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 article represents some high-level concepts and a code example on how to write a PHP class and use it elsewhere. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are some of the key points described later in this article: Why write a PHP class? Key aspects of a PHP class A PHP class – Code Example Using PHP class in PHP files Why Write a PHP Class? As a beginner, I have come across this common thing that PHP developers tend to write one or more functions in the PHP files/scripts. As a matter of fact, I have also come across several projects (profitable ones) which were written with a few PHP files, very large ones, having all the code put in them in form of multiple functions. When learning PHP, it is OK to do in this way. However, for web apps to go to production, this may not be the recommended way. Following are some of the disadvantages of writing PHP scripts with just the functions in it: Low Maintainability: These files with functions are difficult to maintain/manage (change). Thinking of writing unit tests is like next to impossible. In addition to low testability, there may be several functions which could be reused. However, due to the way they get written, the files score low on re-usability as well. It also propagates the code duplication which further impacts code maintainability as changing a functionality would require change at several places. Low Usability: These files are difficult to read and understand. To take care of some of the above issues, one should learn writing PHP using object-oriented manner, e.g., writing code in form of one or more classes. Writing PHP code using classes helps one should segregate similar looking functions in a class (Single Responsibility Principle) and use the class elsewhere in the code (different PHP scripts). As a matter of fact, one could easily follow SOLID principle with PHP and make the code well-structured. Doing this way does propagate high maintainability (high testability, high cohesiveness, high reusability etc) and makes code readable and understandable. Key Aspects of a PHP Class Following are some of the key aspects of a PHP class: Define a class with keyword “class” followed by name of the class Define the constructor method using “__construct” followed by arguments. The object of the class can then be instantiated using “new ClassName( arguments_list )” Define class variables. One could access specifiers such as private, public, protected etc. Define methods using “function” keyword. By default, PHP methods, if not specified with any access specifier becomes public in nature. That is it! A PHP Class – Code Example Following is the code example of a PHP class, User. Pay attention to some of the following: “class” followed by “User”, the class name Member variables such as $name, $age Member functions such as getName, isAdult class User { private $name; private $age; function __construct( $name, $age ) { $this->name = $name; $this->age = $age; } function getName() { return $this->name; } function isAdult() { return $this->age >= 18?"an Adult":"Not an Adult"; } } Save the file as User.php. Don’t forget to put the above code within Using PHP Class in PHP files Finally, its time to use the PHP class. If you are working with a sample project, go to index.php. Assuming that User.php is saved in same folder as index.php, following is how the code would look like. Pay attention to some of the following: “require” keyword used to include User class written inside User.php “new” keyword used to instantiate the User class -> used to invoke methods on the object getName(). "! You are ". $h->isAdult(); ?> getName(). "! You are ". $h->isAdult(); ?>

I’ve been working through the exercises from An Introduction to Statistical Learning and one of them required you to create a pair wise correlation matrix of variables in a data frame. The exercise uses the ‘Carseats’ data set which can be imported like so: > install.packages("ISLR") > library(ISLR) > head(Carseats) Sales CompPrice Income Advertising Population Price ShelveLoc Age Education Urban US 1 9.50 138 73 11 276 120 Bad 42 17 Yes Yes 2 11.22 111 48 16 260 83 Good 65 10 Yes Yes 3 10.06 113 35 10 269 80 Medium 59 12 Yes Yes 4 7.40 117 100 4 466 97 Medium 55 14 Yes Yes 5 4.15 141 64 3 340 128 Bad 38 13 Yes No 6 10.81 124 113 13 501 72 Bad 78 16 No Yes filter the categorical variables from a data frame and If we try to run the ‘cor‘ function on the data frame we’ll get the following error: > cor(Carseats) Error in cor(Carseats) : 'x' must be numeric As the error message suggests, we can’t pass non numeric variables to this function so we need to remove the categorical variables from our data frame. But first we need to work out which columns those are: > sapply(Carseats, class) Sales CompPrice Income Advertising Population Price ShelveLoc Age Education "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "factor" "numeric" "numeric" Urban US "factor" "factor" We can see a few columns of type ‘factor’ and luckily for us there’s a function which will help us identify those more easily: > sapply(Carseats, is.factor) Sales CompPrice Income Advertising Population Price ShelveLoc Age Education FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE Urban US TRUE TRUE Now we can remove those columns from our data frame and create the correlation matrix: > cor(Carseats[sapply(Carseats, function(x) !is.factor(x))]) Sales CompPrice Income Advertising Population Price Age Education Sales 1.00000000 0.06407873 0.151950979 0.269506781 0.050470984 -0.44495073 -0.231815440 -0.051955242 CompPrice 0.06407873 1.00000000 -0.080653423 -0.024198788 -0.094706516 0.58484777 -0.100238817 0.025197050 Income 0.15195098 -0.08065342 1.000000000 0.058994706 -0.007876994 -0.05669820 -0.004670094 -0.056855422 Advertising 0.26950678 -0.02419879 0.058994706 1.000000000 0.265652145 0.04453687 -0.004557497 -0.033594307 Population 0.05047098 -0.09470652 -0.007876994 0.265652145 1.000000000 -0.01214362 -0.042663355 -0.106378231 Price -0.44495073 0.58484777 -0.056698202 0.044536874 -0.012143620 1.00000000 -0.102176839 0.011746599 Age -0.23181544 -0.10023882 -0.004670094 -0.004557497 -0.042663355 -0.10217684 1.000000000 0.006488032 Education -0.05195524 0.02519705 -0.056855422 -0.033594307 -0.106378231 0.01174660 0.006488032 1.000000000 Be Sociable, Share!

