When the first early access versions of Java 8 were made available, what seemed the most important (r)evolution were lambdas. This is now changing and many developers seem to think now that streams are the most valuable Java 8 feature. And this is because they believe that by changing a single word in their programs (replacing stream with parallelStream) they will make these programs work in parallel. Many Java 8 evangelists have demonstrated amazing examples of this. Is there something wrong with this? No. Not something. Many things: Running in parallel may or may not be a benefit. It depends what you are using this feature for. Java 8 parallel streams may make your programs run faster. Or not. Or even slower. Thinking about streams as a way to achieve parallel processing at low cost will prevent developers to understand what is really happening. Streams are not directly linked to parallel processing. Most of the above problems are based upon a misunderstanding: parallel processing is not the same thing as concurrent processing. And most examples shown about “automatic parallelization” with Java 8 are in fact examples of concurrent processing. Thinking about map, filter and other operations as “internal iteration” is a complete nonsense (although this is not a problem with Java 8, but with the way we use it). So, what are streams According to Wikipedia: “a stream is a potentially infinite analog of a list, given by the inductive definition: data Stream a = Cons a (Stream a) Generating and computing with streams requires lazy evaluation, either implicitly in a lazily evaluated language or by creating and forcing thunks in an eager language.” One most important think to notice is that Java is what Wikipedia calls an “eager” language, which means Java is mostly strict (as opposed to lazy) in evaluating things. For example, if you create a List in Java, all elements are evaluated when the list is created. This may surprise you, since you may create an empty list and add elements after. This is only because either the list is mutable (and you are replacing a null reference with a reference to something) or you are creating a new list from the old one appended with the new element. Lists are created from something producing its elements. For example: List list = Arrays.asList(1, 2, 3, 4, 5); Here the producer is an array, and all elements of the array are strictly evaluated. It is also possible to create a list in a recursive way, for example the list starting with 1 and where all elements are equals to 1 plus the previous element and smaller than 6. In Java < 8, this translates into: List list = new ArrayList(); for(int i = 0; i < 6; i++) { list.add(i); } One may argue that the for loop is one of the rare example of lazy evaluation in Java, but the result is a list in which all elements are evaluated. What happens if we want to apply a function to all elements of this list? We may do this in a loop. For example, if with want to increase all elements by 2, we may do this: for(int i = 0; i < list.size(); i++) { list.set(i, list.get(i) * 2); } However, this does not allow using an operation that changes the type of the elements, for example increasing all elements by 10%. The following solution solves this problem: List list2 = new ArrayList(); for(int i = 0; i < list.size(); i++) { list2.add(list.get(i) * 1.2); } This form allows the use of a the Java 5 for each syntax: List list2 = new ArrayList<>(); for(Integer i : list) { list2.add(i * 1.2); } or the Java 8 syntax: List list2 = new ArrayList<>(); list.forEach(x -> list2.add(x * 1.2)); So far, so good. But what if we want to increase the value by 10% and then divide it by 3? The trivial answer would be to do: List list2 = new ArrayList<>(); list.forEach(x -> list2.add(x * 1.2)); List list3 = new ArrayList<>(); list2.forEach(x -> list3.add(x / 3)); This is far from optimal because we are iterating twice on the list. A much better solution is: List list2 = new ArrayList<>(); for(Integer i : list) { list2.add(i * 1.2 / 3); } Let aside the auto boxing/unboxing problem for now. In Java 8, this can be written as: List list2 = new ArrayList<>(); list.forEach(x -> list2.add(x * 1.2 / 3)); But wait... This is only possible because we see the internals of the Consumer bound to the list, so we are able to manually compose the operations. If we had: List list2 = new ArrayList<>(); list.forEach(consumer1); List list3 = new ArrayList<>(); list2.forEach(consumer2); How could we know how to compose them? No way. In Java 8, the Consumer interface has a default method andThen. We could be tempted to compose the consumers this way: list.forEach(consumer1.andThen(consumer2)); but this will result in an error, because andThen is defined as: default Consumer andThen(Consumer after) { Objects.requireNonNull(after); return (T t) -> { accept(t); after.accept(t); }; } This means that we can't use andThen to compose consumers of different types. In fact, we have it all wrong since the beginning. What we need is to bind the list to a function in order to get a new list, such as: Function function1 = x -> x * 1.2; Function function2 = x -> x / 3; list.bind(function1).bind(function2); where the bind method would be defined in a special FList class like: public class FList { final List list; public FList(List list) { this.list = list; } public FList bind(Function f) { List newList = new ArrayList(); for (T t : list) { newList.add(f.apply(t)); } return new FList(newList); } } and we would use it as in the following example: new Flist<>(list).bind(function1).bind(function2); The only trouble we have then is that binding twice would require iterating twice on the list. This is because bind is evaluated strictly. What we would need is a lazy evaluation, so that we could iterate only once. The problem here is that the bind method is not a real binding. It is in reality a composition of a real binding and a reduce. "Reducing" is applying an operation to each element of the list, resulting in the combination of this element and the result of the same operation applied to the previous element. As there is no previous element when we start from the first element, we start with an initial value. For example, applying (x) -> r + x, where r is the result of the operation on the previous element, or 0 for the first element, gives the sum of all elements of