This article is an introduction to using the specification pattern in Java. We also will see how we can combine classic specifications with JPA Criteria queries to retrieve objects from a relational database. Within this post we will use the following Poll class as an example entity for creating specifications. It represents a poll that has a start and end date. In the time between those two dates users can vote among different choices. A poll can also be locked by an administrator before the end date has been reached. In this case, a lock date will be set. @Entity public class Poll { @Id @GeneratedValue private long id; private DateTime startDate; private DateTime endDate; private DateTime lockDate; @OneToMany(cascade = CascadeType.ALL) private List votes = new ArrayList<>(); } For better readability I skipped getters, setters, JPA annotations for mapping Joda DateTime instances and fields that aren't needed in this example (like the question being asked in the poll). Now assume we have two constraints we want to implement: A poll is currently running if it is not locked and if startDate < now < endDate A poll is popular if it contains more than 100 votes and is not locked We could start by adding appropriate methods to Poll like: poll.isCurrentlyRunning(). Alternatively we could use a service method like pollService.isCurrentlyRunning(poll). However, we also want to be able to query the database to get all currently running polls. So we might add a DAO or repository method like pollRepository.findAllCurrentlyRunningPolls(). If we follow this way we implement the isCurrentlyRunning constraint two times in two different locations. Things become worse if we want to combine constraints. What if we want to query the database for a list of all popular polls that are currently running? This is where the specification pattern come in handy. When using the specification pattern we move business rules into extra classes called specifications. To get started with specifications we create a simple interface and an abstract class: public interface Specification { boolean isSatisfiedBy(T t); Predicate toPredicate(Root root, CriteriaBuilder cb); Class getType(); } abstract public class AbstractSpecification implements Specification { @Override public boolean isSatisfiedBy(T t) { throw new NotImplementedException(); } @Override public Predicate toPredicate(Root poll, CriteriaBuilder cb) { throw new NotImplementedException(); } @Override public Class getType() { ParameterizedType type = (ParameterizedType) this.getClass().getGenericSuperclass(); return (Class) type.getActualTypeArguments()[0]; } } Please ignore the AbstractSpecification class with the mysterious getType() method for a moment (we come back to it later). The central part of a specification is the isSatisfiedBy() method, which is used to check if an object satisfies the specification. toPredicate() is an additional method we use in this example to return the constraint as javax.persistence.criteria.Predicate instance which can be used to query a database. For each constraint we create a new specification class that extends AbstractSpecification and implements isSatisfiedBy() and toPredicate(). The specification implementation to check if a poll is currently running looks like this: public class IsCurrentlyRunning extends AbstractSpecification { @Override public boolean isSatisfiedBy(Poll poll) { return poll.getStartDate().isBeforeNow() && poll.getEndDate().isAfterNow() && poll.getLockDate() == null; } @Override public Predicate toPredicate(Root poll, CriteriaBuilder cb) { DateTime now = new DateTime(); return cb.and( cb.lessThan(poll.get(Poll_.startDate), now), cb.greaterThan(poll.get(Poll_.endDate), now), cb.isNull(poll.get(Poll_.lockDate)) ); } } Within isSatisfiedBy() we check if the passed object matches the constraint. In toPredicate() we construct a Predicate using JPA's CriteriaBuilder. We will use the resulting Predicate instance later to build a CriteriaQuery for querying the database. The specification for checking if a poll is popular looks similar: public class IsPopular extends AbstractSpecification { @Override public boolean isSatisfiedBy(Poll poll) { return poll.getLockDate() == null && poll.getVotes().size() > 100; } @Override public Predicate toPredicate(Root poll, CriteriaBuilder cb) { return cb.and( cb.isNull(poll.get(Poll_.lockDate)), cb.greaterThan(cb.size(poll.get(Poll_.votes)), 5) ); } } If we now want to test if a Poll instance matches one of these constraints we can use our newly created specifications: boolean isPopular = new IsPopular().isSatisfiedBy(poll); boolean isCurrentlyRunning = new IsCurrentlyRunning().isSatisfiedBy(poll); For querying the database we need to extend our DAO / repository to support specifications. This can look like the following: public class PollRepository { private EntityManager entityManager = ... public List findAllBySpecification(Specification specification) { CriteriaBuilder criteriaBuilder = entityManager.getCriteriaBuilder(); // use specification.getType() to create a Root instance CriteriaQuery criteriaQuery = criteriaBuilder.createQuery(specification.getType()); Root root = criteriaQuery.from(specification.getType()); // get predicate from specification Predicate predicate = specification.toPredicate(root, criteriaBuilder); // set predicate and execute query criteriaQuery.where(predicate); return entityManager.createQuery(criteriaQuery).getResultList(); } } Here we finally use the getType() method implemented in AbstractSpecification to createCriteriaQuery and Root instances. getType() returns the generic type of theAbstractSpecification instance defined by the subclass. For IsPopular andIsCurrentlyRunning it returns the Poll class. Without getType() we would have to create theCriteriaQuery and Root instances inside toPredicate() of every specification we create. So it is just a small helper to reduce boiler plate code inside specifications. Feel free to replace this with your own implementation if you come up with better approaches. Now we can use our repository to query the database for polls that match a certain specification: List popularPolls = pollRepository.findAllBySpecification(new IsPopular()); List currentlyRunningPolls = pollRepository.findAllBySpecification(new IsCurrentlyRunning()); At this point the specifications are the only components that contain the constraint definitions. We can use it to query the database or to check if an object fulfills the required rules. However one question remains: How do we combine two or more constraints? For example we would like to query the database for all popular polls that are still running. The answer to this is a variation of the composite design pattern called composite specifications. Using a composite specification we can combine specifications in different ways. To query the database for all running and popular pools we need to combine the isCurrentlyRunning with theisPopular specification using the logical and operation. Let's create another specification for this. We name itAndSpecification: public class AndSpecification extends AbstractSpecification { private Specification first; private Specification second; public AndSpecification(Specification first, Specification second) { this.first = first; this.second = second; } @Override public boolean isSatisfiedBy(T t) { return first.isSatisfiedBy(t) && second.isSatisfiedBy(t); } @Override public Predicate toPredicate(Root root, CriteriaBuilder cb) { return cb.and( first.toPredicate(root, cb), second.toPredicate(root, cb) ); } @Override public Class getType() { return first.getType(); } } An AndSpecification is created out of two other specifications. In isSatisfiedBy() and toPredicate()we return the result of both specifications combined by a logical and operation. We can use our new specification like this: Specification popularAndRunning = new AndSpecification<>(new IsPopular(), new IsCurrentlyRunning()); List polls = myRepository.findAllBySpecification(popularAndRunning); To improve readability we can add an and() method to the Specification interface: public interface Specification { Specification and(Specification other); // other methods } and implement it within our abstract implementation: abstract public class AbstractSpecification implements Specification { @Override public Specification and(Specification other) { return new AndSpecification<>(this, other); } // other methods } Now we can chain multiple specification by using the and() method: Specification popularAndRunning = new IsPopular().and(new IsCurrentlyRunning()); boolean isPopularAndRunning = popularAndRunning.isSatisfiedBy(poll); List polls = myRepository.findAllBySpecification(popularAndRunning); When needed we can easily extend this further with other composite specifications (for exampleOrSpecification or NotSpecification). Conclusion When using the specification pattern we move business rules in separate specification classes. These specification classes can be easily combined by using composite specifications. In general, specification improve reusability and maintainability. Additionally specifications can easily be unit tested. For more detailed information about the specification pattern I recommend this article by Eric Evans and Martin Fowler. You can find the source of this example project on GitHub.

Finally the wait is over, Spring 4 is here and one of the best features is that now has a fully support for Groovy. The Spring team did a fantastic job bringing the same concept from Grails into the Spring Framework. In the past if you needed to incorporate Spring in your Groovy Applications or Scripts , normally you did something like this: @Grab('org.springframework:spring-context:4.0.0.RELEASE') import org.springframework.context.support.GenericApplicationContext import org.springframework.context.annotation.ClassPathBeanDefinitionScanner import org.springframework.beans.factory.annotation.Autowired import org.springframework.stereotype.Component @Component class Login { def authorize(User user){ if(user.credentials.username == "guest" && user.credentials.password == "guest"){ "${user.greetings} ${user.credentials.username}" }else "You are not ${user.greetings}" } } @Component class Credentials { String username = "guest" String password = "guest" } @Component class User{ @Autowired Credentials credentials String greetings = "Welcome" } def ctx = new GenericApplicationContext() new ClassPathBeanDefinitionScanner(ctx).scan('') // scan root package for components ctx.refresh() def login = ctx.getBean("login") def user = ctx.getBean("user") println login.authorize(user) One of the benefits that I see is that Groovy removes all the unnecessary boiler plate from Java. Then, if you wanted to add more flavor to your Groovy Apps using the Grail's BeanBuilder , you needed to do something like this: @Grab(group='org.slf4j', module='slf4j-simple', version='1.7.5') @Grab(group='org.grails', module='grails-web', version='2.3.4') import grails.spring.BeanBuilder import groovy.transform.ToString class Login { def authorize(User user){ if(user.credentials.username == "John" && user.credentials.password == "Doe"){ "${user.greetings} ${user.credentials.username}" }else "You are not ${user.greetings}" } } @ToString(includeNames=true) class Credentials{ String username String password } @ToString(includeNames=true) class User{ Credentials credentials String greetings } def bb = new BeanBuilder() bb.beans { login(Login) user(User){ credentials = new Credentials(username:"John", password:"Doe") greetings = 'Welcome!!' } } def ctx = bb.createApplicationContext() def u = ctx.getBean("user") println u def l = ctx.getBean("login") println l.authorize(u) And now with the new Spring 4 you can add all the Groovy flavor to your apps (without all the Grails Dependencies) and using the new GroovyBeanDefinitionReader from Spring 4: @Grab('org.springframework:spring-context:4.0.0.RELEASE') import org.springframework.context.support.GenericApplicationContext import org.springframework.beans.factory.groovy.GroovyBeanDefinitionReader import groovy.transform.ToString class Login { def authorize(User user){ if(user.credentials.username == "John" && user.credentials.password == "Doe"){ "${user.greetings} ${user.credentials.username}" }else "You are not ${user.greetings}" } } @ToString(includeNames=true) class Credentials{ String username String password } @ToString(includeNames=true) class User{ Credentials credentials String greetings } def ctx = new GenericApplicationContext() def reader = new GroovyBeanDefinitionReader(ctx) reader.beans { login(Login) user(User){ credentials = new Credentials(username:"John", password:"Doe") greetings = 'Welcome!!' } } ctx.refresh() def u = ctx.getBean("user") println u def l = ctx.getBean("login") println l.authorize(u) And that's not all, also SpringBoot gives you even more power using Groovy: //File: app.groovy import org.springframework.boot.* import org.springframework.boot.autoconfigure.* import org.springframework.stereotype.* import org.springframework.web.bind.annotation.* import org.springframework.beans.factory.annotation.* @Component class Login { def authorize(User user){ if(user.credentials.username == "John" && user.credentials.password == "Doe"){ "${user.greetings} ${user.credentials.username}" }else "You are not ${user.greetings}" } } @Component class Credentials{ String username String password } @Component class User{ Credentials credentials = new Credentials(username:"John", password:"Doe") String greetings = "Welcome!!" } @Controller @EnableAutoConfiguration class SampleController { @Autowired def login @Autowired def user @RequestMapping("/") @ResponseBody String home() { return login.authorize(user) } } To run the above code, install springboot and then just execute: $ spring run app.groovy and then go to your browser and open http://localhost:8080 and you see: Welcome!! John Congratulations to the Spring Team!! Keep grooving!! References: [1] https://spring.io/blog/2013/12/12/announcing-spring-framework-4-0-ga-release [2] http://groovy.codehaus.org/Using+Spring+Factories+with+Groovy [3] http://grails.org/doc/latest/guide/spring.html [4] http://projects.spring.io/spring-boot/ [5] https://spring.io/guides

