Learn how to use lambda expressions and anonymous functions in Java 8.

creating uber jar’s in java is nothing particular new, even creating executable jar’s was possible with maven long before java 8. with the first release of javafx 2, oracle introduced the javafxpackager tool, which has now been renamed to javapackager (java 8 u20). this enables developers to create native executables for any common platform, even mac app store packages; the drawbacks of the javapackager are that you must create the executable on the target platform and that it will than contain the whole jre to run the application. this means that your 100kb application gets more than 40mb, depending on your target platform. the first step on your way to an executable uber jar is to build your project and to collect all dependencies in a folder or a fat jar. this folder/fat jar will be the input for the javapackager tool, which creates the executable part. solution 1: the maven-dependency-plugin the maven-dependency-plugin contains the goal “unpack-dependencies”. we can use this goal to enhance the default “target/classes” folder with all the dependencies you need to run your application. we assume that the default maven build creates all classes of your project in the “target/classes” folder and the “unpack-dependencies” plugin copies all the project dependencies to this folder. the result is a valid input for the javapackager tool, which creates the executable. the “unpack-dependencies” goal is easy to use; you just need to set the output directory to the “target/classes” folder to ensure everything is together in one folder. a typical configuration looks like this: maven-dependency-plugin 2.6 unpack-dependencies package unpack-dependencies system org.springframework.jmx ${project.build.directory}/classes to create an executable jar from the target/classes directory we use the “exec-maven-plugin” to execute the javapackager commandline tool. org.codehaus.mojo exec-maven-plugin package-jar package exec ${env.java_home}/bin/javapackager -createjar -appclass ${app.main.class} -srcdir ${project.build.directory}/classes -outdir ./target -outfile ${project.artifactid}-app -v now we can create an executable jar, which contains all the project dependencies and that can simply be executed with “java –jar myapp.jar”. in the next step we want to create a native executable or an installer. specific configuration details for the javapackager can be found here: http://docs.oracle.com/javase/8/docs/technotes/tools/unix/javapackager.html , for this tutorial we assume that we want to create a native installer. to do so, we add a second “execution” to your “exec-maven-plugin” like this: package-jar2 package exec ${env.java_home}/bin/javapackager -deploy -native installer -appclass ${app.main.class} -srcfiles ${project.build.directory}/${artifactid}-app.jar -outdir ./target -outfile ${project.artifactid}-app -v once the configuration is done, you can run “mvn clean package” and you will find your executable jar as well as the native installer in your target folder. this solution works fine in most cases, but sometimes you can get in trouble with this plugin. when you have configuration files in your project that also exist in one of your dependencies, the unpack-dependency goal will overwrite your configuration file. for example your project contains a file like “meta-inf/service/com.myconf.file” and any dependency does contain the same file. in this case solution 2 may be a better approach. solution 2: the maven-shade plugin the maven-shade plugin provides the capability to package artifacts into an uber-jar, including its dependencies. it also provides various transformers to merge configuration files or to define the manifest of your jar file. this capability allows us to create an executable uber jar without involving the javapackager, so the packager is only needed to create the native executable. to build an executable uber jar following plugin configuration is needed: org.apache.maven.plugins maven-shade-plugin 2.3 package shade junit:junit jmock:* *:xml-apis … meta-inf/services/conf.file ${app.main.class} ${maven.compile.java.version} ${maven.compile.java.version} this configuration defines a main-class entry in the manifest and merges all “meta-inf/services/conf.files” together. the resulting executable jar file is now a valid input for the javapacker to create a native installer. the configuration to create a native installer with “exec-maven-plugin” and javapacker tool is exactly the same like in solution 1. i provided two example projects on github https://github.com/amoahcp/mvndemos where you can test both configurations. these are two simple projects with a main class starting a jetty webserver on port 8080, so the only dependency is jetty. both solutions can be used for any type of java projects, even with swing or javafx. reference: http://jacpfx.org/2014/10/08/uber-jars.html

In a previous article (Do it in Java 8: Automatic memoization), I wrote about memoization and said that memoization is about handling state between function calls, although the value returned by the function does not change from one call to another as long as the argument is the same. I showed how this could be done automatically. There are however other cases when handling state between function calls is necessary but cannot be done this simple way. One is handling state between recursive calls. In such a situation, the function is called only once from the user point of view, but it is in fact called several times since it calls itself recursively. Most of these functions will not benefit internally from memoization. For example, the factorial function may be implemented recursively, and for one call to f(n), there will be n calls to the function, but not twice with the same value. So this function would not benefit from internal memoization. On the contrary, the Fibonacci function, if implemented according to its recursive definition, will call itself recursively a huge number of times will the same arguments. Here is the standard definition of the Fibonacci function: f(n) = f(n – 1) + f(n – 2) This definition has a major problem: calculating f(n) implies evaluating n² times the function with different values. The result is that, in practice, it is impossible to use this definition for n > 50 because the time of execution increases exponentially. But since we are calculating more than n values, it is obvious that some values are calculated several times. So this definition would be a good candidate for internal memoization. Warning: Java is not a true recursive language, so using any recursive method will eventually blow the stack, unless we use TCO (Tail Call Optimization) as described in my previous article (Do it in Java 8: recursive and corecursive Fibonacci). However, TCO can't be applied to the original Fibonacci definition because it is not tail recursive. So we will write an example working for n limited to a few thousands. The naive implementation Here is how we might implement the original definition: static BigInteger fib(BigInteger n) { return n.equals(BigInteger.ZERO) || n.equals(BigInteger.ONE) ? n : fib(n.subtract(BigInteger.ONE)).add(fib(n.subtract(BigInteger.ONE.add(BigInteger.ONE)))); } Do not try this implementation for values greater that 50. On my machine, fib(50) takes 44 minutes to return! Basic memoized version To avoid computing several times the same values, we can use a map were computed values are stored. This way, for each value, we first look into the map to see if it has already been computed. If it is present, we just retrieve it. Otherwise, we compute it, store it into the map and return it. For this, we will use a special class called Memo. This is a normal HashMap that mimic the interface of functional map, which means that insertion of a new (key, value) pair returns a map, and looking up a key returns an Optional: public class Memo extends HashMap { public Optional retrieve(BigInteger key) { return Optional.ofNullable(super.get(key)); } public Memo addEntry(BigInteger key, BigInteger value) { super.put(key, value); return this; } } Note that this is not a true functional (immutable and persistent) map, but this is not a problem since it will not be shared. We will also need a Tuple class that we define as: public class Tuple { public final A _1; public final B _2; public Tuple(A a, B b) { _1 = a; _2 = b; } } With this class, we can write the following basic implementation: static BigInteger fibMemo1(BigInteger n) { return fibMemo(n, new Memo().addEntry(BigInteger.ZERO, BigInteger.ZERO) .addEntry(BigInteger.ONE, BigInteger.ONE))._1; } static Tuple fibMemo(BigInteger n, Memo memo) { return memo.retrieve(n).map(x -> new Tuple<>(x, memo)).orElseGet(() -> { BigInteger x = fibMemo(n.subtract(BigInteger.ONE), memo)._1 .add(fibMemo(n.subtract(BigInteger.ONE).subtract(BigInteger.ONE), memo)._1); return new Tuple<>(x, memo.addEntry(n, x)); }); } This implementation works fine, provided values of n are not too big. For f(50), it returns in less that 1 millisecond, which should be compared to the 44 minutes of the naive version. Remark that we do not have to test for terminal values (f(0) and f(1). These values are simply inserted into the map at start. So, is there something we should make better? The main problem is that we have to handle the passing of the Memo map by hand. The signature of the fibMemo method is no longer fibMemo(BigInteger n), but fibMemo(BigInteger n, Memo memo). Could we simplify this? We might think about using automatic memoization as described in my previous article (Do it in Java 8: Automatic memoization ). However, this will not work: static Function fib = new Function() { @Override public BigInteger apply(BigInteger n) { return n.equals(BigInteger.ZERO) || n.equals(BigInteger.ONE) ? n : this.apply(n.subtract(BigInteger.ONE)).add(this.apply(n.subtract(BigInteger.ONE.add(BigInteger.ONE)))); } }; static Function fibm = Memoizer.memoize(fib); Beside the fact that we could not use lambdas because reference to this is not allowed, which may be worked around by using the original anonymous class syntax, the recursive call is made to the non memoized function, so it does not make things better. Using the State monad In this example, each computed value is strictly evaluated by the fibMemo method, and this is what makes the memo parameter necessary. Instead of a method returning a value, what we would need is a method returning a function that could be evaluated latter. This function would take a Memo as parameter, and this Memo instance would be necessary only at evaluation time. This is what the State monad will do. Java 8 does not provide the state monad, so we have to create it, but it is very simple. However, we first need an implementation of a list that is more functional that what Java offers. In a real case, we would use a true immutable and persistent List. The one I have written is about 1 000 lines, so I can't show it here. Instead, we will use a dummy functional list, backed by a java.util.ArrayList. Although this is less elegant, it does the same job: public class List { private java.util.List list = new ArrayList<>(); public static List empty() { return new List(); } @SafeVarargs public static List apply(T... ta) { List result = new List<>(); for (T t : ta) result.list.add(t); return result; } public List cons(T t) { List result = new List<>(); result.list.add(t); result.list.addAll(list); return result; } public U foldRight(U seed, Function> f) { U result = seed; for (int i = list.size() - 1; i >= 0; i--) { result = f.apply(list.get(i)).apply(result); } return result; } public List map(Function f) { List result = new List<>(); for (T t : list) { result.list.add(f.apply(t)); } return result; } public List filter(Function f) { List result = new List<>(); for (T t : list) { if (f.apply(t)) { result.list.add(t); } } return result; } public Optional findFirst() { return list.size() == 0 ? Optional.empty() : Optional.of(list.get(0)); } @Override public String toString() { StringBuilder s = new StringBuilder("["); for (T t : list) { s.append(t).append(", "); } return s.append("NIL]").toString(); } } The implementation is not functional, but the interface is! And although there are lots of missing capabilities, we have all we need. Now, we can write the state monad. It is often called simply State but I prefer to call it StateMonad in order to avoid confusion between the state and the monad: public class StateMonad { public final Function> runState; public StateMonad(Function> runState) { this.runState = runState; } public static StateMonad unit(A a) { return new StateMonad<>(s -> new StateTuple<>(a, s)); } public static StateMonad get() { return new StateMonad<>(s -> new StateTuple<>(s, s)); } public static StateMonad getState(Function f) { return new StateMonad<>(s -> new StateTuple<>(f.apply(s), s)); } public static StateMonad transition(Function f) { return new StateMonad<>(s -> new StateTuple<>(Nothing.instance, f.apply(s))); } public static StateMonad transition(Function f, A value) { return new StateMonad<>(s -> new StateTuple<>(value, f.apply(s))); } public static StateMonad> compose(List> fs) { return fs.foldRight(StateMonad.unit(List.empty()), f -> acc -> f.map2(acc, a -> b -> b.cons(a))); } public StateMonad flatMap(Function> f) { return new StateMonad<>(s -> { StateTuple temp = runState.apply(s); return f.apply(temp.value).runState.apply(temp.state); }); } public StateMonad map(Function f) { return flatMap(a -> StateMonad.unit(f.apply(a))); } public StateMonad map2(StateMonad sb, Function> f) { return flatMap(a -> sb.map(b -> f.apply(a).apply(b))); } public A eval(S s) { return runState.apply(s).value; } } This class is parameterized by two types: the value type A and the state type S. In our case, A will be BigInteger and S will be Memo. This class holds a function from a state to a tuple (value, state). This function is hold in the runState field. This is similar to the value hold in the Optional monad. To make it a monad, this class needs a unit method and a flatMap method. The unit method takes a value as parameter and returns a StateMonad. It could be implemented as a constructor. Here, it is a factory method. The flatMap method takes a function from A (a value) to StateMonad and return a new StateMonad. (In our case, A is the same as B.) The new type contains the new value and the new state that result from the application of the function. All other methods are convenience methods: map allows to bind a function from A to B instead of a function from A to StateMonad. It is implemented in terms of flatMap and unit. eval allows easy retrieval of the value hold by the StateMonad. getState allows creating a StateMonad from a function S -> A. transition takes a function from state to state and a value and returns a new StateMonad holding the value and the state resulting from the application of the function. In other words, it allows changing the state without changing the value. There is also another transition method taking only a function and returning a StateMonad. Nothing is a special class: public final class Nothing { public static final Nothing instance = new Nothing(); private Nothing() {} } This class could be replaced by Void, to mean that we do not care about the type. However, Void is not supposed to be instantiated, and the only reference of type Void is normally null. The problem is that null does not carry its type. We could instantiate a Void instance through introspection: Constructor constructor; constructor = Void.class.getDeclaredConstructor(); constructor.setAccessible(true); Void nothing = constructor.newInstance(); but this is really ugly, so we create a Nothing type with a single instance of it. This does the trick, although to be complete, Nothing should be able to replace any type (like null), which does not seem to be possible in Java. Using the StateMonad class, we can rewrite our program: static BigInteger fibMemo2(BigInteger n) { return fibMemo(n).eval(new Memo().addEntry(BigInteger.ZERO, BigInteger.ZERO).addEntry(BigInteger.ONE, BigInteger.ONE)); } static StateMonad fibMemo(BigInteger n) { return StateMonad.getState((Memo m) -> m.retrieve(n)) .flatMap(u -> u.map(StateMonad:: unit).orElse(fibMemo(n.subtract(BigInteger.ONE)) .flatMap(x -> fibMemo(n.subtract(BigInteger.ONE).subtract(BigInteger.ONE)) .map(x::add) .flatMap(z -> StateMonad.transition((Memo m) -> m.addEntry(n, z), z))))); } Now, the fibMemo method only takes a BigInteger as its parameter and returns a StateMonad, which means that when this method returns, nothing has been evaluated yet. The Memo doesn't even exist! To get the result, we may call the eval method, passing it the Memo instance. If you find this code difficult to understand, here is an exploded commented version using longer identifiers: static StateMonad fibMemo(BigInteger n) { /* * Create a function of type Memo -> Optional with a closure * over the n parameter. */ Function> retrieveValueFromMapIfPresent = (Memo memoizationMap) -> memoizationMap.retrieve(n); /* * Create a state from this function. */ StateMonad> initialState = StateMonad.getState(retrieveValueFromMapIfPresent); /* * Create a function for converting the value (BigInteger) into a State * Monad instance. This function will be bound to the Optional resulting * from the lookup into the map to give the result if the value was found. */ Function> createStateFromValue = StateMonad:: unit; /* * The value computation proper. This can't be easily decomposed because it * make heavy use of closures. It first calls recursively fibMemo(n - 1), * producing a StateMonad. It then flatMaps it to a new * recursive call to fibMemo(n - 2) (actually fibMemo(n - 1 - 1)) and get a * new StateMonad which is mapped to BigInteger addition * with the preceding value (x). Then it flatMaps it again with the function * y -> StateMonad.transition((Memo m) -> m.addEntry(n, z), z) which adds * the two values and returns a new StateMonad with the computed value added * to the map. */ StateMonad computedValue = fibMemo(n.subtract(BigInteger.ONE)) .flatMap(x -> fibMemo(n.subtract(BigInteger.ONE).subtract(BigInteger.ONE)) .map(x::add) .flatMap(z -> StateMonad.transition((Memo m) -> m.addEntry(n, z), z))); /* * Create a function taking an Optional as its parameter and * returning a state. This is the main function that returns the value in * the Optional if it is present and compute it and put it into the map * before returning it otherwise. */ Function, StateMonad> computeFiboValueIfAbsentFromMap = u -> u.map(createStateFromValue).orElse(computedValue); /* * Bind the computeFiboValueIfAbsentFromMap function to the initial State * and return the result. */ return initialState.flatMap(computeFiboValueIfAbsentFromMap); } The most important part is the following: StateMonad computedValue = fibMemo_(n.subtract(BigInteger.ONE)) .flatMap(x -> fibMemo_(n.subtract(BigInteger.ONE).subtract(BigInteger.ONE)) .map(x::add) .flatMap(z -> StateMonad.transition((Memo m) -> m.addEntry(n, z), z))); This kind of code is essential to functional programming, although it is sometimes replaced in other languages with “for comprehensions”. As Java 8 does not have for comprehensions we have to use this form. At this point, we have seen that using the state monad allows abstracting the handling of state. This technique can be used every time you have to handle state. More uses of the state monad The state monad may be used for many other cases were state must be maintained in a functional way. Most programs based upon maintaining state use a concept known as a State Machine. A state machine is defined by an initial state and a series of inputs. Each input submitted to the state machine will produce a new state by applying one of several possible transitions based upon a list of conditions concerning both the input and the actual state. If we take the example of a bank account, the initial state would be the initial balance of the account. Possible transition would be deposit(amount) and withdraw(amount). The conditions would be true for deposit and balance >= amount for withdraw. Given the state monad that we have implemented above, we could write a parameterized state machine: public class StateMachine { Function> function; public StateMachine(List, Transition>> transitions) { function = i -> StateMonad.transition(m -> Optional.of(new StateTuple<>(i, m)).flatMap((StateTuple t) -> transitions.filter((Tuple, Transition> x) -> x._1.test(t)).findFirst().map((Tuple, Transition> y) -> y._2.apply(t))).get()); } public StateMonad process(List inputs) { List> a = inputs.map(function); StateMonad> b = StateMonad.compose(a); return b.flatMap(x -> StateMonad.get()); } } This machine uses a bunch of helper classes. First, the inputs are represented by an interface: public interface Input { boolean isDeposit(); boolean isWithdraw(); int getAmount(); } There are two instances of inputs: public class Deposit implements Input { private final int amount; public Deposit(int amount) { super(); this.amount = amount; } @Override public boolean isDeposit() { return true; } @Override public boolean isWithdraw() { return false; } @Override public int getAmount() { return this.amount; } } public class Withdraw implements Input { private final int amount; public Withdraw(int amount) { super(); this.amount = amount; } @Override public boolean isDeposit() { return false; } @Override public boolean isWithdraw() { return true; } @Override public int getAmount() { return this.amount; } } Then come two functional interfaces for conditions and transitions: public interface Condition extends Predicate> {} public interface Transition extends Function, S> {} These act as type aliases in order to simplify the code. We could have used the predicate and the function directly. In the same manner, we use a StateTuple class instead of a normal tuple: public class StateTuple { public final A value; public final S state; public StateTuple(A a, S s) { value = Objects.requireNonNull(a); state = Objects.requireNonNull(s); } } This is exactly the same as an ordinary tuple with named members instead of numbered ones. Numbered members allows using the same class everywhere, but a specific class like this one make the code easier to read as we will see. The last utility class is Outcome, which represent the result returned by the state machine: public class Outcome { public final Integer account; public final List> operations; public Outcome(Integer account, List> operations) { super(); this.account = account; this.operations = operations; } public String toString() { return "(" + account.toString() + "," + operations.toString() + ")"; } } This again could be replaced with a Tuple>>, but using named parameters make the code easier to read. (In some functional languages, we could use type aliases for this.) Here, we use an Either class, which is another kind of monad that Java does not offer. I will not show the complete class, but only the parts that are useful for this example: public interface Either { boolean isLeft(); boolean isRight(); A getLeft(); B getRight(); static Either right(B value) { return new Right<>(value); } static Either left(A value) { return new Left<>(value); } public class Left implements Either { private final A left; private Left(A left) { super(); this.left = left; } @Override public boolean isLeft() { return true; } @Override public boolean isRight() { return false; } @Override public A getLeft() { return this.left; } @Override public B getRight() { throw new IllegalStateException("getRight() called on Left value"); } @Override public String toString() { return left.toString(); } } public class Right implements Either { private final B right; private Right(B right) { super(); this.right = right; } @Override public boolean isLeft() { return false; } @Override public boolean isRight() { return true; } @Override public A getLeft() { throw new IllegalStateException("getLeft() called on Right value"); } @Override public B getRight() { return this.right; } @Override public String toString() { return right.toString(); } } } This implementation is missing a flatMap method, but we will not need it. The Either class is somewhat like the Optional Java class in that it may be used to represent the result of an evaluation that may return a value or something else like an exception, an error message or whatever. What is important is that it can hold one of two things of different types. We now have all we need to use our state machine: public class Account { public static StateMachine createMachine() { Condition predicate1 = t -> t.value.isDeposit(); Transition transition1 = t -> new Outcome(t.state.account + t.value.getAmount(), t.state.operations.cons(Either.right(t.value.getAmount()))); Condition predicate2 = t -> t.value.isWithdraw() && t.state.account >= t.value.getAmount(); Transition transition2 = t -> new Outcome(t.state.account - t.value.getAmount(), t.state.operations.cons(Either.right(- t.value.getAmount()))); Condition predicate3 = t -> true; Transition transition3 = t -> new Outcome(t.state.account, t.state.operations.cons(Either.left(new IllegalStateException(String.format("Can't withdraw %s because balance is only %s", t.value.getAmount(), t.state.account))))); List, Transition>> transitions = List.apply( new Tuple<>(predicate1, transition1), new Tuple<>(predicate2, transition2), new Tuple<>(predicate3, transition3)); return new StateMachine<>(transitions); } } This could not be simpler. We just define each possible condition and the corresponding transition, and then build a list of tuples (Condition, Transition) that is used to instantiate the state machine. There are however to rules that must be enforced: Conditions must be put in the right order, with the more specific first and the more general last. We must be careful to be sure to match all possible cases. Otherwise, we will get an exception. At this stage, nothing has been evaluated. We did not even use the initial state! To run the state machine, we must create a list of inputs and feed it in the machine, for example: List inputs = List.apply( new Deposit(100), new Withdraw(50), new Withdraw(150), new Deposit(200), new Withdraw(150)); StateMonad = Account.createMachine().process(inputs); Again, nothing has been evaluated yet. To get the result, we just evaluate the result, using an initial state: Outcome outcome = state.eval(new Outcome(0, List.empty())) If we run the program with the list above, and call toString() on the resulting outcome (we can't do more useful things since the Either class is so minimal!) we get the following result: // // (100,[-150, 200, java.lang.IllegalStateException: Can't withdraw 150 because balance is only 50, -50, 100, NIL]) This is a tuple of the resulting balance for the account (100) and the list of operations that have been carried on. We can see that successful operations are represented by a signed integer, and failed operations are represented by an error message. This of course is a very minimal example, and as usual, one may think it would be much easier to do it the imperative way. However, think of a more complex example, like a text parser. All there is to do to adapt the state machine is to define the state representation (the Outcome class), define the possible inputs and create the list of (Condition,Transition). Going the functional way does not make the whole thing simpler. However, it allows abstracting the implementation of the state machine from the requirements. The only thing we have to do to create a new state machine is to write the new requirements!

in this article i explained how spring built-in transformers works for while transforming object message to map message. sometimes the messages need to be transformed before they can be consumed to achieve a business purpose. for example, a producer uses a plain xml as its payload to produce a message, while a consumer is interested in java object or types like plain text ,name-value pairs, or json model. spring integration provides endpoints such as service activators, channel adapters, message bridges, gateways, transformers, filters, and routers. in this example how transformers endpoint transform object message to map message. references: spring integration spring with jms spring with junit mockrunner sts high level view spring-mockrunner.xml in spring-mockrunner.xml file, i defined mockqueue, mockqueueconnectionfactory for inbound queue, and outbound queue for quick testing purpose. inboundqueue is where you will publish object message from objecttomaptransformertest.java class. outboundqueue where this queue expecting mapmessage type object and this queue is listing mapmessagelistener.java class. for more information mockrunner works please check my previous article mockrunner with spring jms . pom.xml 4.0.0 org.springframework.samples spring-int-jms-basic 0.0.1-snapshot 1.6 utf-8 utf-8 3.2.3.release 1.0.13 1.7.5 4.11 org.springframework spring-context ${spring-framework.version} org.springframework spring-tx ${spring-framework.version} org.springframework.integration spring-integration-core 2.2.4.release org.springframework.integration spring-integration-jmx 2.2.4.release org.springframework.integration spring-integration-jms 2.2.4.release org.slf4j slf4j-api ${slf4j.version} compile ch.qos.logback logback-classic ${logback.version} runtime org.springframework spring-test ${spring-framework.version} test junit junit ${junit.version} test com.mockrunner mockrunner-jms 1.0.3 javax.jms jms 1.1 org.codehaus.jackson jackson-mapper-asl 1.9.3 compile spring-int-jms.xml the endpoint is configured to connect to a jms server, fetch the messages,and publish them onto a local channel i.e inputchannel. where as connection-factory, and destination referred mockqueueconnectionfactory, and mockqueue(inboundqueue) beans from spring-mockrunner.xml file. inputchannel and outputchannel defined as queue channel objecttomaptransformer: object-to-map-transformer element that takes the payload from the input channel original here mockrunner-in-queue object message and emits a name-value paired map object onto the output channel i.e outputchannel and outboundjmsadapter bean fetch this message and publish to queue i.e mockrunner-out-queue. inboundjmsadapter : inbound-channel-adapter bean is responsible for receiving messages from a jms server here it is reading from mock queue name mockrunner-in-queue see objecttomaptransformertest.java class. outboundjmsadapter : outbound-channel-adapter bean is responsible to fetch messages from the channel i.e outputchannel and publish them to jms queue or topic. in this outbounjmsadapter reading message outputchannel as mapmessage and publish to outboundqueue(mockrunner-out-queue). mapmessagelistener.java package com.spijb.listener; import javax.jms.jmsexception; import javax.jms.mapmessage; import javax.jms.session; import org.slf4j.logger; import org.slf4j.loggerfactory; import org.springframework.jms.listener.sessionawaremessagelistener; public class mapmessagelistener implements sessionawaremessagelistener { private static final logger log = loggerfactory.getlogger(mapmessagelistener.class); @override public void onmessage(mapmessage message, session session) throws jmsexception { log.info("message received \r\n"+message); } } it is plain mapmessagelistener class to print received message from queue. department.java package com.spijb.domain; import java.io.serializable; public class department implements serializable{ private static final long serialversionuid = 1l; private final integer deptno; private final string name; private final string location; public department() { deptno=10; name="sales"; location="tx"; } public department(integer dno,string name,string loc) { this.deptno=dno; this.name=name; this.location=loc; } public integer getdeptno() { return deptno; } public string getname() { return name; } public string getlocation() { return location; } @override public string tostring() { return this.deptno+"-> "+this.name+"->"+this.location; } } domain object to send as a message, by default constructor assign deptno 10 , name as sales, location as tx also provide parameter constructor. spring junit class objecttomaptransformertest.java package com.spijb.invoker; import javax.jms.jmsexception; import javax.jms.message; import javax.jms.objectmessage; import javax.jms.session; import org.junit.test; import org.junit.runner.runwith; import org.springframework.beans.factory.annotation.autowired; import org.springframework.jms.core.jmstemplate; import org.springframework.jms.core.messagecreator; import org.springframework.test.context.contextconfiguration; import org.springframework.test.context.junit4.springjunit4classrunner; import com.mockrunner.mock.jms.mockqueue; import com.spijb.domain.department; @runwith(springjunit4classrunner.class) @contextconfiguration({"classpath:spring-mockrunner.xml","classpath:spring-int-jms.xml"}) public class objecttomaptransformertest { @autowired private jmstemplate jmstemplate; @autowired private mockqueue inboundqueue; @test public void shouldsendmessage() throws interruptedexception { final department defaultdepartment = new department(); jmstemplate.send(inboundqueue,new messagecreator() { @override public message createmessage(session session) throws jmsexception { objectmessage objectmessage = session.createobjectmessage(); objectmessage.setobject(defaultdepartment); return objectmessage; } }); thread.sleep(5000); } } spring with junit class where you can send message to inputchannel i.e inboundqueue using mockrunner. output : info: started inboundjmsadapter oct 06, 2014 1:24:25 pm org.springframework.integration.endpoint.abstractendpoint start info: started org.springframework.integration.config.consumerendpointfactorybean#1 13:24:26.882 [org.springframework.jms.listener.defaultmessagelistenercontainer#0-1] info c.spijb.listener.mapmessagelistener - message received com.mockrunner.mock.jms.mockmapmessage: {location=tx, name=sales, deptno=10} oct 06, 2014 1:24:30 pm org.springframework.context.support.abstractapplicationcontext doclose info: closing org.springframework.context.support.genericapplicationcontext@5840979b: startup date [mon oct 06 13:24:25 cdt 2014]; root of context hierarchy oct 06, 2014 1:24:30 pm org.springframework.context.support.defaultlifecycleprocessor$lifecyclegroup stop info: stopping beans in phase 2147483647 in the above highlighted one is output as map.

This year I’ve been demonstrating how easy it is to create modern web apps using AngularJS, Java and MongoDB. I also use Groovy during this demo to do the sorts of things Groovy is really good at - writing descriptive tests, and creating scripts. Due to the time pressures in the demo, I never really get a chance to go into the details of the script I use, so the aim of this long-overdue blog post is to go over this Groovy script in a bit more detail. Firstly I want to clarify that this is not my original work - I stoleborrowed most of the ideas for the demo from my colleague Ross Lawley. In this blog post he goes into detail of how he built up an application that finds the most popular pub names in the UK. There’s asection in there where he talks about downloading the open street map data and using python to convert the XML into something more MongoDB-friendly - it’s this process that I basically stole, re-worked for coffee shops, and re-wrote for the JVM. I’m assuming if you’ve worked with Java for any period of time, there has come a moment where you needed to use it to parse XML. Since my demo is supposed to be all about how easy it is to work with Java, I didnot want to do this. When I wrote the demo I wasn’t really all that familiar with Groovy, but what I did know was that it has built in support for parsing and manipulating XML, which is exactly what I wanted to do. In addition, creating Maps (the data structures, not the geographical ones) with Groovy is really easy, and this is effectively what we need to insert into MongoDB. Goal Of The Script Parse an XML file containing open street map data of all coffee shops. Extract latitude and longitude XML attributes and transform intoMongoDB GeoJSON. Perform some basic validation on the coffee shop data from the XML. Insert into MongoDB. Make sure MongoDB knows this contains query-able geolocation data. The script is PopulateDatabase.groovy, that link will take you to the version I presented at JavaOne: Firstly, We Need Data I used the same service Ross used in his blog post to obtain the XML file containing “all” coffee shops around the world. Now, the open street map data is somewhat… raw and unstructured (which is why MongoDB is such a great tool for storing it), so I’m not sure I really have all the coffee shops, but I obtained enough data for an interesting demo using http://www.overpass-api.de/api/xapi?*[amenity=cafe][cuisine=coffee_shop] The resulting XML file is in the github project, but if you try this yourself you might (in fact, probably will) get different results. Each XML record looks something like: Each coffee shop has a unique identifier and a latitude and longitude as attributes of a node element. Within this node is a series of tag elements, all with k and v attributes. Each coffee shop has a varying number of these attributes, and they are not consistent from shop to shop (other than amenity and cuisine which we used to select this data). Initialisation Before doing anything else we want to prepare the database. The assumption of this script is that either the collection we want to store the coffee shops in is empty, or full of stale data. So we’re going to use the MongoDB Java Driver to get the collection that we’re interested in, and then drop it. There’s two interesting things to note here: This Groovy script is simply using the basic Java driver. Groovy can talk quite happily to vanilla Java, it doesn’t need to use a Groovy library. There are Groovy-specific libraries for talking to MongoDB (e.g. the MongoDB GORM Plugin), but the Java driver works perfectly well. You don’t need to create databases or collections (collections are a bit like tables, but less structured) explicitly in MongoDB. You simply use the database and collection you’re interested in, and if it doesn’t already exist, the server will create them for you. In this example, we’re just using the default constructor for theMongoClient, the class that represents the connection to the database server(s). This default is localhost:27017, which is where I happen to be running the database. However you can specify your own address and port - for more details on this see Getting Started With MongoDB and Java. Turn The XML Into Something MongoDB-Shaped So next we’re going to use Groovy’s XmlSlurper to read the open street map XML data that we talked about earlier. To iterate over every node we use: xmlSlurper.node.each. For those of you who are new to Groovy or new to Java 8, you might notice this is using a closure to define the behaviour to apply for every “node” element in the XML. Create GeoJSON Since MongoDB documents are effectively just maps of key-value pairs, we’re going to create a Map coffeeShop that contains the document structure that represents the coffee shop that we want to save into the database. Firstly, we initialise this map with the attributes of the node. Remember these attributes are something like: We’re going to save the ID as a value for a new field calledopenStreetMapId. We need to do something a bit more complicated with the latitude and longitude, since we need to store them as GeoJSON, which looks something like: { 'location' : { 'coordinates': [, ], 'type' : 'Point' } } In lines 12-14 you can see that we create a Map that looks like the GeoJSON, pulling the lat and lon attributes into the appropriate places. Insert Remaining Fields Now for every tag element in the XML, we get the k attribute and check if it’s a valid field name for MongoDB (it won’t let us insert fields with a dot in, and we don’t want to override our carefully constructed locationfield). If so we simply add this key as the field and its the matching vattribute as the value into the map. This effectively copies theOpenStreetMap key/value data into key/value pairs in the MongoDB document so we don’t lose any data, but we also don’t do anything particularly interesting to transform it. Save Into MongoDB Finally, once we’ve created a simple coffeeShop Map representing the document we want to save into MongoDB, we insert it into MongoDB if the map has a field called name. We could have checked this when we were reading the XML and putting it into the map, but it’s actually much easier just to use the pretty Groovy syntax to check for a key called namein coffeeShop. When we want to insert the Map we need to turn this into aBasicDBObject, the Java Driver’s document type, but this is easily done by calling the constructor that takes a Map. Alternatively, there’s a Groovy syntax which would effectively do the same thing, which you might prefer: collection.insert(coffeeShop as BasicDBObject) Tell MongoDB That We Want To Perform Geo Queries On This Data Because we’re going to do a nearSphere query on this data, we need to add a “2dsphere” index on our location field. We created the locationfield as GeoJSON, so all we need to do is call createIndex for this field. Conclusion So that’s it! Groovy is a nice tool for this sort of script-y thing - not only is it a scripting language, but its built-in support for XML, really nice Map syntax and support for closures makes it the perfect tool for iterating over XML data and transforming it into something that can be inserted into a MongoDB collection.

Recently, I got a task where licensing was required to be added. I have done such tasks using ant in the past but this time I was supposed to use maven. Some quick search made it clear that maven provides a plugin to do such activities but the documentation was not upto the mark (or I can say it was a bit confusing or too generic). To save other people from such situation I am going to demonstrate it using a simple example. Lets suppose you want to have licensing information given below in all java files of your project: /** * Copyright (C) 2014 My Coaching Company. All rights reserved This software is the confidential * and proprietary information of My Coaching Company. You shall not disclose such confidential * information and shall use it only in accordance with the terms of the license agreement you * entered into with My Coaching Company. * */ Here are steps to do so: 1. Create a txt file named License.txt and place it in parallel with pom.xml and make sure that your license file should not contain comments like /** ... */. It should look like, Copyright (C) 2014 My Coaching Company. All rights reserved This software is the confidential and proprietary information of My Coaching Company. You shall not disclose such confidential information and shall use it only in accordance with the terms of the license agreement you entered into with My Coaching Company. 2. Add following snippet to pom.xml ${basedir} 3. Now add plugin configuration for adding license to java files in maven project, com.mycila.maven-license-plugin maven-license-plugin 1.10.b1 ${license.dir}/license.txt ${project.name} ${project.organization.name} ${project.inceptionYear} ${founder-website} src/main/java/** src/test/java/** format process-sources com.mycila licenses 1 4. Now you are all set to fire the command mvn license:format This will add license information on top of java code. Note: If you have projects under subproject something like project ---| | --> sub-project | --> sub-project2 then you are required to add following snippet into the pom.xml of sub-projects: ${project.parent.basedir} I hope this should help lots of developers around. This is one of the most simple usage of this plugin for more please refer to the official site.

There are some instances when you want to store your passwords in files to be used by programs or scripts. But storing your passwords in plain text is not a good idea. Use the SecurePasswordVault to encrypt your passwords before storing and get it decrypted when you want to use it. You can use the SecurePasswordVault described here to store any number of encrypted passwords. Passwords are stored as key value pairs. Key - any name given by the user for the password Value - encrypted password SecurePasswordVault will create a file with the given name in the working directory if it doesn't exist. If a file exists then the information in that file will be read. Passwords are encrypted using the MAC address of the network card. SecurePasswordVault will use the first network card MAC which is not the loop back interface. So the encrypted file can only be decrypted with that particular MAC address. If you want to reset the pass word details, just delete the password file and run the SecurePasswordVault. You can download the sample code from the following GitHub repository https://github.com/jsdjayanga/secure_password com.wso2.devgov; import org.bouncycastle.util.encoders.Base64; import javax.crypto.*; import javax.crypto.spec.SecretKeySpec; import java.io.*; import java.net.NetworkInterface; import java.net.SocketException; import java.security.InvalidKeyException; import java.security.NoSuchAlgorithmException; import java.security.Security; import java.util.*; /** * Created by jayanga on 3/31/14. */ public class SecurePasswordVault { private static final int AES_KEY_LEN = 32; private static final int PASSWORD_LEN = 256; private static boolean initialized; private final String secureFile; private final byte[] networkHardwareHaddress; private Map secureDataMap; private List secureDataList; SecretKeySpec secretKey; public SecurePasswordVault(String filename, String[] secureData) throws IOException { Security.addProvider(new org.bouncycastle.jce.provider.BouncyCastleProvider()); initialized = false; secureFile = filename; networkHardwareHaddress = SecurePasswordVault.readNetworkHardwareAddress(); secureDataMap = new HashMap(); this.secureDataList = new ArrayList(secureData.length); Collections.addAll(secureDataList, secureData); byte[] key = new byte[AES_KEY_LEN]; Arrays.fill(key, (byte)0); for(int index = 0; index < networkHardwareHaddress.length; index++){ key[index] = networkHardwareHaddress[index]; } secretKey = new SecretKeySpec(key, "AES"); if (!isInitialized()){ readSecureData(secureDataList); persistSecureData(); } readSecureDataFromFile(); } private boolean isInitialized(){ if (initialized == true){ return true; }else{ File file = new File(secureFile); if (file.exists()){ initialized = true; return initialized; } } return false; } private static byte[] readNetworkHardwareAddress() throws SocketException { Enumeration networkInterfaceEnumeration = NetworkInterface.getNetworkInterfaces(); if (networkInterfaceEnumeration != null){ NetworkInterface networkInterface = null; while (networkInterfaceEnumeration.hasMoreElements()){ networkInterface = networkInterfaceEnumeration.nextElement(); if (!networkInterface.isLoopback()){ break; } } if (networkInterface == null){ networkInterface = networkInterfaceEnumeration.nextElement(); } byte[] hwaddr = networkInterface.getHardwareAddress(); return hwaddr; }else{ throw new RuntimeException("Cannot initialize. Failed to generate unique id."); } } private byte[] encrypt(String word) { byte[] password = new byte[PASSWORD_LEN]; Arrays.fill(password, (byte)0); byte[] pw = new byte[0]; try { pw = word.getBytes("UTF-8"); for(int index = 0; index < pw.length; index++){ password[index] = pw[index]; } byte[] cipherText = new byte[password.length]; Cipher cipher = null; try { cipher = Cipher.getInstance("AES/ECB/NoPadding"); try { cipher.init(Cipher.ENCRYPT_MODE, secretKey); int ctLen = 0; try { ctLen = cipher.update(password, 0, password.length, cipherText, 0); ctLen += cipher.doFinal(cipherText, ctLen); return cipherText; } catch (ShortBufferException e) { e.printStackTrace(); } catch (BadPaddingException e) { e.printStackTrace(); } catch (IllegalBlockSizeException e) { e.printStackTrace(); } } catch (InvalidKeyException e) { e.printStackTrace(); } } catch (NoSuchAlgorithmException e) { e.printStackTrace(); } catch (NoSuchPaddingException e) { e.printStackTrace(); } } catch (UnsupportedEncodingException e) { e.printStackTrace(); } return null; } private String decrypt(byte[] cipherText) { byte[] plainText = new byte[PASSWORD_LEN]; Cipher cipher = null; try { cipher = Cipher.getInstance("AES/ECB/NoPadding"); try { cipher.init(Cipher.DECRYPT_MODE, secretKey); int plainTextLen = 0; try { plainTextLen = cipher.update(cipherText, 0, PASSWORD_LEN, plainText, 0); try { plainTextLen += cipher.doFinal(plainText, plainTextLen); String password = new String(plainText); return password.trim(); } catch (IllegalBlockSizeException e) { e.printStackTrace(); } catch (BadPaddingException e) { e.printStackTrace(); } } catch (ShortBufferException e) { e.printStackTrace(); } } catch (InvalidKeyException e) { e.printStackTrace(); } } catch (NoSuchAlgorithmException e) { e.printStackTrace(); } catch (NoSuchPaddingException e) { e.printStackTrace(); } return null; } public void readSecureData(List secureDataList) throws IOException { BufferedReader bufferRead = new BufferedReader(new InputStreamReader(System.in)); for(int index = 0; index < secureDataList.size(); index++){ System.out.println("Please enter the value for :" + secureDataList.get(index)); String value = new String(Base64.encode(encrypt(bufferRead.readLine()))); secureDataMap.put(secureDataList.get(index), value); } } public String getSecureData(String key) { String value = secureDataMap.get(key); if (value != null){ return decrypt(Base64.decode(value.getBytes())); } throw new RuntimeException("Given key is unknown. [key=" + key + "]"); } private void readSecureDataFromFile() throws IOException { BufferedReader br = new BufferedReader(new FileReader(secureFile)); String line; while ((line = br.readLine()) != null){ int dividerPoint = line.indexOf("="); if (dividerPoint > 0){ secureDataMap.put(line.substring(0, dividerPoint), line.substring(dividerPoint + 1)); } } } private void persistSecureData() throws IOException { FileWriter fileWriter = new FileWriter(secureFile); for(String key : secureDataMap.keySet()){ fileWriter.append(key + "=" + secureDataMap.get(key) + "\n"); } fileWriter.close(); } }

Checking method/constructor parameters for null values is a common task problem in Java. To assist you with this, various Java libraries provide validation utilities (see Guava Preconditions, Commons LangValidate or Spring's Assert documentation). However, if you only want to validate for non null values you can use the static requiresNonNull()method of java.util.Objects. This is a little utility introduced by Java 7 that appears to be rarely known. With Objects.requiresNonNull() the following piece of code public void foo(SomeClass obj) { if (obj == null) { throw new NullPointerException("obj must not be null"); } // work with obj } can be replaced with: import java.util.Objects; public void foo(SomeClass obj) { Objects.requireNonNull(obj, "obj must not be null"); // work with obj }

The best coding practices related with Java exception handling that you may want to watch out for while doing coding for exception handling.

Recently I ran into very interesting problem which I thought would take me just a couple of minutes to solve: protecting Apache CXF (current release 3.0.1)/ JAX-RS REST services with Spring Security (current stable version 3.2.5) in the application running inside embedded Jetty container (current release 9.2). At the end, it turns out to be very easy, once you understand how things work together and known subtle intrinsic details. This blog post will try to reveal that. Our example application is going to expose a simple JAX-RS / REST service to manage people. However, we do not want everyone to be allowed to do that so the HTTP basic authentication will be required in order to access our endpoint, deployed at http://localhost:8080/api/rest/people. Let us take a look on thePeopleRestService class: package com.example.rs; import javax.json.Json; import javax.json.JsonArray; import javax.ws.rs.GET; import javax.ws.rs.Path; import javax.ws.rs.Produces; @Path( "/people" ) public class PeopleRestService { @Produces( { "application/json" } ) @GET public JsonArray getPeople() { return Json.createArrayBuilder() .add( Json.createObjectBuilder() .add( "firstName", "Tom" ) .add( "lastName", "Tommyknocker" ) .add( "email", "[email protected]" ) ) .build(); } } As you can see in the snippet above, nothing is pointing out to the fact that this REST service is secured, just couple of familiar JAX-RS annotations. Now, let us declare the desired security configuration following excellent Spring Security documentation. There are many ways to configure Spring Security but we are going to show off two of them: using in-memory authentication and using user details service, both built on top of WebSecurityConfigurerAdapter. Let us start with in-memory authentication as it is the simplest one: package com.example.config; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.context.annotation.Configuration; import org.springframework.security.config.annotation.authentication.builders.AuthenticationManagerBuilder; import org.springframework.security.config.annotation.method.configuration.EnableGlobalMethodSecurity; import org.springframework.security.config.annotation.web.builders.HttpSecurity; import org.springframework.security.config.annotation.web.configuration.EnableWebSecurity; import org.springframework.security.config.annotation.web.configuration.WebSecurityConfigurerAdapter; import org.springframework.security.config.http.SessionCreationPolicy; @Configuration @EnableWebSecurity @EnableGlobalMethodSecurity( securedEnabled = true ) public class InMemorySecurityConfig extends WebSecurityConfigurerAdapter { @Autowired public void configureGlobal(AuthenticationManagerBuilder auth) throws Exception { auth.inMemoryAuthentication() .withUser( "user" ).password( "password" ).roles( "USER" ).and() .withUser( "admin" ).password( "password" ).roles( "USER", "ADMIN" ); } @Override protected void configure( HttpSecurity http ) throws Exception { http.httpBasic().and() .sessionManagement().sessionCreationPolicy( SessionCreationPolicy.STATELESS ).and() .authorizeRequests().antMatchers("/**").hasRole( "USER" ); } } In the snippet above there two users defined: user with the role USER and admin with the roles USER,ADMIN. We also protecting all URLs (/**) by setting authorization policy to allow access only users with roleUSER. Being just a part of the application configuration, let us plug it into the AppConfig class using @Importannotation. package com.example.config; import java.util.Arrays; import javax.ws.rs.ext.RuntimeDelegate; import org.apache.cxf.bus.spring.SpringBus; import org.apache.cxf.endpoint.Server; import org.apache.cxf.jaxrs.JAXRSServerFactoryBean; import org.apache.cxf.jaxrs.provider.jsrjsonp.JsrJsonpProvider; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.context.annotation.DependsOn; import org.springframework.context.annotation.Import; import com.example.rs.JaxRsApiApplication; import com.example.rs.PeopleRestService; @Configuration @Import( InMemorySecurityConfig.class ) public class AppConfig { @Bean( destroyMethod = "shutdown" ) public SpringBus cxf() { return new SpringBus(); } @Bean @DependsOn ( "cxf" ) public Server jaxRsServer() { JAXRSServerFactoryBean factory = RuntimeDelegate.getInstance().createEndpoint( jaxRsApiApplication(), JAXRSServerFactoryBean.class ); factory.setServiceBeans( Arrays.< Object >asList( peopleRestService() ) ); factory.setAddress( factory.getAddress() ); factory.setProviders( Arrays.< Object >asList( new JsrJsonpProvider() ) ); return factory.create(); } @Bean public JaxRsApiApplication jaxRsApiApplication() { return new JaxRsApiApplication(); } @Bean public PeopleRestService peopleRestService() { return new PeopleRestService(); } } At this point we have all the pieces except the most interesting one: the code which runs embedded Jettyinstance and creates proper servlet mappings, listeners, passing down the configuration we have created. package com.example; import java.util.EnumSet; import javax.servlet.DispatcherType; import org.apache.cxf.transport.servlet.CXFServlet; import org.eclipse.jetty.server.Server; import org.eclipse.jetty.servlet.FilterHolder; import org.eclipse.jetty.servlet.ServletContextHandler; import org.eclipse.jetty.servlet.ServletHolder; import org.springframework.web.context.ContextLoaderListener; import org.springframework.web.context.support.AnnotationConfigWebApplicationContext; import org.springframework.web.filter.DelegatingFilterProxy; import com.example.config.AppConfig; public class Starter { public static void main( final String[] args ) throws Exception { Server server = new Server( 8080 ); // Register and map the dispatcher servlet final ServletHolder servletHolder = new ServletHolder( new CXFServlet() ); final ServletContextHandler context = new ServletContextHandler(); context.setContextPath( "/" ); context.addServlet( servletHolder, "/rest/*" ); context.addEventListener( new ContextLoaderListener() ); context.setInitParameter( "contextClass", AnnotationConfigWebApplicationContext.class.getName() ); context.setInitParameter( "contextConfigLocation", AppConfig.class.getName() ); // Add Spring Security Filter by the name context.addFilter( new FilterHolder( new DelegatingFilterProxy( "springSecurityFilterChain" ) ), "/*", EnumSet.allOf( DispatcherType.class ) ); server.setHandler( context ); server.start(); server.join(); } } Most of the code does not require any explanation except the the filter part. This is what I meant by subtle intrinsic detail: the DelegatingFilterProxy should be configured with the filter name which must be exactlyspringSecurityFilterChain, as Spring Security names it. With that, the security rules we have configured are going to apply to any JAX-RS service call (the security filter is executed before the Apache CXF servlet), requiring the full authentication. Let us quickly check that by building and running the project: mvn clean package java -jar target/jax-rs-2.0-spring-security-0.0.1-SNAPSHOT.jar Issuing the HTTP GET call without providing username and password does not succeed and returns HTTP status code 401. > curl -i http://localhost:8080/rest/api/people HTTP/1.1 401 Full authentication is required to access this resource WWW-Authenticate: Basic realm="Realm" Cache-Control: must-revalidate,no-cache,no-store Content-Type: text/html; charset=ISO-8859-1 Content-Length: 339 Server: Jetty(9.2.2.v20140723) The same HTTP GET call with username and password provided returns successful response (with some JSON generated by the server). > curl -i -u user:password http://localhost:8080/rest/api/people HTTP/1.1 200 OK Date: Sun, 28 Sep 2014 20:07:35 GMT Content-Type: application/json Content-Length: 65 Server: Jetty(9.2.2.v20140723) [{"firstName":"Tom","lastName":"Tommyknocker","email":"[email protected]"}] Excellent, it works like a charm! Turns out, it is really very easy. Also, as it was mentioned before, the in-memory authentication could be replaced with user details service, here is an example how it could be done: package com.example.config; import java.util.Arrays; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.security.config.annotation.authentication.builders.AuthenticationManagerBuilder; import org.springframework.security.config.annotation.method.configuration.EnableGlobalMethodSecurity; import org.springframework.security.config.annotation.web.builders.HttpSecurity; import org.springframework.security.config.annotation.web.configuration.EnableWebSecurity; import org.springframework.security.config.annotation.web.configuration.WebSecurityConfigurerAdapter; import org.springframework.security.config.http.SessionCreationPolicy; import org.springframework.security.core.authority.SimpleGrantedAuthority; import org.springframework.security.core.userdetails.User; import org.springframework.security.core.userdetails.UserDetails; import org.springframework.security.core.userdetails.UserDetailsService; import org.springframework.security.core.userdetails.UsernameNotFoundException; @Configuration @EnableWebSecurity @EnableGlobalMethodSecurity(securedEnabled = true) public class UserDetailsSecurityConfig extends WebSecurityConfigurerAdapter { @Autowired public void configureGlobal(AuthenticationManagerBuilder auth) throws Exception { auth.userDetailsService( userDetailsService() ); } @Bean public UserDetailsService userDetailsService() { return new UserDetailsService() { @Override public UserDetails loadUserByUsername( final String username ) throws UsernameNotFoundException { if( username.equals( "admin" ) ) { return new User( username, "password", true, true, true, true, Arrays.asList( new SimpleGrantedAuthority( "ROLE_USER" ), new SimpleGrantedAuthority( "ROLE_ADMIN" ) ) ); } else if ( username.equals( "user" ) ) { return new User( username, "password", true, true, true, true, Arrays.asList( new SimpleGrantedAuthority( "ROLE_USER" ) ) ); } return null; } }; } @Override protected void configure( HttpSecurity http ) throws Exception { http .httpBasic().and() .sessionManagement().sessionCreationPolicy( SessionCreationPolicy.STATELESS ).and() .authorizeRequests().antMatchers("/**").hasRole( "USER" ); } } Replacing the @Import( InMemorySecurityConfig.class ) with @Import( UserDetailsSecurityConfig.class ) in the AppConfig class leads to the same results, as both security configurations define the identical sets of users and their roles. I hope, this blog post will save you some time and gives a good starting point, as Apache CXF and Spring Security are getting along very well under Jetty umbrella! The complete source code is available on GitHub.

There have been a couple of articles on null, NPE's and how to avoid them. They make some point, but could stress the easy, safe, beautiful aspects of Java 8's Optional. This article shows some way of dealing with optional values, without additional utility code. The old way Let's consider this code: String unsafeTypeDirName = project.getApplicationType().getTypeDirName(); System.out.println(unsafeTypeDirName); This can obviously break with NullPointerException if any term is null. A typical way of avoiding this: // safe, ugly, omission-prone if (project != null) { ApplicationType applicationType = project.getApplicationType(); if (applicationType != null) { String typeDirName = applicationType.getTypeDirName(); if (typeDirName != null) { System.out.println(typeDirName); } } } This won't explode, but is just ugly, and it's easy to avoid some null check. Java 8 Let's try with Java 8's Optional: // let's assume you will get this from your model in the future; in the meantime... Optional optionalProject = Optional.ofNullable(project); // safe, java 8, but still ugly and omission-prone if (optionalProject.isPresent()) { ApplicationType applicationType = optionalProject.get().getApplicationType(); Optional optionalApplicationType = Optional.ofNullable(applicationType); if (optionalApplicationType.isPresent()) { String typeDirName = optionalApplicationType.get().getTypeDirName(); Optional optionalTypeDirName = Optional.ofNullable(typeDirName); if (optionalTypeDirName.isPresent()) { System.out.println(optionalTypeDirName); } } As noted in a lot of posts, this isn't a lot better than null checks. Some argue that it makes your intent clear. I don't see any big difference, most null checks being pretty obvious on those kind of situations. Ok, let's use the functional interfaces and get more power from Optional: // safe, prettier Optional optionalTypeDirName = optionalProject .flatMap(project -> project.getApplicationTypeOptional()) .flatMap(applicationType -> applicationType.getTypeDirNameOptional()); optionalTypeDirName.ifPresent(typeDirName -> System.out.println(typeDirName)); flatMap() will always return an Optional, so no nulls possible here, and you avoid having to wrap/unwrap to Optional. Please note that I added *Optional() methods in the types for that. There are other ways to do it (map + flatMap to Optional::ofNullable is one). The best one: only return optional value where it makes sense: if you know the value will always be provided, make it non-optional. By the way, this advice works for old style null checks too. ifPresent() will only run the code if it's there. No default or anything. Let's just use member references to express the same in a tight way: // safe, yet prettier optionalProject .flatMap(Project::getApplicationTypeOptional) .flatMap(ApplicationType::getTypeDirNameOptional) .ifPresent(System.out::println); Or if you know that Project has an ApplicationType anyway: // safe, yet prettier optionalProject .map(Project::getApplicationType) .flatMap(ApplicationType::getTypeDirNameOptional) .ifPresent(System.out::println); Conclusion By using Optional, and never working with null, you could avoid null checks altogether. Since they aren't needed, you also avoid omitting a null check leading to NPEs. Still, make sure that values returned from legacy code (Map, ...), which can be null, are wrapped asap in Optional.

Learn how to resolve the ''failure to transfer'' error encountered in Maven in this quick tutorial.

Gephi comes with tools to analyse properties of a graph.

One of the most frequently voiced criticisms of the Java programming language is the amount of Boilerplate Code it requires. This is especially true for simple classes that should do nothing more than store a few values. You need getters and setters for these values, maybe you also need a constructor, overridingequals() and hashcode() is often required and maybe you want a more useful toString()implementation. In the end you might have 100 lines of code that could be rewritten with 10 lines of Scala or Groovy code. Java IDEs like Eclipse or IntelliJ try to reduce this problem by providing various types of code generation functionality. However, even if you do not have to write the code yourself, you always see it (and get distracted by it) if you open such a file in your IDE. Project Lombok (don't be frightened by the ugly web page) is a small Java library that can help reducing the amount of Boilerplate Code in Java Applications. Project Lombok provides a set of annotations that are processed at development time to inject code into your Java application. The injected code is immediately available in your development environment. Lets have a look at the following Eclipse Screenshot: The defined class is annotated with Lombok's @Data annotation and does not contain any more than three private fields. @Data automatically injects getters, setters (for non final fields), equals(), hashCode(),toString() and a constructor for initializing the final dateOfBirth field. As you can see the generated methods are directly available in Eclipse and shown in the Outline view. Setup To set up Lombok for your application you have to put lombok.jar to your classpath. If you are using Maven you just have to add to following dependency to your pom.xml: org.projectlombok lombok 1.14.8 provided You also need to set up Lombok in the IDE you are using: NetBeans users just have to enable the Enable Annotation Processing in Editor option in their project properties (see: NetBeans instructions). Eclipse users can install Lombok by double clicking lombok.jar and following a quick installation wizard. For IntelliJ a Lombok Plugin is available. Getting started The @Data annotation shown in the introduction is actually a shortcut for various other Lombok annotations. Sometimes @Data does too much. In this case, you can fall back to more specific Lombok annotations that give you more flexibility. Generating only getters and setters can be achieved with @Getter and @Setter: @Getter @Setter public class Person { private final LocalDate birthday; private String firstName; private String lastName; public Person(LocalDate birthday) { this.birthday = birthday; } } Note that getter methods for boolean fields are prefixed with is instead of get (e.g. isFoo() instead ofgetFoo()). If you only want to generate getters and setters for specific fields you can annotate these fields instead of the class. Generating equals(), hashCode() and toString(): @EqualsAndHashCode @ToString public class Person { ... } @EqualsAndHashCode and @ToString also have various properties that can be used to customize their behaviour: @EqualsAndHashCode(exclude = {"firstName"}) @ToString(callSuper = true, of = {"firstName", "lastName"}) public class Person { ... } Here the field firstName will not be considered by equals() and hashCode(). toString() will call super.toString() first and only consider firstName and lastName. For constructor generation multiple annotations are available: @NoArgsConstructor generates a constructor that takes no arguments (default constructor). @RequiredArgsConstructor generates a constructor with one parameter for all non-initialized final fields. @AllArgsConstructor generates a constructor with one parameter for all fields in the class. The @Data annotation is actually an often used shortcut for @ToString, @EqualsAndHashCode, @Getter,@Setter and @RequiredArgsConstructor. If you prefer immutable classes you can use @Value instead of @Data: @Value public class Person { LocalDate birthday; String firstName; String lastName; } @Value is a shortcut for @ToString, @EqualsAndHashCode, @AllArgsConstructor,@FieldDefaults(makeFinal = true, level = AccessLevel.PRIVATE) and @Getter. So, with @Value you get toString(), equals(), hashCode(), getters and a constructor with one parameter for each field. It also makes all fields private and final by default, so you do not have to addprivate or final modifiers. Looking into Lombok's experimental features Besides the well supported annotations shown so far, Lombok has a couple of experimental features that can be found on the Experimental Features page. One of these features I like in particular is the @Builder annotation, which provides an implementation of the Builder Pattern. @Builder public class Person { private final LocalDate birthday; private String firstName; private String lastName; } @Builder generates a static builder() method that returns a builder instance. This builder instance can be used to build an object of the class annotated with @Builder (here Person): Person p = Person.builder() .birthday(LocalDate.of(1980, 10, 5)) .firstName("John") .lastName("Smith") .build(); By the way, if you wonder what this LocalDate class is, you should have a look at my blog post about the Java 8 date and time API ;-) Conclusion Project Lombok injects generated methods, like getters and setters, based on annotations. It provides an easy way to significantly reduce the amount of Boilerplate code in Java applications. Be aware that there is a downside: According to reddit comments (including a comment of the project author), Lombok has to rely on various hacks to get the job done. So, there is a chance that future JDK or IDE releases will break the functionality of project Lombok. On the other hand, these comments where made 5 years ago and Project Lombok is still actively maintained. You can find the source of Project Lombok on GitHub.

Property based testing is an alternative approach to testing, complementingexample based testing. The latter is what we've been doing all our lives: exercising production code against "examples" - inputs we think are representative. Picking these examples is an art on its own: "ordinary" inputs, edge cases, malformed inputs, etc. But why are we limiting ourselves to just few examples? Why not test hundreds, millions... ALL inputs? There are at least two difficulties with that approach: Scale. A pure function taking just one int input would require 4 billion tests. This means few hundred gigabytes of test source code and several months of execution time. Square it if a function takes two ints. For String it practically goes to infinity. Assume we have these tests, executed on a quantum computer or something. How do you know the expected result for each particular input? You either enter it by hand (good luck) or generate expected output. Bygenerate I mean write a program that produces expected value for every input. But aren't we testing such program already in the first place? Are we suppose to write better, error-free version of code under test just to test it? Also known as ugly mirror antipattern. So you understand testing every single input, although ideal, is just a mental experiment, impossible to implement. That being said property based testing tries to get as close as possible to this testing nirvana. Issue #1 is solved by slamming code under test with hundreds or thousands of random inputs. Not all of them, not even a fraction. But a good, random representation. Issue #2 is surprisingly harder. Property based testing can generate random arguments, but it can't figure out what should be the expected outcome for that random input. Thus we need a different mechanism, giving name to whole philosophy. We have to come up with properties (invariants, behaviours) that code under test exhibits no matter what the input is. This sounds very theoretically, but there are many such properties in various scenarios: Absolute value of any number should never be negative Encoding and decoding any string should yield the same String back for every symmetric encoding Optimized version of some old algorithm should produce the same result as the old one for any input Total money in a bank should remain the same after arbitrary number of intra-bank transactions in any order As you can see there are many properties we can think of that do not mention specific example inputs. This is not exhaustive and strict testing. It's more like sampling and making sure samples are "sane". There are many, many libraries supporting property based testing for virtually every language. In this article we will explore Spock and ScalaCheck later. Spock + custom data generators Spock does not support property based testing out-of-the-box. However with help from data driven testing and 3rd-party data generators we can go quite far. Data tables in Spock can be generalized into so-called data pipes: def 'absolute value of #value should not be negative'() { expect: value.abs() >= 0 where: value << randomInts(100) } private static def List randomInts(int count) { final Random random = new Random() (1..count).collect { random.nextInt() } } Code above will generate 100 random integers and make sure for all of them.abs() is non-negative. You might think this test is quite dumb, but to a great surprise it actually discovers one bug! But first let's kill some boilerplate code. Generating random inputs, especially more complex, is cumbersome and boring. I found two libraries that can help us. spock-genesis: import spock.genesis.Gen def 'absolute value of #value should not be negative'() { expect: value.abs() >= 0 where: value << Gen.int.take(100) } Looks great, but if you want to generate e.g. lists of random integers,net.java.quickcheck has nicer API and is not Groovy-specific: import static net.java.quickcheck.generator.CombinedGeneratorsIterables.someLists import static net.java.quickcheck.generator.PrimitiveGenerators.integers def 'sum of non-negative numbers from #list should not be negative'() { expect: list.findAll{it >= 0}.sum() >= 0 where: list << someLists(integers(), 100) } This test is interesting. It makes sure sum of non-negative numbers is never negative - by generating 100 lists of randoms ints. Sounds reasonable. However multiple tests are failing. First of all due to integer overflow sometimes two positiveints add up to a negative one. Duh! Another type of failure that was discovered is actually frightening. While [1,2,3].sum() is 6, obviously, [].sum() is... null(WAT?) As you can see even silliest and most basic property based tests can be useful in finding unusual corner cases in your data. But wait, I said testing absolute of intdiscovered one bug. Actually it didn't, because of poor (too "random") data generators, not returning known edge values in the first place. We will fix that in the next article.

In this article we wanted to share another configuration property that can provide surprising help when setting up your JBoss BPM Suite. Previously we outlined a basic set of configuration properties to provide you with a few tricks when installing your own JBoss BRMS or JBoss BPM Suite products. As the JBoss BPM Suite is a super set, including full JBoss BRMS functionality, the rest of this article will refer only to JBoss BPM Suite but apply to both products. In this article we will show you how to modify your JBoss EAP container configuration to point the products at a custom deployment repository by adjusting a single configuration property. Maven repository The default setup is that the products will look for your maven setting in the default settings.xml as found set in theM2_HOME variable or in the users home directory at .m2/settings.xml. The following system property can be added to JBoss EAP standalone.xml configuration file to point to any file containing your custom settings. kie.maven.settings.custom Location of the maven configuration file where it can find it's settings. Default: the M2_HOME/conf/settings.xml or users home directory .m2/settings.xml Example usage in JBoss EAP When initially setting up the product for use on JBoss EAP containers, one can adjust configuration with the help of system properties. Below we show how to configure an installation to point to our custom maven deployment repository by using a custom settings file we will call bpmsuite-settings.xml We hope this helps you with configuring your own custom deployment repositories and enables you to tie into existing continuous integration infrastructures that might exist in your organization.

This article represents top 4 security vulnerabilities related coding practice to avoid while you are programming with Java language. Recently, I came across few Java projects where these instances were found. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the key points described later in this article: Executing a dynamically generated SQL statement Directly writing an Http Parameter to Servlet output Creating an SQL PreparedStatement from dynamic string Array is stored directly Executing a Dynamically Generated SQL Statement This is most common of all. One can find mention of this vulenrability at several places. As a matter of fact, many developers are also aware of this vulnerability, although this is a different thing they end up making mistakes once in a while. In several DAO classes, the instances such as following code were found which could lead to SQL injection attacks. StringBuilder query = new StringBuilder(); query.append( "select * from user u where u.name in (" + namesString + ")" ); try { Connection connection = getConnection(); Statement statement = connection.createStatement(); resultSet = statement.executeQuery(query.toString()); } Instead of above query, one could as well make use of prepared statement such as that demonstrated in the code below. It not only makes code less vulnerable to SQL injection attacks but also makes it more efficient. StringBuilder query = new StringBuilder(); query.append( "select * from user u where u.name in (?)" ); try { Connection connection = getConnection(); PreparedStatement statement = connection.prepareCall(query.toString()); statement.setString( 1, namesString ); resultSet = statement.execute(); } Directly writing an Http Parameter to Servlet Output In Servlet classes, I found instances where the Http request parameter was written as it is, to the output stream, without any validation checks. Following code demonstrate the same: public void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { String content = request.getParameter("some_param"); // // .... some code goes here // response.getWriter().print(content); } Note that above code does not persist anything. Code like above may lead to what is called reflected (or non-persistent) cross site scripting (XSS) vulnerability. Reflected XSS occur when an attacker injects browser executable code within a single HTTP response. As it goes by definition (being non-persistent), the injected attack does not get stored within the application; it manifests only users who open a maliciously crafted link or third-party web page. The attack string is included as part of the crafted URI or HTTP parameters, improperly processed by the application, and returned to the victim. You could read greater details on following OWASP page on reflect XSS Creating an SQL PreparedStatement from Dynamic Query String What it essentially means is the fact that although PreparedStatement was used, but the query was generated as a string buffer and not in the way recommended for prepared statement (parametrized). If unchecked, tainted data from a user would create a String where SQL injection could make it behave in unexpected and undesirable manner. One should rather make the query statement parametrized and, use the PreparedStatement appropriately. Take a look at following code to identify the vulnerable code. StringBuilder query = new StringBuilder(); query.append( "select * from user u where u.name in (" + namesString + ")" ); try { Connection connection = getConnection(); PreparedStatement statement = connection.prepareStatement(query.toString()); resultSet = statement.executeQuery(); } Array is Stored Directly Instances of this vulnerability, Array is stored directly, could help the attacker change the objects stored in array outside of program, and the program behave in inconsistent manner as the reference to the array passed to method is held by the caller/invoker. The solution is to make a copy within the object when it gets passed. In this manner, a subsequent modification of the collection won’t affect the array stored within the object. You could read the details on following stackoverflow page. Following code represents the vulnerability: // Note that values is a String array in the code below. // public void setValues(String[] somevalues) { this.values = somevalues; }

If you use Java for programming, read on to learn about tools like Eclipse IDE, the Java Development Kit, and other must-know tools.

Raygun, Stack Hunter, Sentry, Takipi and Airbrake: Modern developer tools to help you crush bugs before bugs crush your app With the Java ecosystem going forward, web applications serving growing numbers of requests and users’ demand for high performance - comes a new breed of modern development tools. A fast paced environment with rapid new deployments requires tracking errors and gaining insight to an application's behavior on a level traditional methods can’t sustain. In this post we’ve decided to gather 5 of those tools, see how they integrate with Java and find out what kind of tricks they have up their sleeves. It’s time to smash some bugs. Raygun Mindscape’s Raygun is a web based error management system that keeps track of exceptions coming from your apps. It supports various desktop, mobile and web programming languages, including Java, Scala, .NET, Python, PHP, and JavaScript. Besides that, sending errors to Raygun is possible through a REST API and a few more Providers (that’s how they call language and framework integrations) came to life thanks to developer community involvement. Key Features: Error grouping - Every occurrence of a bug is presented within one group with access to single instances of it, including its stack trace. Full text search - Error groups and all collected data is searchable. View app activity - Every action on an error group is displayed for all your team to see: status updates, comments and more. Affected users - Counts of affected users appear by each error. External integrations - Github, Bitbucket, Asana, JIRA, HipChat and many more. The Java angle: To use Raygun with Java, you’ll need to add some dependencies to your pom.xml file if you’re using Maven or add the jars manually. The second step would be to add an UncaughtExceptionHandler that would create an instance of RaygunClient and send your exceptions to it. In addition, you can also add custom data fields to your exceptions and send them together to Raygun. The full walkthrough is available here. Behind the curtain: Meet Robie Robot, the certified operator of Raygun. As in, the actual ray gun. Check it out on: https://raygun.io Sentry Started as a side-project, Sentry is an open-source web based solution that serves as a real time event logging and aggregation platform. It monitors errors and displays when, where and to whom they happen, promising to do so without relying solely on user feedback. Supported languages and frameworks include Ruby, Python, JS, Java, Django, iOS, .NET and more. Key Features: See the impact of new deployments in real time Provide support to specific users interrupted by an error Detect and thwart fraud as its attempted - notifications of unusual amounts of failures on purchases, authentication, and other sensitive areas External Integrations - GitHub, HipChat, Heroku, and many more The Java angle: Sentry’s Java client is called Raven and supports major existing logging frameworks like java.util.logging, Log4j, Log4j2 and Logback with Slf4j. An independent method to send events directly to Sentry is also available. To set up Sentry for Java with Logback for example, you’ll need to add the dependencies manually or through Maven, then add a new Sentry appender configuration and you’re good to do. Instructions are available here. Behind the curtain: Sentry was an internal project at Disqus back in 2010 to solve exception logging on a Django application by Chris Jennings and David Cramer Check it out on: https://www.getsentry.com/ Takipi Unlike most of the other tools, Takipi is far more than a stack trace prettifier. It was built with a simple objective in mind: Telling developers exactly when and why production code breaks. Whenever a new exception is thrown or a log error occurs – Takipi captures it and shows you the variable state which caused it, across methods and machines. Takipi will overlay this over the actual code which executed at the moment of error – so you can analyze the exception as if you were there when it happened. Key features: Detect – Caught/uncaught exceptions, Http and logged errors. Prioritize – How often errors happen across your cluster, if they involve new or modified code, and whether that rate is increasing. Analyze – See the actual code and variable state, even across different machines and applications. Easy to install - No code or configuration changes needed. Less than 2% overhead. The Java angle: Takipi was built for production environments in Java and Scala. The installation takes less than 1min, and includes attaching a Java agent to your JVM. Behind the curtain: Each exception type and error has a unique monster that represents it. You can find these monster here. Check it out on: http://www.takipi.com/ Airbrake Another tool that has put exception tracking on its eyesights is Rackspace’s Airbrake, taking on the mission of “No More Searching Log Files”. It provides users with a web based interface that includes a dashboard with error details and an application specific view. Supported languages include Ruby, PHP, Java, .NET, Python and even… Swift. Key Features: Detailed stack traces, grouping by error type, users and environment variables Team productivity - Filter importance errors from the noise Team collaboration - See who’s causing bugs and whose fixing them External Integrations - HipChat, GitHub, JIRA, Pivotal and over 30 more The Java angle: Airbrake officially supports only Log4j, although a Logback library is also available. Log4j2 support is currently lacking. The installation procedure is similar to Sentry, adding a few dependencies manually or through Maven, adding an appender, and you’re ready to start. Similarly, a direct way to send messages to Airbrake is also available with AirbrakeNotice and AirbrakeNotifier. More details are available here. Behind the curtain: Airbrake was acquired by Exceptional, which then got acquired by Rackspace. Check it out on: https://airbrake.io/ StackHunter Currently in beta, Stack Hunter provides a self hosted tool to track your Java exceptions. A change of scenery from the past hosted tools. Other than that, it aims to provide a similar feature set to inform developers of their exceptions and help solve them faster. Key Features: A single self hosted web interface to view all exceptions Collections of stack trace data and context including key metrics such as total exceptions, unique exceptions, users affected, & sessions affected Instant email alerts when exceptions occur Exceptions grouping by root cause The Java angle: Built specifically for Java, StackHunter runs on any servlet container running Java 6 or above. Installation includes running StackHunter on a local servlet, configuring an outgoing mail server for alerts, and configuring the application you’re wishing to log. Full instructions are available here. Behind the curtain: StackHunter is developed by Dele Taylor, who also works on Data Pipeline - a tool for transforming and migrating data in Java. Check it out on: http://stackhunter.com/ Bonus: ABRT Another approach to error tracking worth mentioning is used by ABRT, an automatic bug detection and reporting tool from the Fedora ecosystem, which is a Red Hat sponsored community project. Unlike the 5 tools we covered here, this one is intended to be used not only by app developers - but their users as well. Reporting bugs back to Red Hat with richer context that otherwise would have been harder to understand and debug. The Java angle: Support for Java exceptions is still in its proof of concept stage. A Java connector developed by Jakub Filák is available here. Behind the curtain: ABRT is an open-source project developed by Red Hat. Check it out on: https://github.com/abrt/abrt Did we miss any other tools? How do you keep track of your exceptions? Please let me know in the comments section below.

Overview Java 8 has added features such as lambdas and type inference. This makes the language less verbose and cleaner, however it comes with more side effects as you don't have to be as explicit in what you are doing. The return type of a lambda matters Java 8 infers the type of a closure. One way it does this is to look at the return type (or whether anything is returned) This can have a surprising side effect. Consider this code. ExecutorService es = Executors.newSingleThreadExecutor(); es.submit(() -> { try(Scanner scanner = new Scanner(new FileReader("file.txt"))) { String line = scanner.nextLine(); process(line); } return null; }); This code compiles fine. However, the line return null; appears redundant and you might be tempted to remove it. However if you remove the line, you get an error. Error:(12, 39) java: unreported exception java.io.FileNotFoundException; must be caught or declared to be thrown This is complaining about the use of FileReader. What has the return null got to do with catching an uncaught exception !? Type inference. ExecutorService.submit() is an overloaded method. It has two methods which take one argument. ExecutorService.submit(Runnable runnable); ExecutorService.submit(Callable callable); Both these methods take no arguments, so how does the javac compiler infer the type of the lambda? It looks at the return type. If you return null; it is aCallable however if nothing is returned, not even null, it is a Runnable. Callable and Runnable have another important difference. Callable throws checked exceptions, however Runnable doesn't allow checked exceptions to be thrown. The side effect of returning null is that you don't have to handle checked exceptions, these will be stored in the Future submit() returns. If you don't return anything, you have to handle checked exceptions. Conclusion While lambdas and type inference remove significant amounts of boiler plate code, you can find more edge cases, where the hidden details of what the compiler infers can be slightly confusing. Footnote You can be explicit about type inference with a cast. Consider this Callable calls = (Callable & Serializable) () -> { return null; } if (calls instanceof Serializable) // is true This cast has a number of side effects. Not only does the call() method return anInteger and a marker interface added, the code generated for the lambda changes i.e. it adds a writeObject() and readObject() method to support serialization of the lambda. Note: Each call site creates a new class meaning the details of this cast is visible at runtime via reflection.