the list. Applying () -> r + 1 to each element, starting with r = 0 gives the length of the list. (This may not be the more efficient way to get the length of the list, but it is totally functional!) Here, the operation is add(element) and the initial value is an empty list. And this occurs only because the function application is strictly evaluated. What Java 8 streams give us is the same, but lazily evaluated, which means that when binding a function to a stream, no iteration is involved! Binding a Function to a Stream gives us a Stream with no iteration occurring. The resulting Stream is not evaluated, and this does not depend upon the fact that the initial stream was built with evaluated or non evaluated data. In functional languages, binding a Function to a Stream is itself a function. In Java 8, it is a method, which means it's arguments are strictly evaluated, but this has nothing to do with the evaluation of the resulting stream. To understand what is happening, we can imagine that the functions to bind are stored somewhere and they become part of the data producer for the new (non evaluated) resulting stream. In Java 8, the method binding a function T -> U to a Stream, resulting in a Stream is called map. The function binding a function T -> Stream to a Stream, resulting in a Stream is called flatMap. Where is flatten? Most functional languages also offer a flatten function converting a Stream> into a Stream, but this is missing in Java 8 streams. It may not look like a big trouble since it is so easy to define a method for doing this. For example, given the following function: Function> f = x -> Stream.iterate(1, y -> y + 1).limit(x); Stream stream = Stream.iterate(1, x -> x + 1); Stream stream2 = stream.limit(5).flatMap(f); System.out.println(stream2.collect(toList())) to produce: [1, 1, 2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 5] Using map instead of flatMap: Stream stream = Stream.iterate(1, x -> x + 1); Stream stream2 = stream.limit(5).map(f); System.out.println(stream2.collect(toList())) will produce a stream of streams: [java.util.stream.SliceOps$1@12133b1, java.util.stream.SliceOps$1@ea2f77, java.util.stream.SliceOps$1@1c7353a, java.util.stream.SliceOps$1@1a9515, java.util.stream.SliceOps$1@f49f1c] Converting this stream of streams of integers to a stream of integers is very straightforward using the functional paradigm: one just need to flatMap the identity function to it: System.out.println(stream2.flatMap(x -> x).collect(toList())); It is however strange that a flatten method has not been added to the stream, knowing the strong relation that ties map, flatMap, unit and flatten, where unit is the function from T to Stream, represented by the method: Stream Stream.of(T... t) When are stream evaluated? Streams are evaluated when we apply to them some specific operations called terminal operation. This may be done only once. Once a terminal operation is applied to a stream, is is no longer usable. Terminal operations are: forEach forEachOrdered toArray reduce collect min max count anyMatch allMatch noneMatch findFirst findAny iterator spliterator Some of these methods are short circuiting. For example, findFirst will return as soon as the first element will be found. Non terminal operations are called intermediate and can be stateful (if evaluation of an element depends upon the evaluation of the previous) or stateless. Intermediate operations are: filter map mapTo... (Int, Long or Double) flatMap flatMapTo... (Int, Long or Double) distinct sorted peek limit skip sequential parallel unordered onClose Several intermediate operations may be applied to a stream, but only one terminal operation may be use. So what about parallel processing? One most advertised functionality of streams is that they allow automatic parallelization of processing. And one can find the amazing demonstrations on the web, mainly based of the same example of a program contacting a server to get the values corresponding to a list of stocks and finding the highest one not exceeding a given limit value. Such an example may show an increase of speed of 400 % and more. But this example as little to do with parallel processing. It is an example of concurrent processing, which means that the increase of speed will be observed also on a single processor computer. This is because the main part of each “parallel” task is waiting. Parallel processing is about running at the same time tasks that do no wait, such as intensive calculations. Automatic parallelization will generally not give the expected result for at least two reasons: The increase of speed is highly dependent upon the kind of task and the parallelization strategy. And over all things, the best strategy is dependent upon the type of task. The increase of speed in highly dependent upon the environment. In some environments, it is easy to obtain a decrease of speed by parallelizing. Whatever the kind of tasks to parallelize, the strategy applied by parallel streams will be the same, unless you devise this strategy yourself, which will remove much of the interest of parallel streams. Parallelization requires: A pool of threads to execute the subtasks, Dividing the initial task into subtasks, Distributing subtasks to threads, Collating the results. Without entering the details, all this implies some overhead. It will show amazing results when: Some tasks imply blocking for a long time, such as accessing a remote service, or There are not many threads running at the same time, and in particular no other parallel stream. If all subtasks imply intense calculation, the potential gain is limited by the number of available processors. Java 8 will by default use as many threads as they are processors on the computer, so, for intensive tasks, the result is highly dependent upon what other threads may be doing at the same time. Of course, if each subtask is essentially waiting, the gain may appear to be huge. The worst case is if the application runs in a server or a container alongside other applications, and subtasks do not imply waiting. In such a case, (for example running in a J2EE server), parallel streams will often be slower that serial ones. Imagine a server serving hundreds of requests each second. There are great chances that several streams might be evaluated at the same time, so the work is already