a common and difficult problem acquiring data is extracting tables from a pdf. previously, i described how to extract the text from a pdf with pdf.js , a pdf rendering library made by mozilla labs. the rendering process requires an html canvas object, and then draws each object (character, line, rectangle, etc) on it. the easiest way to get a list of these is to to intercept all the calls pdf.js makes to drawing functions on the canvas object. (see “ self modifying javascripts ” for a similar technique). the “set” method below adds a wrapper closure to each function, which logs the call. function replace(ctx, key) { var val = ctx[key]; if (typeof(val) == "function") { ctx[key] = function() { var args = array.prototype.slice.call(arguments); console.log("called " + key + "(" + args.join(",") + ")"); return val.apply(ctx, args); } } } for (var k in context) { replace(context, k); } var rendercontext = { canvascontext: context, viewport: viewport }; page.render(rendercontext); this lets us see a series of calls: called transform(1,0,0,1,150.42,539.67) called translate(0,0) called scale(1,-1) called scale(0.752625,0.752625) called measuretext(c) called save() called scale(0.9701818181818181,1) called filltext(c,0,0) called restore() called restore() called save() called transform(1,0,0,1,150.42,539.6 we can easily retrieve the text by noting the first argument to each “filltext” call: "congregations ranked by growth and decline in membership and worship attendance, 2006 to 2011philadelphia presbytery - table 16net membership changenet worship changepercent changepercent changeworship 2006worship 2011membership 2006membership 2011abington, abington- 143(74)-13.18%(57)0(15)0.00%(22)numberrank3003001,085942anchor, wrightstown0(23)0.00%(27)-12(25)-21.43%(52)numberrank56449797arch street, philadelphia-117(71)-68.42%(117)27(5)90.00% (2)numberrank305717154aston, aston3(21)3.53%(22)-5(19)-9.43% (31)numberrank53488588beaconno reportboth yearsno reportboth yearsnumberrankbensalem, bensalem-23(39)-13.94%(62)-28(36)-28.57% (64)numberrank9870165142berean, philadelphia106(4)44.92%(4)no reportboth yearsnumberrank00236342bethany collegiate, havertown- 188(76)-42.44%(110)43(3)21.29%(7)numberrank202245443255bethel, philadelphia-13(33)-13.68%(60)-27(35)-35.06% (71)numberrank77509582bethesda, philadelphia9(18)5.56%(18)no reportboth yearsnumberrank1150162171beverly hills, upper darby-3(26)-3.03% (32)-11(24)-20.00%(48)numberrank55449996bridesburg, philadelphia0(23)0.00%(27)no reportboth yearsnumberrank004444bristol, bristolno reportboth yearsno reportboth yearsnumberrankpage 1 of 10report prepared by research services, presbyterian church (u.s.a.)1- 800-728-7228, ext #204006-oct-12" notable, this doesn’t track line endings, and not all the characters are recorded in the expected order (the first line is rendered after the second). the calls to transform, translate, and scale control where text is placed. the filltext method also takes an (x, y) parameter set that moves the individual letters between words. the exact position is a combination of successive operations, which are modeled as a stack of matrix operations. thankfully, pdf.js tracks the output of these operations as it renders, so we don’t have to recalculate it. thus, we can make a method that records the letters and their real positions. this method takes the internal context object, the type of state transition, and the arguments to the transition. this method is then called from the ‘record’ function listed above. var chars = []; var cur = {}; function record(ctx, state, args) { if (state === 'filltext') { var c = args[0]; cur.c = c; cur.x = ctx._transformmatrix[4] + args[1]; cur.y = ctx._transformmatrix[5] + args[2]; chars[chars.length] = cur; cur = {}; } } these results can be sorted by position (x and y). the sort method arranges letters by position – if they are shifted up or down a small amount, they are considered to be on one line. chars.sort( function(a, b) { var dx = b.x - a.x; var dy = b.y - a.y; if (math.abs(dy) < 0.5) { return dx * -1; } else { return dy * -1; } } ); this presents several difficulties: this doesn’t detect right-to-left text, and it’s becoming clear that we’re going to have a hard time knowing when you’re in a table and when we aren’t. to do this, we define a function which can transform the array of letters and positions into a csv style output. this tracks from letter to letter – if it sees a “large” change in y, it makes a new line. if it sees a “large” change in x, it treats it as a new column. the real challenge is defining “large” which for my test pdf were around 15 and 20, for dx and dy. function gettext(marks, ex, ey, v) { var x = marks[0].x; var y = marks[0].y; var txt = ''; for (var i = 0; i < marks.length; i++) { var c = marks[i]; var dx = c.x - x; var dy = c.y - y; if (math.abs(dy) > ey) { txt += "\"\n\""; if (marks[i+1]) { // line feed - start from position of next line x = marks[i+1].x; } } if (math.abs(dx) > ex) { txt += "\",\""; } if (v) { console.log(dx + ", " + dy); } txt += c.c; x = c.x; y = c.y; } return txt; } this algorithm doesn’t handle newlines in rows, and oddly, the columns don’t come out in the right order, but they appear to be consistently out of order. line with large spaces (e.g. an em-dash) are detected as having multiple columns, but this can be cleaned up later – here is some sample output. you can see an example below, and the final source is available on github . congregations ranked by growth and decline in m","embership and w","orship attendance, 2006 to 2011" "","philadelphia presbytery"," - table 16" "","net ","membership ","change" "","net worship ","change","percent ","change","percent ","change","worship"," 2006","worship"," 2011","membership"," 2006","membership"," 2011" "","abington, abington","-143","(74)","-13.18%(57)","0","(15)","0.00%(22)","number","rank","300","300","1,085","942" "","anchor, wrightstown","0","(23)","0.00%(27)","-12","(25)","-21.43%(52)","number","rank","56","44","97","97" "","arch street, philadelphia","-117","(71)","-68.42%","(117)","27(5)","90.00%(2)","number","rank","30","57","171","54" "","aston, aston","3","(21)","3.53%(22)","-5","(19)","-9.43%(31)","number","rank","53","48","85","88" "","beacon","no report","both years","no report","both years","number","rank" "","bensalem, bensalem","-23","(39)","-13.94%(62)","-28","(36)","-28.57%(64)","number","rank","98","70","165","142" "","berean, philadelphia","106(4)","44.92%(4)","no report","both years","number","rank","0","0","236","342" "","bethany collegiate, havertown","-188","(76)","-42.44%","(110)","43(3)","21.29%(7)","number","rank","202","245","443","255" "","bethel, philadelphia","-13","(33)","-13.68%(60)","-27","(35)","-35.06%(71)","number","rank","77","50","95","82" "","bethesda, philadelphia","9","(18)","5.56%(18)","no report","both years","number","rank","115","0","162","171" "","beverly hills, upper darby","-3","(26)","-3.03%(32)","-11","(24)","-20.00%(48)","number","rank","55","44","99","96" "","bridesburg, philadelphia","0","(23)","0.00%(27)","no report","both years","number","rank","0","0","44","44" "","bristol, bristol","no report","both years","no report","both years","number","rank" "","page 1 of 10","report prepared by research services, presbyterian church (u.s.a.)","1-800-728-7228, ext #2040","06-oct-12"

this post is not about variables in my application code (which i debug). it is about using variables in eclipse for building projects. eclipse variables allow me to make my projects ‘position independent’ whenever i cannot use a path relative to my projects or workspace. eclipse variables which variables are used where in eclipse might be sometimes not very clear. depending in which context variables are used, not everything might be available. this link for example gives a list of variables which can be used to invoke an external tool. build variables eclipse comes with many built-in variables, especially for the build system. if i want to see what variables are already defined, i can show them in the project properties, under c/c++ build > build variables with enabled option ‘show system variables’: system build variables with the ‘add…’ button i can define and add my own variables, available for that project: define a new build variable if above operation is done on a project, then the setting is for the project only. if i want to add a variable for the workspace, i can do this using the menu window > preferences : workspace build variables global system variables eclipse automatically includes the system (e.g. windows) environment variables. many dialogs have the ‘variables…’ button where i can use my variables, including the variables defined on system level: system variables system variables: one way or the other so if i want to have a variable for every workspace, one way is to define it at the system level. however, this is not a good way as this clutter the variables for every application. batch file a solution to this to create my custom batch file where i define my variables, and at the end of this batch file i launch eclipse. that way the extra variables are only for this eclipse session. cwide-env file another very nice way codewarrior eclipse offers is using the cwide-env file located in the eclipse sub-folder of the installation: cwide-env file i can define variables here, or extend existing ones: -add : add string to the variable at the end -prepend : add string to the variable at the beginning that way i can easily manipulate existing system variables or create new ones which then are used by eclipse. summary variables in eclipse help me to define paths to source files and folders outside of a project or workspace. with variables i avoid using absolute paths which would make porting projects from one machine to another difficult. i can define variables for projects, for the workspace or use system variables. with codewarrior i have a cwide-env file which is used to extend the system variables. happy variabling

the upcoming version of jboss application server will no longer use tomcat as the integrated webserver, but instead, will replace it with undertow . the architecture of undertow is based on handlers that can be added dynamically via a builder api to the server. this approach is similar to the way of constructing a webserver in node.js . it allows developers to embed the undertow webserver easily into their applications. as the addition of features is done via the builder api, one can only add the features that are really required in one’s application. beyond that undertow supports websockets and the servlet api in version 3.1. it can be run as a blocking or non-blocking server and it is reported that first benchmarks have proven that undertow is the fastest webserver written in java. as all of this sounds very promising, so let’s try to set up a simple websocket server. as usual we start by creating a simple java project and add the undertow maven dependency: io.undertow undertow-core 1.0.0.beta20 with undertow’s builder api our buildandstartserver() method looks like this: public void buildandstartserver(int port, string host) { server = undertow.builder() .addlistener(port, host) .sethandler(getwebsockethandler()) .build(); server.start(); } we just add a listener that specifies the port and host to listen for incoming connections and afterwards add a websocket handler. as the websocket handler code is a little bit more comprehensive, i have put it into its own method: private pathhandler getwebsockethandler() { return path().addpath("/websocket", websocket(new websocketconnectioncallback() { @override public void onconnect(websockethttpexchange exchange, websocketchannel channel) { channel.getreceivesetter().set(new abstractreceivelistener() { @override protected void onfulltextmessage(websocketchannel channel, bufferedtextmessage message) { string data = message.getdata(); lastreceivedmessage = data; logger.info("received data: "+data); websockets.sendtext(data, channel, null); } }); channel.resumereceives(); } })) .addpath("/", resource(new classpathresourcemanager(websocketserver.class.getclassloader(), websocketserver.class.getpackage())) .addwelcomefiles("index.html")); } let’s go line by line through this code snippet. first of all, we add a new path: /websocket. the second argument of the addpath() methods lets us specify what kind of protocol we want to use for this path. in our case we create a new websocket. the anonymous implementation has a onconnect() method in which we set an implementation of abstractreceivelistener. here we have a convenient method onfulltextmessage() that is called when a client has sent us a text message. a call of getdata() fetches the actual message we have received. in this simple example we just echo this string back to client to validate that the roundtrip from the client to server and back works. to perform some simple manual tests we also add a second resource under the path / which serves some static html and javascript files. the directory that contains these files is given as an instance of classpathresourcemanager. the call of addwelcomefiles() tells undertow which file to server when the client asks for the path /. the index.html looks like this: our javascript code is swapped out to the websocket.js file. we use jquery and the jquery-plugin gracefulwebsocket to ease the client side development: var ws = $.gracefulwebsocket("ws://127.0.0.1:8080/websocket"); ws.onmessage = function(event) { var messagefromserver = event.data; $('#output').append('received: '+messagefromserver+''); } function send(message) { ws.send(message); } after having created a websocket object by calling $.gracefulwebsocket() we can register a callback function for incoming messages. in this method we only append the message string to the dom of the page. the send() method is just a call to gracefulwebsocket’s send() method. when we now start our application and open the url http://127.0.0.1:8080/ in our webbrowser we see the following page: entering some string and hitting the “send web socket data” button sends the message to the server, which in response echos it back to the client. now that we know that everything works as expected, we want to protect our code against regression with a junit test case. as a websocket client i have chosen the library jetty-websocket: org.eclipse.jetty jetty-websocket 8.1.0.rc5 test in the test case we build and start the websocket server to open a new connection to the websocket port. the websocket implementation of jetty-websocket allows us to implement two callback methods for the open and close events. within the open callback we send the test message to the client. the rest of the code waits for the connection to be established, closes it and asserts that the server has received the message: @test public void teststartandbuild() throws exception { subject = new websocketserver(); subject.buildandstartserver(8080, "127.0.0.1"); websocketclient client = new websocketclient(); future connectionfuture = client.open(new uri("ws://localhost:8080/websocket"), new websocket() { @override public void onopen(connection connection) { logger.info("onopen"); try { connection.sendmessage("testmessage"); } catch (ioexception e) { logger.error("failed to send message: "+e.getmessage(), e); } } @override public void onclose(int i, string s) { logger.info("onclose"); } }); websocket.connection connection = connectionfuture.get(2, timeunit.seconds); assertthat(connection, is(notnullvalue())); connection.close(); subject.stopserver(); thread.sleep(1000); assertthat(subject.lastreceivedmessage, is("testmessage")); } as usual you can find the source code on github . conclusion: undertow’s builder api makes it easy to construct a websocket server and in general an embedded webserver that fits your needs. this also eases automatic testing as you do not need any specific maven plugin that starts and stops your server before and after your integration tests. beyond that the jquery plugin jquery-graceful-websocket lets you send and receive messages over websockets with only a few lines of code.

One alternative to using SQLite on Android is to store Java objects in SharedPreferences.

Your mission-critical projects need complex event processing, realtime management and monitoring. Talend 5.4 (released in December 2013, https://www.talend.com) offers a great new feature: Talend Event Logging. It allows logging, processing and monitoring of all technical events and business data. In this article, I will focus on how to process, filter and monitor business data. You can find more details about monitoring technical events and logs (e.g. OSGi events of the ESB container) in Talend’s documentation (www.help.talend.com). This new feature is very powerful, but also extendable to fit custom requirements. You can solve and monitor much more complex scenarios than the one I describe here. Talend Event Logging with Logstash, ElasticSearch and Kibana First, let’s take a look at the components / projects which are integrated and extended into Talend’s products for implementing this new feature. logstash (http://logstash.net) is a tool for managing events and logs. You can use it to collect different (distributed) logs, parse them, and store them for later use. Speaking of searching, logstash comes with a simple, but fine web interface for searching and drilling into all of your logs. It uses ElasticSearch under the hood. So, you can easily query through all your logs for specific errors or business analytics (e.g. searching for all lines matching an unique order id). Additional to the pure collection of events, the Event Logging feature supports custom processing (e.g. custom filtering, customer data enrichment/reduction), aggregation, signing and also server side custom pre- and post-processing of events - e.g. to send them to an intrusion detection system or to any other kind of potential higher level log processing /management system. Kibana (http://www.elasticsearch.org/overview/kibana) is a browser based analytics and search interface to logstash and other timestamped data sets stored in ElasticSearch. Kibana strives to be easy to get started with, while also being flexible and powerful, just like logstash and ElasticSearch. Main difference to logstash is a much more powerful HTML5 based web interface. You can • use multiple concurrent search inputs • highlight to drill down bar charts • create line charts, stacked, unstacked, filled or unfilled, with or without points • create Pie and donut charts that compare top terms or the results of multiple queries • create custom dashboards with multiple charts • and much more… Therefore, logstash, Elasticsearch and Kibana are a perfect combination. You can use Kibana to analyze and monitor your data as you do with logstash, however, Kibana’s web interface is much more powerful and comfortable than logstash. There is a great book about logstash: “The logstash book” – for just 9.99 USD. I can really recommend this book for getting started: http://www.logstashbook.com/. For Elasticsearch, you can find several books on Amazon. Unfortunately, Kibana has no good and extensive documentation yet. I heard from its developers that this topic is addressed for Q1 2014. Integration of Event Logging and Monitoring into Talend’s Unified Platform Talend 5.4 has integrated logstash and Kibana into its Unified Platform. Talend Administration Center (TAC) is Talend’s central web application for management and monitoring. It got a new logging view: Here, you can use very flexible and powerful realtime search capabilities of Elasticsearch. Some dashboards are available by default. You can also create your own custom dashboards easily within this site thanks to Kibana. Many panel types are available, such as pie, histogram, table, hits or trends. You can analyze every technical event or business data down to the message level: By default, you see fields such as message, source, timestamp, type, and others. Of course, you can also add custom fields suitable for your business case. This way, you have a central monitoring capability which allows analyzing data on distributed clusters easily. Under the hood, this data comes from logstash. Many alternative inputs are available for logstash, such as log4j input or tcp input. For business data, I often use file input (http://logstash.net/docs/1.2.2/inputs/file) to analyze files such as CSV. Adding new inputs is very simple. You just have to add an input to your logstash configuration file of your logstash server. As I mentioned already, all this is integrated into the Talend’s Unified Platform: In this example, there are three log4j inputs and one file input. I use the file input to analyze text files in a specific directory. In this case, output is an embedded Elasticsearch instance. In production, you should use an external Elasticsearch cluster, of course. Processing such as filtering can also be configured in this file. Thus, you can process, analyze and monitor all your different inputs within one central monitoring application thanks to Kibana. Building Talend Integration Jobs, Routes and Web Services As mentioned before, you can analyze almost all data with logstash, Elasticsearch and Kibana. It does not matter if your input is technical events from a container (e.g. OSGi events) or any business data such as CSV files, log4j logs, or something else. Talend implicitly supports technical events which are created by the ESB container, by MDM, etc. However, you can also add your custom business data from your Talend jobs (integration perspective), SOAP / REST Web Services (integration perspective) or Talend routes (mediation perspective), easily: This is just one example (part of Talend’s DI demos which are included in every DI installation). The job generates some random data and stores it to a CSV file. You just have to add the configured file or directory of tFileOutputDelimited to your logstash configuration using file input (with wild cards for more complex scenarios). That’s it. You can now monitor and analyze the business data in realtime. This example showed a Talend DI Job (i.e. ETL job). However, you can also monitor your business data from SOAP / REST Web Services or Talend Routes the same way. Conclusion Your mission-critical projects need management and monitoring. Today, this is not just possible with complex and expensive tools of large vendors, but also with Talend’s Unified Platform products such as Talend ESB or Talend MDM. Under the hood, Talend integrates and extends widely used open source products: logstash, Elasticsearch and Kibana. Have fun with Talend 5.4’s new event logging and monitoring features… Best regards, Kai Wähner (@KaiWaehner) CONTENT FROM MY BLOG: http://www.kai-waehner.de/blog/2013/12/17/realtime-event-logging-complex-event-processing-cep-and-monitoring-with-talends-unified-platform-5-4-di-esb-dq-mdm-bpm-using-elasticsearch-logstash-and-kibana/

Editor's note: Be sure to check out part 2 as well. This is the fourth of an introductory series of blogs on Apache HBase. In the third part, we saw a high level view of HBase architecture . In this part, we'll use the HBase Java API to create tables, insert new data, and retrieve data by row key. We'll also see how to setup a basic table scan which restricts the columns retrieved and also uses a filter to page the results. Having just learned about HBase high-level architecture, now let's look at the Java client API since it is the way your applications interact with HBase. As mentioned earlier you can also interact with HBase via several flavors of RPC technologies like Apache Thrift plus a REST gateway, but we're going to concentrate on the native Java API. The client APIs provide both DDL (data definition language) and DML (data manipulation language) semantics very much like what you find in SQL for relational databases. Suppose we are going to store information about people in HBase, and we want to start by creating a new table. The following listing shows how to create a new table using the HBaseAdmin class. Configuration conf = HBaseConfiguration.create(); HBaseAdmin admin = new HBaseAdmin(conf); HTableDescriptor tableDescriptor = new HTableDescriptor(TableName.valueOf("people")); tableDescriptor.addFamily(new HColumnDescriptor("personal")); tableDescriptor.addFamily(new HColumnDescriptor("contactinfo")); tableDescriptor.addFamily(new HColumnDescriptor("creditcard")); admin.createTable(tableDescriptor); The people table defined in preceding listing contains three column families: personal, contactinfo, and creditcard. To create a table you create an HTableDescriptor and add one or more column families by adding HColumnDescriptor objects. You then call createTable to create the table. Now we have a table, so let's add some data. The next listing shows how to use the Put class to insert data on John Doe, specifically his name and email address (omitting proper error handling for brevity). Configuration conf = HBaseConfiguration.create(); HTable table = new HTable(conf, "people"); Put put = new Put(Bytes.toBytes("doe-john-m-12345")); put.add(Bytes.toBytes("personal"), Bytes.toBytes("givenName"), Bytes.toBytes("John")); put.add(Bytes.toBytes("personal"), Bytes.toBytes("mi"), Bytes.toBytes("M")); put.add(Bytes.toBytes("personal"), Bytes.toBytes("surame"), Bytes.toBytes("Doe")); put.add(Bytes.toBytes("contactinfo"), Bytes.toBytes("email"), Bytes.toBytes("[email protected]")); table.put(put); table.flushCommits(); table.close(); In the above listing we instantiate a Put providing the unique row key to the constructor. We then add values, which must include the column family, column qualifier, and the value all as byte arrays. As you probably noticed, the HBase API's utility Bytes class is used a lot; it provides methods to convert to and from byte[] for primitive types and strings. (Adding a static import for the toBytes() method would cut out a lot of boilerplate code.) We then put the data into the table, flush the commits to ensure locally buffered changes take effect, and finally close the table. Updating data is also done via the Put class in exactly the same manner as just shown in the prior listing. Unlike relational databases in which updates must update entire rows even if only one column changed, if you only need to update a single column then that's all you specify in the Put and HBase will only update that column. There is also a checkAndPut operation which is essentially a form of optimistic concurrency control - the operation will only put the new data if the current values are what the client says they should be. Retrieving the row we just created is accomplished using the Get class, as shown in the next listing. (From this point forward, listings will omit the boilerplate code to create a configuration, instantiate the HTable, and the flush and close calls.) Get get = new Get(Bytes.toBytes("doe-john-m-12345")); get.addFamily(Bytes.toBytes("personal")); get.setMaxVersions(3); Result result = table.get(get); The code in the previous listing instantiates a Get instance supplying the row key we want to find. Next we use addFamily to instruct HBase that we only need data from the personal column family, which also cuts down the amount of work HBase must do when reading information from disk. We also specify that we'd like up to three versions of each column in our result, perhaps so we can list historical values of each column. Finally, calling get returns a Result instance which can then be used to inspect all the column values returned. In many cases you need to find more than one row. HBase lets you do this by scanning rows, as shown in the second part which showed using a scan in the HBase shell session. The corresponding class is the Scan class. You can specify various options, such as the start and ending row key to scan, which columns and column families to include and the maximum versions to retrieve. You can also add filters, which allow you to implement custom filtering logic to further restrict which rows and columns are returned. A common use case for filters is pagination. For example, we might want to scan through all people whose last name is Smith one page (e.g. 25 people) at a time. The next listing shows how to perform a basic scan. Scan scan = new Scan(Bytes.toBytes("smith-")); scan.addColumn(Bytes.toBytes("personal"), Bytes.toBytes("givenName")); scan.addColumn(Bytes.toBytes("contactinfo"), Bytes.toBytes("email")); scan.setFilter(new PageFilter(25)); ResultScanner scanner = table.getScanner(scan); for (Result result : scanner) { // ... } In the above listing we create a new Scan that starts from the row key smith- and we then use addColumn to restrict the columns returned (thus reducing the amount of disk transfer HBase must perform) to personal:givenName and contactinfo:email. A PageFilter is set on the scan to limit the number of rows scanned to 25. (An alternative to using the page filter would be to specify a stop row key when constructing the Scan.) We then get a ResultScanner for the Scan just created, and loop through the results performing whatever actions are necessary. Since the only method in HBase to retrieve multiple rows of data is scanning by sorted row keys, how you design the row key values is very important. We'll come back to this topic later. You can also delete data in HBase using the Delete class, analogous to the Put class to delete all columns in a row (thus deleting the row itself), delete column families, delete columns, or some combination of those. Connection Handling In the above examples not much attention was paid to connection handling and RPCs (remote procedure calls). HBase provides the HConnection class which provides functionality similar to connection pool classes to share connections, for example you use the getTable() method to get a reference to an HTable instance. There is also an HConnectionManager class which is how you get instances of HConnection. Similar to avoiding network round trips in web applications, effectively managing the number of RPCs and amount of data returned when using HBase is important, and something to consider when writing HBase applications. Conclusion to Part 4 In this part we used the HBase Java API to create a people table, insert a new person, and find the newly inserted person information. We also used the Scan class to scan the people table for people with last name "Smith" and showed how to restrict the data retrieved and finally how to use a filter to limit the number of results. In the next part, we'll learn how to deal with the absence of SQL and relations when modeling schemas in HBase. References HBase web site, http://hbase.apache.org/ HBase wiki, http://wiki.apache.org/hadoop/Hbase HBase Reference Guide http://hbase.apache.org/book/book.html HBase: The Definitive Guide, http://bit.ly/hbase-definitive-guide Google Bigtable Paper, http://labs.google.com/papers/bigtable.html Hadoop web site, http://hadoop.apache.org/ Hadoop: The Definitive Guide, http://bit.ly/hadoop-definitive-guide Fallacies of Distributed Computing, http://en.wikipedia.org/wiki/Fallacies_of_Distributed_Computing HBase lightning talk slides, http://www.slideshare.net/scottleber/hbase-lightningtalk Sample code, https://github.com/sleberknight/basic-hbase-examples

In Lucene's facet module we recently added support for dynamic range faceting, to show how many hits match each of a dynamic set of ranges. For example, the Updated drill-down in the Lucene/Solr issue search application uses range facets. Another example is distance facets (< 1 km, < 2 km, etc.), where the distance is dynamically computed based on the user's current location. Price faceting might also use range facets, if the ranges cannot be established during indexing. To implement range faceting, for each hit, we first calculate the value (the distance, the age, the price) to be aggregated, and then lookup which ranges match that value and increment its counts. Today we use a simple linear search through all ranges, which has O(N) cost, where N is the number of ranges. But this is inefficient! Segment Trees There are fun data structures like segment trees and interval trees with O(log(N) + M) cost per lookup, where M is the number of ranges that match the given value. I chose to explore segment trees, as Lucene only requires looking up by a single value (interval trees can also efficiently look up all ranges overlapping a provided range) and also because all the ranges are known up front (interval trees also support dynamically adding or removing ranges). If the ranges will never overlap, you can use a simple binary search; Guava's ImmutableRangeSet takes this approach. However, Lucene's range faceting allows overlapping ranges so we can't do that. Segment trees are simple to visualize: you "project" all ranges down on top of one another, creating a one-dimensional Venn diagram, to define the elementary intervals. This classifies the entire range of numbers into a minimal number of distinct ranges, each elementary interval, such that all points in each elementary interval always match the same set of input ranges. The lookup process is then a binary search to determine which elementary interval a point belongs to, recording the matched ranges as you recurse down the tree. Consider these ranges; the lower number is inclusive and the upper number is exclusive: 0: 0 – 10 1: 0 – 20 2: 10 – 30 3: 15 – 50 4: 40 – 70 The elementary intervals (think Venn diagram!) are: -∞ – 0 0 – 10 10 – 15 15 – 20 20 – 30 30 – 40 40 – 50 50 – 70 70 – ∞ Finally, you build a binary tree on top of the elementary ranges, and then add output range indices to both internal nodes and the leaves of that tree, necessary to prevent adversarial cases that would require too much (O(N^2)) space. During lookup, as you walk down the tree, you gather up the output ranges (indices) you encounter; for our example, each elementary range is assigned the follow range indices as outputs: -∞ – 0 → 0 – 10 → 0 10 – 15 → 1, 2 15 – 20 → 1, 2, 3 20 – 30 → 2, 3 30 – 40 → 3 40 – 50 → 3, 4 50 – 70 → 4 70 – ∞ → Some ranges correspond to 1 elementary interval, while other ranges correspond to 2 or 3 or more, in general. Some, 2 in this example, may have no matching input ranges. Looking Up Matched Ranges I've pushed all sources described below to new Google code project; the code is still somewhat rough and exploratory, so there are likely exciting bugs lurking, but it does seem to work: it includes (passing!) tests and simple micro-benchmarks. I started with a basic segment tree implementation as described on the Wikipedia page, for long values, called SimpleLongRangeMultiSet; here's the recursive lookup method: private int lookup(Node node, long v, int[] answers, int upto) { if (node.outputs != null) { for(int range : node.outputs) { answers[upto++] = range; } } if (node.left != null) { if (v <= node.left.end) { upto = lookup(node.left, v, answers, upto); } else { upto = lookup(node.right, v, answers, upto); } } return upto; } This worked correctly, but I realized there must be non-trivial overhead for the recursion, checking for nulls, the for loop over the output values, etc. Next, I tried switching to parallel arrays to hold the binary tree (ArrayLongRangeMultiSet), where the left child of node N is at 2*N and the right child is at 2*N+1, but this turned out to be slower. After that I tested a code specializing implementation, first by creating dynamic Java source code from the binary tree. This eliminates the recursion and creates a single simple method that uses a series of if statements, specialized to the specific ranges, to do the binary search and record the range indices. Here's the resulting specialized code, compiled from the above ranges: void lookup(long v, int[] answers) { int upto = 0; if (v <= 19) { if (v <= 9) { if (v >= 0) { answers[upto++] = 0; answers[upto++] = 1; } } else { answers[upto++] = 1; answers[upto++] = 2; if (v >= 15) { answers[upto++] = 3; } } } else { if (v <= 39) { answers[upto++] = 3; if (v <= 29) { answers[upto++] = 2; } } else { if (v <= 49) { answers[upto++] = 3; answers[upto++] = 4; } else { if (v <= 69) { answers[upto++] = 4; } } } } } Finally, using the ASM library, I compiled the tree directly to specialized Java bytecode, and this proved to be fastest (up to 2.5X faster in some cases). As a baseline, I also added the simple linear search method, LinearLongRangeMultiSet; as long as you don't have too many ranges (say 10 or less), its performance is often better than the Java segment tree. The implementation also allows you to specify the allowed range of input values (for example, maybe all values are >=0 in your usage), which can save an if statement or two in the lookup method. Counting All Matched Ranges While the segment tree allows you to quickly look up all matching ranges for a given value, after a nice tip from fellow Lucene committee Robert Muir, we realized Lucene's range faceting does not need to know the ranges for each value; instead, it only requires the aggregated counts for each range in the end, after seeing many values. This leads to an optimization: compute counts for each elementary interval and then in the end, roll up those counts to get the count for each range. This will only work for single-valued fields, since for a multi-valued field you'd need to carefully never increment the same range more than once per hit. So based on that approach, I created a new LongRangeCounter abstract base class, and the SimpleLongRangeCounter Java implementation, and also the ASM specialized version, and the results are indeed faster (~20 to 50%) than using the lookup method to count; I'll use this approach with Lucene. Segment trees are normally always "perfectly" balanced but one final twist I explored was to use a training set of values to bias the order of the if statements. For example, if your ranges cover a tiny portion of the search space, as is the case for the Updated drill-down, then it should be faster to use a slightly unbalanced tree, by first checking if the value is less than the maximum range. However, in testing, while there are some cases where this "training" is a bit faster, often it's slower; I'm not sure why. Lucene I haven't folded this into Lucene yet, but I plan to; all the exploratory code lives in the segment-trees Google code project for now. Results on the micro-benchmarks can be entirely different once the implementations are folded into a "real" search application. While ASM is a powerful way to generate specialized code, and it gives sizable performance gains at least in the micro-benchmarks, it is an added dependency and complexity for ongoing development and many more developers know Java than ASM. It may also confuse hotspot, causing deoptimizations when there are multiple implementations for an abstract base class. Furthermore, if there are many ranges, the resulting specialized bytecode can be become quite large (but, still O(N*log(N)) in size), which may cause other problems. On balance I'm not sure the sizable performance gains (on a micro-benchmark) warrant using ASM in Lucene's range faceting.

To serialize a Business object marked with [DataContract] and properties marked with [DataMember] attributes… public static string DataContractSerializeObject(T objectToSerialize) { using (var output = new StringWriter()) { using (var writer = new XmlTextWriter(output) { Formatting = Formatting.Indented }) { var dataContractSerializer = new DataContractSerializer(typeof(T), EntityUtilities.GetAllKnownTypes(), int.MaxValue, true, true, null); dataContractSerializer.WriteObject(writer, objectToSerialize); return output.GetStringBuilder().ToString(); } } } Then to deserialize back to your DataContract type, use this logic… public static T Deserialize(string xml) { using (Stream stream = new MemoryStream()) { byte[] data = System.Text.Encoding.UTF8.GetBytes(xml); stream.Write(data, 0, data.Length); stream.Position = 0; var dataContractSerializer = new DataContractSerializer(typeof(T), EntityUtilities.GetAllKnownTypes(), int.MaxValue, true, true, null); return (T)dataContractSerializer.ReadObject(stream); } }

A typical Maven and Spring web application has a fair amount of XML and verbosity to it. Add in Jersey and Spring Security and you can have hundreds of lines of XML before you even start to write your Java code. As part of a recent project, I was tasked with upgrading a webapp like this to use Spring 4 and Spring Boot. I also figured I'd try to minimize the XML. This is my story on how I upgraded to Spring 4, Jersey 2, Java 8 and Spring Boot 0.5.0 M6. When I started, the app was using Spring 3.2.5, Spring Security 3.1.4 and Jersey 1.18. The pom.xml had four Jersey dependencies, three Spring dependencies and three Spring Security dependencies, along with a number of exclusions for "jersey-spring". Upgrading to Spring 4 Upgrading to Spring 4 was easy, I changed the version property to 4.0.0.RC2 and added the new Spring bill of materials to my pom.xml. I also add the Spring milestone repo since Spring 4 won't be released to Maven central until tomorrow. org.springframework spring-framework-bom ${spring.framework.version} pom import spring-milestones http://repo.spring.io/milestone true Next, I removed all the references to ${spring.framework.version} in dependencies since it'd be controlled by Maven's dependency management feature. org.springframework spring-web - ${spring.framework.version} I also changed to use Maven 3's wildcard syntax to exclude multiple dependencies. com.sun.jersey.contribs jersey-spring org.springframework - spring - - - org.springframework - spring-core - - - org.springframework - spring-web - - - org.springframework - spring-beans - - - org.springframework - spring-context + * I confirmed the upgrade worked by running "mvn dependency:tree | grep spring", followed by "mvn jetty:run" and viewing the app in my browser. Upgrading to Jersey 2 The next item I tackled was upgrading to Jersey 2.4.1. I changed the version number in my pom.xml, then added the Jersey BOM. org.glassfish.jersey jersey-bom ${jersey.version} pom import You might ask "why Jersey?" if we already have Spring MVC and its REST support? You might also ask why not Play or Grails instead of a Java + Spring stack? For this particular project, I recommended technology options, and these were certainly among them. However, the team chose differently and I support their decision. The project is creating an iOS app, as well as a responsive HTML5 mobile/desktop app. We figured we had enough risk with new technologies on the front-end that we should play it a bit safer on the backend. To make the backend work a bit sexier, we've decided to allow Spring 4, Java 8 and possibly some reactive principles. Next, I changed from the old com.sun.jersey dependencies to org.glassfish.jersey and removed jersey-spring. org.glassfish.jersey.containers jersey-container-servlet org.glassfish.jersey.media jersey-media-json-jackson The last thing I needed to do was change the servlet-class and param-name in web.xml: jersey-servlet org.glassfish.jersey.servlet.ServletContainer jersey.config.server.provider.packages com.raibledesigns.boot.service 1 Requiring Java 8 Requiring Java 8 to compile was easy enough. I added the maven-compiler-plugin to enforce a minimum version. maven-compiler-plugin 3.1 1.8 1.8 I downloaded the latest Java 8 SDK and installed it. Then I set my JAVA_HOME to use it. export JAVA_HOME=`/usr/libexec/java_home -v 1.8` Integrating Spring Boot I learned about Spring Boot a few weeks ago at Devoxx. Josh Long gave me a 3-minute demo at the speaker's dinner and showed me enough to peak my interest. To integrate it into my project, I started with the Quick Start. I added the boot-parent, dependencies for web, security and actuator (logging, metrics, etc.) and the Maven plugin. I removed all the Spring and Spring Security dependencies. org.springframework.boot spring-boot-starter-parent 0.5.0.M6 ... spring-milestones http://repo.spring.io/milestone ... org.springframework.boot spring-boot-starter-web org.springframework.boot spring-boot-starter-security org.springframework.boot spring-boot-starter-actuator ... org.springframework.boot spring-boot-maven-plugin Upon restarting my app, I got an error about spring-security.xml using a 3.1 XSD. I fixed it by changing to 3.2. Next, I wanted to eliminate web.xml. First of all, I created an ApplicationInitializer so the WAR could be started from the command line. package com.raibledesigns.boot.config; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.EnableAutoConfiguration; import org.springframework.boot.builder.SpringApplicationBuilder; import org.springframework.boot.web.SpringBootServletInitializer; import org.springframework.context.annotation.ComponentScan; import org.springframework.context.annotation.Configuration; @Configuration @EnableAutoConfiguration @ComponentScan public class ApplicationInitializer extends SpringBootServletInitializer { @Override protected SpringApplicationBuilder configure(SpringApplicationBuilder application) { return application.sources(ApplicationInitializer.class); } public static void main(String[] args) { SpringApplication.run(ApplicationInitializer.class, args); } } However, after adding this, I received the following error on startup: org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'org.springframework.boot.context.properties.ConfigurationPropertiesBindingPostProcessor': Invocation of init method failed; nested exception is java.lang.AbstractMethodError: org.hibernate.validator.internal.engine.ConfigurationImpl .getDefaultParameterNameProvider()Ljavax/validation/ParameterNameProvider; Adding hibernate-validator as a dependency solved this problem: org.hibernate hibernate-validator To configure Spring Security without web.xml and spring-security.xml, I created WebSecurityConfig.java: package com.raibledesigns.boot.config; import org.springframework.context.annotation.Configuration; import org.springframework.core.Ordered; import org.springframework.core.annotation.Order; import org.springframework.security.config.annotation.authentication.builders.AuthenticationManagerBuilder; import org.springframework.security.config.annotation.web.builders.HttpSecurity; import org.springframework.security.config.annotation.web.configuration.EnableWebSecurity; import org.springframework.security.config.annotation.web.configuration.WebSecurityConfigurerAdapter; @Configuration @EnableWebSecurity @Order(Ordered.LOWEST_PRECEDENCE - 6) public class WebSecurityConfig extends WebSecurityConfigurerAdapter { @Override protected void configure(HttpSecurity http) throws Exception { http.authorizeRequests() .antMatchers("/", "/home").permitAll() .antMatchers("/v1.0/**").hasRole("USER") .anyRequest().authenticated(); http.httpBasic().realmName("My API"); } @Override protected void configure(AuthenticationManagerBuilder authManagerBuilder) throws Exception { authManagerBuilder.inMemoryAuthentication() .withUser("test").password("test123").roles("USER"); } } To configure Jersey without web.xml, I created a JerseyConfig class: package com.raibledesigns.boot.config; import org.glassfish.jersey.filter.LoggingFilter; import org.glassfish.jersey.jackson.JacksonFeature; import org.glassfish.jersey.server.ResourceConfig; import org.glassfish.jersey.server.ServerProperties; import javax.ws.rs.ApplicationPath; @ApplicationPath("/v1.0") public class JerseyConfig extends ResourceConfig { public JerseyConfig() { packages("com.raibledesigns.boot.service"); property(ServerProperties.BV_SEND_ERROR_IN_RESPONSE, true); property(ServerProperties.JSON_PROCESSING_FEATURE_DISABLE, false); property(ServerProperties.MOXY_JSON_FEATURE_DISABLE, true); property(ServerProperties.WADL_FEATURE_DISABLE, true); register(LoggingFilter.class); register(JacksonFeature.class); } } Finally, I created MvcConfig.java to set the welcome page. package com.raibledesigns.boot.config; import org.springframework.context.annotation.Configuration; import org.springframework.web.servlet.config.annotation.ViewControllerRegistry; import org.springframework.web.servlet.config.annotation.WebMvcConfigurerAdapter; @Configuration public class MvcConfig extends WebMvcConfigurerAdapter { @Override public void addViewControllers(ViewControllerRegistry registry) { registry.addViewController("/").setViewName("index"); } } To cleanup, I deleted src/main/webapp/WEB-INF and created src/main/resources/logback.xml: Since Boot doesn't support JSP out-of-the-box, I renamed my index.jsp file to index.html and changed the URL in it to point to "/v1.0/hello". I was pleased to see that everything worked nicely. I learned shortly after that I could remove the Spring BOM since Spring Boot uses a property to control its Spring version. The only issue I found is when started the app with "mvn package && java -jar target/app.war", it failed to initialize Jersey. I tried adding a @Bean for the servlet: @Bean public ServletRegistrationBean jerseyServlet() { ServletRegistrationBean registration = new ServletRegistrationBean(new ServletContainer(), "/v1.0/*"); registration.addInitParameter(ServletProperties.JAXRS_APPLICATION_CLASS, JerseyConfig.class.getName()); return registration; } Unfortunately, when running it using "java -jar", I get the following error: org.glassfish.hk2.api.MultiException: A MultiException has 1 exceptions. They are: 1. org.glassfish.jersey.server.internal.scanning.ResourceFinderException: java.io.FileNotFoundException: /.../target/app.war!/WEB-INF/classes (No such file or directory) at org.jvnet.hk2.internal.Utilities.justCreate(Utilities.java:869) at org.jvnet.hk2.internal.ServiceLocatorImpl.create(ServiceLocatorImpl.java:814) at org.jvnet.hk2.internal.ServiceLocatorImpl.createAndInitialize(ServiceLocatorImpl.java:906) at org.jvnet.hk2.internal.ServiceLocatorImpl.createAndInitialize(ServiceLocatorImpl.java:898) at org.glassfish.jersey.server.ApplicationHandler.createApplication(ApplicationHandler.java:300) at org.glassfish.jersey.server.ApplicationHandler.(ApplicationHandler.java:279) at org.glassfish.jersey.servlet.WebComponent.(WebComponent.java:302) This seems strange since there is a WEB-INF/classes in my WAR. Regardless, this is not a Boot problem per se, but more of a Jersey issue. From one of the Boot developers: The whole idea with Boot is that servlets are just a transport - they are a means to an end, and hopefully not the only one - the "container" is Spring, not the servlet container. We probably could add some form of support for SCI but only by hacking the containers since the spec really doesn't allow for much control of their lifecycle. It hasn't been a priority so far. Summary I hope this article is useful to see how you to upgrade your Java webapps to use Spring 4 and Spring Boot. I've created a boot-makeover project on GitHub with all the code mentioned. You can also view the commits for each step.

CMS, or content management systems, are platforms for managing and administering website content. There is no denying that CMSes are important in today's web ecosystem. These content management systems not only provide an easy way to build and maintain websites, but they also lend a helping hand in updating and editing website content without the need to spend hours or days writing and altering codes and scripts. Some of the leading CMSes are PHP-based, Ruby on Rails-based, ASP.NET-based, and Java-based. Among these, due to scalability, modernized architecture and open-source standards of a few, Java-based CMSs are getting quite a lot of attention lately, especially for enterprise websites, because of the scalable, modern, open source technology behind most of them. There are plenty of CMS tools based on Java to help developers create multi-lingual and multi-channel websites. But how do we decide on the best one for our use case? In this article, we’re going to explore the top 24 content management systems based on Java. Let’s have a look at each of them in detail: 1. Alfresco : Alfresco is one of the top open-source content management systems of Java. It comes with enterprise repository and portlet capabilities along with document management, collaboration, records management, knowledge management, web content management, imaging, and a lot more. Alfresco has a modular architecture and enables end users to efficiently manage websites across the cloud, mobile, hybrid and on-premise environments using open source Java technologies, such as Spring, Hibernate, Lucene and JSF. 2. Magnolia : Magnolia is a well-documented, easy to use, enterprise-grade open source CMS based on the Java Content Repository Standard. It is a highly popular CMS due to its out-of-the-box functionality and ease of use under an open source license. Moreover, Magnolia supports unique content delivery capabilities in a search-engine optimized manner and also follows W3C standards. Magnolia CMS has been deployed by enterprises and governments in more than 100 countries across the world. Here's a case study on Magnolia-based website development 3. LogicalDOC : Though less known than other software such as Alfresco, LogicalDOC is emerging as a powerful and more affordable alternative. With primary focus on Document Management, it offers very interesting content management, knowledge management and collaboration features, and all this in a really efficient way. A peculiar aspect of the interface is the use of Google GWT , this makes the user interface very responsive while the data transfer with the server is minimum. Also the availability of Free Apps for Android and Apple devices (iPhone and iPad) is an interesting feature. 4. Asbru: Asbru is another powerful, fully-featured, easy to use content management system with database-driven capabilities. It is built on the Spring framework with integrated community, databases, eCommerce and statistics modules, which helps developers to create, publish and manage rich and user-friendly internet, extranet and intranet websites on the go. Available in various editions, Asbru provides users with a simple, user-friendly platform to manage websites along with a host of other benefits and features such as custom templates and data, password protected content, multi-lingual content, communities, eCommerce and website analytics, a cutting-edge WYSIWYG content editor and a lot more. 5. OpenCMS : OpenCMS is based on Java and XML technology that allows you to build highly customizable and interactive websites and portals. It comes integrated with a WYSIWYG editor and fully-featured Template Engine which is fully compliant with W3C standards. OpenCMS can be deployed both in an open-source environment (Linux, Apache, Tomcat, MySQL) as well as a commercial environment (Windows NT, IIS, BEA Weblogic, Oracle) 6. Walrus: Walrus is yet another Spring-based CMS that provides unique and effective content management capabilities with a smart administrative interface and drag-and-drop facilities. Easy-to-setup and undo/redo features make Walrus a highly preferred and suitable CMS for government and non-profit enterprises. 7. Pulse : Pulse is a Java-based framework and portal solution that offers easy-to-use and extensible patterns for creating rich browser web applications and responsive websites. It brings a bunch of innovative and powerful components including content management, web shops, user management and more. A few of its key features include a WebDAV based virtual file system for digital asset management, mature user and role management, built-in internationalization, and more. 8. MeshCMS: MeshCMS is an easy to use online editing system written in Java. It comes with a host of features that you will find in any ideal content management system however, it uses a conventional approach in managing and editing website content. It is considered one of the fastest CMSes for editing files online, managing files, and building some very common components like menus, breadcrumbs, mail forms and so on. MeshCMS is accompanied by cross-browser capabilities, a WYSIWYG editor, hot-linking prevention, and tag-library that makes content management an interesting affair. 9. Liferay: Liferay is one of the most popular CMSes based on Java, and is recommended by many industry experts. It comes with awesome features that can make your content management tasks simple. Liferay is a very popular for developing personal as well as professional websites with ease. 10. DotCMS: DotCMS is a next-gen enterprise CMS that wears an open-source hat. It is highly popular and widely used CMS due to its open APIs, extensible and scalable architecture that it used to create personalized and engaging websites, intranets, extranets and applications with ease. 11. Jease: Jease highly known as ‘Java with ease’ is another open source content management system that is built on popular Java technologies like db40, Perst, Lucence and ZK. It is an extremely lightweight CMS with excellent Ajax interface. Due to its intuitive and interactive interface, it is highly simple and easy to customize and deploy websites in Jease even for inexperienced Java developers. 12. Hippo: Hippo is again a powerful open-source CMS made in Java that features enterprise level capabilities that helps in delivering personalized websites and channels. Hippo outlines its competitor by delivering outstanding customer experience through innovative solutions. Hippo has come a long way since 1999 serving medium to large organizations by offering a personalized multichannel content distribution platform including website, mobile, tablet, extranets and intranets. Its major version update was in December 2012 and since then it is seeing minor updates every couple of months. 13. Apache Lenya: Apache Lenya is another open-source Java CMS that features revision control , multisite management, scheduling, search, WYSIWYG editors, and workflow which makes website development and management quite interesting and easy for developers. Available in a variety of languages, Apache Lenya is highly preferred CMS among enterprises that desire to develop multi-lingual websites. 14. Contelligent: Conteligent is another smart CMS solution offered under Java technology stack. It is fully compliant with J2EE and offers great solution for creating and managing personalized websites. 15. InfoGlue: InfoGlue again is a Java-based CMS that is known for its advanced, scalable and robust open-source architecture. It is a highly flexible CMS built on JSR-168 and comes with full multi-language support, excellent information reuse and high integration capabilities. 16. OpenEdit: OpenEdit CMS is a dynamic tool for managing website content with online editing capabilities. Built in open-source architecture, OpenEdit provides facilities like user manager, file manager, version control and notification tools for managing media-rich websites. OpenCMS features enterprise grade plugins such as eCommerce, Content Management, Blog, Events Calendar, Social Networking Tools and more. 17. AtLeap: Atleap is a multi-lingual CMS based on Java which offers amazing content delivery assistance with SEO and full text search functionalities. AtLeap, a product of Blandware, is not only a CMS but a highly robust framework for developing website and web applications 18. Weceem: Weceem is yet another open source content management system, unlikely other CMS it is built upon well-known Java framework grails, spring and Java itself. Weceem has garner positive reviews and is an ideal CMS when it comes to grails, but faces tough competition in best Java CMS category. I came across a LinkedIn discussion which was enough for me to put this CMS in the Best Java CMS list. 19. Nuxeo: Nuxeno is a powerful open source CMS built on Java-based architecture. It offers solutions related to document management, case management and digital asset management. It is free from licensing free but do costs you when reach out for support and maintenance help. It has strong groups of customers including Electronic Arts, U.S. Navy and as stated on the company website, it’s been used in over 145 countries across thousands of organizations. 20. XperienCentral: Xperien central is currently the only CMS that offers unique content to a visitor as per his earlier journey, so you can tailor the content to increase the conversion. It offers multi-channel content delivery across website, mobile social media channels and applications. It is built on Java and hence it is extremely scalable and agile. 21 Atex: Atex is a web CMS that uses polopoly technology to deliver content. As per claims, it is the only industry leading CMS with built in paywall. Atex again is one of the premium CMS that offers amazing solutions for managing websites and helps marketers deliver the right content to relevant audiences. It has rich set of clientele. 22 Escenic: Customers of escenic include News of the World, The Sun, The Times, the Independent titles. It’s a closed source Java framework. Both Atex and Escenic are found to be highly popular in Sweden. Some of the biggest sites in Sweden use both these CMS. idg.se uses Atex and Aftonbladet.se uses Escenic 23. Adobe Experience Manager/ CQ5 : Best CMS list cannot be completed without including adobe experience manager. It is an all-round CMS which offers all kinds of agility and flexibility an organization may want. It helps deliver unique customer experience by delivering different content on different channels. Adobe Experience Manager was recently named a leader in web content management by Garnters magic quadrant. Earlier it was known as CQ5 but later was acquire by Adobe in 2010 24. SDL Tridion Again a well known CMS and highly recommended by industry experts. Its simple intuitive UI makes it simple to manage the content and deliver it uniformly across all channels. It recently received top score in overall content management experience according to an independent research firm - Forrester Research, Inc., This completes the list 23 top Java-based content management system. Hope after reading about all the CMS, you have got enough inferences and insight as to which CMS would be best for your website development project.

European countries are moving towards Single Euro Payments Area (SEPA) initiative and therefore a lot of companies are implementing International Bank Account Numbers (IBAN) or replacing existing bank account details with IBAN. Recently I was dealing with International Bank Account Numbers and couldn't find any open source library neither to generate them nor to validate them. To fill the gap I implemented iban4j Java library for International Bank Account Number (IBAN) generation and validation. Using iban4j is as easy as: Iban iban = new Iban.Builder() .countryCode(CountryCode.AT) .bankCode("19043") .accountNumber("00234573201") .build(); Library is available in maven central: org.iban4j iban4j 1.0.0 For more details check out github page

Jetty 9.1 is finally released, bringing Java WebSockets (JSR-356) to non-EE environments. It's awesome news and today's post will be about using this great new API along with Spring Framework. JSR-356 defines concise, annotation-based model to allow modern Java web applications easily create bidirectional communication channels using WebSockets API. It covers not only server-side, but client-side as well, making this API really simple to use everywhere. Let's get started! Our goal would be to build a WebSockets server which accepts messages from the clients and broadcasts them to all other clients currently connected. To begin with, let's define the message format, which server and client will be exchanging, as this simple Message class. We can limit ourselves to something like a String, but I would like to introduce to you the power of another new API - Java API for JSON Processing (JSR-353). package com.example.services; public class Message { private String username; private String message; public Message() { } public Message( final String username, final String message ) { this.username = username; this.message = message; } public String getMessage() { return message; } public String getUsername() { return username; } public void setMessage( final String message ) { this.message = message; } public void setUsername( final String username ) { this.username = username; } } To separate the declarations related to the server and the client, JSR-356 defines two basic annotations:@ServerEndpoint and @ClientEndpoit respectively. Our client endpoint, let's call itBroadcastClientEndpoint, will simply listen for messages server sends: package com.example.services; import java.io.IOException; import java.util.logging.Logger; import javax.websocket.ClientEndpoint; import javax.websocket.EncodeException; import javax.websocket.OnMessage; import javax.websocket.OnOpen; import javax.websocket.Session; @ClientEndpoint public class BroadcastClientEndpoint { private static final Logger log = Logger.getLogger( BroadcastClientEndpoint.class.getName() ); @OnOpen public void onOpen( final Session session ) throws IOException, EncodeException { session.getBasicRemote().sendObject( new Message( "Client", "Hello!" ) ); } @OnMessage public void onMessage( final Message message ) { log.info( String.format( "Received message '%s' from '%s'", message.getMessage(), message.getUsername() ) ); } } That's literally it! Very clean, self-explanatory piece of code: @OnOpen is being called when client got connected to the server and @OnMessage is being called every time server sends a message to the client. Yes, it's very simple but there is a caveat: JSR-356 implementation can handle any simple objects but not the complex ones like Message is. To manage that, JSR-356 introduces concept of encoders and decoders. We all love JSON, so why don't we define our own JSON encoder and decoder? It's an easy task which Java API for JSON Processing (JSR-353) can handle for us. To create an encoder, you only need to implementEncoder.Text< Message > and basically serialize your object to some string, in our case to JSON string, using JsonObjectBuilder. package com.example.services; import javax.json.Json; import javax.json.JsonReaderFactory; import javax.websocket.EncodeException; import javax.websocket.Encoder; import javax.websocket.EndpointConfig; public class Message { public static class MessageEncoder implements Encoder.Text< Message > { @Override public void init( final EndpointConfig config ) { } @Override public String encode( final Message message ) throws EncodeException { return Json.createObjectBuilder() .add( "username", message.getUsername() ) .add( "message", message.getMessage() ) .build() .toString(); } @Override public void destroy() { } } } For decoder part, everything looks very similar, we have to implement Decoder.Text< Message > and deserialize our object from string, this time using JsonReader. package com.example.services; import javax.json.JsonObject; import javax.json.JsonReader; import javax.json.JsonReaderFactory; import javax.websocket.DecodeException; import javax.websocket.Decoder; public class Message { public static class MessageDecoder implements Decoder.Text< Message > { private JsonReaderFactory factory = Json.createReaderFactory( Collections.< String, Object >emptyMap() ); @Override public void init( final EndpointConfig config ) { } @Override public Message decode( final String str ) throws DecodeException { final Message message = new Message(); try( final JsonReader reader = factory.createReader( new StringReader( str ) ) ) { final JsonObject json = reader.readObject(); message.setUsername( json.getString( "username" ) ); message.setMessage( json.getString( "message" ) ); } return message; } @Override public boolean willDecode( final String str ) { return true; } @Override public void destroy() { } } } And as a final step, we need to tell the client (and the server, they share same decoders and encoders) that we have encoder and decoder for our messages. The easiest thing to do that is just by declaring them as part of @ServerEndpoint and @ClientEndpoit annotations. import com.example.services.Message.MessageDecoder; import com.example.services.Message.MessageEncoder; @ClientEndpoint( encoders = { MessageEncoder.class }, decoders = { MessageDecoder.class } ) public class BroadcastClientEndpoint { } To make client's example complete, we need some way to connect to the server usingBroadcastClientEndpoint and basically exchange messages. The ClientStarter class finalizes the picture: package com.example.ws; import java.net.URI; import java.util.UUID; import javax.websocket.ContainerProvider; import javax.websocket.Session; import javax.websocket.WebSocketContainer; import org.eclipse.jetty.websocket.jsr356.ClientContainer; import com.example.services.BroadcastClientEndpoint; import com.example.services.Message; public class ClientStarter { public static void main( final String[] args ) throws Exception { final String client = UUID.randomUUID().toString().substring( 0, 8 ); final WebSocketContainer container = ContainerProvider.getWebSocketContainer(); final String uri = "ws://localhost:8080/broadcast"; try( Session session = container.connectToServer( BroadcastClientEndpoint.class, URI.create( uri ) ) ) { for( int i = 1; i <= 10; ++i ) { session.getBasicRemote().sendObject( new Message( client, "Message #" + i ) ); Thread.sleep( 1000 ); } } // Application doesn't exit if container's threads are still running ( ( ClientContainer )container ).stop(); } } Just couple of comments what this code does: we are connecting to WebSockets endpoint atws://localhost:8080/broadcast, randomly picking some client name (from UUID) and generating 10 messages, every with 1 second delay (just to be sure we have time to receive them all back). Server part doesn't look very different and at this point could be understood without any additional comments (except may be the fact that server just broadcasts every message it receives to all connected clients). Important to mention here: new instance of the server endpoint is created every time new client connects to it (that's why peers collection is static), it's a default behavior and could be easily changed. package com.example.services; import java.io.IOException; import java.util.Collections; import java.util.HashSet; import java.util.Set; import javax.websocket.EncodeException; import javax.websocket.OnClose; import javax.websocket.OnMessage; import javax.websocket.OnOpen; import javax.websocket.Session; import javax.websocket.server.ServerEndpoint; import com.example.services.Message.MessageDecoder; import com.example.services.Message.MessageEncoder; @ServerEndpoint( value = "/broadcast", encoders = { MessageEncoder.class }, decoders = { MessageDecoder.class } ) public class BroadcastServerEndpoint { private static final Set< Session > sessions = Collections.synchronizedSet( new HashSet< Session >() ); @OnOpen public void onOpen( final Session session ) { sessions.add( session ); } @OnClose public void onClose( final Session session ) { sessions.remove( session ); } @OnMessage public void onMessage( final Message message, final Session client ) throws IOException, EncodeException { for( final Session session: sessions ) { session.getBasicRemote().sendObject( message ); } } } In order this endpoint to be available for connection, we should start the WebSockets container and register this endpoint inside it. As always, Jetty 9.1 is runnable in embedded mode effortlessly: package com.example.ws; import org.eclipse.jetty.server.Server; import org.eclipse.jetty.servlet.DefaultServlet; import org.eclipse.jetty.servlet.ServletContextHandler; import org.eclipse.jetty.servlet.ServletHolder; import org.eclipse.jetty.websocket.jsr356.server.deploy.WebSocketServerContainerInitializer; import org.springframework.web.context.ContextLoaderListener; import org.springframework.web.context.support.AnnotationConfigWebApplicationContext; import com.example.config.AppConfig; public class ServerStarter { public static void main( String[] args ) throws Exception { Server server = new Server( 8080 ); // Create the 'root' Spring application context final ServletHolder servletHolder = new ServletHolder( new DefaultServlet() ); final ServletContextHandler context = new ServletContextHandler(); context.setContextPath( "/" ); context.addServlet( servletHolder, "/*" ); context.addEventListener( new ContextLoaderListener() ); context.setInitParameter( "contextClass", AnnotationConfigWebApplicationContext.class.getName() ); context.setInitParameter( "contextConfigLocation", AppConfig.class.getName() ); server.setHandler( context ); WebSocketServerContainerInitializer.configureContext( context ); server.start(); server.join(); } } The most important part of the snippet above is WebSocketServerContainerInitializer.configureContext: it's actually creates the instance of WebSockets container. Because we haven't added any endpoints yet, the container basically sits here and does nothing. Spring Framework and AppConfig configuration class will do this last wiring for us. package com.example.config; import javax.annotation.PostConstruct; import javax.inject.Inject; import javax.websocket.DeploymentException; import javax.websocket.server.ServerContainer; import javax.websocket.server.ServerEndpoint; import javax.websocket.server.ServerEndpointConfig; import org.eclipse.jetty.websocket.jsr356.server.AnnotatedServerEndpointConfig; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.web.context.WebApplicationContext; import com.example.services.BroadcastServerEndpoint; @Configuration public class AppConfig { @Inject private WebApplicationContext context; private ServerContainer container; public class SpringServerEndpointConfigurator extends ServerEndpointConfig.Configurator { @Override public < T > T getEndpointInstance( Class< T > endpointClass ) throws InstantiationException { return context.getAutowireCapableBeanFactory().createBean( endpointClass ); } } @Bean public ServerEndpointConfig.Configurator configurator() { return new SpringServerEndpointConfigurator(); } @PostConstruct public void init() throws DeploymentException { container = ( ServerContainer )context.getServletContext(). getAttribute( javax.websocket.server.ServerContainer.class.getName() ); container.addEndpoint( new AnnotatedServerEndpointConfig( BroadcastServerEndpoint.class, BroadcastServerEndpoint.class.getAnnotation( ServerEndpoint.class ) ) { @Override public Configurator getConfigurator() { return configurator(); } } ); } } As we mentioned earlier, by default container will create new instance of server endpoint every time new client connects, and it does so by calling constructor, in our caseBroadcastServerEndpoint.class.newInstance(). It might be a desired behavior but because we are usingSpring Framework and dependency injection, such new objects are basically unmanaged beans. Thanks to very well-thought (in my opinion) design of JSR-356, it's actually quite easy to provide your own way of creating endpoint instances by implementing ServerEndpointConfig.Configurator. TheSpringServerEndpointConfigurator is an example of such implementation: it's creates new managed bean every time new endpoint instance is being asked (if you want single instance, you can create singleton of the endpoint as a bean in AppConfig and return it all the time). The way we retrieve the WebSockets container is Jetty-specific: from the attribute of the context with name"javax.websocket.server.ServerContainer" (it probably might change in the future). Once container is there, we are just adding new (managed!) endpoint by providing our own ServerEndpointConfig (based onAnnotatedServerEndpointConfig which Jetty kindly provides already). To build and run our server and clients, we need just do that: mvn clean package java -jar target\jetty-web-sockets-jsr356-0.0.1-SNAPSHOT-server.jar // run server java -jar target/jetty-web-sockets-jsr356-0.0.1-SNAPSHOT-client.jar // run yet another client As an example, by running server and couple of clients (I run 4 of them, '392f68ef', '8e3a869d', 'ca3a06d0', '6cb82119') you might see by the output in the console that each client receives all the messages from all other clients (including its own messages): Nov 29, 2013 9:21:29 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Hello!' from 'Client' Nov 29, 2013 9:21:29 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #1' from '392f68ef' Nov 29, 2013 9:21:29 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #2' from '8e3a869d' Nov 29, 2013 9:21:29 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #7' from 'ca3a06d0' Nov 29, 2013 9:21:30 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #4' from '6cb82119' Nov 29, 2013 9:21:30 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #2' from '392f68ef' Nov 29, 2013 9:21:30 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #3' from '8e3a869d' Nov 29, 2013 9:21:30 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #8' from 'ca3a06d0' Nov 29, 2013 9:21:31 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #5' from '6cb82119' Nov 29, 2013 9:21:31 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #3' from '392f68ef' Nov 29, 2013 9:21:31 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #4' from '8e3a869d' Nov 29, 2013 9:21:31 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #9' from 'ca3a06d0' Nov 29, 2013 9:21:32 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #6' from '6cb82119' Nov 29, 2013 9:21:32 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #4' from '392f68ef' Nov 29, 2013 9:21:32 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #5' from '8e3a869d' Nov 29, 2013 9:21:32 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #10' from 'ca3a06d0' Nov 29, 2013 9:21:33 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #7' from '6cb82119' Nov 29, 2013 9:21:33 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #5' from '392f68ef' Nov 29, 2013 9:21:33 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #6' from '8e3a869d' Nov 29, 2013 9:21:34 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #8' from '6cb82119' Nov 29, 2013 9:21:34 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #6' from '392f68ef' Nov 29, 2013 9:21:34 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #7' from '8e3a869d' Nov 29, 2013 9:21:35 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #9' from '6cb82119' Nov 29, 2013 9:21:35 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #7' from '392f68ef' Nov 29, 2013 9:21:35 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #8' from '8e3a869d' Nov 29, 2013 9:21:36 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #10' from '6cb82119' Nov 29, 2013 9:21:36 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #8' from '392f68ef' Nov 29, 2013 9:21:36 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #9' from '8e3a869d' Nov 29, 2013 9:21:37 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #9' from '392f68ef' Nov 29, 2013 9:21:37 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #10' from '8e3a869d' Nov 29, 2013 9:21:38 PM com.example.services.BroadcastClientEndpoint onMessage INFO: Received message 'Message #10' from '392f68ef' 2013-11-29 21:21:39.260:INFO:oejwc.WebSocketClient:main: Stopped org.eclipse.jetty.websocket.client.WebSocketClient@3af5f6dc Awesome! I hope this introductory blog post shows how easy it became to use modern web communication protocols in Java, thanks to Java WebSockets (JSR-356), Java API for JSON Processing (JSR-353) and great projects such as Jetty 9.1! As always, complete project is available on GitHub.

Data access, specifically SQL access from within Java, has never been nice. This is in large part due to the fact that the JDBC api has a lot of ceremony. Java 7 vastly improved things with ARM blocks by taking away a lot of the ceremony around managing database objects such as Statements and ResultSets but fundamentally the code flow is still the same. Java 8 Lambdas gives us a very nice tool for improving the flow of JDBC. Out first attempt at improving things here is very simply to make it easy to work with ajava.sql.ResultSet. Here we simply wrap the ResultSet iteration and then delegate it to Lambda function. This is very similar in concept to Spring's JDBCTemplate. NOTE: I've released All the code snippets you see here under an Apache 2.0 license on Github. First we create a functional interface called ResultSetProcessor as follows: @FunctionalInterface public interface ResultSetProcessor { public void process(ResultSet resultSet, long currentRow) throws SQLException; } Very straightforward. This interface takes the ResultSet and the current row of theResultSet as a parameter. Next we write a simple utility to which executes a query and then calls ourResultSetProcessor each time we iterate over the ResultSet: public static void select(Connection connection, String sql, ResultSetProcessor processor, Object... params) { try (PreparedStatement ps = connection.prepareStatement(sql)) { int cnt = 0; for (Object param : params) { ps.setObject(++cnt, param)); } try (ResultSet rs = ps.executeQuery()) { long rowCnt = 0; while (rs.next()) { processor.process(rs, rowCnt++); } } catch (SQLException e) { throw new DataAccessException(e); } } catch (SQLException e) { throw new DataAccessException(e); } } Note I've wrapped the SQLException in my own unchecked DataAccessException. Now when we write a query it's as simple as calling the select method with a connection and a query: select(connection, "select * from MY_TABLE",(rs, cnt)-> { System.out.println(rs.getInt(1)+" "+cnt) }); So that's great, but I think we can do more... One of the nifty Lambda additions in Java is the new Streams API. This would allow us to add very powerful functionality with which to process a ResultSet. Using the Streams API over a ResultSet however creates a bit more of a challenge than the simple select with Lambda in the previous example. The way I decided to go about this is create my own Tuple type which represents a single row from a ResultSet. My Tuple here is the relational version where a Tuple is a collection of elements where each element is identified by an attribute, basically a collection of key value pairs. In our case the Tuple is ordered in terms of the order of the columns in the ResultSet. The code for the Tuple ended up being quite a bit so if you want to take a look, see the GitHub project in the resources at the end of the post. Currently the Java 8 API provides the java.util.stream.StreamSupport object which provides a set of static methods for creating instances of java.util.stream.Stream. We can use this object to create an instance of a Stream. But in order to create a Stream it needs an instance ofjava.util.stream.Spliterator. This is a specialised type for iterating and partitioning a sequence of elements, the Stream needs for handling operations in parallel. Fortunately the Java 8 api also provides the java.util.stream.Spliterators class which can wrap existing Collection and enumeration types. One of those types being ajava.util.Iterator. Now we wrap a query and ResultSet in an Iterator: public class ResultSetIterator implements Iterator { private ResultSet rs; private PreparedStatement ps; private Connection connection; private String sql; public ResultSetIterator(Connection connection, String sql) { assert connection != null; assert sql != null; this.connection = connection; this.sql = sql; } public void init() { try { ps = connection.prepareStatement(sql); rs = ps.executeQuery(); } catch (SQLException e) { close(); throw new DataAccessException(e); } } @Override public boolean hasNext() { if (ps == null) { init(); } try { boolean hasMore = rs.next(); if (!hasMore) { close(); } return hasMore; } catch (SQLException e) { close(); throw new DataAccessException(e); } } private void close() { try { rs.close(); try { ps.close(); } catch (SQLException e) { //nothing we can do here } } catch (SQLException e) { //nothing we can do here } } @Override public Tuple next() { try { return SQL.rowAsTuple(sql, rs); } catch (DataAccessException e) { close(); throw e; } } } This class basically delegates the iterator methods to the underlying result set and then on the next() call transforms the current row in the ResultSet into my Tuple type. And that's the basics done (This class will need a little bit more work though). All that's left is to wire it all together to make a Stream object. Note that due to the nature of a ResultSet it's not a good idea to try process them in parallel, so our stream cannot process in parallel. public static Stream stream(final Connection connection, final String sql, final Object... parms) { return StreamSupport .stream(Spliterators.spliteratorUnknownSize( new ResultSetIterator(connection, sql), 0), false); } Now it's straightforward to stream a query. In the usage example below I've got a table TEST_TABLE with an integer column TEST_ID which basically filters out all the non even numbers and then runs a count: long result = stream(connection, "select TEST_ID from TEST_TABLE") .filter((t) -> t.asInt("TEST_ID") % 2 == 0) .limit(100) .count(); And that's it! We now have a very powerful way of working with a ResultSet. So all this code is available under an Apache 2.0 license on GitHub here. I've rather lamely dubbed the project "lambda tuples," and the purpose really is to experiment and see where you can take Java 8 and Relational DB access, so please download or feel free to contribute.

This is a code snippet to retrieve the path of the current running web application project in java public String getPath() throws UnsupportedEncodingException { String path = this.getClass().getClassLoader().getResource("").getPath(); String fullPath = URLDecoder.decode(path, "UTF-8"); String pathArr[] = fullPath.split("/WEB-INF/classes/"); System.out.println(fullPath); System.out.println(pathArr[0]); fullPath = pathArr[0]; String reponsePath = ""; // to read a file from webcontent reponsePath = new File(fullPath).getPath() + File.separatorChar + "newfile.txt"; return reponsePath; }

This post demonstrates a Groovy script for uncompressing files with the 7-Zip archive format. The two primary objectives of this post are to demonstrate uncompressing 7-Zip files with Groovy and the handy 7-Zip-JBindingand to call out and demonstrate some key characteristics of Groovy as a scripting language. The 7-Zip page describes 7-Zip as "a file archiver with a high compression ratio." The page further adds, "7-Zip is open source software. Most of the source code is under the GNU LGPL license." More license information in available on the site along with information on the 7z format ("LZMA is default and general compression method of 7z format"). The 7-Zip page describes it as "a Java wrapper for 7-Zip C++ library" that "allows extraction of many archive formats using a very fast native library directly from Java through JNI." The 7z format is based on "LZMA andLZMA2 compression." Although there is an LZMA SDK available, it is easier to use the open source (SourceForge)7-Zip-JBinding project when manipulating 7-Zip files with Java. A good example of using Java with 7-Zip-JBinding to uncompress 7z files is available in the StackOverflowthread Decompress files with .7z extension in java. Dark Knight's response indicates how to use Java with 7-Zip-JBinding to uncompress a 7z file. I adapt Dark Knight's Java code into a Groovy script in this post. To demonstrate the adapted Groovy code for uncompressing 7z files, I first need a 7z file that I can extract contents from. The next series of screen snapshots show me using Windows 7-Zip installed on my laptop to compress the six PDFs available under the Guava Downloads page into a single 7z file called Guava.7z. Six Guava PDFs Sitting in a Folder Contents Selected and Right-Click Menu to Compress to 7z Format Guava.7z Compressed Archive File Created With a 7z file in place, I now turn to the adapted Groovy script that will extract the contents of this Guava.7zfile. As mentioned previously, this Groovy script is an adaptation of the Java code provided by Dark Knight on aStackOverflow thread. unzip7z.groovy // // This Groovy script is adapted from Java code provided at // http://stackoverflow.com/a/19403933 import static java.lang.System.err as error import java.io.File import java.io.FileNotFoundException import java.io.FileOutputStream import java.io.IOException import java.io.RandomAccessFile import java.util.Arrays import net.sf.sevenzipjbinding.ExtractOperationResult import net.sf.sevenzipjbinding.ISequentialOutStream import net.sf.sevenzipjbinding.ISevenZipInArchive import net.sf.sevenzipjbinding.SevenZip import net.sf.sevenzipjbinding.SevenZipException import net.sf.sevenzipjbinding.impl.RandomAccessFileInStream import net.sf.sevenzipjbinding.simple.ISimpleInArchive import net.sf.sevenzipjbinding.simple.ISimpleInArchiveItem if (args.length < 1) { println "USAGE: unzip7z.groovy .7z\n" System.exit(-1) } def fileToUnzip = args[0] try { RandomAccessFile randomAccessFile = new RandomAccessFile(fileToUnzip, "r") ISevenZipInArchive inArchive = SevenZip.openInArchive(null, new RandomAccessFileInStream(randomAccessFile)) ISimpleInArchive simpleInArchive = inArchive.getSimpleInterface() println "${'Hash'.center(10)}|${'Size'.center(12)}|${'Filename'.center(10)}" println "${'-'.multiply(10)}+${'-'.multiply(12)}+${'-'.multiply(10)}" simpleInArchive.getArchiveItems().each { item -> final int[] hash = new int[1] if (!item.isFolder()) { final long[] sizeArray = new long[1] ExtractOperationResult result = item.extractSlow( new ISequentialOutStream() { public int write(byte[] data) throws SevenZipException { //Write to file try { File file = new File(item.getPath()) file.getParentFile()?.mkdirs() FileOutputStream fos = new FileOutputStream(file) fos.write(data) fos.close() } catch (Exception e) { printExceptionStackTrace("Unable to write file", e) } hash[0] ^= Arrays.hashCode(data) // Consume data sizeArray[0] += data.length return data.length // Return amount of consumed data } }) if (result == ExtractOperationResult.OK) { println(String.format("%9X | %10s | %s", hash[0], sizeArray[0], item.getPath())) } else { error.println("Error extracting item: " + result) } } } } catch (Exception e) { printExceptionStackTrace("Error occurs", e) System.exit(1) } finally { if (inArchive != null) { try { inArchive.close() } catch (SevenZipException e) { printExceptionStackTrace("Error closing archive", e) } } if (randomAccessFile != null) { try { randomAccessFile.close() } catch (IOException e) { printExceptionStackTrace("Error closing file", e) } } } /** * Prints the stack trace of the provided exception to standard error without * Groovy meta data trace elements. * * @param contextMessage String message to precede stack trace and provide context. * @param exceptionToBePrinted Exception whose Groovy-less stack trace should * be printed to standard error. * @return Exception derived from the provided Exception but without Groovy * meta data calls. */ def Exception printExceptionStackTrace( final String contextMessage, final Exception exceptionToBePrinted) { error.print "${contextMessage}: ${org.codehaus.groovy.runtime.StackTraceUtils.sanitize(exceptionToBePrinted).printStackTrace()}" } In my adaptation of the Java code into the Groovy script shown above, I left most of the exception handling in place. Although Groovy allows exceptions to be ignored whether they are checked or unchecked, I wanted to maintain this handling in this case to make sure resources are closed properly and that appropriate error messages are presented to users of the script. One thing I did change was to make all of the output that is error-related be printed to standard error rather than to standard output. This required a few changes. First, I used Groovy's capability to rename something that is statically imported (see my related post Groovier Static Imports) to reference "java.lang.System.err" as "error" so that I could simply use "error" as a handle in the script rather than needing to use "System.err" to access standard error for output. Because Throwable.printStackTrace() already writes to standard error rather than standard output, I just used it directly. However, I placed calls to it in a new method that would first runStackTraceUtils.sanitize(Throwable) to remove Groovy-specific calls associated with Groovy's runtime dynamic capabilities from the stack trace. There were some other minor changes to the script as part of making it Groovier. I used Groovy's iteration on the items in the archive file rather than the Java for loop, removed semicolons at the ends of statements, used Groovy's String GDK extension for more controlled output reporting [to automatically center titles and tomultiply a given character by the appropriate number of times it needs to exist], and took advantage ofGroovy's implicit inclusion of args to add a check to ensure file for extraction was provided to the script. With the file to be extracted in place and the Groovy script to do the extracting ready, it is time to extract the contents of the Guava.7z file I demonstrated generating earlier in this post. The following command will run the script and places the appropriate 7-Zip-JBinding JAR files on the classpath. groovy -classpath "C:/sevenzipjbinding/lib/sevenzipjbinding.jar;C:/sevenzipjbinding/lib/sevenzipjbinding-Windows-x86.jar" unzip7z.groovy C:\Users\Dustin\Downloads\Guava\Guava.7z Before showing the output of running the above script against the indicated Guava.7z file, it is important to note the error message that will occur if the native operating system specific 7-Zip-JBinding JAR (sevenzipjbinding-Windows-x86.jar in my laptop's case) is not included on the classpath of the script. As the last screen snapshot indicates, neglecting to include the native JAR on the classpath leads to the error message: "Error occurs: java.lang.RuntimeException: SevenZipJBinding couldn't be initialized automaticly using initialization from platform depended JAR and the default temporary directory. Please, make sure the correct 'sevenzipjbinding-.jar' file is in the class path or consider initializing SevenZipJBinding manualy using one of the offered initialization methods: 'net.sf.sevenzipjbinding.SevenZip.init*()'" Although I simply added C:/sevenzipjbinding/lib/sevenzipjbinding-Windows-x86.jar to my script's classpath to make it work on this laptop, a more robust script might detect the operating system and apply the appropriate JAR to the classpath for that operating system. The 7-Zip-JBinding Download pagefeatures multiple platform-specific downloads (including platform-specific JARs) such assevenzipjbinding-4.65-1.06-rc-extr-only-Windows-amd64.zip, sevenzipjbinding-4.65-1.06-rc-extr-only-Mac-x86_64.zip, sevenzipjbinding-4.65-1.06-rc-extr-only-Mac-i386.zip, and sevenzipjbinding-4.65-1.06-rc-extr-only-Linux-i386.zip. Once the native 7-Zip-JBinding JAR is included on the classpath along with the core sevenzipjbinding.jar JAR, the script runs beautifully as shown in the next screen snapshot. The script extracts the contents of the 7z file into the same working directory as the Groovy script. A further enhancement would be to modify the script to accept a directory to which to write the extracted files or might write them to the same directory as the 7z archive file by default instead. Use of Groovy's built-in CLIBuilder support could also improve the script. Groovy is my preferred language of choice when scripting something that makes use of the JVM and/or of Java libraries and frameworks. Writing the script that is the subject of this post has been another reminder of that.

when you build applications based on mule ee (enterprise edition) and you are using maven to build your projects, you will notice you have dependencies to libraries that are not available in the public maven repos. to add these libraries to your local maven repo the mule distribution comes with a script ‘populate_m2_repo’ which is described here how to use it. now that is okay if you are the only developer and you are running your continuous integration on your local machine. in my case we are using artifactory as our company maven repository and also our build server is using it as the maven repo. so what i wanted was not to populate my local repository but the artifactory instance with all mule libraries. to do so i did two things: first make sure that maven is authorised to add libraries to artifactory. you can do this by adding the following to your settings.xml: artifactory admin password second step is to modify the original ‘populate_m2_repo.groovy’ script. replace the following line: mvn(["install:install-file", "-dgroupid=${project.groupid}", "-dartifactid=${project.artifactid}", "-dversion=${version}", "-dpackaging=pom", "-dfile=${localpom.canonicalpath}"]) with mvn(["deploy:deploy-file", "-dgroupid=${project.groupid}", "-dartifactid=${project.artifactid}", "-dversion=${version}", "-dpackaging=pom", "-dfile=${localpom.canonicalpath}", "-drepositoryid=arti", "-durl=http://localhost:8080/artifactory/libs-release-local" ]) and do the same for the line: def args = ["install:install-file", "-dgroupid=${pomprops.groupid}", "-dartifactid=${pomprops.artifactid}", "-dversion=${pomprops.version}", "-dpackaging=jar", "-dfile=${f.canonicalpath}", "-dpomfile=${localpom.canonicalpath}"] by replacing it with: def args = ["deploy:deploy-file", "-dgroupid=${pomprops.groupid}", "-dartifactid=${pomprops.artifactid}", "-dversion=${pomprops.version}", "-dpackaging=jar", "-dfile=${f.canonicalpath}", "-dpomfile=${localpom.canonicalpath}", "-drepositoryid=arti", "-durl=http://localhost:8080/artifactory/libs-release-local" ] now you can run the script with: ./populate_m2_repo bla as you can see it doesn’t really matter what you supply as m2_repo_home here because the libraries are uploaded to artifactory anyway. if you want you can replace the hardcoded url for artifactory in the script with the supplied parameter but in my case this solution was sufficient

This post is following up the experiment we ran exactly a year ago comparing the performance of different GC algorithms in real-life settings. We took the same experiment, expanded the tests to contain the G1 garbage collector and ran the tests on different platform. This year our tests were run with the following Garbage Collectors: -XX:+UseParallelOldGC -XX:+UseConcMarkSweepGC -XX:+UseG1GC Description of the environment The experiment was ran on out-of-the-box JIRA configuration. The motivation for the test run was loud and clear – Minecraft, Dalvik-based Angry Bird and Eclipse asides, JIRA should be one of the most popular Java applications out there. And opposed to the alternatives it is a more typical representative of what most of us are dealing with on the everyday business – after all Java is still by far most used in server side Java EE apps. What also affected our decision was – the engineers from Atlassian ship nicely packaged load tests along the JIRA download. So we had a benchmark to use for our configuration. We carefully unzipped our fresh JIRA 6.1 download and installed it on a Mac OS X Mavericks. And ran the bundled tests without changing anything in the default memory settings. The Atlassian team had been kind enough to set them for us: -Xms256m -Xmx768m -XX:MaxPermSize=256m The tests used JIRA functionality in different common ways – creating tasks, assigning tasks, resolving tasks, searching and discovering tasks, etc. Total runtime for the test was 30 minutes. We ran the test using three different garbage collection algorithms – Parallel, CMS and G1 were used in our case. Each test started with a fresh JVM boot, followed by prepopulating the storage to the exactly the same state. Only after the preparations we launched the load generation. Results During each run we have collected GC logs using -XX:+PrintGCTimeStamps -Xloggc:/tmp/gc.log -XX:+PrintGCDetails and analyzed this statistics with the help of GCViewer The results can be aggregated as follows. Note that all measurements are in milliseconds: Parallel CMS G1 Total GC pauses 20 930 18 870 62 000 Max GC pause 721 64 50 Interpretation and results First stop – Parallel GC (-XX:+UseParallelOldGC). Out of the 30 minutes the tests took to complete, we spent close to 21 seconds in GC pauses with the parallel collector. And the longest pause took 721 milliseconds. So let us take this as the baseline: GC cycles reduced the throughput by 1.1% of the total runtime. And the worst-case latency was 721ms. Next contestant: CMS (-XX:+UseConcMarkSweepGC). Again, 30 minutes of tests out of which we lost a bit less than 19 seconds to GC. Throughput-wise this is roughly in the same neighbourhood as the parallel mode. Latency on the other hand has been improved significantly - the worst-case latency is reduced more than 10 times! We are now facing just 64ms as the maximum pause time from the GC. Last experiment used the newest and shiniest GC algorithm available – G1 (-XX:+UseG1GC). The very same tests were run and throughput-wise we saw results suffering severely. This time our application spent more than a minute waiting for the GC to complete. Comparing this to the just 1% of the overhead with CMS, we are now facing close to 3.5% effect on the throughput. But if you really do not care about throughput and want to squeeze out the last bit from the latency then – we have improved around 20% comparing to the already-good CMS – using G1 saw the longest GC pause only taking 50ms. Conclusion As always, trying to summarize such an experiment into a single conclusion is dangerous. So if you have time and required skills – definitely go ahead and measure your own environment instead of adopting to one-size-fits-all solution. But if I would dare to make such a conclusion, I would say that CMS is still the best “default” option to go with. G1 throughput is still so much worse that the improved latency is usually not worth it. If you enjoyed the content, consider subscribe to either our RSS feed or Twitter stream – we continue to publish on different performance optimization topics.

eclipse is very workspace centric: it only knows and deals with files in the workspace. so it is easy to compare and merge files present in the workspace: i select both files/folders and compare them with each other: compare with each other but what if the files and folders are not in the workspace? hidden option to compare external files as outlined in this post , it needs a special plugin to search for files outside the eclipse workspace. and doing a file or folder compare outside of the workspace requires a trick as shown in this post . thanks to a tip from john there is another (hidden) way in eclipse to compare external files keyboard shortcut the trick is described in this article and requires a keyboard shortcut assigned. select the menu window > preferences > general > keys and assign a shortcut key for ‘ compare with other resource ‘: compare with other resource key binding (i’m using ctrl+shift+home above). comparing to compare, i have first to select a file, folder or project, then i press my shortcut. then the following dialog shows up (with the selection as default): select resource to compare if the dialog does not show up, then i probably have not selected a file or folder in the eclipse project view. now i can select the external files or folders to compare with: selected external files with this, i can now compare and merge my files and folders with the eclipse compare view: compare view in eclipse i can select two files/folders and then press the shortcut, and it will populate the search dialog values. drag & drop that compare dialog has a nice feature: i can drag&drop files and folders too: c:\programdata\processor expert\cwmcu_pe5_00\examples\frdm-kl25z\frdm-kl25z_rnet\sources summary with ‘ compare with other resource ‘ i have a way to compare files/folders, and i’m not limited to the workspace files. the only disadvantage is that i need to assign a shortcut for it first. beside of that: yet another hidden treasure in eclipse . happy comparing