If you need to process large database result sets from Java, you can opt for JDBC to give you the low level control required. On the other hand, if you are already using an ORM in your application, falling back to JDBC might imply some extra pain. You would be losing features such as optimistic locking, caching, automatic fetching when navigating the domain model and so forth. Fortunately most ORMs, like Hibernate, have some options to help you with that. While these techniques are not new, there are a couple of possibilities to choose from. A simplified example; let's assume we have a table (mapped to class "DemoEntity") with 100.000 records. Each record consists of a single column (mapped to the property "property" in DemoEntity) holding some random alphanumerical data of about ~2KB. The JVM is ran with -Xmx250m. Let's assume that 250MB is the overall maximum memory that can be assigned to the JVM on our system. Your job is to read all records currently in the table, doing some not further specified processing, and finally store the result. We'll assume that the entities resulting from our bulk operation are not modified. To start we'll try the obvious first, performing a query to simply retrieve all data: new TransactionTemplate(txManager).execute(new TransactionCallback() { @Override public Void doInTransaction(TransactionStatus status) { Session session = sessionFactory.getCurrentSession(); List demoEntitities = (List) session.createQuery("from DemoEntity").list(); for(DemoEntity demoEntity : demoEntitities){ //Process and write result } return null; } }); After a couple of seconds: Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit exceeded Clearly this won't cut it. To fix this we will be switching to Hibernate scrollable result sets as probably most developers are aware of. The above example instructs hibernate to execute the query, map the entire results to entities and return them. When using scrollable result sets records are transformed to entities one at a time: new TransactionTemplate(txManager).execute(new TransactionCallback() { @Override public Void doInTransaction(TransactionStatus status) { Session session = sessionFactory.getCurrentSession(); ScrollableResults scrollableResults = session.createQuery("from DemoEntity").scroll(ScrollMode.FORWARD_ONLY); int count = 0; while (scrollableResults.next()) { if (++count > 0 && count % 100 == 0) { System.out.println("Fetched " + count + " entities"); } DemoEntity demoEntity = (DemoEntity) scrollableResults.get()[0]; //Process and write result } return null; } }); After running this we get: ... Fetched 49800 entities Fetched 49900 entities Fetched 50000 entities Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit exceeded Although we are using a scrollable result set, every returned object is an attached object and becomes part of the persistence context (aka session). The result is actually the same as our first example in which we used "session.createQuery("from DemoEntity").list()". However, with that approach we had no control; everything happens behind the scenes and you get a list back with all the data if hibernate has done its job. using a scrollable result set on the other hand gives us a hook into the retrieval process and allows us to free memory up when needed. As we have seen it does not free up memory automatically, you have to instruct Hibernate to actually do it. Following options exist: Evicting the object from the persistent context after processing it Clearing the entire session every now and then We will opt for the first. In the above example under line 13 (//Process and write result) we'll add: session.evict(demoEntity); Important: If you were to perform any modification to the entity (or entities it has associations with that are cascade evicted alongside), make sure to flush the session PRIOR evicting or clearing, otherwise queries hold back because of Hibernate's write behind will not be sent to the database Evicting or clearing does not remove the entities from second level cache. If you enabled second level cache and are using it and you want to remove them as well use the desired sessionFactory.getCache().evictXxx() method From the moment you evict an entity it will be no longer attached (no longer associated with a session). Any modification done to the entity at that stage will no longer be reflected to the database automatically. If you are using lazy loading, accessing any property that was not loaded prior the eviction will yield the famous org.hibernate.LazyInitializationException. So basically, make sure the processing for that entity is done (or it is at least initialized for further needs) before you evict or clear After we run the application again, we see that it now successfully executes: ... Fetched 99800 entities Fetched 99900 entities Fetched 100000 entities Btw; you can also set the query read-only allowing hibernate to perform some extra optimizations: ScrollableResults scrollableResults = session.createQuery("from DemoEntity").setReadOnly(true).scroll(ScrollMode.FORWARD_ONLY); Doing this only gives a very marginal difference in memory usage, in this specific test setup it enabled us to read about 300 entities extra with the given amount of memory. Personally I would not use this feature merely for memory optimizations alone but only if it suits in your overall immutability strategy. With hibernate you have different options to make entities read-only: on the entity itself, the overall session read-only and so forth. Setting read only false on the query individually is probably the least preferred approach. (eg. entities loaded in the session before will remain unaffected, possibly modifiable. Lazy associations will be loaded modifiable even if the root objects returned by the query are read only). Ok, we were able to process our 100.000 records, life is good. But as it turns out Hibernate has another another option for bulk operations: the stateless session. You can obtain a scrollable result set from a stateless session the same way as from a normal session. A stateless session lies directly above JDBC. Hibernate will run in nearly "all features disabled" mode. This means no persistent context, no 2nd level caching, no dirty detection, no lazy loading, basically no nothing. From the javadoc: /** * A command-oriented API for performing bulk operations against a database. * A stateless session does not implement a first-level cache nor interact with any * second-level cache, nor does it implement transactional write-behind or automatic * dirty checking, nor do operations cascade to associated instances. Collections are * ignored by a stateless session. Operations performed via a stateless session bypass * Hibernate's event model and interceptors. Stateless sessions are vulnerable to data * aliasing effects, due to the lack of a first-level cache. For certain kinds of * transactions, a stateless session may perform slightly faster than a stateful session. * * @author Gavin King */ The only thing it does is transforming records to objects. This might be an appealing alternative because it helps you getting rid of that manual evicting/flushing: new TransactionTemplate(txManager).execute(new TransactionCallback() { @Override public Void doInTransaction(TransactionStatus status) { sessionFactory.getCurrentSession().doWork(new Work() { @Override public void execute(Connection connection) throws SQLException { StatelessSession statelessSession = sessionFactory.openStatelessSession(connection); try { ScrollableResults scrollableResults = statelessSession.createQuery("from DemoEntity").scroll(ScrollMode.FORWARD_ONLY); int count = 0; while (scrollableResults.next()) { if (++count > 0 && count % 100 == 0) { System.out.println("Fetched " + count + " entities"); } DemoEntity demoEntity = (DemoEntity) scrollableResults.get()[0]; //Process and write result } } finally { statelessSession.close(); } } }); return null; } }); Besides the fact that the stateless session has the most optimal memory usage, using the it has some side effects. You might have noticed that we are opening a stateless session and closing it explicitly: there is no sessionFactory.getCurrentStatelessSession() nor (at the time of writing) any Spring integration for managing the stateless session.Opening a stateless session allocates a new java.sql.Connection by default (if you use openStatelessSession()) to perform its work and therefore indirectly spawns a second transaction. You can mitigate these side effects by using the Hibernate work API as in the example which supplies the current Connection and pass it along to openStatelessSession(Connection connection). Closing the session in the finally has no impact on the physical connection since that is captured by the Spring infrastructure: only the logical connection handle is closed and a new logical connection handle was created when opening the stateless session. Also note that you have to deal with closing the stateless session yourself and that the above example is only good for read-only operations. From the moment you are going to modify using the stateless session there are some more caveats. As said before, hibernate runs in "all feature disabled" mode and as a direct consequence entities are returned in detached state. For each entity you modify, you'll have to call: statelessSession.update(entity) explicitly. First I tried this for modifying an entity: new TransactionTemplate(txManager).execute(new TransactionCallback() { @Override public Void doInTransaction(TransactionStatus status) { sessionFactory.getCurrentSession().doWork(new Work() { @Override public void execute(Connection connection) throws SQLException { StatelessSession statelessSession = sessionFactory.openStatelessSession(connection); try { DemoEntity demoEntity = (DemoEntity) statelessSession.createQuery("from DemoEntity where id = 1").uniqueResult(); demoEntity.setProperty("test"); statelessSession.update(demoEntity); } finally { statelessSession.close(); } } }); return null; } }); The idea is that we open a stateless session with the existing database Connection. As the StatelessSession javadoc indicates that no write behind occurs, I was convinced that each statement performed by the stateless session would be sent directly to the database. Eventually when the transaction (started by the TransactionTemplate) would be committed the results would become visible in the database. However, hibernate does BATCH statements using a stateless session. I'm not 100% sure what the difference is between batching and write behind, but the result is the same and thus contra dictionary with the javadoc as statements are queued and flushed at a later time. So, if you don't do anything special, statements that are batched will not be flushed and this is what happened in my case: the "statelessSession.update(demoEntity);" was batched and never flushed. One way to force the flush is to use the hibernate transaction API: StatelessSession statelessSession = sessionFactory.openStatelessSession(); statelessSession.beginTransaction(); ... statelessSession.getTransaction().commit(); ... While this works, you probably don't want to start controlling your transactions programatically just because you are using a stateless session. Also, doing this we are again running our stateless session work in a second transaction scenario since we didn't pass along our Connection and thus a new database connection will be acquired. The reason we can't pass along the outer Connection is because if we commit the inner transaction (the "stateless session transaction") and it would be using the same connection as the outer transaction (started by the TransactionTemplate) it would break the outer transaction atomicity as statements from the outer transaction sent to database would be committed along with the inner transaction. So not passing along the connections means opening a new connection and thus creating a second transaction. A better alternative would be just to trigger Hibernate to flush the stateless session. However, statelessSession has no "flush" method to manually trigger a flush. A solution here is to depend a bit on the Hibernate internal API. This solution makes the manual transaction handling and the second transaction obsolete: all statements become part of our (one and only) outer transaction: StatelessSession statelessSession = sessionFactory.openStatelessSession(connection); try { DemoEntity demoEntity = (DemoEntity) statelessSession.createQuery("from DemoEntity where id = 1").uniqueResult(); demoEntity.setProperty("test"); statelessSession.update(demoEntity); ((TransactionContext) statelessSession).managedFlush(); } finally { statelessSession.close(); } Fortunately there is an even better solution very recently posted on the Spring jira: https://jira.springsource.org/browse/SPR-2495 This is not yet part of Spring, but the factory bean implementation is pretty straight forward: StatelessSessionFactoryBean.java when using this you could simple inject the StatelessSession: @Autowired private StatelessSession statelessSession; It will inject a stateless session proxy which is equivalent to the way the normal "current" session works (with the minor difference that you inject a SessionFactory and need to obtain the currentSession each time). When the proxy is invoked it will lookup the stateless session bound to the running transaction. If none exists already it will create one with the same connection as the normal session (like we did in the example) and register a custom transaction synchronization for the stateless session. When the transaction is committed the stateless session is flushed thanks to the synchronization and finally closed. Using this you can inject the stateless session directly and use it as a current session (or the same way as you would inject a JPA PeristentContext for that matter). This relieves you from dealing with the opening and closing of the stateless session and having to deal with one way or the other to make it flush. The implementation is JPA aimed, but the JPA part is limited to obtaining the physical connection in obtainPhysicalConnection(). You can easily leave out the EntityManagerFactory and get the physical connection directly from the Hibernate session. Very careful conclusion: it is clear that the best approach will depend on your situation. If you use the normal session you will have to deal with eviction yourself when reading or persisting entities. Besides the fact you have to do this manually, it might also impact further use of the session if you have a mixed transaction; you both perform 'bulk' and 'normal' operations in the same transaction. If you continue with the normal operations you will have detached entities in your session which might lead to unexpected results (as dirty detection will no longer work and so forth). On the other hand you will still have the major hibernate benefits (as long as the entity isn't evicted) such as lazy loading, caching, dirty detection and the likes. Using the stateless session at the time of writing requires some extra attention on managing it (opening, closing and flushing) which can also be error prone. In the assumption you can proceed with the proposed factory bean, you have a very bare bone session which is separately from your normal session but still participating in the same transaction. With this you have a powerful tool to perform bulk operations without having to think about memory management. The downside is that you don't have any other hibernate functionality available.

what is codenvy exactly? well, their website states: codenvy is a cloud environment for coding, building, and debugging apps. basically, it’s an ide in the cloud (“ide as a service?”) accessible by all the major browsers . it started out as an additional feature to the exo platform in early 2009 and gained a lot of traction after the first paas (openshift) and git integration was added mid-2011. codenvy targets me as a (java) software developer to run and debug applications in their hosted cloud ide, while being able to share and collaborate during development and finally publish to a repository – e.g. git – or a number of deployment platforms – e.g. amazon, openshift or google app engine. i first encountered their booth at javaone last september, but they couldn’t demo their product right there on the spot over the wifi, because their on-line demo workspace never finished loading well i got the t-shirt instead then, but now’s the time to see what codenvy has in store as a cloud ide. signing up signing up took 3 seconds. all you have to do is go to codenvy.com , use the “sign up” button, choose an email address and a name for your workspace , confirm the email they’ll send you and you’re done. the “workspace” holds all your projects and is part of the url codenvy will create for you, like “ https://codenvy.com/ide/ . although not very clear during the registration process – which of course nowadays is usually minimalistic as can be – it seems that i’ve signed up for codenvy’s free community plan , which gives me an unlimited number of public projects. you can even start coding without registration. after confirming the registration mail, i’m in. finally i’ll end up in the browser where your (empty) workspace has been opened. empty workspace a few options a possible for here on, as seen in the figure above: create a new project from scratch – generate an empty project from predefined project types import from github – import projects from your github account clone a git repository – create a new project from any public git reposiroty browse documentation invite people – get team members on board support – questions, feedback and troubleshooting let’s… create a new project from scratch this option allows you to name the new project – e.g. “myproject”, choose a technology and a paas . the technology is a defined set of languages of frameworks to develop with. available technologies at the moment the technologies are: java jar java war java spring javascript ruby on rails python php node.js android maven multi-module at the time of writing java 1.6 is supported. available paas at the moment the available platforms are: amazon webservices (aws) elastic beanstalk savvis cloud appfrog cloudbees google app engine (gae) heroku manymo android emulator red hat’s openshift none depending on the choice of technology, or or more paas options become available. a single jar can not be deployed onto any of the platforms, leaving only the option “none” available. a java web application (war) can be deployed onto any number of platforms, except heroku and manymo. node.js can only be deployed to openshift. creating a simple jar project after having selected a jar (and no platform) one can select a project template . e.g. if webapplication (war) would have been selected, codenvy would present project templates, such as google app engine java project illustrating simple examples that use the search api , java web project with datasource usage or a demonstration of accessing amazon s3 buckets using the java sdk . the jar technology has only one project: simple jar project . after having finished the wizard, our jar project has been created in our workspace. we’ll see two views of our project: a project explorer and a package explorer. project- and package explorer what we can see is that our jar project has been given a maven pom.xml with the following content: view source print ? 01. < project xmlns = " http://maven.apache.org/pom/4.0.0 " xmlns:xsi = " http://www.w3.org/2001/xmlschema-instance " 02. xsi:schemalocation = " http://maven.apache.org/pom/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd " > 03. < modelversion >4.0.0 04. < groupid >com.codenvy.workspaceyug8g52wjwb5im13 05. < artifactid >testjarproject 06. < version >1.0-snapshot 07. < packaging >jar 08. 09. < name >sample-lib 10. 11. < properties > 12. < project.build.sourceencoding >utf-8 13. 14. 15. < dependencies > 16. < dependency > 17. < groupid >junit 18. < artifactid >junit 19. < version >3.8.1 20. < scope >test 21. 22. 23. we have a generated group id com.codenvy.workspaceyug8g52wjwb5im13 , our own artifact id and the junit dependency, which is a decent choice for many java developers use it as a testing framework. the source encoding has already been set to utf-8, which is also a sensible choice. as a convenience we’ve also been given a hello.sayhello class, so we know we’re actually in a java project say hello file & project management so what about the browser-based editor we’re working in? on top we’re seeing a few menu’s, like file, project, edit, view, run, git, paas, window, share and help . i’ll be highlighting a few. file- and project menu the file menu allows to creating folders , packages and various kind of filetypes , such as text, xml (1.0 at time of writing) , html (4.1) , css (2.0), java classes and jsp’s (2.1). although i’m in a jar project, i am still also able to create here e.g. ruby, php or python files. a very convenient feature is to upload existing files to the workspace, either separately or in zip archives. i’ve tried dropping a file onto the package explorer from the file system, but the browser (in this case, chrome) tries to open it instead the project menu allows to create new projects, either by launching the create project wizard again, but also allows for importing from github . in order to clone a repository, you’ll have to authorize codenvy to access github.com to be able to import a project. after having authorized github, codeenvy presents me with a list of projects to choose from. after having imported all necessary stuff, it somehow needs to know what kind of project i’m importing. selecting a file type after importing a project from github the project i imported didn’t give codenvy any clues as to what kind of project it is (which is right since i only had a readme.md in it), so it lists a few options to choose from. i chose the maven multi-module type after which the output window shows: git@github.com:tvinke/examples.git was successfully cloned. [info] project type updated. if you’d have a pom.xml in the root of your project, it would immediately recognize it a s a maven project. apart from going through the project > import from github option, you can also go directly to the git menu, and choose clone repository . this allows you to manually enter the remote repository uri, wanted project name and the remote name (e.g. “origin”). cloning a repository one you have pulled in a git project, the git menu allows all kinds of common operations, such as adding and removing files, committing, pushing, pulling and much more. git menu the ssh keys can be found under menu window > preferences where you can view the github.com entry, where one can view the details or delete it. also a new key can be either generated or uploaded here. sharing the project one of the unique selling points of codenvy are their collaboration possibilities which come along with any project. you can: invite other developers with read-only rights or full read-write rights to your workspace and every project in it.when you’re pair-programming like this, or co-editing a file with a colleague, you can also send each other code pointers – small shortcuts to code lines. use factories to create temporary workspaces , through cloning, off one source project (“factory”) and represent the cloning mechanism as a url which can be given to other developers. a use case might be to get a colleague quickly started on a project by providing a fully working development environment.there’s a lot more about creating factories in the docs (such as through rest), but the nice thing is that once you have a factory url, you can embed it as a button, send it through email of publish it somewhere for others! a factory url to load up e.g. their twitter bootstrap sample – as they use on their website themselves – looks like: https://codenvy.com/factory?v=1.0&pname=sample-twitterbootstrap&wname=codenvy-factories&vcs=git&vcsurl=http%3a%2f%2fcodenvy.com%2fgit%2f04%2f0f%2f7f%2fworkspacegcpv6cdxy1q34n1i%2fsample-twitterbootstrap&idcommit=c1443ecea63471f5797f172c081cd802bac6e6b0&action=openproject&ptype=javascript conclusion applications are run in the cloud nowadays, so why not create them there too? codenvy brings some interesting features, such as being able to instantly provision workspaces (through factory urls) and share projects in real-time. it supports common operations with projects, files and version control. with a slew of languages and platforms and as an ide being always accessible through the internet, it could lower the barrier to actually code anytime and anywhere. in a future post i will try and see whether or not it can actually replace my conventional desktop ide for java development.

I first came across Reactive Extensions about 4 years ago on Matthew Podwysocki’s blog but then haven’t heard much about it until I saw Matthew give a talk at Code Mesh a few weeks ago. It seems to have grown in popularity recently and I noticed that’s there’s now a Java version called RxJavawritten by Netflix. I thought I’d give it a try by changing some code I wrote while exploring cypher’s MERGE function to expose an Observable instead of Futures. To recap, we have 50 threads and we do 100 iterations where we create random (user, event) pairs. We create a maximum of 10 users and 50 events and the goal is to concurrently send requests for the same pairs. In the example of my other post I was throwing away the result of each query whereas here I returned the result back so I had something to subscribe to. The outline of the code looks like this: public class MergeTimeRx { public static void main( final String[] args ) throws InterruptedException, IOException { String pathToDb = "/tmp/foo"; FileUtils.deleteRecursively( new File( pathToDb ) ); GraphDatabaseService db = new GraphDatabaseFactory().newEmbeddedDatabase( pathToDb ); final ExecutionEngine engine = new ExecutionEngine( db ); int numberOfThreads = 50; int numberOfUsers = 10; int numberOfEvents = 50; int iterations = 100; Observable events = processEvents( engine, numberOfUsers, numberOfEvents, numberOfThreads, iterations ); events.subscribe( new Action1() { @Override public void call( ExecutionResult result ) { for ( Map row : result ) { } } } ); .... } } The nice thing about using RxJava is that there’s no mention of how we got our collection of ExecutionResults, it’s not important. We just have a stream of them and by calling the subscribe function on the Observablewe’ll be informed whenever another one is made available. Most of the examples I found show how to generate events from a single thread but I wanted to use a thread pool so that I could fire off lots of requests at the same time. The processEvents method ended up looking like this: private static Observable processEvents( final ExecutionEngine engine, final int numberOfUsers, final int numberOfEvents, final int numberOfThreads, final int iterations ) { final Random random = new Random(); final List userIds = generateIds( numberOfUsers ); final List eventIds = generateIds( numberOfEvents ); return Observable.create( new Observable.OnSubscribeFunc() { @Override public Subscription onSubscribe( final Observer observer ) { final ExecutorService executor = Executors.newFixedThreadPool( numberOfThreads ); List> jobs = new ArrayList<>(); for ( int i = 0; i < iterations; i++ ) { Future job = executor.submit( new Callable() { @Override public ExecutionResult call() { Integer userId = userIds.get( random.nextInt( numberOfUsers ) ); Integer eventId = eventIds.get( random.nextInt( numberOfEvents ) ); return engine.execute( "MERGE (u:User {id: {userId})\n" + "MERGE (e:Event {id: {eventId})\n" + "MERGE (u)-[:HAS_EVENT]->(e)\n" + "RETURN u, e", MapUtil.map( "userId", userId, "eventId", eventId ) ); } } ); jobs.add( job ); } for ( Future future : jobs ) { try { observer.onNext( future.get() ); } catch ( InterruptedException | ExecutionException ignored ) { } } observer.onCompleted(); executor.shutdown(); return Subscriptions.empty(); } } ); } I’m not sure if that’s the correct way of using Observables so please let me know in the comments if I’ve got it wrong. I wasn’t sure what the proper way of handling errors was. I initially had a call to observer#onError in the catch block but that means that no further events are produced which wasn’t what I wanted. The code is available as a gist if you want to play around with it. I added the following dependency to get the RxJava library: com.netflix.rxjava rxjava-core 0.15.1

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.

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("john.m.doe@gmail.com")); 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.

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; }

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

One of the most welcoming features of the new Mule Studio 3.4 is the Maven support. I was very keen to try out this new feature. I grabbed one of the projects I was working on, and imported it into Mule Studio through File -> Import -> Existing Maven Projects. Everything is as good as it gets. However I had one issue. Every time I wanted to run my flows as Mule Application, Mule Studio was doing a whole build of my project, including running the tests. Since this was a large project, I wanted to avoid running all the tests every time I needed to start the application. So I started by adding -DskipTests=true as a VM argument in the run configuration, but this did not work. My second attempt was to add the MAVEN_OPTS environmental variable and set it to -DskipTests=true, so back to the Mule Studio run configuration, clicked the Environment Variables table, set it there. Again, unfortunately this did not work. Worry not, there is a way. The third and final attempt was to check if the Mule Studio Maven support provides its own configuration, and luckily it does. So to fix it, Window -> Preferences (or MuleStudio -> Preference if you are using a MAC), navigate to Mule Studio on the left hand panel, expand that, and choose “Maven Settings”. In the configuration panel on the right hand side, you can type -DskipTests=true in the text box labelled as “MAVEN_OPTS environment variable”. Running my flow now does not run the tests. One small tip, -DskipTests=true and -Dmaven.test.skip=true are slightly different. If you go for the second option, Maven won’t even build your test classes, hence if you try to run any JUnit test from Mule Studio after a build, it will fail with ClassNotFoundException. Therefore I recommend the first option.

The lack of Base64 encoding API in Java is, in my opinion, by far one of the most annoying holes in the libraries. Finally Java 8 includes a decent API for it in the java.util package. Here is a short introduction of this new API (apparently it has a little more than the regular encode/decode API). The java.util.Base64 Utility Class The entry point to Java 8's Base64 support is the java.util.Base64 class. It provides a set of static factory methods used to obtain one of the three Base64 encodes and decoders: Basic URL MIME Basic Encoding The standard encoding we all think about when we deal with Base64: no line feeds are added to the output and the output is mapped to characters in the Base64 Alphabet: A-Za-z0-9+/ (we see in a minute why is it important). Here is a sample usage: // Encode String asB64 = Base64.getEncoder().encodeToString("some string".getBytes("utf-8")); System.out.println(asB64); // Output will be: c29tZSBzdHJpbmc= // Decode byte[] asBytes = Base64.getDecoder().decode("c29tZSBzdHJpbmc="); System.out.println(new String(asBytes, "utf-8")); // And the output is: some string It can't get simpler than that and, unlike in earlier versions of Java, there is any need for external dependencies (commons-codec), Sun classes (sun.misc.BASE64Decoder) or JAXB's DatatypeConverter (Java 6 and above). However it gets even better. URL Encoding Most of us are used to get annoyed when we have to encode something to be later included in a URL or as a filename - the problem is that the Base64 Alphabet contains meaningful characters in both URIs and filesystems (most specifically the forward slash (/)). The second type of encoder uses the alternative "URL and Filename safe" Alphabet which includes -_ (minus and underline) instead of +/. See the following example: String basicEncoded = Base64.getEncoder().encodeToString("subjects?abcd".getBytes("utf-8")); System.out.println("Using Basic Alphabet: " + basicEncoded); String urlEncoded = Base64.getUrlEncoder().encodeToString("subjects?abcd".getBytes("utf-8")); System.out.println("Using URL Alphabet: " + urlEncoded); The output will be: Using Basic Alphabet: c3ViamVjdHM/YWJjZA== Using URL Alphabet: c3ViamVjdHM_YWJjZA== The sample above illustrates some content which if encoded using a basic encoder will result in a string containing a forward slash while when using a URL safe encoder the output will include an underscore instead (URL Safe encoding is described in clause 5 of the RFC) MIME Encoding The MIME encoder generates a Base64 encoded output using the Basic Alphabet but in a MIME friendly format: each line of the output is no longer than 76 characters and ends with a carriage return followed by a linefeed (\r\n). The following example generates a block of text (this is needed just to make sure we have enough 'body' to encode into more than 76 characters) and encodes it using the MIME encoder: StringBuilder sb = new StringBuilder(); for (int t = 0; t < 10; ++t) { sb.append(UUID.randomUUID().toString()); } byte[] toEncode = sb.toString().getBytes("utf-8"); String mimeEncoded = Base64.getMimeEncoder().encodeToString(toEncode); System.out.println(mimeEncoded); The output: NDU5ZTFkNDEtMDVlNy00MDFiLTk3YjgtMWRlMmRkMWEzMzc5YTJkZmEzY2YtM2Y2My00Y2Q4LTk5 ZmYtMTU1NzY0MWM5Zjk4ODA5ZjVjOGUtOGMxNi00ZmVjLTgyZjctNmVjYTU5MTAxZWUyNjQ1MjJj NDMtYzA0MC00MjExLTk0NWMtYmFiZGRlNDk5OTZhMDMxZGE5ZTYtZWVhYS00OGFmLTlhMjgtMDM1 ZjAyY2QxNDUyOWZiMjI3NDctNmI3OC00YjgyLThiZGQtM2MyY2E3ZGNjYmIxOTQ1MDVkOGQtMzIz Yi00MDg0LWE0ZmItYzkwMGEzNDUxZTIwOTllZTJiYjctMWI3MS00YmQzLTgyYjUtZGRmYmYxNDA4 Mjg3YTMxZjMxZmMtYTdmYy00YzMyLTkyNzktZTc2ZDc5ZWU4N2M5ZDU1NmQ4NWYtMDkwOC00YjIy LWIwYWItMzJiYmZmM2M0OTBm Wrapping All encoders and decoders created by the java.util.Base64 class support streams wrapping - this is a very elegant construct - both coding and efficiency wise (since no redundant buffering are needed) - to stream in and out buffers via encoders and decoders. The sample bellow illustrates how a FileOutputStream can be wrapped with an encoder and a FileInputStream is wrapped with a decoder - in both cases there is no need to buffer the content: public void wrapping() throws IOException { String src = "This is the content of any resource read from somewhere" + " into a stream. This can be text, image, video or any other stream."; // An encoder wraps an OutputStream. The content of /tmp/buff-base64.txt will be the // Base64 encoded form of src. try (OutputStream os = Base64.getEncoder().wrap(new FileOutputStream("/tmp/buff-base64.txt"))) { os.write(src.getBytes("utf-8")); } // The section bellow illustrates a wrapping of an InputStream and decoding it as the stream // is being consumed. There is no need to buffer the content of the file just for decoding it. try (InputStream is = Base64.getDecoder().wrap(new FileInputStream("/tmp/buff-base64.txt"))) { int len; byte[] bytes = new byte[100]; while ((len = is.read(bytes)) != -1) { System.out.print(new String(bytes, 0, len, "utf-8")); } } }

It is a very rare usecase, but sometimes you need it. How to embed Maven in your application, so that you can programatically run goals? Short answer is: it's tricky. I dabbled into the matter for my java webapp automatic syncing project, and at some point I decided not to embed it. Ultimately, I used a library that does what I needed, but anyway, here are the steps and tools that might be helpful. What you usually need the embedded maven for, is to execute some goals on a maven project. There are two scenarios. The first one is, if you are running inside the maven container, i.e. you are writing a mojo/plugin. Then it's fairly easy, because you have everything managed by the already-initialized plexus container. In that case you can use the mojo-executor. Easy to use, but expects a "project", "pluginManager" and "session", which you can't easily obtain. The second scenario is completely embedded maven. There is a library that does what I needed it to do (thanks to MariuszS for pointing it out) - it's Maven Embedder. Its usage is described in this SO question. Use both the first and the second answer. Before finding that library, I tried two more libraries: the jenkins maven embedded and the Maven Invoker. The problem in both libraries is: they need a maven home. That is, the path to where a maven installation resides. Which is kind of contrary to the idea of "embedded" maven. If the Maven Embedder suits you, you can stop reading. However, there might be cases where the Maven Embedder might not be what you are looking for. In that case, you should use one of the two aforementioned libraries. So, how to find and set a maven home? Ask the user to specify it. Not too much of a hassle, probably Use M2_HOME. One of the libraries uses that by default, but the problem is it might not be set. I don't usually set it, for example. If it is not, you can fallback to the previous approach Scan the entire file system for a maven installation - sounds ok, and it can be done only once, and then stored in some entry. The problem is - there might not be a maven installation. Even if it's a developer's machine - IDEs (Eclipse, at least) have an "embedded" maven. And while it probably stores it somewhere internally in the same format a manual installation would, it may change it's path or structure depending on the version. You can, of course, re-scan the file tree every once in a while to find such an installation Download Maven programatically yourself. Then you can be sure where it is located and that it will always be located there in the same format. The problem here is version mismatch - the user might be using another version of maven. Making the version configurable is an option. All of these work in some cases, and don't work in others. So, in order of preference: 1. make sure you really need to embed maven 2. use the Maven Embedder 3. use another option with its considerations

we all know junit test-classes can be parameterized , which means that for a given set of test-elements, the test class is instantiated a few times, but using constructors for that isn’t always what you want. i’ve taken the stringsorttest from this blog as an example. @runwith(parameterized.class) public class stringsorttest { @parameters public static collection data() { return arrays.aslist(new object[][] { { "abc", "abc" }, { "cba", "abc" }, }); } private final string input; private final string expected; public stringsorttest(final string input, final string expected) { this.input = input; this.expected = expected; } @test public void testsort() { assertequals(expected, mysortmethod(input)); } } this is pretty darn obnoxious sometimes if you have multiple sets of data for various tests, which all go through the constructor, which would force you to write multiple test classes. testng solves this better by allowing you to provide separate data sets to individual test methods using the @dataprovider annotation . but don’t worry, now you can achieve the same with the junit-dataprovider , available on github. pull in the dependency with e.g. maven. com.tngtech.java junit-dataprovider 1.5.0 test the above example now could be rewritten as: @runwith(dataproviderrunner.class) public class stringsorttest { @dataprovider public static object[][] data() { return new object[][] { { "abc", "abc" }, { "cba", "abc" }, }; } @test @usedataprovider("data") public void testsort(final string input, final string expected) { assertequals(expected, mysortmethod(input)); } } you’ll see: no constructor. in this example it doesn’t have many benefits, except maybe for less boiler-plate, but now you can create as many @dataprovider-annotated methods which are fed directly to your @usedataprovider-annotated testmethod(s). sidenote for eclipse: if you’re using that ide, the junit plugin is unable to map the names of the passed/failed testmethods to the ones in the testclass correctly. if a method fails, you’ll have to find it back manually in the testclass (or vote for the patch andreas smidt created to get this fixed in eclipse). in the mean time, if you’re stuck with junit and you’d love to use this feature you’re so accustomed to using with testng, go ahead and try the junit-dataprovider now.

This blog is more of a tutorial where we describe the development of a simple data access module, more for fun and learning than anything else. All code can be found here for those who don’t want to type along: https://github.com/ricston-git/tododb As a heads-up, we will be covering the following: Using Groovy in a Maven project within Eclipse Using Groovy to interact with our database Testing our code using the Spock framework We include Spring in our tests with ContextConfiguration A good place to start is to write a pom file as shown here. The only dependencies we want packaged with this artifact are groovy-all and commons-lang. The others are either going to be provided by Tomcat or are only used during testing (hence the scope tags in the pom). For example, we would put the jar with PostgreSQL driver in Tomcat’s lib, and tomcat-jdbc and tomcat-dbcp are already there. (Note: regarding the postgre jar, we would also have to do some minor configuration in Tomcat to define a DataSource which we can get in our app through JNDI – but that’s beyond the scope of this blog. See here for more info). Testing-wise, I’m depending on spring-test, spock-core, and spock-spring (the latter is to get spock to work with spring-test). Another significant addition in the pom is the maven-compiler-plugin. I have tried to get gmaven to work with Groovy in Eclipse, but I have found the maven-compiler-plugin to be a lot easier to work with. With your pom in an empty directory, go ahead and mkdir -p src/main/groovy src/main/java src/test/groovy src/test/java src/main/resources src/test/resources. This gives us a directory structure according to the Maven convention. Now you can go ahead and import the project as a Maven project in Eclipse (install the m2e plugin if you don’t already have it). It is important that you do not mvn eclipse:eclipse in your project. The .classpath it generates will conflict with your m2e plugin and (at least in my case), when you update your pom.xml the plugin will not update your dependencies inside Eclipse. So just import as a maven project once you have your pom.xml and directory structure set up. Okay, so our tests are going to be integration tests, actually using a PostgreSQL database. Since that’s the case, lets set up our database with some data. First go ahead and create a tododbtest database which will only be used for testing purposes. Next, put the following files in your src/test/resources: Note, fill in your username/password: DROP TABLE IF EXISTS todouser CASCADE; CREATE TABLE todouser ( id SERIAL, email varchar(80) UNIQUE NOT NULL, password varchar(80), registered boolean DEFAULT FALSE, confirmationCode varchar(280), CONSTRAINT todouser_pkey PRIMARY KEY (id) ); insert into todouser (email, password, registered, confirmationCode) values ('abc.j123@gmail.com', 'abc123', FALSE, 'abcdefg') insert into todouser (email, password, registered, confirmationCode) values ('def.123@gmail.com', 'pass1516', FALSE, '123456') insert into todouser (email, password, registered, confirmationCode) values ('anon@gmail.com', 'anon', FALSE, 'codeA') insert into todouser (email, password, registered, confirmationCode) values ('anon2@gmail.com', 'anon2', FALSE, 'codeB') Basically, testContext.xml is what we’ll be configuring our test’s context with. The sub-division into datasource.xml and initdb.xml may be a little too much for this example… but changes are usually easier that way. The gist is that we configure our data source in datasource.xml (this is what we will be injecting in our tests), and the initdb.xml will run the schema.sql and test-data.sql to create our table and populate it with data. So lets create our test, or should I say, our specification. Spock is specification framework that allows us to write more descriptive tests. In general, it makes our tests easier to read and understand, and since we’ll be using Groovy, we might as well make use of the extra readability Spock gives us. package com.ricston.blog.sample.model.spec; import javax.sql.DataSource import org.springframework.beans.factory.annotation.Autowired import org.springframework.test.annotation.DirtiesContext import org.springframework.test.annotation.DirtiesContext.ClassMode import org.springframework.test.context.ContextConfiguration import spock.lang.Specification import com.ricston.blog.sample.model.data.TodoUser import com.ricston.blog.sample.model.dao.postgre.PostgreTodoUserDAO // because it supplies a new application context after each test, the initialize-database in initdb.xml is // executed for each test/specification @DirtiesContext(classMode=ClassMode.AFTER_EACH_TEST_METHOD) @ContextConfiguration('classpath:testContext.xml') class PostgreTodoUserDAOSpec extends Specification { @Autowired DataSource dataSource PostgreTodoUserDAO postgreTodoUserDAO def setup() { postgreTodoUserDAO = new PostgreTodoUserDAO(dataSource) } def "findTodoUserByEmail when user exists in db"() { given: "a db populated with a TodoUser with email anon@gmail.com and the password given below" String email = 'anon@gmail.com' String password = 'anon' when: "searching for a TodoUser with that email" TodoUser user = postgreTodoUserDAO.findTodoUserByEmail email then: "the row is found such that the user returned by findTodoUserByEmail has the correct password" user.password == password } } One specification is enough for now, just to make sure that all the moving parts are working nicely together. The specification itself is easy enough to understand. We’re just exercising the findTodoUserByEmail method of PostgreTodoUserDAO – which we will be writing soon. Using the ContextConfiguration from Spring Test we are able to inject beans defined in our context (the dataSource in our case) through the use of annotations. This keeps our tests short and makes them easier to modify later on. Additionally, note the use of DirtiesContext. Basically, after each specification is executed, we cannot rely on the state of the database remaining intact. I am using DirtiesContext to get a new Spring context for each specification run. That way, the table creation and test data insertions happen all over again for each specification we run. Before we can run our specification, we need to create at least the following two classes used in the spec: TodoUser and PostgreTodoUserDAO package com.sample.data import org.apache.commons.lang.builder.ToStringBuilder class TodoUser { long id; String email; String password; String confirmationCode; boolean registered; @Override public String toString() { ToStringBuilder.reflectionToString(this); } } package com.ricston.blog.sample.model.dao.postgre import groovy.sql.Sql import javax.sql.DataSource import com.ricston.blog.sample.model.dao.TodoUserDAO import com.ricston.blog.sample.model.data.TodoUser class PostgreTodoUserDAO implements TodoUserDAO { private Sql sql public PostgreTodoUserDAO(DataSource dataSource) { sql = new Sql(dataSource) } /** * * @param email * @return the TodoUser with the given email */ public TodoUser findTodoUserByEmail(String email) { sql.firstRow """SELECT * FROM todouser WHERE email = $email""" } } package com.ricston.blog.sample.model.dao; import com.ricston.blog.sample.model.data.TodoUser; public interface TodoUserDAO { /** * * @param email * @return the TodoUser with the given email */ public TodoUser findTodoUserByEmail(String email); } We’re just creating a POGO in TodoUser, implementing its toString using common’s ToStringBuilder. In PostgreTodoUserDAO we’re using Groovy’s SQL to access the database, for now, only implementing the findTodoUserByEmail method. PostgreTodoUserDAO implements TodoUserDAO, an interface which specifies the required methods a TodoUserDAO must have. Okay, so now we have all we need to run our specification. Go ahead and run it as a JUnit test from Eclipse. You should get back the following error message: org.codehaus.groovy.runtime.typehandling.GroovyCastException: Cannot cast object '{id=3, email=anon@gmail.com, password=anon, registered=false, confirmationcode=codeA}' with class 'groovy.sql.GroovyRowResult' to class 'com.ricston.blog.sample.model.data.TodoUser' due to: org.codehaus.groovy.runtime.metaclass.MissingPropertyExceptionNoStack: No such property: confirmationcode for class: com.ricston.blog.sample.model.data.TodoUser Possible solutions: confirmationCode at com.ricston.blog.sample.model.dao.postgre.PostgreTodoUserDAO.findTodoUserByEmail(PostgreTodoUserDAO.groovy:23) at com.ricston.blog.sample.model.spec.PostgreTodoUserDAOSpec.findTodoUserByEmail when user exists in db(PostgreTodoUserDAOSpec.groovy:37) Go ahead and connect to your tododbtest database and select * from todouser; As you can see, our confirmationCode varchar(280), ended up as the column confirmationcode with a lower case ‘c’. In PostgreTodoUserDAO’s findTodoUserByEmail, we are getting back GroovyRowResult from our firstRow invocation. GroovyRowResult implements Map and Groovy is able to create a POGO (in our case TodoUser) from a Map. However, in order for Groovy to be able to automatically coerce the GroovyRowResult into a TodoUser, the keys in the Map (or GroovyRowResult) must match the property names in our POGO. We are using confirmationCode in our TodoUser, and we would like to stick to the camel case convention. What can we do to get around this? Well, first of all, lets change our schema to use confirmation_code. That’s a little more readable. Of course, we still have the same problem as before since confirmation_code will not map to confirmationCode by itself. (Note: remember to change the insert statements in test-data.sql too). One way to get around this is to use Groovy’s propertyMissing methods as show below: def propertyMissing(String name, value) { if(isConfirmationCode(name)) { this.confirmationCode = value } else { unknownProperty(name) } } def propertyMissing(String name) { if(isConfirmationCode(name)) { return confirmationCode } else { unknownProperty(name) } } private boolean isConfirmationCode(String name) { 'confirmation_code'.equals(name) } def unknownProperty(String name) { throw new MissingPropertyException(name, this.class) } By adding this to our TodoUser.groovy we are effectively tapping in on how Groovy resolves property access. When we do something like user.confirmationCode, Groovy automatically calls getConfirmationCode(), a method which we got for free when declared the property confirmationCode in our TodoUser. Now, when user.confirmation_code is invoked, Groovy doesn’t find any getters to invoke since we never declared the property confirmation_code, however, since we have now implemented the propertyMissing methods, before throwing any exceptions it will use those methods as a last resort when resolving properties. In our case we are effectively checking whether a get or set on confirmation_code is being made and mapping the respective operations to our confirmationCode property. It’s as simple as that. Now we can keep the auto coercion in our data access object and the property name we choose to have in our TodoUser. Assuming you’ve made the changes to the schema and test-data.sql to use confirmation_code, go ahead and run the spec file and this time it should pass. That’s it for this tutorial. In conclusion, I would like to discuss some finer points which someone who’s never used Groovy’s SQL before might not know. As you can see in PostgreTodoUserDAO.groovy, our database interaction is pretty much a one-liner. What about resource handling (e.g. properly closing the connection when we’re done), error logging, and prepared statements? Resource handling and error logging are done automatically, you just have to worry about writing your SQL. When you do write your SQL, try to stick to using triple quotes as used in the PostgreTodoUserDAO.groovy example. This produces prepared statements, therefore protecting against SQL injection and avoids us having to put ‘?’ all over the place and properly lining up the arguments to pass in to the SQL statement. Note that transaction management is something which the code using our artifact will have to take care of. Finally, note that a bunch of other operations (apart from findTodoUserByEmail) are implemented in the project on GitHub: https://github.com/ricston-git/tododb. Additionally, there is also a specification test for TodoUser, making sure that the property mapping works correctly. Also, in the pom.xml, there is some maven-surefire-plugin configuration in order to get the surefire-plugin to pick up our Spock specifications as well as any JUnit tests which we might have in our project. This allows us to run our specifications when we, for example, mvn clean package. After implementing all the operations you require in PostgreTodoUserDAO.groovy, you can go ahead and compile the jar or include in a Maven multi-module project to get a data access module you can use in other applications.

We'll use a custom CXF interceptor to add these headers.

What is Functional Programming? Functional programming is a specific way to look at problems and model their solutions. Pragmatically, functional programming is a coding style that exhibits the following characteristics: Power and flexibility. We can solve many general real-world problems using functional constructs Simplicity. Most functional programs exhibit a small set of keywords and concise syntax for expressing concepts Suitable for parallel processing. Via immutable values and operators, functional programs lend themselves to asynchronous and parallel processing. In functional programming, programs are executed by evaluating expressions, in contrast with imperative programming where programs are composed of statements which change global state when executed. Functional programming typically avoids using mutable state. Everything is a mathematical function. Functional programming languages can have objects, but generally those objects are immutable -- either arguments or return values to functions. There are no for/next loops, as those imply state changes. Instead, that type of looping is performed with recursion and by passing functions as arguments. Functional Programming vs. Imperative Programming: We can think of imperative programming as writing code that describes in exacting detail the steps the software must take to execute a given computation. These steps are generally expressed as a combination of statement executions, state changes, loops and conditional branches. Many programming languages support programming in both functional and imperative style but the syntax and facilities of a language are typically optimised for only one of these styles, and social factors like coding conventions and libraries often force the programmer towards one of the styles. Therefore, programming languages may be categorized into functional and imperative ones. Why Functional Programming? While you can develop concurrent, scalable and asynchronous software without embracing functional programming, it's simpler, safer, and easier to use the right tool for the job. Functional programming enables you to take advantage of multi-core systems, develop robust concurrent algorithms, parallelize compute-intensive algorithms, and to readily leverage the growing number of cloud computing platforms. Imagine you've implemented a large program in a purely functional way. All the data is properly threaded in and out of functions, and there are no truly destructive updates to speak of. Now pick the two lowest-level and most isolated functions in the entire codebase. They're used all over the place, but are never called from the same modules. Now make these dependent on each other: function A behaves differently depending on the number of times function B has been called and vice-versa. In addition, if you've not already explored non-imperative programming, it's a great way to expand your problem solving skills and your horizons. The new concepts that you'll learn will help you to look at many problems from a different perspective and will help you to become a better and more insightful OO programmer. I encourage everyone who wants to be a better programmer: consider learning a functional language. Haskell and OCaml are both great choices, and F# and Erlang are pretty good as well. It won’t be easy, but that is probably a good sign. Try and identify the difficult concepts you encounter and see if other people are leveraging them; frequently you can break through a mental roadblock by finding out what the intent of an unfamiliar abstraction really is. While you’re learning, do be careful not to take it too seriously. Like anything that requires time and effort, there is a danger of becoming over-invested in FP. Falling into this cognitive trap will ruin your investment. It’s easy to forget how many models of computation there are out there, and even easier to forget how much beautiful software has been written with any of them. It’s a narrow path to walk down, but on the other side, you emerge with more core concepts and models to leverage in your everyday programming. You will almost certainly become more comfortable with denser code, and will certainly gain new insights into how to be a better software engineer.