parallelized. A new layer of parallelization at the business level will most probably make things slower. Worst: there are great chances that the business applications will see a speed increase in the development environment and a decrease in production. And that is the worst possible situation. Edit: for a better understanding of why parallel streams in Java 8 (and the Fork/Join pool in Java 7) are broken, refer to these excellent articles by Edward Harned: A Java Fork-Join Calamity A Java Parallel Calamity What streams are good for Stream are a useful tool because they allow lazy evaluation. This is very important in several aspect: They allow functional programming style using bindings. They allow for better performance by removing iteration. Iteration occurs with evaluation. With streams, we can bind dozens of functions without iterating. They allow easy parallelization for task including long waits. Streams may be infinite (since they are lazy). Functions may be bound to infinite streams without problem. Upon evaluation, there must be some way to make them finite. This is often done through a short circuiting operation. What streams are not good for Streams should be used with high caution when processing intensive computation tasks. In particular, by default, all streams will use the same ForkJoinPool, configured to use as many threads as there are cores in the computer on which the program is running. If evaluation of one parallel stream results in a very long running task, this may be split into as many long running sub-tasks that will be distributed to each thread in the pool. From there, no other parallel stream can be processed because all threads will be occupied. So, for computation intensive stream evaluation, one should always use a specific ForkJoinPool in order not to block other streams. To do this, one may create a Callable from the stream and submit it to the pool: List list = // A list of objects Stream stream = list.parallelStream().map(this::veryLongProcessing); Callable> task = () -> stream.collect(toList()); ForkJoinPool forkJoinPool = new ForkJoinPool(4); List newList = forkJoinPool.submit(task).get() This way, other parallel streams (using their own ForkJoinPool) will not be blocked by this one. In other words, we would need a pool of ForkJoinPool in order to avoid this problem. If a program is to be run inside a container, one must be very careful when using parallel streams. Never use the default pool in such a situation unless you know for sure that the container can handle it. In a Java EE container, do not use parallel streams. Previous articles What's Wrong with Java 8, Part I: Currying vs Closures What's Wrong in Java 8, Part II: Functions & Primitives

In a previous post, we had shared a small function that generated random string in Java. It turns out that similar functionality is available from a class in the extremely useful apache commons lang library. If you are using maven, download the jar using the following dependency: commons-lang commons-lang 20030203.000129 The class we are interested in is RandomStringUtils. Listed below are some functions you may find useful. Generate and print a random string of length 5 from all characters available System.out.println(RandomStringUtils.random(5)); Generate and print random string of length 10 from upper and lower case alphabets System.out.println(RandomStringUtils.randomAlphabetic(10)); Generate and print a random number of length 12 System.out.println(RandomStringUtils.randomNumeric(12)); Generate and print a random string of length 5 using only a, b, c and d characters System.out.println(RandomStringUtils.random(10,new char[]{'a','b','c','d'}));

Facts and Terminology As you probably know, Java uses UTF-16 to represent Strings. In order to understand the confusion about String.length(), you need to be familiar with some Encoding/Unicode terms. Code Point: A unique integer value which represents a character in the code space. Code Unit: A bit sequence used to encode characters (Code Points). One or more Code Units may be required to represent a Code Point. UTF-16 Unicode Code Points are logically divided into 17 planes. The first plane, the Basic Multilingual Plane (BMP) contains the “classic” characters (from U+0000 to U+FFFF). The other planes contain the supplementary characters (from U+10000 to U+10FFFF). Characters (Code Points) from the first plane are encoded in one 16-bit Code Unit with the same value. Supplementary characters (Code Points) are encoded in two Code Units (encoding-specific, see Wiki for the explanation). Example Character: A Unicode Code Point: U+0041 UTF-16 Code Unit(s): 0041 Character: Mathematical double-struck capital A Unicode Code Point: U+1D538 UTF-16 Code Unit(s): D835 DD38 As you can see here, there are characters which are encoded in two Code Units. String.length() Let’s take a look at the Javadoc of the length() method: public int length() Returns the length of this string. The length is equal to the number of Unicode code units in the string. So if you have one supplementary character which consists of two code units, the length of that single character is two. // Mathematical double-struck capital A String str = "\uD835\uDD38"; System.out.println(str); System.out.println(str.length()); //prints 2 Which is correct according to the documentation, but maybe it’s not expected. ~Solution You need to count the code points not the code units: String str = "\uD835\uDD38"; System.out.println(str); System.out.println(str.codePointCount(0, str.length())); See: codePointCount(int beginIndex, int endIndex) References/Sources The Java Language Specification Unicode Glossary: Code Point Wiki: Code Point Unicode Glossary: Code Unit Wiki: Code Unit Wiki: Unicode Wiki: UTF-16 Supplementary Characters in the Java Platform Wiki: Unicode Planes

If you need to compare the java.io.file.Path object, be aware that Path.endsWith(String) will ONLY match another sub-element of Path object in your original path, not the path name string portion! If you want to match the string name portion, you would need to call the Path.toString() first. For example // Match all jar files. Files.walk(dir).forEach(path -> { if (path.toString().endsWith(".jar")) System.out.println(path); }); With out the "toString()" you will spend many fruitless hours wonder why your program didn't work.

