crunch the data, and then serve it up in Solr. And we’re not talking about just free-text search; Solr can be used as a key-value store (i.e. a NoSQL database) via its support for range queries. Even on a single server, Solr can easily handle many millions of records (“documents” in Lucene lingo). Even better, Solr now supports sharding and replication via the new, cutting-edge SolrCloud functionality. Background I started using Hadoop & Solr about five years ago, as key pieces of the Krugle code search startup I co-founded in 2005. Back then, Hadoop was still part of the Nutch web crawler we used to extract information about open source projects. And Solr was fresh out of the oven, having just been released as open source by CNET. At Bixo Labs we use Hadoop, Solr, Cascading, Mahout, and many other open source technologies to create custom data processing workflows. The web is a common source of our input data, which we crawl using the Bixo open source project. The Problem During a web crawl, the state of the crawl is contained in something commonly called a “crawl DB”. For broad crawls, this has to be something that works with billions of records, since you need one entry for each known URL. Each “record” has the URL as the key, and contains important state information such as the time and result of the last request. For Hadoop-based crawlers such as Nutch and Bixo, the crawl DB is commonly kept in a set of flat files, where each file is a Hadoop “SequenceFile”. These are just a packed array of serialized key/value objects. Sometimes we need to poke at this data, and here’s where the simple flat-file structure creates a problem. There’s no easy way run queries against the data, but we can’t store it in a traditional database since billions of records + RDBMS == pain and suffering. Here is where scalable NoSQL solutions shine. For example, the Nutch project is currently re-factoring this crawl DB layer to allow plugging in HBase. Other options include Cassandra, MongoDB, CouchDB, etc. But for simple analytics and exploration on smaller datasets, a Solr-based solution works and is easier to configure. Plus you get useful and surprising fun functionality like facets, geospatial queries, range queries, free-form text search, and lots of other goodies for free. Architecture So what exactly would such a Hadoop + Solr system look like? As mentioned previously, in this example our input data comes from a Bixo web crawler’s CrawlDB, with one entry for each known URL. But the input data could just as easily be log files, or records from a traditional RDBMS, or the output of another data processing workflow. The key point is that we’re going to take a bunch of input data, (optionally) munge it into a more useful format, and then generate a Lucene index that we access via Solr. Hadoop For the uninitiated, Hadoop implements both a distributed file system (aka “HDFS”) and an execution layer that supports the map-reduce programming model. Typically data is loaded and transformed during the map phase, and then combined/saved during the reduce phase. In our example, the map phase reads in Hadoop compressed SequenceFiles that contain the state of our web crawl, and our reduce phase write out Lucene indexes. The focus of this article isn’t on how to write Hadoop map-reduce jobs, but I did want to show you the code that implements the guts of the job. Note that it’s not typical Hadoop key/value manipulation code, which is painful to write, debug, and maintain. Instead we use Cascading, which is an open source workflow planning and data processing API that creates Hadoop jobs from shorter, more representative code. The snippet below reads SequenceFiles from HDFS, and pipes those records into a sink (output) that stores them using a LuceneScheme, which in turn saves records as Lucene documents in an index. Tap source = new Hfs(new SequenceFile(CRAWLDB_FIELDS), inputDir); Pipe urlPipe = new Pipe("crawldb urls"); urlPipe = new Each(urlPipe, new ExtractDomain()); Tap sink = new Hfs(new LuceneScheme(SOLR_FIELDS, STORE_SETTINGS, INDEX_SETTINGS, StandardAnalyzer.class, MAX_FIELD_LENGTH), outputDir, true); FlowConnector fc = new FlowConnector(); fc.connect(source, sink, urlPipe).complete(); We defined CRAWLDB_FIELDS and SOLR_FIELDS to be the set of input and output data elements, using names like “url” and “status”. We take advantage of the Lucene Scheme that we’ve created for Cascading, which lets us easily map from Cascading’s view of the world (records with fields) to Lucene’s index (documents with fields). We don’t have a Cascading Scheme that directly supports Solr (wouldn’t that be handy?), but we can make-do for now since we can do simple analysis for this example. We indexed all of the fields so that we can perform queries against them. Only the status message contains normal English text, so that’s the only one we have to analyze (i.e., break the text up into terms using spaces and other token delimiters). In addition, the ExtractDomain operation pulls the domain from the URL field and builds a new Solr field containing just the domain. This will allow us to do queries against the domain of the URL as well as the complete URL. We could also have chosen to apply a custom analyzer to the URL to break it into several pieces (i.e., protocol, domain, port, path, query parameters) that could have been queried individually. Running the Hadoop Job For simplicity and pay-as-you-go, it’s hard to beat Amazon’s EC2 and Elastic Mapreduce offerings for running Hadoop jobs. You can easily spin up a cluster of 50 servers, run your job, save the results, and shut it down – all without needing to buy hardware or pay for IT support. There are many ways to create and configure a Hadoop cluster; for us, we’re very familiar with the (modified) EC2 Hadoop scripts that you can find in the Bixo distribution. Step-by-step instructions are available at http://openbixo.org/documentation/running-bixo-in-ec2/ The code for this article is available via GitHub, at http://github.com/bixolabs/hadoop2solr. The README displayed on that page contains step-by-step instructions for building and running the job. After the job is done, we’ll copy the resulting index out of the Hadoop distributed file system (HDFS) and onto the Hadoop cluster’s master server, then kill off the one slave we used. The Hadoop master is now ready to be configured as our Solr server. Solr On the Solr side of things, we need to create a schema that matches the index we’re generating. The key section of our schema.xml file is where we define the fields. These fields have a one-to-one correspondence with the SOLR_FIELDS we defined in our Hadoop workflow. They also need to use the same Lucene settings as what we defined in the static IndexWorkflow.java STORE_SETTINGS and INDEX_SETTINGS. Once we have this defined, all that’s left is to set up a server that we can use. To keep it simple, we’ll use the single EC2 instance in Amazon’s cloud (m1.large) that we used as our master for the Hadoop job, and run the simple Solr search server that relies on embedded Jetty to provide the webapp container. Similar to the Hadoop job, step-by-step instructions are in the README for the hadoop2solr project on GitHub. But in a nutshell, we’ll copy and unzip a Solr 1.4.1 setup on the EC2 server, do the same for our custom Solr configuration, create a symlink to the index, and then start it running with: Giving it a Try Now comes the interesting part. Since we opened up the default Jetty port used by Solr (8983) on this EC2 instance, we can directly access Solr’s handy admin console by pointing our browser at http://:8983/solr/admin % cd solr % java -Dsolr.solr.home=../solr-conf -Dsolr.data.dir=../solr-data -jar start.jar From here we can run queries against Solr: We can also use curl to talk to the server via HTTP requests: curl http://:8983/solr/select/?q=-status%3AFETCHED+and+-status%3AUNFETCHED The response is XML by default. Below is an example of the response from the above request, where we found 2,546 matches in 94ms. Now here’s what I find amazing. For an index of 82 million documents, running on a fairly wimpy box (EC2 m1.large = 2 virtual cores), the typical response time for a simple query like “status:FETCHED” is only 400 milliseconds, to find 9M documents. Even a complex query such as (status not FETCHED and not UNFETCHED) only takes six seconds. Scaling Obviously we could use beefier boxes. If we switched to something like m1.xlarge (15GB of memory, 4 virtual cores) then it’s likely we could handle upwards of 200M “records” in our Solr index and still get reasonable response times. If we wanted to scale beyond a single box, there are a number of solutions. Even out of the box Solr supports sharding, where your HTTP request can specify multiple servers to use in parallel. More recently, the Solr trunk has support for SolrCloud. This uses the ZooKeeper open source project to simplify coordination of multiple Solr servers. Finally, the Katta open source project supports Lucene-level distributed search, with many of the features needed for production quality distributed search that have not yet been added to SolrCloud. Summary The combination of Hadoop and Solr makes it easy to crunch lots of data and then quickly serve up the results via a fast, flexible search & query API. Because Solr supports query-style requests, it’s suitable as a NoSQL replacement for traditional databases in many situations, especially when the size of the data exceeds what is reasonable with a typical RDBMS. Solr has some limitations that you should be aware of, specifically: · Updating the index works best as a batch operation. Individual records can be updated, but each commit (index update) generates a new Lucene segment, which will impact performance. · Current support for replication, fail-over, and other attributes that you’d want in a production-grade solution aren’t yet there in SolrCloud. If this matters to you, consider Katta instead. · Many SQL queries can’t be easily mapped to Solr queries. The code for this article is available via GitHub, at http://github.com/bixolabs/hadoop2solr. The README displayed on that page contains additional technical details.

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/

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/

Sonar is an open source web-based application to manage code quality which covers seven axes of code quality as: Architecture and design, comments, duplications, unit tests, complexity, potential bugs and coding rules. Developed in Java and can cover projects in Java, Flex, PHP, PL/SQL, Cobol and Visual Basic 6. It's very efficient to navigate, offering visual reporting and you can follow metrics evolution of your project and combine them. There is an online project called Nemo dedicated to open source projects, as you can see projects like Jetty, Apache Lucene and Apache Tomcat. So, let's config Sonar to work together with Maven. First of all set up Sonar server and other configurations in Maven's settings.xml file: sonar true jdbc:postgresql://localhost/sonar org.postgresql.Driver user password http://localhost:9000 You must to have a Sonar server running and define it in sonar.host.url parameter. In this example I'm using the default Sonar URL, http://localhost:9000 , and you must set user name and password for your database. To install local Sonar Server you can see this link. After that, to execute the code analysers and save results in Sonar database just execute mvn sonar:sonar. You can config Sonar to execute in your CI (Continuous Integration) application. Hudson is a perfect match, because there is a Sonar plugin for it. See the final result below: I prefer Teamcity CI, but there isn't a stable plugin as you can see in compatibility matrix. To resolve this plugin's problem, just add sonar:sonar in the maven command executed by Teamcity. Sonar is an essential tool for your software project to help you evaluating how cohesive your classes are, warning for future problems as code complexity or duplication problems arise, and will help you to have a cleaner code. If you have any question or some difficulties, contact me.

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);

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.