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.

It is possible to disable HTTP access on S3 bucket, limiting S3 traffic to only HTTPS requests. The documentation is scattered around the Amazon AWS documentation, but the solution is actually straightforward. All you need to do to block HTTP traffic on an S3 bucket is add a Condition in your bucket's policy. AWS supports a global condition for verifying SSL. So you can add a condition like this: "Condition": { "Bool": { "aws:SecureTransport": "true" } } Here's a complete example: { "Version": "2008-10-17", "Id": "some_policy", "Statement": [ { "Sid": "AddPerm", "Effect": "Allow", "Principal": { "AWS": "*" }, "Action": "s3:GetObject", "Resource": "arn:aws:s3:::my_bucket/*", "Condition": { "Bool": { "aws:SecureTransport": "true" } } } ] } Now accessing the contents of my_bucket over HTTP will produce a 403 error, while using HTTPS will work fine.

In MongoDB there are multiple guarantee levels available for reporting the success of a write operation, called Write Concerns. The strength of the write concerns determine the level of guarantee. A weak Write Concern has better performance at the cost of lesser guarantee, while a strong Write Concern has higher guarantee as clients wait to confirm the write operations. MongoDB provides different levels of write concern to better address the specific needs of applications. Clients may adjust write concern to ensure that the most important operations persist successfully to an entire MongoDB deployment. For other less critical operations, clients can adjust the write concern to ensure faster performance rather than ensure persistence to the entire deployment. Write Concern Levels MongoDB has the following levels of conceptual write concern, listed from weakest to strongest: Unacknowledged With an unacknowledged write concern, MongoDB does not acknowledge the receipt of write operations. Unacknowledged is similar to errors ignored; however, drivers will attempt to receive and handle network errors when possible. The driver’s ability to detect network errors depends on the system’s networking configuration. Acknowledged With a receipt acknowledged write concern, the mongod confirms the receipt of the write operation. Acknowledged write concern allows clients to catch network, duplicate key, and other errors. This is default write concern. Journaled With a journaled write concern, the MongoDB acknowledges the write operation only after committing the data to the journal. This write concern ensures that MongoDB can recover the data following a shutdown or power interruption. You must have journaling enabled to use this write concern. Replica Acknowledged Replica sets present additional considerations with regards to write concern. The default write concern only requires acknowledgement from the primary. With replica acknowledged write concern, you can guarantee that the write operation propagates to additional members of the replica set. Write operation to a replica set with write concern level of w:2 or write to the primary and at least one secondary.

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.

I’ve been playing around with PostgreSQL recently and in particular the Northwind dataset typically used as an introductory data set for relational databases. Having imported the data I wanted to take a quick look at the employees table: postgres=# SELECT * FROM employees LIMIT 1; EmployeeID | LastName | FirstName | Title | TitleOfCourtesy | BirthDate | HireDate | Address | City | Region | PostalCode | Country | HomePhone | Extension | Photo | Notes | ReportsTo | PhotoPath ------------+----------+-----------+----------------------+-----------------+------------+------------+-----------------------------+---------+--------+------------+---------+----------------+-----------+-------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+-------------------------------------- 1 | Davolio | Nancy | Sales Representative | Ms. | 1948-12-08 | 1992-05-01 | 507 - 20th Ave. E.\nApt. 2A | Seattle | WA | 98122 | USA | (206) 555-9857 | 5467 | \x | Education includes a BA IN psychology FROM Colorado State University IN 1970. She also completed "The Art of the Cold Call." Nancy IS a member OF Toastmasters International. | 2 | http://accweb/emmployees/davolio.bmp (1 ROW) That works fine but what if I only want to return the ‘EmployeeID’ field? postgres=# SELECT EmployeeID FROM employees LIMIT 1; ERROR: COLUMN "employeeid" does NOT exist LINE 1: SELECT EmployeeID FROM employees LIMIT 1; I hadn’t realised (or had forgotten) that field names get lower cased so we need to quote the name if it’s been stored in mixed case: postgres=# SELECT "EmployeeID" FROM employees LIMIT 1; EmployeeID ------------ 1 (1 ROW) From my reading the suggestion seems to be to have your field names lower cased to avoid this problem but since it’s just a dummy data set I guess I’ll just put up with the quoting overhead for now.

This article shows how to mock your JMS infrastructure using MockRunner and test it using Spring.

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(); } }

Now that SQL Server 2014 and SQL Server Compact 4 has been released, some developers are curious about the differences between SQL Server Compact 4.0 and SQL Server Express 2014 (including LocalDB) I have updated the comparison table from the excellent discussion of the differences between Compact 3.5 and Express 2005 here to reflect the changes in the newer versions of each product. Information about LocalDB comes from here and SQL Server 2014 Books Online. LocalDB is the full SQL Server Express engine, but invoked directly from the client provider. It is a replacement of the current “User Instance” feature in SQL Server Express. Feature SQL Server Compact 3.5 SP2 SQL Server Compact 4.0 SQLite, incl SQLite ADO.NET Provider SQL Server Express 2012 SQL Server 2012 LocalDB Deployment/ Installation Features Installation size 2.5 MB download size 12 MB expanded on disk 2.5 MB download size 18 MB expanded on disk 10 MB download, 14 MB expanded on disk 120 MB download size > 300 MB expanded on disk 32 MB download size > 160 MB on disk ClickOnce deployment Yes Yes Yes Yes Yes Privately installed, embedded, with the application Yes Yes Yes No No Non-admin installation option Yes Yes Yes No No Runs under ASP.NET No Yes Yes Yes Yes Runs on Windows Mobile / Windows Phone platform Yes No Yes No No Runs on WinRT (Phone/Store Apps) No No Yes No No Runs on non-Microsoft platforms No No Yes No No Installed centrally with an MSI Yes Yes Yes Yes Yes Runs in-process with application Yes Yes Yes No No (as process started by app) 64-bit support Yes Yes Yes Yes Yes Runs as a service No – In process with application No - In process with application No - In process with application Yes No – as launched process Data file features File format Single file Single file Single file Multiple files Multiple files Data file storage on a network share No No No No No Support for different file extensions Yes Yes Yes No No Database size support 4 GB 4 GB 140 TB 10 GB 10 GB XML storage Yes – stored as ntext Yes - stored as ntext Yes, stored as text Yes, native Yes, native Binary (BLOB) storage Yes – stored as image Yes - stored as image Yes Yes Yes FILESTREAM support No No No Yes No Code free, document safe, file format Yes Yes Yes No No Programmability Transact-SQL - Common Query Features Yes Yes No Yes Yes Procedural T-SQL - Select Case, If, features No No Limited Yes Yes Remote Data Access (RDA) Yes No (not supported) No No No ADO.NET Sync Framework Yes No No Yes Yes LINQ to SQL Yes No (not supported) No Yes Yes ADO.NET Entity Framework 4.1 Yes (no Code First) Yes Yes Yes Yes ADO.NET Entity Framework 6 Yes (fully) Yes (fully) Yes (limited) Yes Yes Subscriber for merge replication Yes No No Yes No Simple transactions Yes Yes Yes Yes Yes Distributed transactions No No No Yes Yes Native XML, XQuery/XPath No No No Yes Yes Stored procedures, views, triggers No No Views and triggers Yes Yes Role-based security No No No Yes Yes Number of concurrent connections 256 (100) 256 Unlimited Unlimited Unlimited (but only local) There is also a table here that allows you to determine which Transact-SQL commands, features, and data types are supported by SQL Server Compact 3.5 (which are the same a 4.0 with very few exceptions), compared with SQL Server 2005 and 2008.

The concept of resource fragmentation is common in the IT world. In the simplest of contexts, resource fragmentation occurs when blocks of capacity (compute, storage, whatever) are allocated, freed, and ultimately re-allocated to create noncontiguous blocks. While the most familiar setting for fragmentation is memory allocation, the phenomenon plays itself out within the datacenter as well. But what does resource fragmentation look like in the datacenter? And more importantly, what is the remediation? The impacts of virtualization Server virtualization does for applications and compute what fragmentation and noncontiguous memory blocks did for storage. By creating virtual machines on servers, each with a customizable resource footprint, the once large contiguous blocks of compute capacity (each server) can be divided into much smaller subdivisions. And as applications take advantage of this architectural compute model, they become more distributed. The result of this is an application environment where individual components are distributed across multiple devices, effectively occupying a noncontiguous set of compute resources that must be unified via the network. It is not a stretch to say that for server virtualization to deliver against its promise of higher utilization, the network must act as the Great Uniter. Not just a virtual phenomenon While fragmentation is easily explained in a virtualized context, the phenomenon is certainly not only a virtual one. After creation, datacenters grow organically. In the best of times, they grow at very predictable, steady rates. More frequently, they grow in fits and spurts as business requirements heap new application demands on top of existing infrastructure. This growth model is made even more chaotic because of physical constraints. Rows are finite and have an end. If you want to rack up additional compute next to existing compute for a particular application, you might have to move a row over. But what about when that row itself is taken? Then maybe you move a couple rows over. Or a room over. Or maybe into another datacenter entirely. Physical locations are also constrained by how much space they have. Even if you have the will to expand, there might simply be no additional real estate to consume. So you build up, in which case the resources you need are now separated by a floor. Or maybe you build out and separate resources by a short distance across the campus. Or across the city. Or maybe even across the country. Sometimes it’s not even the physical space. With very large footprints, trying to pull enough power from the grid might be impossible. And then there are all the business continuity requirements that frequently lead to datacenter resource sprawl across physical locations. The point is that growth is rarely linear, and this means that physical resources cannot normally be guaranteed to be in close proximity. What started as a nicely groomed cluster of compute and storage turns into a set of noncontiguous resources spread out across whatever physical footprint your datacenter (or datacenters) occupies. Unifying contiguous resources There are, of course, ways to unify resources that suffer from this type of sprawl. In the best of cases, if all of your servers are equivalent, you can migrate VMs over time to achieve continuity. The orchestration of such a feat is nightmarish enough, forgetting for a moment the impact of all that activity and the risk it incurs. So if there is no datacenter equivalent for defragmentation, what do you do? The network ends up playing a unifying role. So long as resources are connected, they can work in concert to deliver some application workload. But not all networks are the same, and depending on the spread of resources, the type of network needed varies. Not all networks are the same If resources are contained now and forever in a fairly tight geographical space, then providing rack-to-rack or row-to-row connectivity is fairly straightforward. But what if the applications across those resources are more bandwidth hungry? You might need to consider cross-connect and offload solutions. How about if those applications are particularly latency-sensitive? You might favor completely flat architectures over more traditional two- and three-tier networks. If resources are not so easily contained, the network choices expand. If application workloads are distributed across different rooms in a datacenter, you have to consider the impact of room-to-room connectivity. Is that done through a WAN connection, in which case you take on yet another networking layer? Or do you use optical equipment to stretch an L2 domain across some physical distance, in which case you have to consider laying or leasing fiber? And even then, as distances grow from a few hundred meters to a few thousand kilometers, the considerations change again. Conditions will change Finally, the complexity only increases as you consider that all of this is a moving target. When your business is smaller, perhaps you can keep everything in one location. A few years down the road, maybe you outgrow your site or leasing terms change. Your company acquires another company, and you now have resource sprawl with a datacenter consolidation project on the horizon. Accounting for all of the potential outcomes is challenging. The best that you can do is create solid architectural building blocks that provide the most optionality for whatever outcomes exist. In that regard, planning for growth is about considering how that growth might materialize and including flexibility as one of the primary requirements around the underlying infrastructure. The bottom line As datacenters grow, application resources will become fragmented. The question is not whether you will have to deal with this but rather how quickly your infrastructure can adapt. Architecting with this explicitly in mind could mean the difference between natural evolution or the types of transformation initiatives that stop companies dead in their tracks every 3-5 years. [Today’s fun fact: Chewing gum while peeling onions will prevent you from crying. It doesn’t work as well in romantic comedies.]

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 }

Sometimes one would want to handle strings which contain characters not included in UTF-8 or the default encoding (set in mule-deploy.properties). In these scenarios a different encoding which is capable of handling these characters (such as UTF-16 or UTF-32) can be used. To do so the default encoding can be easily changed by making a few modifications according to the type of transformer being used. Changing Encoding with the Datamapper When using the datamapper with data such as XML, one can easily choose the encoding by clicking on the settings button in the mapping (this should be set properly for both input and output) : Settings button datamapper A similar panel to the one below should appear: Changing Encoding when using “simple” transformers When using transformers such as object-to-string or byte-array-to-string, one would think that setting the “encoding” attribute on the transformer would do the trick: Unfortunately this doesn’t work, since the current Mule’s behaviour is to use this property just to set the MULE_ENCODING outbound property after the transformation is done. However, instead we should make sure that MULE_ENCODING outbound property is set properly before invoking the transformer. The transformer would then be able to transform the payload correctly for us.

eclipse has a great user interface (ui). but what if i want to do things from the command line, without the gui? for example to build one or more projects in the workspace without using the eclipse ui? with this, i can do automated check-outs and do automated builds. performed a command line project build with eclipse the solution to this: there is a command line version of eclipse which i can use to run eclipse in the command line version. inside the eclipse folder on windows, there is the eclipsec program which is the command-line version of eclipse: eclipsec program, a command line version of eclipse the options of this command line version (for eclipse kepler) are described here: http://help.eclipse.org/kepler/index.jsp?topic=%2forg.eclipse.platform.doc.isv%2freference%2fmisc%2fruntime-options.html for example eclipsec.exe -nosplash -application org.eclipse.cdt.managedbuilder.core.headlessbuild -data c:\my_wsp -build k64f will launch eclipse without splash screen ( -nosplash ), uses the - application command to load the managed make builder (which is used to build projects), with -data i specify the workspace to be used, and with the -build command it will the project k64f. more options and details are shown here: http://stackoverflow.com/questions/344797/build-several-cdt-c-projects-from-commandline and a very good article with additional background information how to use it with the gnu arm eclipse plubins can be found here: http://gnuarmeclipse.livius.net/blog/headless-builds/ happy headlessing :-)

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

in this mobile ui patterns article we will build a flowchart depicting the screens needed to handle user registration, login and logout in a mobile application. as a ux designer, it’s very important that you are familiar with these screens and how the interact with each other. let’s start by taking a look at the typical application launch sequence. an application’s launch sequence a large number of applications have a launch sequence that takes users to a landing page or landing screen, however you like to call it, from where they have access to deeper areas of the app. we could draw this ui pattern like this: the login flowchart most apps provide personalization features that require a user to have an account in the app. in those applications the launch sequence changes quite a bit. for starters, when users arrive at the landing page and you don’t know who they are, you need to redirect them to a login page where they will enter their credentials: upon successful login you will take users back to the landing page. to protect users and your app, you should consider locking user accounts for a period of time, or permanently, after a number of unsuccessful login attempts. if you do so, you will need to add an account locked page where you will let users know what happened. the signup flowchart as users must have an account in the app in order to log in, you need to create a page that will allow them to sign up for such account. on the login page you will offer a path to this signup page: after users enter their profiles through the signup page, you will send them a message asking them to confirm their email address. the message will contain a link to a page that can be in or outside your app, which they will need to access in order for you to confirm that they received the message. you will take users back to the login page once they confirm their email address. the password reset flowchart you need to offer an easy way for the users of your app to reset their passwords. a common approach to password reset consists of emailing users a temporary password, while giving them access to a page where they can use the temporary password to create a new permanent password. after sending the temporary password, you will display a confirmation page that will offer access to the page where the user can create a new password. alternatively, you can skip the confirmation page a navigate directly to the “new password” page. the logout and user profile pages we are missing two more pages to complete this screen flow. first would be the logout page, which will allow users to manually end their session in the app; and last would be the user profile page, which is where users can change their personal information in the app. summary and next steps in this mobile ui patterns article we built a flow chart depicting the screens that you would need to create in order to implement user registration, login and logout features in a mobile app. these screens are essential in modern applications, as they are the foundation of any personalization features that we build into the apps. while there are variations to the approach i showed you in this article, the concepts behind them are similar. do you follow a different approach? please let us know by leaving a comment. stay tuned don’t miss any articles. get free updates in your inbox.

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.

These are two concepts that I mix up occasionally - a WebApplicationInitializer and an ApplicationContextInitializer, and wanted to describe each of them to clarify them for myself. I have previously blogged about WebApplicationInitializerhere and here. It is relevant purely in a Servlet 3.0+ spec compliant servlet container and provides a hook to programmatically configure the servlet context. How does this help - you can have a web application without potentially any web.xml file, typically used in a Spring based web application to describe the root application context and the Spring web front controller called theDispatcherServlet. An example of using WebApplicationInitializer is the following: public class CustomWebAppInitializer extends AbstractAnnotationConfigDispatcherServletInitializer { @Override protected Class[] getRootConfigClasses() { return new Class[]{RootConfiguration.class}; } @Override protected Class[] getServletConfigClasses() { return new Class[]{MvcConfiguration.class}; } @Override protected String[] getServletMappings() { return new String[]{"/"}; } } Now, what is an ApplicationContextInitializer. It is essentially code that gets executed before the Spring application context gets completely created. A good use case for using an ApplicationContextInitializer would be to set a Spring environment profile programmatically, along these lines: public class DemoApplicationContextInitializer implements ApplicationContextInitializer { @Override public void initialize(ConfigurableApplicationContext ac) { ConfigurableEnvironment appEnvironment = ac.getEnvironment(); appEnvironment.addActiveProfile("demo"); } } If you have a Spring-Boot based application then registering an ApplicationContextInitializer is fairly straightforward: @Configuration @EnableAutoConfiguration @ComponentScan public class SampleWebApplication { public static void main(String[] args) { new SpringApplicationBuilder(SampleWebApplication.class) .initializers(new DemoApplicationContextInitializer()) .run(args); } } For a non Spring-Boot Spring application though, it is a little more tricky, if it is a programmatic configuration of web.xml, then the configuration is along these lines: public class CustomWebAppInitializer implements WebApplicationInitializer { @Override public void onStartup(ServletContext container) { AnnotationConfigWebApplicationContext rootContext = new AnnotationConfigWebApplicationContext(); rootContext.register(RootConfiguration.class); ContextLoaderListener contextLoaderListener = new ContextLoaderListener(rootContext); container.addListener(contextLoaderListener); container.setInitParameter("contextInitializerClasses", "mvctest.web.DemoApplicationContextInitializer"); AnnotationConfigWebApplicationContext webContext = new AnnotationConfigWebApplicationContext(); webContext.register(MvcConfiguration.class); DispatcherServlet dispatcherServlet = new DispatcherServlet(webContext); ServletRegistration.Dynamic dispatcher = container.addServlet("dispatcher", dispatcherServlet); dispatcher.addMapping("/"); } } If it a normal web.xml configuration then the initializer can be specified this way: contextInitializerClasses com.myapp.spring.SpringContextProfileInit org.springframework.web.context.ContextLoaderListener So to conclude, except for the Initializer suffix, both WebApplicationInitializer and ApplicationContextInitializer serve fairly different purposes. Whereas the WebApplicationInitializer is used by a Servlet Container at startup of the web application and provides a way for programmatic creating a web application(replacement for a web.xml file), ApplicationContextInitializer provides a hook to configure the Spring application context before it gets fully created.