Before some time I have written a blog post – Converting a C# object into JSON string in that post one of reader, Thomas Levesque commented that mostly people are using JSON.NET a popular high performance JSON for creating for .NET Created by James Newton- King. I agree with him if we are using .NET Framework 4.0 or higher version for earlier version still JavaScriptSerializer is good. So in this post we are going to learn How we can convert C# object into JSON string with JSON.NET framework. What is JSON.NET: JSON.NET is a very high performance framework compared to other serializer for converting C# object into JSON string. It is created by James Newton-Kind. You can find more information about this framework from following link. http://james.newtonking.com/json How to convert C# object into JSON string with JSON.NET framework: For this I am going to use old application that I have used in previous post. Following is a employee class with two properties first name and last name. public class Employee { public string FirstName { get; set; } public string LastName { get; set; } } I have created same object of “Employee” class as I have created in previous post like below. Employee employee=new Employee {FirstName = "Jalpesh", LastName = "Vadgama"}; Now it’s time to add JSON.NET Nuget package. You install Nuget package via following command. I have installed like below. Now we are done with adding NuGet package. Following is code I have written to convert C# object into JSON string. string jsonString = Newtonsoft.Json.JsonConvert.SerializeObject(employee); Console.WriteLine(jsonString); Let's run application and following is a output as expected. That’s it. It’s very easy. Hope you like it. Stay tuned for more.

Recently I had to integrate a Node.js based server application with a C# DLL. Our software (a web-app) offers the possibility to execute payments over a POS terminal. This latter one is controllable through a dedicated DLL which exposes interfaces like ExecutePayment(operation, amount) and so on. As I mentioned, there is the Node.js server that somehow exposes the functionality of the POS (and some more) as a REST api. (The choice for using Node.js had specific reasons which I wouldn't want to outline right now). When you start with such an undertaking, then there are different possibilities. One is to use Edge.js which allows you to embed, reference and invoke .Net CLR objects from within your Node.js based applications. Something like this: var hello = require('edge').func({ assemblyFile: 'My.Edge.Samples.dll', typeName: 'Samples.FooBar.MyType', methodName: 'MyMethod' // Func> }); hello('Node.js', function (error, result) { ... }); Edge is a very interesting project and has a lot of potential. In fact, I just tried it quickly with a simple DLL and it worked right away. However, when using it from my Node app within node-webkit it didn't work. I'm not yet sure whether it was related to node-webkit or the POS DLL itself (because it might be COM exposed etc..). However, if you need simple integrations this might work well for you. Process invocation A second option that came to my mind is to design the DLL as a self-contained process and to invoke it using Node.js's process api. Turns out this is quite simple. Just prepare your C# application to read it's invocation arguments s.t. you can do something like.. IntegrationConsole.exe ExecutePayment 1 100 ..to "ExecutePayment" with operation number 1 and an amount of 1€. The C# console application needs to communicate it's return values to the STDOUT (you may use JSON for creating a more structured information exchange protocol format). Once you have this, you can simply execute the process from Node.js and read the according STDOUT: var process = require('child_process'); ... process.exec(execCmd, function (error, stdout, stderr) { var result = stdout; ... writeToResponse(stdout); }); execCmd is holds the instructions required to launch the EXE with the required invocation arguments. In this approach you execute the process, it does its job, returns the response and terminates. If for some reason however, you need to keep the process running for having a longer, kind of more interactive communication between the two components, you can communicate through the STDIN/STDOUT of the process. Your C# console application starts and listens on the STDIN.. static void Main(string[] args) { ... string line; do { line = Console.ReadLine(); try { // do something meaningful with the input // write to STDOUT to respond to the caller } catch (Exception e) { Console.WriteLine(e.Message); } } while (line != null); } On the Node.js side you do not exec your process, but instead you spawn a child process. var spawn = require('child_process').spawn; ... var posProc = spawn('IntegrationConsole.exe', ['ExecutePayment', 1, 100]); For getting the responses, you simply register on the STDOUT of the process... posProc.stdout.once('data', function (data) { // write it back on the response object writeToResponse(data); }); ..and you may also want to listen for when the process dies to eventually perform some cleanup. posProc.on('exit', function (code) { ... }); Writing to the STDIN of the process is simple as well: posProc.stdin.setEncoding ='utf-8'; posProc.stdin.write('...'); In this way you have a more interactive, "stateful communication", where you send a command to the EXE which responds (STDOUT) and based on the response you again react and send some other command (STDIN). Embedding this in the Request/Response pattern To expose everything as a REST api (on Node), you need to pay some attention on the registration of the event handlers on STDOUT. Suppose you do something like app.post('/someEndpoint',function(req, res){ posProc = spawn('IntegrationConsole.exe',['ExecutePayment',1,100]);... posProc.stdout.on('data',function(data){// return the result of this execution// on the response});}), app.post('/someOtherEndpoint',function(req, res){... posProc.stdout.on('data',function(data){// return the result of this execution// on the response});// write to the stdin of the before created child process posProc.stdin.setEncoding ='utf-8'; posProc.stdin.write('...');}); I excluded proper edge case handling like what happens if your process died before etc.. but the key point here is that you cannot register your events by using on(..), as otherwise you'll end up having multiple data event handlers on the stdout. So you can either register and de-register the event by using the removeListener('event name', callback) syntax or use the more handy once registration mechanism (as I did already in my samples at the beginning of the article): posProc.stdout.once('data',function(data){// write it back on the response object writeToResponse(data);});

To convert a byte[] array to a String we can simply use the new String(byte[]) constructor. But if the array contains non-printable bytes we don't get a good representation. In Groovy we can use the method encodeHex() to transform a byte[] array to a hex String value. The byteelements are converted to their hexadecimal equivalents. final byte[] printable = [109, 114, 104, 97, 107, 105] // array with non-printable bytes 6, 27 (ACK, ESC) final byte[] nonprintable = [109, 114, 6, 27, 104, 97, 107, 105] assert new String(printable) == 'mrhaki' assert new String(nonprintable) != 'mr haki' // encodeHex() returns a Writable final Writable printableHex = printable.encodeHex() assert printableHex.toString() == '6d7268616b69' final nonprintableHex = nonprintable.encodeHex().toString() assert nonprintableHex == '6d72061b68616b69' // Convert back assert nonprintableHex.decodeHex() == nonprintable Code written with Groovy 2.2.1

Gradle, a versatile JVM build tool, effectively handles JavaScript and CSS tasks for web applications and server components.

over the past few months i've had a series of articles ( part 1 , part 2 , part 3 ) discussing indexeddb. in the last article i built a full, if rather simple, application that let you write notes. (i'm a sucker for note taking applications.) when i built the application, i intentionally did not use a framework. i tried to write nice, clear code of course, but i wanted to avoid anything that wasn't 100% necessary to demonstrate the application and indexeddb. in the perspective of an article, i think this was the right decision to make. i wanted my readers to focus on the feature and not anything else. but i thought this would be an excellent opportunity to try angularjs again. for the most part, this conversion worked perfectly. this may sound lame, but i found myself grinning as i built this application. i'm a firm believer that if something makes you happy then it is probably good for you. ;) i still find myself a bit... not confused... but slowed down by the module system and dependency injection. these are both things i grasp in general, but in angularjs they feel a bit awkward to me. it feels like something i'll never be able to code from memory, but will need to reference older applications to remind me. i'm not saying they are wrong of course, they just don't feel natural to me yet. on the flip side, the binding support is incredible. i love working with html templates and $scope. it feels incredibly powerful. heck, being able to add an input field and use it as a filter in approximately 30 seconds was mind blowing. one issue i ran into and i'm not convinced i created the best solution for was the async nature of indexeddb's database open logic. angularjs has a promises library built in and it works incredibly well for my application in general. but i needed the entire application to be bootstrapped to an async call for database startup. i got around that with two things that felt a bit like a hack. first, my home view (get all notes) ran a call to an init function to ensure the db was already open. so consider this init(): function init() { var deferred = $q.defer(); if(setup) { deferred.resolve(true); return deferred.promise; } var openrequest = window.indexeddb.open("indexeddb_angular",1); openrequest.onerror = function(e) { console.log("error opening db"); console.dir(e); deferred.reject(e.tostring()); }; openrequest.onupgradeneeded = function(e) { var thisdb = e.target.result; var objectstore; //create note os if(!thisdb.objectstorenames.contains("note")) { objectstore = thisdb.createobjectstore("note", { keypath: "id", autoincrement:true }); objectstore.createindex("titlelc", "titlelc", { unique: false }); objectstore.createindex("tags","tags", {unique:false,multientry:true}); } }; openrequest.onsuccess = function(e) { db = e.target.result; db.onerror = function(event) { // generic error handler for all errors targeted at this database's // requests! deferred.reject("database error: " + event.target.errorcode); }; setup=true; deferred.resolve(true); }; return deferred.promise; } this logic is similar to what i had in the non-framework app but i've made use of promises and a flag to remember when i've already opened the database. this lets me then tie to init() in my getnotes logic. function getnotes() { var deferred = $q.defer(); init().then(function() { var result = []; var handleresult = function(event) { var cursor = event.target.result; if (cursor) { result.push({key:cursor.key, title:cursor.value.title, updated:cursor.value.updated}); cursor.continue(); } }; var transaction = db.transaction(["note"], "readonly"); var objectstore = transaction.objectstore("note"); objectstore.opencursor().onsuccess = handleresult; transaction.oncomplete = function(event) { deferred.resolve(result); }; }); return deferred.promise; } all of this worked ok - but i ran into an issue on the other pages of my application. if for example you bookmarked the edit link for a note, you would run into an error. i could have applied the same fix in my service layer (run init first), but it just felt wrong. so instead i did this in my app.js: $rootscope.$on("$routechangestart", function(event,currentroute, previousroute){ if(!persistanceservice.ready() && $location.path() != '/home') { $location.path('/home'); }; }); the ready call was simply a wrapper to the flag variable. so yeah, this worked for me, but i still think there is (probably) a nicer solution. anyway, if you want to check it out, just hit the demo link below. i want to give a shoutout to sharon diorio for giving me a lot of help/tips/support while i built this app. p.s. i assume this is obvious, but i'm not really offering this up as a "best practices" angularjs application. i assume i could have done about every part better. ;)

Note: the following article is purely theoretical. I don’t know if it fits a real-life use-case, but the point is just too good to miss Java’s List sorting has two flavors: one follows the natural ordering of collection objects, the other requires an external comparator. In the first case, Java assumes objects are naturally ordered. From a code point of view, this means types of objects in the list must implement the Comparable interface. For example, such is the case for String and Date objects. If this is not the case, or if objects cannot be compared to one another (because perhapsthey belong to incompatible type as both String and Date). The second case happens when the natural order is not relevant and a comparator has to be implemented. For example, strings are sorted according to the character value, meaning case is relevant. When the use-case requires a case-insensitive sort, the following code will do (using Java 8 enhanced syntax): Collections.sort(strings, (s1, s2) -> s1.compareToIgnoreCase(s2)); The Comparable approach is intrinsic, the Comparator extrinsic; the former case rigid, the latter adaptable to the required context. What applies to lists, however, cannot be applied to Java sets. Objects added to sets have to define equals() and hashCode() and both properties (one could say that it’s only one since they are so coupled together) are intrinsic. There is no way to define an equality that can change depending on the context in the JDK. Enters Trove: The Trove library provide primitive collections with similar APIs to the above. This gap in the JDK is often addressed by using the “wrapper” classes (java.lang.Integer, java.lang.Float, etc.) with Object-based collections. For most applications, however, collections which store primitives directly will require less space and yield significant performance gains. Let’s be frank, Trove is under-documented. However, it offers what is missing regarding extrinsic equality: it provides a dedicated set implementation, that accepts its own extrinsic equality abstraction. A sample code would look like that: HashingStrategy strategy = new MyCustomStrategy(); Set dates = new TCustomHashSet(strategy); A big bonus for using Trove is performance, though: It probably is the first argument to use Trove I never tested that in any context To go further, just have a look at Trove for yourself.

This post was originally written by Brett Lawson. So, recently I added support to our Node.js client for executing N1QL queries against your cluster, providing you are running an instance of the N1QL engine (to get a hold of the updated version of the Node.js client with this support, point npm to our github master branch at https://github.com/couchbase/couchnode). When I implemented it, I didn’t have very much to test against at the time, so I figured it would be a interesting endeavor to see how nice the Node.js’s beer-sample example would look if we used entirely N1QL queries rather than using any views. I first started by converting over the basic queries which simply selected all beers or breweries from the sample data, and then moved on to converting the live-search querying to use N1QL as well. I figured I would write a little blog post on the conversions and make some remarks about what I noticed along the way. Here is our first query: var q = { limit : ENTRIES_PER_PAGE, stale : false }; db.view( "beer", "by_name", q).query(function(err, values) { var keys = _.pluck(values, 'id'); db.getMulti( keys, null, function(err, results) { var beers = _.map(results, function(v, k) { v.value.id = k; return v.value; }); res.render('beer/index', {'beers':beers}); }) }); and the converted version: db.query( "SELECT META().id AS id, * FROM beer-sample WHERE type='beer' LIMIT " + ENTRIES_PER_PAGE, function(err, beers) { res.render('beer/index', {'beers':beers}); }); As you can see, we no longer need to do two separate operations to retrieve the list. We can execute our N1QL query which will returns all the information that we need, and formats it appropriately; rather than needing to reformat the data and add our id values, we can simply select it as part of the result set. I find the N1QL version here is much more concise and appreciate how simple it was to construct the query. I then converted the brewery listing function following a similar path, and here is what I ended up with, as you can see, it is similarly beautiful and concise: db.query( "SELECT META().id AS id, name FROM beer-sample WHERE type='brewery' LIMIT " + ENTRIES_PER_PAGE, function(err, breweries) { res.render('brewery/index', {'breweries':breweries}); }); Next I converted the searching methods. These were a bit more of a challenge as looking at the original code directly, without thinking about what it was trying to achieve, the semantics were not immediately obvious, here is a look at what it looked like: var q = { startkey : value, endkey : value + JSON.parse('"\u0FFF"'), stale : false, limit : ENTRIES_PER_PAGE } db.view( "beer", "by_name", q).query(function(err, values) { var keys = _.pluck(values, 'id'); db.getMulti( keys, null, function(err, results) { var beers = []; for(var k in results) { beers.push({ 'id': k, 'name': results[k].value.name, 'brewery_id': results[k].value.brewery_id }); } res.send(beers); }); }); Again, we have quite a bit of code to achieve something which you should expect to be quite simple. In case you can’t tell, the map/reduce query above retrieves a listing of beers whose names begin with the value entered by the user. We are going to convert this to a N1QL LIKE clause, and as an added bonus, we will allow the search term to appear anywhere in the string, instead of requiring it at the beginning: db.query( "SELECT META().id, name, brewery_id FROM beer-sample WHERE type='beer' AND LOWER(name) LIKE '%" + term + "%' LIMIT " + ENTRIES_PER_PAGE, function(err, beers) { res.send(beers); }); We have again collapsed a large amount of vaguely understandable code down to a simple and concise query. I believe this begins to show the power of N1QL and why I am personally so excited to see N1QL. There is however one caveat I noticed while doing this, and this is that similar to SQL, you need to be careful about what kind of user-data you are passing into your queries. I wrote a simple cleaning function to try and prevent any malicious intent (though N1QL is currently read-only anyways), but my cleaning code is by no means extensive. Another issue I noticed is that our second query with the LIKE clause executed significantly slower as a N1QL query then it did when using map/reduce. I believe this is simply a result of N1QL still being developer preview, and there is lots of optimizations left to be done by the N1QL team. If you want to see the fully converted source code, take a look at the n1ql branch of the beersample-node repository available here, https://github.com/couchbaselabs/beersample-node/tree/n1ql. Thanks! Brett

I'm passionate about front end optimization and have been for years. My original inspiration was Steve Souders and his Even Faster Web Sites talk at OSCON 2008. Since then, I've optimized this blog, made it even faster with a new design, doubled the speed of several apps for clients and showed how to make AppFuse faster. As part of my Devoxx 2013 presentation, I showed how to do page speed optimization in a Java webapp. I developed a couple AngularJS apps last year. To concat and minify their stylesheets and scripts, I used mechanisms that already existed in the projects. On one project, it was Ant and its concat task. On the other, it was part of a Grails application, so I used the resources and yui-minify-resources plugins. The Angular project I'm working on now will be published on a web server, as well as bundled in an iOS native app. Therefore, I turned to Grunt to do the optimization this time. I found it to be quite simple, once I figured out how to make it work with Angular. Based on my findings, I submitted a pull request to add Grunt to angular-seed. Below are the steps I used to add Grunt to my Angular project. Install Grunt's command line interface with "sudo npm install -g grunt-cli". Edit package.json to include a version number (e.g. "version": "1.0.0"). Add Grunt plugins in package.json to do concat/minify/asset versioning: "grunt": "~0.4.1", "grunt-contrib-concat": "~0.3.0", "grunt-contrib-uglify": "~0.2.7", "grunt-contrib-cssmin": "~0.7.0", "grunt-usemin": "~2.0.2", "grunt-contrib-copy": "~0.5.0", "grunt-rev": "~0.1.0", "grunt-contrib-clean": "~0.5.0" Create a Gruntfile.js that runs all the plugins. module.exports = function (grunt) { grunt.initConfig({ pkg: grunt.file.readJSON('package.json'), clean: ["dist", '.tmp'], copy: { main: { expand: true, cwd: 'app/', src: ['**', '!js/**', '!lib/**', '!**/*.css'], dest: 'dist/' }, shims: { expand: true, cwd: 'app/lib/webshim/shims', src: ['**'], dest: 'dist/js/shims' } }, rev: { files: { src: ['dist/**/*.{js,css}', '!dist/js/shims/**'] } }, useminPrepare: { html: 'app/index.html' }, usemin: { html: ['dist/index.html'] }, uglify: { options: { report: 'min', mangle: false } } }); grunt.loadNpmTasks('grunt-contrib-clean'); grunt.loadNpmTasks('grunt-contrib-copy'); grunt.loadNpmTasks('grunt-contrib-concat'); grunt.loadNpmTasks('grunt-contrib-cssmin'); grunt.loadNpmTasks('grunt-contrib-uglify'); grunt.loadNpmTasks('grunt-rev'); grunt.loadNpmTasks('grunt-usemin'); // Tell Grunt what to do when we type "grunt" into the terminal grunt.registerTask('default', [ 'copy', 'useminPrepare', 'concat', 'uglify', 'cssmin', 'rev', 'usemin' ]); }; Add comments to app/index.html so usemin knows what files to process. The comments are the important part, your files will likely be different. ... A couple of things to note: 1) the copy task copies the "shims" directory from Webshims lib because it loads files dynamically and 2) setting "mangle: false" on the uglify task is necessary for Angular's dependency injection to work. I tried to use grunt-ngmin with uglify and had no luck. After making these changes, I'm able to run "grunt" and get an optimized version of my app in the "dist" folder of my project. For development, I continue to run the app from my "app" folder, so I don't currently have a need for watching and processing assets on-the-fly. That could change if I start using LESS or CoffeeScript. The results speak for themselves: from 27 requests to 5 on initial load, and only 3 requests for less than 2K after that. YSlow Page Speed No optimization 75 27 HTTP requests / 464K 55/100 Apache optimization (gzip and expires headers) 89 initial load: 26 requests / 166K primed cache: 4 requests / 40K 88/100 Apache + concat/minified/versioned files 98 initial load: 5 requests / 136K primed cache: 3 requests / 1.4K 93/100

Welcome, dear reader, in this article, we’ll talk about a web framework that has become popular in the construction of single-page web applications: Angularjs. But after all, what is a single-page application? Single-page applications Single-page applications, as the name implies, consist of applications where only a single “page” – or as we also call, HTML document – is submitted to the client, and after this initial load, only fragments of the page are reloaded, by Ajax requests, without ever making a full page reload. The main advantage we have in this web application model is that, due to minimizing the data traffic between the client and the server, it provides the user with a highly dynamic application, with low “latency” between user actions on the interface. A point of attention, however, is that in this application model, much of the “weight” of the application processing falls to the client side, then the device’s capabilities where the user is accessing the application can be a problem for the adoption of the model, especially if we are talking about applications accessed on mobile devices. Developed by Google, Angularjs brought features that allow you to build in a well structured way applications in single-page model, through the use of javascript as an extension built on top of HTML pages. One of the advantages of the framework is that it integrates seamlessly into HTML, allowing the developer team to, for example, reuse the pages of a prototype made by a web designer. Architecture The framework architecture works with the concept of been a html extension of the page to which it is linked. As a javascript library, imported within the pages, which through the use of policies – kind of attributes that are embedded within their own html tags, usually with the prefix “NG” – performs the compilation of all the code belonging to the framework generating dynamic code – html, css and javascript – that the user use through his browser. For readers who are interested in knowing more deeply the architecture behind the framework, the presentation below speaks in detail about this topic: Angularjs architecture overview Layout Although the framework has high flexibility in the construction of layouts due to the use of pure html, it lacks ready layout options, so to make the application with a more pleasant graphical interface, it enters the bootstrap, providing CSS styles – plus pre-build behavior in javacript and dynamic html – that enable an even richer layout for the application. At the end of this article, beyond the official angularjs site, you can also access the link to the official site of the bootstrap. The MVC model In the web world, is well known the pattern MVC (Model – View – Controller). In this pattern, we define that the web application is defined in 3 layers with different responsibilities: View: In this layer, we have the code related to the presentation to the end user, with pages and rules related to navigation, such as control of the flags of a register in the “wizard” format, for example. Controller: This layer is the code that bridges between the navigation and the source of application data, represented by the model layer. Processing and business rules typically belong to this layer. Model: In this layer is the code responsible for performing the persistence of the application data. In a traditional web application, we commonly have a DBMS as a data source. