this is an old yet still popular question. there are multiple reasons that string is designed to be immutable in java. a good answer depends on good understanding of memory, synchronization, data structures, etc. in the following, i will summarize some answers. 1. requirement of string pool string pool (string intern pool) is a special storage area in java heap. when a string is created and if the string already exists in the pool, the reference of the existing string will be returned, instead of creating a new object and returning its reference. the following code will create only one string object in the heap. string string1 = "abcd"; string string2 = "abcd"; here is how it looks: if string is not immutable, changing the string with one reference will lead to the wrong value for the other references. 2. allow string to cache its hashcode the hashcode of string is frequently used in java. for example, in a hashmap. being immutable guarantees that hashcode will always the same, so that it can be cashed without worrying the changes.that means, there is no need to calculate hashcode every time it is used. this is more efficient. 3. security string is widely used as parameter for many java classes, e.g. network connection, opening files, etc. were string not immutable, a connection or file would be changed and lead to serious security threat. the method thought it was connecting to one machine, but was not. mutable strings could cause security problem in reflection too, as the parameters are strings. here is a code example: boolean connect(string s){ if (!issecure(s)) { throw new securityexception(); } //here will cause problem, if s is changed before this by using other references. causeproblem(s); } in summary, the reasons include design, efficiency, and security. actually, this is also true for many other “why” questions in a java interview.

Just about every 6 months or so an article appears cursing Maven, attracting both proponents as opponents to Maven and Ant. While it’s real fun to watch (I really get a laugh when people start to advocate the return to Ant), most of the time it’s always the same arguments. Maven lacks flexibility, the plugin system sucks (when will people learn to use plugin versions…), you can’t use scripting and the all time favorite: the release plugin sucks. Well, I am a Maven addict and I’m happy to say: yes, I agree, the release plugin sucks. Big time. But here’s something you may have forgotten: you don’t need it! Even more: you shouldn’t use it. The Maven release plugin tries to make releasing software a breeze. That’s where the plugin authors got it wrong to start with. Releases are not something done on a whim. They are carefully planned and orchestrated actions, preceded by countless rules and followed by more rules. Assuming you can bundle all that in a simple mvn release:release is just plain naive. Even Maven’s most fierce supporters agree on this. The Maven release plugin just tries to do too much stuff at once: build your software, tag it, build it again, deploy it, build the site (triggering yet another build in the process) and deploy the site. And whilst doing that, running the tests x times. Most of the time, you’re making candidate releases, so building the complete documentation is a complete waste of time. Now, if you break down the release plugin into sensible steps, you’ll really save yourself a whole lot of trouble. I use these steps to release something. As a sidenote: I use git and git-flow standards (as described here). Assume the POM’s version’s currently on 1.0-SNAPSHOT. Announce the release process Very important. As I said, you don’t release on a whim. Make sure everyone on your team knows a release is pending and has all their stuff pushed to the development branch that needs to be included. Branch the development branch into a release branch. Following git-flow rules, I make a release branch 1.0. Update the POM version of the development branch. Update the version to the next release version. For example mvn versions:set -DnewVersion=2.0-SNAPSHOT. Commit and push. Now you can put resources developing towards the next release version. Update the POM version of the release branch. Update the version to the standard CR version. For example mvn versions:set -DnewVersion=1.0.CR-SNAPSHOT. Commit and push. Run tests on the release branch. Run all the tests. If one or more fail, fix them first. Create a candidate release from the release branch. Use the Maven version plugin to update your POM’s versions. For example mvn versions:set -DnewVersion=1.0.CR1. Commit and push. Make a tag on git. Use the Maven version plugin to update your POM’s versions back to the standard CR version. For example mvn versions:set -DnewVersion=1.0.CR-SNAPSHOT. Commit and push. Checkout the new tag. Do a deployment build (mvn clean deploy). Since you’ve just run your tests and fixed any failing ones, this shouldn’t fail. Put deployment on QA environment. Iterate until QA gives a green light on the candidate release. Fix bugs. Fix bugs reported on the CR releases on the release branch. Merge into development branch on regular intervals (or even better, continuous). Run tests continuously, making bug reports on failures and fixing them as you go. Create a candidate release. Use the Maven version plugin to update your POM’s versions. For example mvn versions:set -DnewVersion=1.0.CRx. Commit and push. Make a tag on git. Use the Maven version plugin to update your POM’s versions back to the standard CR version. For example mvn versions:set -DnewVersion=1.0.CR-SNAPSHOT. Commit and push. Checkout the new tag. Do a deployment build (mvn clean deploy). Since you’ve run your tests continuously, this shouldn’t fail. Put deployment on QA environment. Once QA has signed off on the release, create a final release. Check whether there are no new commits since the last release tag (if there are, slap developers as they have done stuff that wasn’t needed or asked for). Use the Maven version plugin to update your POM’s versions. For example mvn versions:set -DnewVersion=1.0. Commit and push. Tag the release branch. Merge into the master branch. Checkout the master branch. Do a deployment build (mvn clean deploy). Start production release and deployment process (in most companies, not a small feat). This can involve building the site and doing other stuff, some not even Maven related. There’s no way in hell Maven can automate this process and if you try, you’ll bump into the many pitfalls the release plugin has to offer. The release plugin is just a combination of the versions, scm, deploy and site plugin that seriously violates the single responsibility principle. The release plugin is one of the reasons Maven has gotten a bad reputation with some people. It’s long due for an overhaul, but if you ask me, they should just remove it altogether. Releasing software is a process, not a single command on the command line. The process I just described isn’t perfect in any way, but it works and I avoid using the release plugin as it just does too much stuff. Have fun bashing Maven, but please, keep it clean :) .

MongoDB is an open source document-oriented NoSQL database system which stores data as JSON-like documents with dynamic schemas. As it doesn't store data in tables as is done in the usual relational database setup, it doesn't map well to the JPA way of storing data. Morphia is an open source lightweight type-safe library designed to bridge the gap between the MongoDB Java driver and domain objects. It can be an alternative to SpringData if you're not using the Spring Framework to interact with MongoDB. This post will cover the basics of persisting and querying entities along the lines of JPA by using Morphia and a MongoDB database instance. There are four POJOs this example will be using. First we have BaseEntity which is an abstract class containing the Id and Version fields: package com.city81.mongodb.morphia.entity; import org.bson.types.ObjectId; import com.google.code.morphia.annotations.Id; import com.google.code.morphia.annotations.Property; import com.google.code.morphia.annotations.Version; public abstract class BaseEntity { @Id @Property("id") protected ObjectId id; @Version @Property("version") private Long version; public BaseEntity() { super(); } public ObjectId getId() { return id; } public void setId(ObjectId id) { this.id = id; } public Long getVersion() { return version; } public void setVersion(Long version) { this.version = version; } } Whereas JPA would use @Column to rename the attribute, Morphia uses @Property. Another difference is that @Property needs to be on the variable whereas @Column can be on the variable or the get method. The main entity we want to persist is the Customer class: package com.city81.mongodb.morphia.entity; import java.util.List; import com.google.code.morphia.annotations.Embedded; import com.google.code.morphia.annotations.Entity; @Entity public class Customer extends BaseEntity { private String name; private List accounts; @Embedded private Address address; public String getName() { return name; } public void setName(String name) { this.name = name; } public List getAccounts() { return accounts; } public void setAccounts(List accounts) { this.accounts = accounts; } public Address getAddress() { return address; } public void setAddress(Address address) { this.address = address; } } As with JPA, the POJO is annotated with @Entity. The class also shows an example of @Embedded: The Address class is also annotated with @Embedded as shown below: package com.city81.mongodb.morphia.entity; import com.google.code.morphia.annotations.Embedded; @Embedded public class Address { private String number; private String street; private String town; private String postcode; public String getNumber() { return number; } public void setNumber(String number) { this.number = number; } public String getStreet() { return street; } public void setStreet(String street) { this.street = street; } public String getTown() { return town; } public void setTown(String town) { this.town = town; } public String getPostcode() { return postcode; } public void setPostcode(String postcode) { this.postcode = postcode; } } Finally, we have the Account class of which the customer class has a collection of: package com.city81.mongodb.morphia.entity; import com.google.code.morphia.annotations.Entity; @Entity public class Account extends BaseEntity { private String name; public String getName() { return name; } public void setName(String name) { this.name = name; } } The above show only a small subset of what annotations can be applied to domain classes. More can be found at http://code.google.com/p/morphia/wiki/AllAnnotations The Example class shown below goes through the steps involved in connecting to the MongoDB instance, populating the entities, persisting them and then retrieving them: package com.city81.mongodb.morphia; import java.net.UnknownHostException; import java.util.ArrayList; import java.util.List; import com.city81.mongodb.morphia.entity.Account; import com.city81.mongodb.morphia.entity.Address; import com.city81.mongodb.morphia.entity.Customer; import com.google.code.morphia.Datastore; import com.google.code.morphia.Key; import com.google.code.morphia.Morphia; import com.mongodb.Mongo; import com.mongodb.MongoException; /** * A MongoDB and Morphia Example * */ public class Example { public static void main( String[] args ) throws UnknownHostException, MongoException { String dbName = new String("bank"); Mongo mongo = new Mongo(); Morphia morphia = new Morphia(); Datastore datastore = morphia.createDatastore(mongo, dbName); morphia.mapPackage("com.city81.mongodb.morphia.entity"); Address address = new Address(); address.setNumber("81"); address.setStreet("Mongo Street"); address.setTown("City"); address.setPostcode("CT81 1DB"); Account account = new Account(); account.setName("Personal Account"); List accounts = new ArrayList(); accounts.add(account); Customer customer = new Customer(); customer.setAddress(address); customer.setName("Mr Bank Customer"); customer.setAccounts(accounts); Key savedCustomer = datastore.save(customer); System.out.println(savedCustomer.getId()); } Executing the first few lines will result in the creation of a Datastore. This interface will provide the ability to get, delete and save objects in the 'bank' MongoDB instance. The mapPackage method call on the morphia object determines what objects are mapped by that instance of Morphia. In this case all those in the package supplied. Other alternatives exist to map classes, including the method map which takes a single class (this method can be chained as the returning object is the morphia object), or passing a Set of classes to the Morphia constructor. After creating instances of the entities, they can be saved by calling save on the datastore instance and can be found using the primary key via the get method. The output from the Example class would look something like the below: 11-Jul-2012 13:20:06 com.google.code.morphia.logging.MorphiaLoggerFactory chooseLoggerFactory INFO: LoggerImplFactory set to com.google.code.morphia.logging.jdk.JDKLoggerFactory 4ffd6f7662109325c6eea24f Mr Bank Customer There are many other methods on the Datastore interface and they can be found along with the other Javadocs at http://morphia.googlecode.com/svn/site/morphia/apidocs/index.html An alternative to using the Datastore directly is to use the built in DAO support. This can be done by extending the BasicDAO class as shown below for the Customer entity: package com.city81.mongodb.morphia.dao; import com.city81.mongodb.morphia.entity.Customer; import com.google.code.morphia.Morphia; import com.google.code.morphia.dao.BasicDAO; import com.mongodb.Mongo; public class CustomerDAO extends BasicDAO { public CustomerDAO(Morphia morphia, Mongo mongo, String dbName) { super(mongo, morphia, dbName); } } To then make use of this, the Example class can be changed (and enhanced to show a query and a delete): ... CustomerDAO customerDAO = new CustomerDAO(morphia, mongo, dbName); customerDAO.save(customer); Query query = datastore.createQuery(Customer.class); query.and( query.criteria("accounts.name").equal("Personal Account"), query.criteria("address.number").equal("81"), query.criteria("name").contains("Bank") ); QueryResults retrievedCustomers = customerDAO.find(query); for (Customer retrievedCustomer : retrievedCustomers) { System.out.println(retrievedCustomer.getName()); System.out.println(retrievedCustomer.getAddress().getPostcode()); System.out.println(retrievedCustomer.getAccounts().get(0).getName()); customerDAO.delete(retrievedCustomer); } ... With the output from running the above shown below: 11-Jul-2012 13:30:46 com.google.code.morphia.logging.MorphiaLoggerFactory chooseLoggerFactory INFO: LoggerImplFactory set to com.google.code.morphia.logging.jdk.JDKLoggerFactory Mr Bank Customer CT81 1DB Personal Account This post only covers a few brief basics of Morphia but shows how it can help bridge the gap between JPA and NoSQL.

When you have a piece of code that often fails and must be retried, this Java 7/8 library provides rich and unobtrusive API with fast and scalable solution to this problem: ScheduledExecutorService scheduler = Executors.newSingleThreadScheduledExecutor(); RetryExecutor executor = new AsyncRetryExecutor(scheduler). retryOn(SocketException.class). withExponentialBackoff(500, 2). //500ms times 2 after each retry withMaxDelay(10_000). //10 seconds withUniformJitter(). //add between +/- 100 ms randomly withMaxRetries(20); You can now run arbitrary block of code and the library will retry it for you in case it throws SocketException: final CompletableFuture future = executor.getWithRetry(() -> new Socket("localhost", 8080) ); future.thenAccept(socket -> System.out.println("Connected! " + socket) ); Please look carefully! getWithRetry() does not block. It returns CompletableFuture immediately and invokes given function asynchronously. You can listen for that Future or even for multiple futures at once and do other work in the meantime. So what this code does is: trying to connect to localhost:8080 and if it fails with SocketException it will retry after 500 milliseconds (with some random jitter), doubling delay after each retry, but not above 10 seconds. Equivalent but more concise syntax: executor. getWithRetry(() -> new Socket("localhost", 8080)). thenAccept(socket -> System.out.println("Connected! " + socket)); This is a sample output that you might expect: TRACE | Retry 0 failed after 3ms, scheduled next retry in 508ms (Sun Jul 21 21:01:12 CEST 2013) java.net.ConnectException: Connection refused at java.net.PlainSocketImpl.socketConnect(Native Method) ~[na:1.8.0-ea] //... TRACE | Retry 1 failed after 0ms, scheduled next retry in 934ms (Sun Jul 21 21:01:13 CEST 2013) java.net.ConnectException: Connection refused at java.net.PlainSocketImpl.socketConnect(Native Method) ~[na:1.8.0-ea] //... TRACE | Retry 2 failed after 0ms, scheduled next retry in 1919ms (Sun Jul 21 21:01:15 CEST 2013) java.net.ConnectException: Connection refused at java.net.PlainSocketImpl.socketConnect(Native Method) ~[na:1.8.0-ea] //... TRACE | Successful after 2 retries, took 0ms and returned: Socket[addr=localhost/127.0.0.1,port=8080,localport=46332] Connected! Socket[addr=localhost/127.0.0.1,port=8080,localport=46332] Imagine you connect to two different systems, one is slow, second unreliable and fails often: CompletableFuture stringFuture = executor.getWithRetry(ctx -> unreliable()); CompletableFuture intFuture = executor.getWithRetry(ctx -> slow()); stringFuture.thenAcceptBoth(intFuture, (String s, Integer i) -> { //both done after some retries }); thenAcceptBoth() callback is executed asynchronously when both slow and unreliable systems finally reply without any failure. Similarly (using CompletableFuture.acceptEither()) you can call two or more unreliable servers asynchronously at the same time and be notified when the first one succeeds after some number of retries. I can’t emphasize this enough - retries are executed asynchronously and effectively use thread pool, rather than sleeping blindly. Rationale Often we are forced to retry given piece of code because it failed and we must try again, typically with a small delay to spare CPU. This requirement is quite common and there are few ready-made generic implementations with retry support in Spring Batch through RetryTemplate class being best known. But there are few other, quite similar approaches ([1], [2]). All of these attempts (and I bet many of you implemented similar tool yourself!) suffer the same issue - they are blocking, thus wasting a lot of resources and not scaling well. This is not bad per se because it makes programming model much simpler - the library takes care of retrying and you simply have to wait for return value longer than usual. But not only it creates leaky abstraction (method that is typically very fast suddenly becomes slow due to retries and delay), but also wastes valuable threads since such facility will spend most of the time sleeping between retries. Therefore Async-Retry utility was created, targeting Java 8 (with Java 7 backport existing) and addressing issues above. The main abstraction is RetryExecutor that provides simple API: public interface RetryExecutor { CompletableFuture doWithRetry(RetryRunnable action); CompletableFuture getWithRetry(Callable task); CompletableFuture getWithRetry(RetryCallable task); CompletableFuture getFutureWithRetry(RetryCallable> task); } Don’t worry about RetryRunnable and RetryCallable - they allow checked exceptions for your convenience and most of the time we will use lambda expressions anyway. Please note that it returns CompletableFuture. We no longer pretend that calling faulty method is fast. If the library encounters an exception it will retry our block of code with preconfigured backoff delays. The invocation time will sky-rocket from milliseconds to several seconds. CompletableFuture clearly indicates that. Moreover it’s not a dumb java.util.concurrent.Future we all know - CompletableFuture in Java 8 is very powerful and most importantly - non-blocking by default. If you need blocking result after all, just call .get() on Future object. Basic API The API is very simple. You provide a block of code and the library will run it multiple times until it returns normally rather than throwing an exception. It may also put configurable delays between retries: RetryExecutor executor = //... executor.getWithRetry(() -> new Socket("localhost", 8080)); Returned CompletableFuture will be resolved once connecting to localhost:8080 succeeds. Optionally we can consume RetryContext to get extra context like which retry is currently being executed: executor. getWithRetry(ctx -> new Socket("localhost", 8080 + ctx.getRetryCount())). thenAccept(System.out::println); This code is more clever than it looks. During first execution ctx.getRetryCount() returns 0, therefore we try to connect to localhost:8080. If this fails, next retry will try localhost:8081 (8080 + 1) and so on. And if you realize that all of this happens asynchronously you can scan ports of several machines and be notified about first responding port on each host: Arrays.asList("host-one", "host-two", "host-three"). stream(). forEach(host -> executor. getWithRetry(ctx -> new Socket(host, 8080 + ctx.getRetryCount())). thenAccept(System.out::println) ); For each host RetryExecutor will attempt to connect to port 8080 and retry with higher ports. getFutureWithRetry() requires special attention. I you want to retry method that already returns CompletableFuture: e.g. result of asynchronous HTTP call: private CompletableFuture asyncHttp(URL url) { /*...*/} //... final CompletableFuture> response = executor.getWithRetry(ctx -> asyncHttp(new URL("http://example.com"))); Passing asyncHttp() to getWithRetry() will yield CompletableFuture>. Not only it’s awkward to work with, but also broken. The library will barely call asyncHttp() and retry only if it fails, but not if returned CompletableFuture fails. The solution is simple: final CompletableFuture response = executor.getFutureWithRetry(ctx -> asyncHttp(new URL("http://example.com"))); In this case RetryExecutor will understand that whatever was returned from asyncHttp() is the actually just a Future and will (asynchronously) wait for result or failure. This library is much more powerful, so let’s dive into: Configuration Options In general there are two important factors you can configure: RetryPolicy that controls whether next retry attempt should be made and Backoff - that optionally adds delay between subsequent retry attempts. By default RetryExecutor repeats user task infinitely on every Throwable and adds 1 second delay between retry attempts. Creating an Instance of RetryExecutor Default implementation of RetryExecutor is AsyncRetryExecutor which you can create directly: ScheduledExecutorService scheduler = Executors.newSingleThreadScheduledExecutor(); RetryExecutor executor = new AsyncRetryExecutor(scheduler); //... scheduler.shutdownNow(); The only required dependency is standard ScheduledExecutorService from JDK. One thread is enough in many cases but if you want to concurrently handle retries of hundreds or more tasks, consider increasing the pool size. Notice that the AsyncRetryExecutor does not take care of shutting down the ScheduledExecutorService. This is a conscious design decision which will be explained later. AsyncRetryExecutor has few other constructors but most of the time altering the behaviour of this class is most convenient with calling chained with*() methods. You will see plenty of examples written this way. Later on we will simply use executor reference without defining it. Assume it’s of RetryExecutor type. Retrying Policy Exceptions By default every Throwable (except special AbortRetryException) thrown from user task causes retry. Obviously this is configurable. For example in JPA you may want to retry a transaction that failed due to OptimisticLockException - but every other exception should fail immediately: executor. retryOn(OptimisticLockException.class). withNoDelay(). getWithRetry(ctx -> dao.optimistic()); Where dao.optimistic() may throw OptimisticLockException. In that case you probably don’t want any delay between retries, more on that later. If you don’t like the default of retrying on every Throwable, just restrict that using retryOn(): executor.retryOn(Exception.class) Of course the opposite might also be desired - to abort retrying and fail immediately in case of certain exception being thrown rather than retrying. It’s that simple: executor. abortOn(NullPointerException.class). abortOn(IllegalArgumentException.class). getWithRetry(ctx -> dao.optimistic()); Clearly you don’t want to retry NullPointerException or IllegalArgumentException as they indicate programming bug rather than transient failure. And finally you can combine both retry and abort policies. User code will retry in case of any retryOn() exception (or subclass) unless it should abortOn() specified exception. For example we want to retry every IOException or SQLException but abort if FileNotFoundException or java.sql.DataTruncation is encountered (order is irrelevant): executor. retryOn(IOException.class). abortIf(FileNotFoundException.class). retryOn(SQLException.class). abortIf(DataTruncation.class). getWithRetry(ctx -> dao.load(42)); If this is not enough you can provide custom predicate that will be invoked on each failure: executor. abortIf(throwable -> throwable instanceof SQLException && throwable.getMessage().contains("ORA-00911") ); Max Number of Retries Another way of interrupting retrying “loop” (remember that this process is asynchronous, there is no blocking loop) is by specifying maximum number of retries: executor.withMaxRetries(5) In rare cases you may want to disable retries and barely take advantage from asynchronous execution. In that case try: executor.dontRetry() Delays Between Retries (backoff) Retrying immediately after failure is sometimes desired (see OptimisticLockException example) but in most cases it’s a bad idea. If you can’t connect to external system, waiting a little bit before next attempt sounds reasonably. You save CPU, bandwidth and other server’s resources. But there are quite a few options to consider: should we retry with constant intervals or increase delay after each failure? should there be a lower and upper limit on waiting time? should we add random “jitter” to delay times to spread retries of many tasks in time? This library answers all these questions. Fixed Interval Between Retries By default each retry is preceded by 1 second waiting time. So if initial attempt fails, first retry will be executed after 1 second. Of course we can change that default, e.g. to 200 milliseconds: executor.withFixedBackoff(200) If we are already here, by default backoff is applied after executing user task. If user task itself consumes some time, retries will be less frequent. For example with retry delay of 200ms and average time it takes before user task fails at about 50ms RetryExecutor will retry about 4 times per second (50ms + 200ms). However if you want to keep retry frequency at more predictable level you can use fixedRate flag: executor. withFixedBackoff(200). withFixedRate() This is similar to “fixed rate” vs. “fixed delay” approaches in ScheduledExecutorService. BTW don’t expect RetryExecutor to be very precise, it does it’s best but it heavily depends on aforementioned ScheduledExecutorService accuracy. Exponentially Growing Intervals Between Retries It’s probably an active research subject, but in general you may wish to expand retry delay over time, assuming that if the user task fails several times we should try less frequently. For example let’s say we start with 100ms delay until first retry attempt is made but if that one fails as well, we should wait two times more (200ms). And later 400ms, 800ms… You get the idea: executor.withExponentialBackoff(100, 2) This is an exponential function that can grow very fast. Thus it’s useful to set maximum backoff time at some reasonable level, e.g. 10 seconds: executor. withExponentialBackoff(100, 2). withMaxDelay(10_000) //10 seconds Random Jitter One phenomena often observed during major outages is that systems tend to synchronize. Imagine a busy system that suddenly stops responding. Hundreds or thousands of requests fail and are retried. It depends on your backoff but by default all these requests will retry exactly after one second producing huge wave of traffic at one point in time. Finally such failures are propagated to other systems that, in turn, synchronize as well. To avoid this problem it’s useful to spread retries over time, flattening the load. A simple solution is to add random jitter to delay time so that not all request are scheduled for retry at the exact same time. You have choice between uniform jitter (random value from -100ms to 100ms): executor.withUniformJitter(100) //ms …and proportional jitter, multiplying delay time by random factor, by default between 0.9 and 1.1 (10%): executor.withProportionalJitter(0.1) //10% You may also put hard lower limit on delay time to avoid to short retry times being scheduled: executor.withMinDelay(50) //ms Implementation Details This library was built with Java 8 in mind to take advantage of lambdas and new CompletableFuture abstraction (but Java 7 port with Guava dependency exists). It uses ScheduledExecutorService underneath to run tasks and schedule retries in the future - which allows best thread utilization. But what is really interesting is that the whole library is fully immutable, there is no single mutable field, at all. This might be counter-intuitive at first, take for example this trivial code sample: ScheduledExecutorService scheduler = Executors.newSingleThreadScheduledExecutor(); AsyncRetryExecutor first = new AsyncRetryExecutor(scheduler). retryOn(Exception.class). withExponentialBackoff(500, 2); AsyncRetryExecutor second = first.abortOn(FileNotFoundException.class); AsyncRetryExecutor third = second.withMaxRetries(10); It might seem that all with*() methods or retryOn()/abortOn() mutate existing executor. But that’s not the case, each configuration change creates new instance, leaving the old one untouched. So for example while first executor will retry on FileNotFoundException, the second and third won’t. However they all share the same scheduler. This is the reason why AsyncRetryExecutor does not shut down ScheduledExecutorService (it doesn’t even have any close() method). Since we have no idea how many copies of AsyncRetryExecutor exist pointing to the same scheduler, we don’t even try to manage its lifecycle. However this is typically not a problem (see Spring integration below). You might be wondering, why such an awkward design decision? There are three reasons: when writing a concurrent code immutability can greatly reduce risk of multi-threading bugs. For example RetryContext holds number of retries. But instead of mutating it we simply create new instance (copy) with incremented but final counter. No race condition or visibility can ever occur. if you are given an existing RetryExecutor which is almost exactly what you want but you need one minor tweak, you simply call executor.with...() and get a fresh copy. You don’t have to worry about other places where the same executor was used (see: Spring integration for further examples) functional programming and immutable data structures are sexy these days ;-). N.B.: AsyncRetryExecutor is not marked final, does you can break immutability by subclassing it and adding mutable state. Please don’t do this, subclassing is only permitted to alter behaviour. Dependencies This library requires Java 8 and SLF4J for logging. Java 7 port additionally depends on Guava. Spring Integration If you are just about to use RetryExecutor in Spring - feel free, but the configuration API might not work for you. Spring promotes (or used to promote) the convention of mutable services with plenty of setters. In XML you define bean and invoke setters (via ) on it. This convention assumes the existence of mutating setters. But I found this approach error-prone and counter-intuitive under some circumstances. Let’s say we globally defined org.springframework.transaction.support.TransactionTemplate bean and injected it in multiple places. Great. Now there is this one single request that requires slightly different timeout: @Autowired private TransactionTemplate template; and later in the same class: final int oldTimeout = template.getTimeout(); template.setTimeout(10_000); //do the work template.setTimeout(oldTimeout); This code is wrong on so many levels! First of all if something fails we never restore oldTimeout. OK, finally to the rescue. But also notice how we changed global, shared TransactionTemplate instance. Who knows how many other beans and threads are just about to use it, unaware of changed configuration? And even if you do want to globally change the transaction timeout, fair enough, but it’s still wrong way to do this. private timeout field is not volatile and thus changes made to it may or may not be visible to other threads. What a mess! The same problem appears with many other classes like JmsTemplate. You see where I’m going? Just create one, immutable service class and safely adjust it by creating copies whenever you need it. And using such services is equally simple these days: @Configuration class Beans { @Bean public RetryExecutor retryExecutor() { return new AsyncRetryExecutor(scheduler()). retryOn(SocketException.class). withExponentialBackoff(500, 2); } @Bean(destroyMethod = "shutdownNow") public ScheduledExecutorService scheduler() { return Executors.newSingleThreadScheduledExecutor(); } } Hey! It’s 21st century, we don’t need XML in Spring any more. Bootstrap is simple as well: final ApplicationContext context = new AnnotationConfigApplicationContext(Beans.class); final RetryExecutor executor = context.getBean(RetryExecutor.class); //... context.close(); As you can see integrating modern, immutable services with Spring is just as simple. BTW if you are not prepared for such a big change when designing your own services, at least consider constructor injection. Maturity This library is covered with a strong battery of unit tests. However it wasn’t yet used in any production code and the API is subject to change. Of course you are encouraged to submit bugs, feature requests and pull requests. It was developed with Java 8 in mind but Java 7 backport exists with slightly more verbose API and mandatory Guava dependency (ListenableFuture instead of CompletableFuture from Java 8). Full source code on GitHub.

Quick reference for converting Java objects to various formats (byte array, JSON, XML) and back, using different libraries for serialization and deserialization.

Recently a respected member of the Apache community tried Log4j 2 and wrote on Twitter: (Quote from Mark Struberg: @TheASF #log4j2 rocks big times! Performance is close to insane ^^ http://logging.apache.org/log4j/2.x/ ) It happened shortly after Remko Popma contributed something which is now called the “AsyncLoggers”. Some of you might know Log4j 2 has AsyncAppenders already. They are similar like the ones you can find in Log4j 1 and other logging frameworks. I am honest: I wasn’t so excited about the new feature until I read the tweet on its performance and became curious. Clearly Java logging has many goals. Among them: logging must be as fast as hell. Nobody wants his logging framework to become a bottleneck. Of course you’ll always have a cost when logging. There is some operation the CPU must perform. Something is happening, even when you decide NOT to write a log statement. Logging is expected to be invisible. Until now, the well-known logging frameworks were similar in speed. Benchmarks are unreliable after all. We have made some benchmarks over at Apache Logging. Sometimes one logging frameworks wins, sometimes the other. But at the end of the day you can say they are all very good and you can choose whatever your liking is. Until we got Remko’s contribution and Log4j 2 became “insanely fast”. Small software projects running one thread might not care about performance so much. When running a SaaS you simply don’t know when your app gets so much attraction that you need to scale. Then you suddenly need some extra power. With Log4j 2, running 64 threads might bring you twelve times more logging throughput than with comparable frameworks. We speak of more than 18,000,000 messages per second, while others do around 1,500,000 or less in the same environment. I saw the chart, but simply couldn’t believe it. There must be something wrong. I rechecked. I ran the tests myself. It’s like that: Log4j 2 is insanely fast. Async Performance, last read on July 19, 2013 As of now, we have a logging framework which performs lots better than every other logging framework out there. As of now we need to justify our decision when we do not want to use Log4j 2, if speed matters.Everything else than Log4j 2 can become a bottleneck and a risk. With such a fast logging framework you might even consider to log a bit more in production than you did before. Eventually I wrote Remko an e-mail and asked him what exactly the difference between the old AsyncAppenders and the new Asynchronous Loggers is. The difference between old AsynAppenders and new AsyncLoggers “The Asynchronous Loggers do two things differently than the AsyncAppender”, he told me, “they try to do the minimum amount of work before handing off the log message to another thread, and they use a different mechanism to pass information between the producer and consumer threads. AsyncAppender uses an ArrayBlockingQueue to hand off the messages to the thread that writes to disk, and Asynchronous Loggers use the LMAX Disruptor library. Especially the Disruptor has made a large performance difference.” In other terms, the AsyncAppender use a first-in-first-out Queue to work through messages. But the Async Logger uses something new – the Disruptor. To be honest, I had never heard of it. And furthermore, I never thought much about scaling my logging framework. When somebody said “scale the system”, I thought about the database, the app server and much more, but usually not logging. In production, logging was off. End of story. But Remko thinks about scaling when it comes to logging. “Looking at the performance test results for the Asynchronous Loggers, the first thing you notice is that some ways of logging scale much better than others. By scaling better I mean that you get more throughput when you add more threads. If your throughput increases a constant amount with every thread you add, you have linear scalability. This is very desirable but can be difficult to achieve.”, he wrote me. “Comparing synchronous to asynchronous, you would expect any asynchronous mechanism to scale much better than synchronous logging because you don’t do the I/O in the producing thread any more, and we all know that ‘I/O is slow’ (and I’ll get back to this in a bit)”. Yes, exactly my understanding. I thought it would be enough to send something to a queue, and something else would pick it up and write the message. The app would go on. This is exactly what the old AsyncAppender does, wrote Remko: “With AsyncAppender, all your application thread needs to do is create a LogEvent object and put it on the ArrayBlockingQueue; the consuming thread will then take these events off the queue and do all the time-consuming work. That is, the work of turning the event into bytes and writing these bytes to the I/O device. Since the application threads do not need to do the I/O, you would expect this to scale better, meaning adding threads will allow you to log more events.” If you believed that like me, take a seat and a deep breath. We were wrong. “What may surprise you is that this is not the case.”, he wrote. “If you look at the performance numbers for the AsyncAppenders of all logging frameworks, you’ll see that every time you double the number of threads, your throughput per thread roughly halves.” “So your total throughput remains more or less flat! AsyncAppenders are faster than synchronous logging, but they are similar in the sense that neither of them gives you more total throughput when you add more threads.”, he told me. It hit me like a hammer. Basically instead of making your logging faster with adding more threads you made basically: nothing. After all Appenders didn’t scale until now. I asked Remko why this was the case. “It turns out that queues are not the most optimal data structure to pass information between threads. The concurrent queues that are part of the standard Java libraries use locks to make sure that values don’t get corrupted and to ensure data visibility between threads.”. LMAX Disruptor? “The LMAX team did a lot of research on this and found that these queues have a lot of lock contention. An interesting thing they found is that queues are always either full or empty: If your producer is faster, your queue will be full most of the time (and that may be a problem in itself ). If your consumer is fast enough, your queue will be empty most of the time. Either way, you will have contention on the head or on the tail of the queue, where both the producer and the consumer thread want to update the same field. To resolve this, the LMAX team came up with the Disruptor library, which is a lock-free data structure for passing messages between threads. Here is a performance comparison between the Disruptor and ArrayBlockingQueue:Performance Comparison.” Wow. After all these years of Java programming I actually felt a bit like a Junior programmer again. I missed the LMAX disruptor and even never considered it a performance problem to use the Queue. I wonder what other performance problems I did not discover so far. I realized, I had to re-learn Java. I asked Remko how he could find a library like the LMAX disruptor. I mean nobody writes software, creates an instance of a Queue-class, doubts its performance and finally searches the internet for “something better”. Or are there really people of that kind? “How I found about the Disruptor? The short answer is, it was all a mistake.”, he started. “Okay, perhaps that was a bit too short, so here is the longer answer: a colleague of mine wrote a small logger, essentially adding a time-stamped log message to a queue, with a background thread that took these strings off the queue and wrote them to disk. He did this because he needed better performance than what he could get with log4j-1.x. I did some testing and found it was faster, I don’t remember exactly by how much. I was quite surprised because I had been using log4j for years and had never thought it would be easily outperformed. Until then I had assumed that the well-known libraries would be fast, because, well… To be honest, I had just assumed. So this was a bit of an eye-opener for me. However, the custom logger was a bit bare-bones in terms of functionality so I started to look around for alternatives.” “Before I start talking about the Disruptor, I have to confess something. I recently went back to see how much faster the custom logger was than log4j-1.x, but when I measured it it was actually slower! It turned out that I had been comparing the custom logger to an old beta of log4j-2.0, I think beta3 or beta4. AsyncAppender in those betas still had a performance issue (LOG4J2-153 if you’re curious). If I had compared the custom logger to the AsyncAppender in log4j-1.x, I would have found that log4j-1.x was faster and I would not have thought about it further. But because I made this mistake I started to look for other high-performance logging libraries that were richer in functionality. I did not find such a logging library, but I ran into a whole bunch of other interesting stuff, including the Disruptor. Eventually I decided to try to combine Log4j-2, which has a very nice code base, with the Disruptor. The result of this was eventually accepted into Log4j-2 itself, and the rest, as they say, was history.” “One thing I came across that I should mention here is Peter Lawrey’sChronicle library. Chronicle uses memory-mapped files to write tens of millions of messages per second to disk with very low latency. Remember that above I said that “we all know that I/O is slow”? Chronicle shows that synchronous I/O can be very, very fast.“. “It was via Peter’s work that I came across the Disruptor. There is a lot of good material out there about the Disruptor. Just to give you a few pointers: Martin Fowler: LMAX Trisha Lee on LMAX under the hood (slightly outdated now but the most detailed material I know of) …and video presentations like this The Disruptor google group is also highly recommended. Recommended readings on Java performance in general are: Martin Thompson’s “Mechanical Sympathy” Martin Thompson Presentations. Martin Thompson has done a number of articles and presentations on various aspects of high performance computing in java. He does a great job of making the complex stuff that is going on under the hood accessible.” My bookmarks folder went full after reading this e-mail, and I appreciate the lots of starting points for improving my knowledge on Java performance. Should I use AsyncLoggers by default? I was sure I want to use the new Async Loggers. This all sounds just fantastic. But on the other hand, I am a bit scared and even a little paranoid to include new dependencies or new technologies like the new Log4j 2 Async Loggers. I asked Remko if he would use the new feature by default or if he would enable them just for a few, limited use cases. “I use Async Loggers by default, yes.”, he wrote me. “One use case when you would _not_ want to use asynchronous logging is when you use logging for audit purposes. In that case a logging error is a problem that your application needs to know about and deal with. I believe that most applications are different, in that they don’t care too much about logging errors. Most applications don’t want to stop if a logging exception occurs, in fact, they don’t even want to know about it. By default, appenders in Log4j-2.0 will suppress exceptions so the application doesn’t need to try/catch every log statement. If that is your usage, then you will not lose anything by using asynchronous loggers, so you get only the benefits, which is improved performance.” “One nice little detail I should mention is that both Async Loggers and Async Appenders fix something that has always bothered me in Log4j-1.x, which is that they will flush the buffer after logging the last event in the queue. With Log4j-1.x, if you used buffered I/O, you often could not see the last few log events, as they were still stuck in the memory buffer. Your only option was setting immediateFlush to true, which forces disk I/O on every single log event and has a performance impact. With Async Loggers and Appenders in Log4j-2.0 your log statements are all flushed to disk, so they are always visible, but this happens in a very efficient manner.” Isn’t it risky to log to use Log4js AsyncLoggers? But considering that Log4j-1 had serious threading issues and the modern world uses cloud computing and clustering all the time to scale their apps,isn’t asynchronous logging some kind of additional risk? Or is it safe? I knew my questions would sound like the questions of a decision maker, not of an developer. But the whole LMAX thing was so new to me and since I maintain the old and really ugly Log4j 1 code, I simply had to ask. Remko: “There are a number of questions in there. First, is Log4j-2 safer from a concurrency perspective than Log4j-1.x? I believe so. The Log4j-2 team has put in considerable effort to support multi-threaded applications, and the asynchronous loggers are just a very recent and relatively small addition to the project. Log4j-2 uses more granular locking than log4j-1.x, and is architecturally simpler, which should result in fewer issues, and any issues that do come up will be easier to fix.” “On the other hand, Log4j-2 is still in beta and is under active development, although recently I think most effort is being spent on fixing things and tying up loose ends rather than adding new features. I believe it is stable enough for production use. If you are considering using Log4j-2, for performance or other reasons, I’d suggest you do your due diligence and test, just like you would before adopting any other 3rd party library in your project.” (Sidenote: A stable version of Log4j2 can be expected soon, most likely autumn 2013). Sounded good to me. And yes, I can perfectly agree with that from my own observations on the project, though I personally did not write code in the Log4j 2 repository. “The other question I see is: Is asynchronous logging riskier than synchronous logging? I don’t think so, in fact, if your application is multi threaded the opposite may be the case: once the log event has been handed off to the consumer thread that does the I/O, there is only that one thread dealing with the layouts, appenders and all the other logging-related components. So after the hand-off you’re single-threaded and you don’t need to worry about any threading issues like deadlock and liveliness etc any more.” “You can take this one step further and make your business logic completely single-threaded, using the disruptor for all I/O or communication with external systems. Single-threaded business logic without lock contention can be blazingly fast. The results at LMAX (6 million transactions/sec, with less than 10 ms latency) speak for themselves.” Reading Remko’s message I learned three things. First, I had to learn more about Java performance. Second, I definitely want to make my applications use Log4j 2. As first step, I will enable it in my Struts 2 apps, which I use often. Third, a web application framework using the LMAX Disruptor might blow us all away. I would like to give a big thank you and a hug to Remko Popma for answering my questions and working on this blog post with me. All errors are my own.

In my previous post quite sometime back I had written about Internal DSLs using Java. In the book Domain Specific Languages by Martin Fowler, he discusses about another type of DSL called external DSLs in which the DSL is written in another language which is then parsed by the host language to populate the semantic model. In the previous example I was discussing about creating a DSL for defining a graph. The advantage of using an external dsl is that any change in the graph data would not require recompilation of the program instead the program can just load the external dsl, create a parse tree and then populate the semantic model. The semantic model will remain the same and the advantage of using the semantic model is that one can make modification to the DSL without making much changes to the semantic model. In the example between Internal DSLs and external DSLs I have not modified the semantic model. To create an external DSL I am making use of ANTLR. What is ANTLR? The definition as given on the official site is: ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It’s widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build and walk parse trees. The notable features of ANTLR from the above definition are: parser generator for structured text or binary files can build and walk parse trees Semantic Model In this example I will exploit the above features of ANTLR to parse a DSL and then walk through the parse tree to populate the Semantic model. To recap, the semantic model consists of Graph, Edge and Vertex classes which represent a Graph and an Edge and a Vertex of the Graph respectively. The below code shows the class definitions: public class Graph { private List edges; private Set vertices; public Graph() { edges = new ArrayList<>(); vertices = new TreeSet<>(); } public void addEdge(Edge edge){ getEdges().add(edge); getVertices().add(edge.getFromVertex()); getVertices().add(edge.getToVertex()); } public void addVertice(Vertex v){ getVertices().add(v); } public List getEdges() { return edges; } public Set getVertices() { return vertices; } public static void printGraph(Graph g){ System.out.println("Vertices..."); for (Vertex v : g.getVertices()) { System.out.print(v.getLabel() + " "); } System.out.println(""); System.out.println("Edges..."); for (Edge e : g.getEdges()) { System.out.println(e); } } } public class Edge { private Vertex fromVertex; private Vertex toVertex; private Double weight; public Edge() { } public Edge(Vertex fromVertex, Vertex toVertex, Double weight) { this.fromVertex = fromVertex; this.toVertex = toVertex; this.weight = weight; } @Override public String toString() { return fromVertex.getLabel() + " to " + toVertex.getLabel() + " with weight " + getWeight(); } public Vertex getFromVertex() { return fromVertex; } public void setFromVertex(Vertex fromVertex) { this.fromVertex = fromVertex; } public Vertex getToVertex() { return toVertex; } public void setToVertex(Vertex toVertex) { this.toVertex = toVertex; } public Double getWeight() { return weight; } public void setWeight(Double weight) { this.weight = weight; } } public class Vertex implements Comparable { private String label; public Vertex(String label) { this.label = label.toUpperCase(); } @Override public int compareTo(Vertex o) { return (this.getLabel().compareTo(o.getLabel())); } public String getLabel() { return label; } public void setLabel(String label) { this.label = label; } } Creating the DSL Lets come up with the structure of the language before going into creating grammar rules. The structure which I am planning to come up is something like: Graph { A -> B (10) B -> C (20) D -> E (30) } Each line in the Graph block represents an edge and the vertices involved in the edge and the value in the braces represent the weight of the edge. One limitation which I am enforcing is that the Graph cannot have dangling vertices i.e vertices which are not part of any edge. This limitation can be removed by slightly changing the grammar, but I would leave that as an exercise for the readers of this post. The first task in creating the DSL is to define the grammar rules. These are the rules which your lexer and parser will use to convert the DSL into a Abstract Syntax tree/parse tree. ANTLR then makes use of this grammar to generate the Parser, Lexer and a Listener which are nothing but java classes extending/implementing some classes from the ANTLR library. The creators of the DSL must make use of these java classes to load the external DSL, parse it and then using the listener populate the semantic model as and when the parser encounters certain nodes (think of this as a variant of SAX parser for XML) Now that we know in very brief what ANTLR can do and the steps in using ANTLR, we would have to setup ANTLR i.e download ANTLR API jar and setup up some scripts for generating the parser and lexer and then trying out the language via the command line tool. For that please visit this official tutorial from ANTLR which shows how to setup ANTLR and a simple Hello World example. Grammar for the DSL Now that you have ANTLR setup let me dive into the grammar for my DSL: grammar Graph; graph: 'Graph {' edge+ '}'; vertex: ID; edge: vertex '->' vertex '(' NUM ')' ; ID: [a-zA-Z]+; NUM: [0-9]+; WS: [ \t\r\n]+ -> skip; Lets go rule: graph: 'Graph {' edge+ '}'; The above grammar rule which is the start rule says that the language should start with ‘Graph {‘ and end with ‘}’ and has to contain at lease one edge or more than one edge. vertex: ID; edge: vertex '->' vertex '(' NUM ')' ; ID: [a-zA-Z]+; NUM: [0-9]+; The above four rules say that a vertex should have atleast one character or more than one character. And an edge is defined as collection of two vertices separated by a ‘->’ and with the some digits in the ‘()’. I have named the grammar language as “Graph” and hence once we use ANTLR to generate the java classes i.e parser and lexer we will end up seeing the following classes: GraphParser, GraphLexer, GraphListener and GraphBaseListener. The first two classes deal with the generation of parse tree and the last two classes deal with the parse tree walk through. GraphListener is an interface which contains all the methods for dealing with the parse tree i.e dealing with events such as entering a rule, exiting a rule, visiting a terminal node and in addition to these it contains methods for dealing with events related to entering the graph rule, entering the edge rule and entering the vertex rule. We will be making use of these methods to intercept the data present in the dsl and then populate the semantic model. Populating the semantic model I have created a file graph.gr in the resource package which contains the DSL for populating the graph. As the files in the resource package are available to the ClassLoader at runtime, we can use the ClassLoader to read the DSL script and then pass it on to the Lexer and parser classes. The DSL script used is: Graph { A -> B (10) B -> C (20) D -> E (30) A -> E (12) B -> D (8) } And the code which loads the DSL and populates the semantic model: //Please resolve the imports for the classes used. public class GraphDslAntlrSample { public static void main(String[] args) throws IOException { //Reading the DSL script InputStream is = ClassLoader.getSystemResourceAsStream("resources/graph.gr"); //Loading the DSL script into the ANTLR stream. CharStream cs = new ANTLRInputStream(is); //Passing the input to the lexer to create tokens GraphLexer lexer = new GraphLexer(cs); CommonTokenStream tokens = new CommonTokenStream(lexer); //Passing the tokens to the parser to create the parse trea. GraphParser parser = new GraphParser(tokens); //Semantic model to be populated Graph g = new Graph(); //Adding the listener to facilitate walking through parse tree. parser.addParseListener(new MyGraphBaseListener(g)); //invoking the parser. parser.graph(); Graph.printGraph(g); } } /** * Listener used for walking through the parse tree. */ class MyGraphBaseListener extends GraphBaseListener { Graph g; public MyGraphBaseListener(Graph g) { this.g = g; } @Override public void exitEdge(GraphParser.EdgeContext ctx) { //Once the edge rule is exited the data required for the edge i.e //vertices and the weight would be available in the EdgeContext //and the same can be used to populate the semantic model Vertex fromVertex = new Vertex(ctx.vertex(0).ID().getText()); Vertex toVertex = new Vertex(ctx.vertex(1).ID().getText()); double weight = Double.parseDouble(ctx.NUM().getText()); Edge e = new Edge(fromVertex, toVertex, weight); g.addEdge(e); } } And the output when the above would be executed would be: Vertices... A B C D E Edges... A to B with weight 10.0 B to C with weight 20.0 D to E with weight 30.0 A to E with weight 12.0 B to D with weight 8.0 To summarize, this post creates a external DSL for populating the data for graphs by making use of ANTLR. I will enhance this simple DSL and expose it as an utility which can be used by programmers working on graphs. The post is very heavy on concepts and code, feel free to drop in any queries you have so that I can try to address them for benefit of others as well.

An overview starting with some basic classes of the Java 8 package: Instant, LocalDate, LocalTime, and LocalDateTime.

I previously wrote a blog post describing how I’ve been trying to learn more about network sockets in which I created some server sockets and connected to them using netcat. The next step was to do the same thing in Java and I started out by writing a server socket which echoed any messages sent by the client: public class EchoServer { public static void main(String[] args) throws IOException { int port = 4444; ServerSocket serverSocket = new ServerSocket(port, 50, InetAddress.getByAddress(new byte[] {0x7f,0x00,0x00,0x01})); System.err.println("Started server on port " + port); while (true) { Socket clientSocket = serverSocket.accept(); System.err.println("Accepted connection from client: " + clientSocket.getRemoteSocketAddress() ); In in = new In (clientSocket); Out out = new Out(clientSocket); String s; while ((s = in.readLine()) != null) { out.println(s); } System.err.println("Closing connection with client: " + clientSocket.getInetAddress()); out.close(); in.close(); clientSocket.close(); } } } public final class In { private Scanner scanner; public In(java.net.Socket socket) { try { InputStream is = socket.getInputStream(); scanner = new Scanner(new BufferedInputStream(is), "UTF-8"); } catch (IOException ioe) { System.err.println("Could not open " + socket); } } public String readLine() { String line; try { line = scanner.nextLine(); } catch (Exception e) { line = null; } return line; } public void close() { scanner.close(); } } public class Out { private PrintWriter out; public Out(Socket socket) { try { out = new PrintWriter(socket.getOutputStream(), true); } catch (IOException ioe) { ioe.printStackTrace(); } } public void close() { out.close(); } public void println(Object x) { out.println(x); out.flush(); } } I ran the main method of the class and this creates a server socket on port 4444 listening on the 127.0.0.1 interface and we can connect to it using netcat like so: $ nc -v 127.0.0.1 4444 Connection to 127.0.0.1 4444 port [tcp/krb524] succeeded! hello hello The output in my IntelliJ console looked like this: Started server on port 4444 Accepted connection from client: /127.0.0.1:63222 Closing connection with client: /127.0.0.1 Using netcat is fine but what I actually wanted to do was write some test code which would check that I’d made sure the server socket on port 4444 was accessible via all interfaces i.e. bound to 0.0.0.0. There are actually some quite nice classes in Java which make this very easy to do and wiring those together I ended up with the following client code: public static void main(String[] args) throws IOException { Enumeration nets = NetworkInterface.getNetworkInterfaces(); for (NetworkInterface networkInterface : Collections.list(nets)) { for (InetAddress inetAddress : Collections.list(networkInterface.getInetAddresses())) { Socket socket = null; try { socket = new Socket(inetAddress, 4444); System.out.println(String.format("Connected using %s [%s]", networkInterface.getDisplayName(), inetAddress)); } catch (ConnectException ex) { System.out.println(String.format("Failed to connect using %s [%s]", networkInterface.getDisplayName(), inetAddress)); } finally { if (socket != null) { socket.close(); } } } } } } If we run the main method of that class we’ll see the following output (on my machine at least!): Failed to connect using en0 [/fe80:0:0:0:9afe:94ff:fe4f:ee50%4] Failed to connect using en0 [/192.168.1.89] Failed to connect using lo0 [/0:0:0:0:0:0:0:1] Failed to connect using lo0 [/fe80:0:0:0:0:0:0:1%1] Connected using lo0 [/127.0.0.1] Interestingly we can’t even connect via the loopback interface using IPv6 which is perhaps not that surprising in retrospect given we bound using an IPv4 address. If we tweak the second line of EchoServer from: ServerSocket serverSocket = new ServerSocket(port, 50, InetAddress.getByAddress(new byte[] {0x7f,0x00,0x00,0x01})); to ServerSocket serverSocket = new ServerSocket(port, 50, InetAddress.getByAddress(new byte[] {0x00,0x00,0x00,0x00})); And restart the server before re-running the client we can now connect through all interfaces: Connected using en0 [/fe80:0:0:0:9afe:94ff:fe4f:ee50%4] Connected using en0 [/192.168.1.89] Connected using lo0 [/0:0:0:0:0:0:0:1] Connected using lo0 [/fe80:0:0:0:0:0:0:1%1] Connected using lo0 [/127.0.0.1] We can then wrap the EchoClient code into our testing framework to assert that we can connect via all the interfaces.

Overview OpenHFT/Java Lang started as an Apache 2.0 library to provide the low level functionality used by Java Chronicle without the need to persist to a file. This allows serializable and deserialization of data and random access to memory in native space (off heap) It supports writing and reading enumerable types with object pooling. e.g. writing and reading String without creating an object (if it has been pooled). It also supports writing and read primitive types in binary and text without creating any garbage. Small messages can be serialized and deserialized in under a micro-second. Recent additions Java Lang supports a DirectStore which is like a ByteBuffer but can be any size (up to 40 to 48-bit on most systems) It support 64-bit sizes and offset. It support compacted types, and object serialization. It also supports thread safety features such as volatile reads, ordered (lazy) writes, CAS operations and using an int (4 bytes) as a lock in native memory. Testing a native memory lock in Java This test has one lock and a value which is toggled. One thread changes the value from 0 to 1 and the other switches it from 1 to 0. This goes around 20 million times, but has been run for longer final DirectStore store1 = DirectStore.allocate(1L << 12); final int lockCount = 20 * 1000 * 1000; new Thread(new Runnable() { @Override public void run() { manyToggles(store1, lockCount, 1, 0); } }).start(); manyToggles(store1, lockCount, 0, 1); store1.free(); The manyToggles method is more interesting. Note is using the 4 bytes at offset 0 as a lock. You can arrange any number of locks in native space this way. E.g. you might have fixed length records and want to be able to lock them before updating or access them. You can place a lock at the "head" of the record. private void manyToggles(DirectStore store1, int lockCount, int from, int to) { long id = Thread.currentThread().getId(); assertEquals(0, id >>> 24); System.out.println("Thread " + id); DirectBytes slice1 = store1.createSlice(); for (int i = 0; i < lockCount; i++) { assertTrue( slice1.tryLockNanosInt(0L, 10 * 1000 * 1000)); int toggle1 = slice1.readInt(4); if (toggle1 == from) { slice1.writeInt(4L, to); } else { i--; } slice1.unlockInt(0L); } } The size of the DataStore and the offsets within it are long, allowing you to allocate a continuous block of native memory into the many GB, and access it as you required. On my 2.6 GHz i5 laptop I get the following output for this test Contended lock rate was 9,096,824 per second This looks great but under heavy contention, one thread can be staved out. This is more useful for lots of locks and lower contention. Note: if I drop the timeout from 10 ms to 1 ms, it eventually fails meaning sometimes it takes more then 1 ms to get a lock! Conclusion The Java Lang library is taking the step of making it easier to use native memory with the same functionality available on the heap. The language support is not as good, but if you need to store say 128 GB of data you will get a much better GC behavior using off heap memory.

In the last post I talked about our need for some improved testing tools, our choice of Spock as something to spike, and how mocking looks in Spock. As that blog got rather long, I saved the next installment for a separate post. Today I want to look at stubbing. Stubbing Mocking is great for checking outputs - in the example in the last post, we're checking that the process of encoding an array calls the right things on the way out, if you like - that the right stuff gets poked onto the bsonWriter. Stubbing is great for faking your inputs (I don't know why this difference never occurred to me before, but Colin's talk at Devoxx UK made this really clear to me). One of the things we need to do in the compatibility layer of the new driver is to wrap all the new style Exceptions that can be thrown by the new architecture layer and turn them into old-style Exceptions, for backwards compatibility purposes. Sometimes testing the exceptional cases is... challenging. So I opted to do this with Spock. class DBCollectionSpecification extends Specification { private final Mongo mongo = Mock() private final ServerSelectingSession session = Mock() private final DB database = new DB(mongo, 'myDatabase', new DocumentCodec()) @Subject private final DBCollection collection = new DBCollection('collectionName', database, new DocumentCodec()) def setup() { mongo.getSession() >> { session } } def 'should throw com.mongodb.MongoException if rename fails'() { setup: session.execute(_) >> { throw new org.mongodb.MongoException('The error from the new Java layer') } when: collection.rename('newCollectionName'); then: thrown(com.mongodb.MongoException) } } So here we can use a real DB class, but with a mock Mongo that will return us a "mock" Session. It's not actually a mock though, it's more of a stub because we want to tell it how to behave when it's called - in this test, we want to force it to throw an org.mongodb.MongoException whenever execute is called. It doesn't matter to us what get passed in to the execute method (that's what the underscore means on line 16), what matters is that when it gets called it throws the correct type of Exception. Like before, the when: section shows the bit we're actually trying to test. In this case, we want to callrename. Then finally the then: section asserts that we received the correct sort of Exception. It's not enormously clear, although I've kept the full namespace in to try and clarify, but the aim is that anyorg.mongodb.MongoException that gets thrown by the new architecture gets turned into the appropriate com.mongodb.MongoException. We're sort of "lucky" because the old code is in the wrong package structure, and in the new architecture we've got a chance to fix this and put stuff into the right place. Once I'd tracked down all the places Exceptions can escape and started writing these sorts of tests to exercise those code paths, not only did I feel more secure that we wouldn't break backwards compatibility by leaking the wrong Exceptions, but we also found our test coverage went up - and more importantly, in the unhappy paths, which are often harder to test. I mentioned in the last post that we already did some simple stubbing to help us test the data driver. Why not just keep using that approach? Well, these stubs end up looking like this: private static class TestAsyncConnectionFactory implements AsyncConnectionFactory { @Override public AsyncConnection create(final ServerAddress serverAddress) { return new AsyncConnection() { @Override public void sendMessage(final List byteBuffers, final SingleResultCallback callback) { throw new UnsupportedOperationException(); } @Override public void receiveMessage(final ResponseSettings responseSettings, final SingleResultCallback callback) { throw new UnsupportedOperationException(); } @Override public void close() { } @Override public boolean isClosed() { throw new UnsupportedOperationException(); } @Override public ServerAddress getServerAddress() { throw new UnsupportedOperationException(); } }; } } Ick. And you end up extending them so you can just override the method you're interested in (particularly in the case of forcing a method to throw an exception). Most irritatingly to me, these stubs live away from the actual tests, so you can't easily see what the expected behaviour is. In the Spock test, the expected stubbed behaviour is defined on line 16, the call that will provoke it is on line 19 and the code that checks the expectation is on line 22. It's all within even the smallest monitor's window. So stubbing in Spock is painless. Next!

A recent Java production performance problem forced me to revisit and truly appreciate the Java VM Just-In-Time (JIT) compiler. Most Java developers and support individuals have heard of this JVM run time performance optimization but how many truly understand and appreciate its benefits? This article will share with you a troubleshooting exercise I was involved with following the addition of a new virtual server (capacity improvement and horizontal scaling project). For a more in-depth coverage of JIT, I recommend the following articles: ## Just-in-time compilation http://en.wikipedia.org/wiki/Just-in-time_compilation ## The Java HotSpot Performance Engine Architecture http://www.oracle.com/technetwork/java/whitepaper-135217.html ## Understanding Just-In-Time Compilation and Optimization http://docs.oracle.com/cd/E15289_01/doc.40/e15058/underst_jit.htm ## How the JIT compiler optimizes code http://pic.dhe.ibm.com/infocenter/java7sdk/v7r0/index.jsp?topic=%2Fcom.ibm.java.zos.70.doc%2Fdiag%2Funderstanding%2Fjit_overview.html JIT compilation overview The JIT compilation is essentially a process that improves the performance of your Java applications at run time. The diagram below illustrates the different JVM layers and interaction. It describes the following high level process: Java source files are compiled by the Java compiler into platform independent bytecode or Java class files. After your fire your Java application, the JVM loads the compiled classes at run time and execute the proper computation semantic via the Java interpreter. When JIT is enabled, the JVM will analyze the Java application method calls and compile the bytecode (after some internal thresholds are reached) into native, more efficient, machine code. The JIT process is normally prioritized by the busiest method calls first. Once such method call is compiled into machine code, the JVM executes it directlyinstead of “interpreting” it. The above process leads to improved run time performance over time. Case study Now here is the background on the project I was referring to earlier. The primary goal was to add a new IBM P7 AIX virtual server (LPAR) to the production environment in order to improve the platform’s capacity. Find below the specifications of the platform itself: Java EE server: IBM WAS 6.1.0.37 & IBM WCC 7.0.1 OS: AIX 6.1 JDK: IBM J2RE 1.5.0 (SR12 FP3 +IZ94331) @64-bit RDBMS: Oracle 10g Platform type: Middle tier and batch processing In order to achieve the existing application performance levels, the exact same hardware specifications were purchased. The AIX OS version and other IBM software’s were also installed using the same version as per existing production. The following items (check list) were all verified in order to guarantee the same performance level of the application: Hardware specifications (# CPU cores, physical RAM, SAN…). OS version and patch level; including AIX kernel parameters. IBM WAS & IBM WCC version, patch level; including tuning parameters. IBM JRE version, patch level and tuning parameters (start-up arguments, Java heap size…). The network connectivity and performance were also assessed properly. After the new production server build was completed, functional testing was performed which did also confirm a proper behaviour of the online and batch applications. However, a major performance problem was detected on the very first day of its production operation. You will find below a summary matrix of the performance problems observed. Production server Operation elapsed time Volume processed (# orders) CPU % (average) Middleware health Existing server 10 hours 250 000 (baseline) 20% healthy *New* server 10 hours 50 000 -500% 80% +400% High thread utilization As you can see from the above view, the performance results were quite disastrous on the first production day. Not only much less orders were processed by the new production server but the physical resource utilization such as CPU % was much higher compared with the existing production servers. The situation was quite puzzling given the amount of time spent ensuring that the new server was built exactly like the existing ones. At that point, another core team was engaged in order to perform extra troubleshooting and identify the source of the performance problem. Troubleshooting: searching for the culprit... The troubleshooting team was split in 2 in order to focus on the items below: Identify the source of CPU % from the IBM WAS container and compare the CPU footprint with the existing production server. Perform more data and file compares between the existing and new production server. In order to understand the source of the CPU %, we did perform an AIX CPU per Thread analysis from the IBM JVM running IBM WAS and IBM WCC. As you can see from the screenshot below, many threads were found using between 5-20% each. The same analysis performed on the existing production server did reveal fewer # of threads with CPU footprint always around 5%. Conclusion: the same type of business process was using 3-4 times more CPU vs. the existing production server. In order to understand the type of processing performed, JVM thread dumps were captured at the same time of the CPU per Thread data. Now the first thing that we realized after reviewing the JVM thread dump (Java core) is that JIT was indeed disabled! The problem was also confirmed by running the java –version command from the running JVM processes. This finding was quite major, especially given that JIT was enabled on the existing production servers. Around the same time, the other team responsible of comparing the servers did finally find differences between the environment variables of the AIX user used to start the application. Such compare exercise was missed from the earlier gap analysis. What they found is that the new AIX production server had the following extra entry: JAVA_COMPILER=NONE As per the IBM documentation, adding such environment variable is one of the ways to disableJIT. Complex root cause analysis, simple solution In order to understand the impact of disabling JIT in our environment, you have to understand its implication. Disabling JIT essentially means that the entire JVM is now running ininterpretation mode. For our application, running in full interpretation mode not only reduces the application throughput significantly but also increases the pressure point on the server CPU utilization since each request/thread takes 3-4 more times CPU than a request executed with JIT (remember, when JIT is enabled, the JVM will perform many calls to the machine/native code directly). As expected, the removal of this environment variable along with the restart of the affected JVM processes did resolve the problem and restore the performnance level. Assessing the JIT benefits for your application I hope you appreciated this case study and short revisit of the JVM JIT compilation process. In order to understand the impact of not using JIT for your Java application, I recommend that you preform the following experiment: Generate load to your application with JIT enabled and capture some baseline data such as CPU %, response time, # requests etc. Disable JIT. Redo the same testing and compare the results. I’m looking forward for your comments and please share any experience you may have with JIT.

Introduction The purpose of this article is to see how to integrate Spring Security on top of an Openxava standalone application. Openxava builds portlets as well as standalone applications. When working with portlets deployed on a portal such as Liferay, they handle secured access by configuration. A standalone application lets you have to handle this functionality yourself. This page will illustrate how to add spring security (authentication/authorisation) functionalities. The focus will be on the authorizations aspects since authorization is often enterprise-environment specific. To demonstrate the integration, this article will use the minuteproject Lazuly showcase application generated for Openxava. The first part identifies and explains the actions to undertake. The second part explains what minuteproject can do to fasten your development by generated a customed spring-security integration for you Openxava application. Eventually a set of tests will ensure that the resulting application is correctly protected for URL direct access as well as content display. Furthermore, the integration is technologically non-intruisive. You do not have to change Openxava code for it to work. Spring-Security Openxava integration Technical Access URL access The url pattern is the following http://servername:port/applicationcontext/xava/module.jsp?application=appName&module=moduleName given like that it is hard to protect. The module and application are passed as parameters. The URL has to be revisited with http://servername:port/applicationcontext/applicationPath/module And the 'parameter' access are banned. Enabling new URL access Add a servlet package net.sf.minuteproject.openxava.web.servlet; import java.io.*; import javax.servlet.*; import javax.servlet.http.*; public class ModuleHomeServlet extends HttpServlet { protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { RequestDispatcher dispatcher; String [] uri = request.getRequestURI().split("/"); if (uri.length < 4) { dispatcher = request.getRequestDispatcher("/xava/homeMenu.jsp"); } else { dispatcher = request.getRequestDispatcher( "/xava/home.jsp?application=" + uri[1] + "&module=" + uri[3]); } dispatcher.forward(request, response); } protected void doPost(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { doGet(request, response); } } homeMenu.jsp is a page including a header with menu (to protect and whose menu link URL are correspond to the secured format) and a footer. Add a servlet configuration Servlet configuration snippet done in Openxava servlets.xml. moduleHome net.sf.minuteproject.openxava.web.servlet.ModuleHomeServlet moduleHome /MenuModules/* This snippet will be package in war web.xml at build time by OpenXava ant script. Jsp access Prohibit any Openxava jsp access except the one of the menu To do that add an spring applicationContext-security.xml in you classpath (ex: Openxava src folder). ... This means that all path after xava will be accessible (ex: css...) safe jsp expect one homeMenu.jsp is available to all registered user (ie having role ROLE_APPLICATION_USER cf attribution at authorisation part further). Of course ensure that the role ROLE_NOT_PRESENT is really not present in your app. Business Access The idea is to give CRUD access on a entity base on role. Define roles and UC To be more explicit, I define 3 roles with their scope. Administrator can administrate ROLE and COUNTRY entities Application_user can manage all the other conference related tables safe the master data table mentionned above. Reviewer can access to the statistic views but not the administration. Both reviewer and Administrator can do what Application_user can do. In applicationContext-security.xml the role can be mapped to specific URLs Impact of the roles access on your model modal navigation Be coherent As I said before 'the CRUD access on a entity is role based' but the affectation mechanism has to reflect that. OpenXava has annotation to create an entity from another one. It is then logical that we cannot create entity B from entity A, if we do not have CRUD rights on entity B. The mechanism will consist in this case of affectation only with search functionalities. In our scenario it means that a user with 'application_user' only can select a country but can not create any (no create or update icons available). It is also true at the menu level, a user is entitled to see only its menu items corresponding to its profile. Here the menu is done in JSP. To secure the access you can wrap to code to secure with taglib code coming with spring security or add a little taglib such as the following isUserInRole.tag located in web/WEB-INF/tags/common. Wrap the code to protect here the administrator menu and each menu item Administration CountryRole Authentication/Authorization For the user to operate, he must be authenticated and authorised (moment where his role profile is loaded granting him with business access rights). I use an simple authentication and authorisation based a DB information. Of course you are not supposed to use that in production ;) In applicationContext-security.xml add the following snippet. java:comp/env/jdbc/conferenceDS Both authorisation and authentication queries have to be valid. Here, they are done on top of views, which means that you have to implement 2 views: user_authentication and user_authorisation. The datasource is the same as the one of the Openxava application View gives you flexibility because if you have indirection level of granularity such as (user-role-permission), your view can associate user to role Authentication flow Eventually you need to handle an authentication flow composed of welcome page login page access denied page logout link The flow is handled by applicationContext-security.xml Add the following snippet. Login.jsp is strongly inspired by spring petclinic sample Login test Locale is: Your login attempt was not successful, try again. Reason: . User:Password:Don't ask for my password for two weeks index.jsp Welcome to Conference login accessDenied.jsp Access denied! Not to forget a logout functionality here added on the menu Logoff Spring security dependencies Add spring security jars into web/WEB-INF/lib spring-aop-3.0.4.RELEASE.jar spring-asm-3.0.4.RELEASE.jar spring-beans-3.0.4.RELEASE.jar spring-context-3.0.4.RELEASE.jar spring-core-3.0.4.RELEASE.jar spring-expression-3.0.4.RELEASE.jar spring-jdbc-3.0.4.RELEASE.jar spring-security-acl-2.0.3.jar spring-security-config-3.1.0.M1.jar spring-security-core-2.0.3.jar spring-security-core-3.1.0.M1.jar spring-security-core-tiger-2.0.3.jar spring-security-taglibs-2.0.3.jar spring-security-web-3.1.0.M1.jar spring-tx-3.0.4.RELEASE.jar spring-web-3.0.4.RELEASE.jar Spring security context Spring security context had been mentioned at different level, here is the complete version java:comp/env/jdbc/conferenceDS Reference the context Openxava listeners.xml is the place where you can set web.xml-snippets to be package in web.xml at Openxava build time Add the following snippet org.springframework.web.context.ContextLoaderListener contextConfigLocation classpath:applicationContext-security.xml springSecurityFilterChain org.springframework.web.filter.DelegatingFilterProxy springSecurityFilterChain /* The minuteproject way Doing the integration can be time consuming. As you can notice there is some effort to have the code compliant for a webapp here Openxava to be bodyguard by Spring-Security. Meanwhile when dealing with data centric application, this knowledge can be crystalized to be instantly available. Because...there is an underlying concept that guides our choice and lead to best pratices. It is one thing to execute them, it is another to state it. The question is how do we specify which entity to access and to which role. The idea is to express with simplicity the relationship between role or permission and action. In our case the actions are: a full CRUD an affectation mechanism The full CRUD is associated to a specific role. The affection (linkage of an entity from another by search) is when to entities are linked but not all the role of the main entities are the same as the roles of the target. Otherwise affection goes with creation and update. And the roles are: Administrator Application_user Reviewer Now it is time for a primary school exercice If you represent an entity-relationship diagram, you should see boxes and links. Boxes for entities and links for relationships. Give each role/permission a color. Paint all the boxes that are full CRUD with the corresponding role color... Yes, you may paint the same box twice (resulting is color combination). The result gives you the Color access spectrum of your DB. Of course, we can further decline the gradient with other function (read-only, controller specific...) But the underlying idea is evident. What Minuteproject allows you to do it by enriching your model with this color spectrum at the entity level or at the package level. This enables you to work with concept only closed to UC agnostic of technology implementations. Minuteproject configuration snippet Generation Minuteproject configuration full The configuration is similar to lazuly show case enhanced with security aspects org.gjt.mm.mysql.Driver jdbc:mysql://127.0.0.1:3306/conference root mysql The main points are exclude entities starting with user_ (i.e. the security entity used by spring configuration) add security access on package level package admin is accessible by role administrator only package statistics is accessible by role reviewer only default package (conference) is accessible by any application_user add spring-security track in the target add reference in openxava to spring-security The track springsecurity holding the configuration is not yet bundled in minuteproject release 0.8 but will be present for 0.8.1+. Set up Database Implement the views Here a very dummy implementation. create view user_authentication as select email as username, first_name as password, '1' as active from conference_member ; create view user_authorisation as select cm.email as username, r.name as role from conference_member cm, role r, member_role mr where mr.role_id = r.id and mr.conference_member_id = cm.id union select cm.email as username, concat('ROLE_',r.name) as role from conference_member cm, role r, member_role mr where mr.role_id = r.id and mr.conference_member_id = cm.id ; As you can not there is a little redundancy in the user_authentication view, since sometimes the role administrator is refered sometimes role_administrator. This will be homogenized in next release. Add some default value Here a very dummy implementation. INSERT INTO country (id, name, iso_name) VALUES (-1, 'France', 'FR'); INSERT INTO address (id, street1, street2, country_id) VALUES(-1, 'rue 1', 'rue 2', -1); INSERT INTO conference_member (id, conference_id, first_name, last_name, email, address_id, status ) VALUES (-1, -1, 'f', 'a', '[email protected]', -1, 'ACTIVE' ); INSERT INTO role (id, name) VALUES (-1, 'ADMINSTRATOR' ); INSERT INTO role (id, name) VALUES (-2, 'ROLE_APPLICATION_USER' ); INSERT INTO member_role (conference_member_id, role_id) VALUES (-1, -1); INSERT INTO member_role (conference_member_id, role_id) VALUES (-1, -2); So when user [email protected] connects he will get the role Administrator which allows him to access the administrator menu and create a new role called 'REVIEWER'. He can also create a new conference member and associate with the role 'REVIEWER'. Set up Application Download the lazuly-openxava-springsecurity minuteproject configuration from google code minuteproject. Copy file into /mywork/config Execute In /mywork/config: model-generation.cmd mp-config-LAZULY-Openxava-with-spring-security.xml The generated code goes to /DEV/output/openxava-springsecurity/conference Packaging Here the packaging/deployment is a 2 steps exercices (unfortunately): there is no more the start-tomcat/stop-tomcat command in OX distribution spring dependencies are not included Steps Check that Openxava 4.3 is available, and OX_HOME is set to Openxava 4.3 from /DEV/output/openxava-springsecurity/conference run build-conference(.cmd/sh). This will trigger the build that is successful but not the deployment due to information before. Open the project generated by the build in Openxava workspace Add Spring security dependencies Start tomcat server (remark: The Datasource for the application is present in tomcat/config/context.xml) Deploy Enjoy Testing Welcome page Default URL at context root of the application. Login page Any other direct called where the user is not authenticated will be intercepted and routed to this page Contextual Menu The user have access to the admin and conference part not the statistics. The URLs have been modified. When the user tries to access the standard OX style URL he recieves an access denied (ex: module.jsp) Add role reviewer Add user Affect user with role reviewer and default (application_user) Logoff (click logoff) Login as Reviewer On login page enter [email protected] and password=b In the contextual menu you do see the 'admin' package' And you get an access deny when manipulating directly the URL Now the application is secured. Conclusion This article showed the configuration and manipulation to integrate spring security with openxava in a non-intrusive manner. It stressed a new concept 'DB color access spectrum' and how to densify the security information in minuteproject configuration. DB color access spectrum is a concept which ask only to be extended: Ad-hoc functions, controllers Store procedures It is simple to express and analyst friendly. It is not bound to a technology. It is a step in easily defining fine grain access, its combination with profile based access and state based access (to do manually... for the moment ;)) could pave the way to intuitive and implicit workflows instead of heavy BPM solutions.

Strategy Pattern is one of the patterns from the Design Patterns : Elements of Reusable Object book. The intent of the strategy pattern as stated in the book is: Define a family of algorithms, encapsulate each one, and make them interchangeable. Strategy lets the algorithm vary independently from clients that use it. In this post I would like to give an example or two on strategy pattern and then rewrite the same example using lambda expressions to be introduced in Java 8. Strategy Pattern: An example Consider an interface declaring the strategy: interface Strategy{ public void performTask(); } Consider two implementations of this strategy: class LazyStratgey implements Strategy{ @Override public void performTask() { System.out.println("Perform task a day before deadline!"); } } class ActiveStratgey implements Strategy{ @Override public void performTask() { System.out.println("Perform task now!"); } } The above strategies are naive and I have kept it simple to help readers grasp it quickly. And lets see these strategies in action: public class StartegyPatternOldWay { public static void main(String[] args) { List strategies = Arrays.asList( new LazyStratgey(), new ActiveStratgey() ); for(Strategy stg : strategies){ stg.performTask(); } } } The output for the above is: Perform task a day before deadline! Perform task now! Strategy Pattern: An example with Lambda expressions Lets look at the same example using Lambda expressions. For this we will retain our Strategy interface, but we need not create different implementation of the interface, instead we make use of lambda expressions to create different implementations of the strategy. The below code shows it in action: import java.util.Arrays; import java.util.List; public class StrategyPatternOnSteroids { public static void main(String[] args) { System.out.println("Strategy pattern on Steroids"); List strategies = Arrays.asList( () -> {System.out.println("Perform task a day before deadline!");}, () -> {System.out.println("Perform task now!");} ); strategies.forEach((elem) -> elem.performTask()); } } The output for the above is: Strategy pattern on Steroids Perform task a day before deadline! Perform task now! In the example using lambda expression, we avoided the use of class declaration for different strategies implementation and instead made use of the lambda expressions. Strategy Pattern: Another Example This example is inspired from Neal Ford’s article on IBM Developer works: Functional Design Pattern-1. The idea of the example is exactly similar, but Neal Ford uses Scala and I am using Java for the same with a few changes in the naming conventions. Lets look at an interface Computation which also declares a generic type T apart from a method compute which takes in two parameters. interface Computation { public T compute(T n, T m); } We can have different implementations of the computation like: IntSum – which returns the sum of two integers, IntDifference – which returns the difference of two integers and IntProduct – which returns the product of two integers. class IntSum implements Computation { @Override public Integer compute(Integer n, Integer m) { return n + m; } } class IntProduct implements Computation { @Override public Integer compute(Integer n, Integer m) { return n * m; } } class IntDifference implements Computation { @Override public Integer compute(Integer n, Integer m) { return n - m; } } Now lets look at these strategies in action in the below code: public class AnotherStrategyPattern { public static void main(String[] args) { List computations = Arrays.asList( new IntSum(), new IntDifference(), new IntProduct() ); for (Computation comp : computations) { System.out.println(comp.compute(10, 4)); } } }public class AnotherStrategyPattern { public static void main(String[] args) { List computations = Arrays.asList( new IntSum(), new IntDifference(), new IntProduct() ); for (Computation comp : computations) { System.out.println(comp.compute(10, 4)); } } } The output for the above is: 14 6 40 Strategy Pattern: Another Example with lambda expressions Now lets look at the same example using Lambda expressions. As in the previous example as well we need not declare classes for different implementation of the strategy i.e the Computation interface, instead we make use of lambda expressions to achieve the same. Lets look at an example: public class AnotherStrategyPatternWithLambdas { public static void main(String[] args) { List> computations = Arrays.asList( (n, m)-> { return n+m; }, (n, m)-> { return n*m; }, (n, m)-> { return n-m; } ); computations.forEach((comp) -> System.out.println(comp.compute(10, 4))); } } The output for above is 14 6 40 From the above examples we can see that using Lambda expressions will help in reducing lot of boilerplate code to achieve more concise code. And with practice one can get used to reading lambda expressions.

in-line predicates can create a maintenance nightmare. writing in-line lambda expressions and using the stream interfaces to perform common operations on collections can be awesome. assume the following example: list getadultmales (list persons) { return persons.stream().filter(p -> p.getage() > adult && p.getsex() == sexenum.male ).collect(collectors.tolist()); } that’s fun! but things like this also lead to software that is costly to maintain. at least in an enterprise application, where most of your code handles business logic, your development team will grow the tenancy to write the same similar set of predicate rules again and again. that is not what you want on your project. it breaks three important principles for growing maintainable and stable enterprise applications: dry (don’t repeat yourself): writing code more than once is not a good fit for a lazy developer it also makes your software more difficult to maintain because it becomes harder to make your business logic consistent readability : following clean-code best practices, 80% of writing code is reading the code that already exists. having complicated lambda expressions is still a bit hard to read compared to a simple one-line statement. testability : your business logic needs to be well-tested. it is adviced to unit-test your complex predicates. and that is just much easier to do when you separate your business predicate from your operational code. and from a personal point of view… that method still contains too much boilerplate code… imports to the rescue! fortunately, we have a very good suggestion in the world of unit testing on how we could improve on this. imagine the following example: import static somepackage.personpredicate; ... list getadultmales (list persons) { return persons.stream().filter( isadultmale() ).collect(collectors.tolist()); } what we did here was: create a personpredicate class define a “factory” method that creates the lambda predicate for us statically import the factory method into our old class this is how such a predicate class could look like, located next to your person domain entity: public personpredicate { public static predicate isadultmale() { return p -> p.getage() > adult && p.getsex() == sexenum.male; } } wait… why don’t we just create a “ismaleadult” boolean function on the person class itself like we would do in domain driven development? i agreed, that is also an option… but as time goes on and your software project becomes bigger and loaded with functionality and data… you will again break your clean code principles: the class becomes bloated with all kind of function and conditions your class and tests become huge, more difficult to handle and change (*) (*) and yes… even if you do your best to separate your concerns and use composition patterns adding some defaults… working with domain objects, we can imagine that some operations (such as filter) are often executed on domain entities. taking that into account, it would make sense to let our entities implement some interface that offers us some default methods. for example: public interface domainoperations { default list filter(predicate predicate) { return persons.stream().filter( predicate ) .collect(collectors.tolist()); } } when our person entity implements this interface, we can clean-up our code even more: list getadultmales (list persons) { return persons.filter( isadultmale() ); } and there we go… conclusion moving your predicates to a predicate helper class offers some good advantages in the long run: predicate classes are easy to test and change your domain objects remain clean and focussed on representing your domain, not your business logic you optimize the re-usability of your code and, in the end, reduce your maintenance you seperate your business from operational concerns references clean code: a handbook of agile software craftsmanship [robert c. martin] practical unit testing with junit and mockito [tomek kaczanowski] state of the collections [http://cr.openjdk.java.net/~briangoetz/lambda/collections-overview.html] notes the code above is served as an example to illustrate the principles i wanted to discuss. however, i did not proof-run this code yet (it’s still on my todo list). some modifications may be needed for your project.

A lot of times you need to to assign some functionality to some keyboard shortcut like F5 or Ctrl+R in your application. JavaFx also provides KeyCodeCombination API for handling multiple key events. scene.setOnKeyPressed(new EventHandler() { public void handle(final KeyEvent keyEvent) { if (keyEvent.getCode() == KeyCode.F5) { System.out.println("F5 pressed"); //Stop letting it do anything else keyEvent.consume(); } } }); final KeyCombination keyComb1 = new KeyCodeCombination(KeyCode.R, KeyCombination.CONTROL_DOWN); scene.addEventHandler(KeyEvent.KEY_RELEASED, new EventHandler() { @Override public void handle(KeyEvent event) { if (keyComb1.match(event)) { System.out.println("Ctrl+R pressed"); } } });

This technical tip shows how to Add, Delete & Get Attachment in a PDF Document using Aspose.Pdf for Java. In order to add attachment in a PDF document, you need to create a FileSpecification object with the file, which needs to be added, and the file description. After that the FileSpecification object can be added to EmbeddedFiles collection of Document object using add(..) method of EmbeddedFiles collection. The attachments of the PDF document can found in the EmbeddedFiles collection of the Document object. In order to delete all the attachments, you only need to call the delete(..) method of the EmbeddedFiles collection and then save the updated file using save method of the Document object. //Add attachment in a PDF document. //open document com.aspose.pdf.Document pdfDocument = new com.aspose.pdf.Document("input.pdf"); //setup new file to be added as attachment com.aspose.pdf.FileSpecification fileSpecification = new com.aspose.pdf.FileSpecification("sample.txt", "Sample text file"); //add attachment to document's attachment collection pdfDocument.getEmbeddedFiles().add(fileSpecification); // Save updated document containing table object pdfDocument.save("output.pdf"); //Delete all the attachments from the PDF document. //open document com.aspose.pdf.Document pdfDocument = new com.aspose.pdf.Document("input.pdf"); //delete all attachments pdfDocument.getEmbeddedFiles().delete(); //save updated file pdfDocument.save("output.pdf"); //Get an individual attachment from the PDF document. //open document com.aspose.pdf.Document pdfDocument = new com.aspose.pdf.Document("input.pdf"); //get particular embedded file com.aspose.pdf.FileSpecification fileSpecification = pdfDocument.getEmbeddedFiles().get_Item(1); //get the file properties System.out.printf("Name: - " + fileSpecification.getName()); System.out.printf("\nDescription: - " + fileSpecification.getDescription()); System.out.printf("\nMime Type: - " + fileSpecification.getMIMEType()); // get attachment form PDF file try { InputStream input = fileSpecification.getContents(); File file = new File(fileSpecification.getName()); // create path for file from pdf file.getParentFile().mkdirs(); // create and extract file from pdf java.io.FileOutputStream output = new java.io.FileOutputStream(fileSpecification.getName(), true); byte[] buffer = new byte[4096]; int n = 0; while (-1 != (n = input.read(buffer))) output.write(buffer, 0, n); // close InputStream object input.close(); output.close(); } catch (IOException e) { e.printStackTrace(); } // close Document object pdfDocument.dispose();

My team has recently started a new Java web application project and we picked gradle as our build tool. Most of us were extremely familiar with maven, but decided to give gradle a try. Today I had to figure out how to do resource filtering in gradle. And to be honest it wasn't as easy as I thought it should be; as least coming from a maven background. I eventually figured it out, but wanted to post my solution to make it easier for others. What is Resource Filtering? First, for those that may not know, what is resource filtering? It's basically a way to avoid hard coding values in files and make them more dynamic. For example, I may want to display the version of my application in my application. The version is usually defined in your build file and this value can be injected or replaced in your configuration file during assembly. So I could have a file called config.properties under src/main/resources with the following content: application.version=${application.version}. With resource filtering the ${application.version} value gets replaced with 1.0.0 during assembly, then my application can load config.properties and display the application version. It's an extremely valuable and powerful feature in build tools like maven and one that I took advantage of often. Resource Filtering in Gradle With this being my first gradle project, I needed to find the recommended way to enable resource filtering in gradle. My first problem I had to figure out was where to define the property. In maven this would typically be defined in the project's pom.xml file as a maven property: 1.0.0 For gradle the appropriate place seemed to be the project's gradle.properties file. So you would add the following to your project's gradle.properties file (Note, I'm not suggesting you would hardcode the modules version in the gradle.properties file. Obviously the value would be derived from the version property in your project. I'm just using this for a simple example): application.version=1.0.0 The next, and most difficult, problem I had to track down was how to actually enable resource filtering. I was hoping to just set some enableFiltering option and define the includes/excludes list, but that doesn't seem to be the case (extra tip: don't do filtering on binary files like images). I did find some resources online, but this one seemed to be the best approach. So you will need to add the following to your build.gradle file: import org.apache.tools.ant.filters.* processResources { filter ReplaceTokens, tokens: [ "application.version": project.property("application.version") ] } Next you need to update your resource file. So put a config.properties file under src/main/resources and add this: [email protected]@ Note, the use of @ instead of ${}. This is because gradle is based on ant, and ant by default uses the @ character as the token identifier whereas maven uses ${}. Finally, if you build your project you can look under build/resources/main and you should see a config.properties file with a value of 1.0.0. You can also open up your artifact and see the same result. Dot notation One thing to note is I typically use a period or dot to separate words for properties: application.version instead of applicationVersion. So you will notice the surrounding quotes around "application.version" in the build.gradle file. This is required as failing to surround the key by quotes will fail the build. Probably because groovy's dynamic nature thinks you are traversing an object. Overriding I also investigated the best approach to overriding properties in gradle, as this appeared to be slightly different then how it's done in maven. In maven, properties can be overridden by properties defined in the user's setting.xml file or on the command line with the -D option. To override application.version in gradle on the command line I had to run the following: gradle assemble -Papplication.version=2.0.0 If you want to override it for all projects you can add the property in your gradle.properties file under /user_home/.gradle. Also, if you are overriding the value via the command line and your property value contains special characters like a single quote, you can wrap the value with double quotes like the following to get it to work: gradle assemble -Papplication.version="2.0.0'6589" Summary Well I hope this helps and if anyone from the gradle community sees a better way to perform resource filtering I'd love to hear about it. I'd also like to see something as important as resource filtering becoming easier to perform in gradle. I think it's crazy having to add an import statement to perform something so simple.

Hi, I'm excited to announce the release of our QuartzDesk product. QuartzDesk is an advanced Java Quartz scheduler management and monitoring GUI / tool with many powerful and unique features. To name just a few: Support for Quartz 1.x and 2.x schedulers. Persistent job execution history. Job execution log message capturing. Notifications (email, all popular IM protocols, web-service). Interactive execution statistics and charts. REST API for job / trigger / scheduler monitoring. QuartzAnywhere web-service to manage / monitor Quartz schedulers from applications. and more To keep this announcement short, I kindly refer you to the QuartzDesk Features page for details and screenshots. The product is aimed at Java developers and system administrators. Jan Moravec (Founder) & The QuartzDesk Team

I was recently working a bit with Twitter’s Storm, and it got me wondering, how does it compare to another high-performance, concurrent-data-processing framework, Akka. WHAT’S AKKA AND STORM? Let’s start with a short description of both systems. Storm is a distributed, real-time computation system. On a Storm cluster, you execute topologies, which process streams of tuples (data). Each topology is a graph consisting of spouts (which produce tuples) and bolts (which transform tuples). Storm takes care of cluster communication, fail-over and distributing topologies across cluster nodes. Akka is a toolkit for building distributed, concurrent, fault-tolerant applications. In an Akka application, the basic construct is an actor; actors process messages asynchronously, and each actor instance is guaranteed to be run using at most one thread at a time, making concurrency much easier. Actors can also be deployed remotely. There’s a clustering module coming, which will handle automatic fail-over and distribution of actors across cluster nodes. Both systems scale very well and can handle large amounts of data. But when to use one, and when to use the other? There’s another good blog post on the subject, but I wanted to take the comparison a bit further: let’s see how elementary constructs in Storm compare to elementary constructs in Akka. COMPARING THE BASICS Firstly, the basic unit of data in Storm is a tuple. A tuple can have any number of elements, and each tuple element can be any object, as long as there’s a serializer for it. In Akka, the basic unit is amessage, which can be any object, but it should be serializable as well (for sending it to remote actors). So here the concepts are almost equivalent. Let’s take a look at the basic unit of computation. In Storm, we have components: bolts andsprouts. A bolt can be any piece of code, which does arbitrary processing on the incoming tuples. It can also store some mutable data, e.g. to accumulate results. Moreover, bolts run in a single thread, so unless you start additional threads in your bolts, you don’t have to worry about concurrent access to the bolt’s data. This is very similar to an actor, isn’t it? Hence a Storm bolt/sprout corresponds to an Akka actor. How do these two compare in detail? Actors can receive arbitrary messages; bolts can receive arbitrary tuples. Both are expected to do some processing basing on the data received. Both have internal state, which is private and protected from concurrent thread access. ACTORS & BOLTS: DIFFERENCES One crucial difference is how actors and bolts communicate. An actor can send a message to any other actor, as long as it has the ActorRef (and if not, an actor can be looked up by-name). It can also send back a reply to the sender of the message that is being handled. Storm, on the other hand is one-way. You cannot send back messages; you also can’t send messages to arbitrary bolts. You can also send a tuple to a named channel (stream), which will cause the tuple (message) to be broadcast to all listeners, defined in the topology. (Bolts also ack messages, which is also a form of communication, to the ackers.) In Storm, multiple copies of a bolt’s/sprout’s code can be run in parallel (depending on theparallelism setting). So this corresponds to a set of (potentially remote) actors, with a load-balancer actor in front of them; a concept well-known from Akka’s routing. There are a couple of choices on how tuples are routed to bolt instances in Storm (random, consistent hashing on a field), and this roughly corresponds to the various router options in Akka (round robin, consistent hashing on the message). There’s also a difference in the “weight” of a bolt and an actor. In Akka, it is normal to have lots of actors (up to millions). In Storm, the expected number of bolts is significantly smaller; this isn’t in any case a downside of Storm, but rather a design decision. Also, Akka actors typically share threads, while each bolt instance tends to have a dedicated thread. OTHER FEATURES Storm also has one crucial feature which isn’t implemented in Akka out-of-the-box: guaranteed message delivery. Storm tracks the whole tree of tuples that originate from any tuple produced by a sprout. If all tuples aren’t acknowledged, the tuple will be replayed. Also the cluster management of Storm is more advanced (automatic fail-over, automatic balancing of workers across the cluster; based on Zookeeper); however the upcoming Akka clustering module should address that. Finally, the layout of the communication in Storm – the topology – is static and defined upfront. In Akka, the communication patterns can change over time and can be totally dynamic; actors can send messages to any other actors, or can even send addresses (ActorRefs). So overall, Storm implements a specific range of usages very well, while Akka is more of a general-purpose toolkit. It would be possible to build a Storm-like system on top of Akka, but not the other way round (at least it would be very hard).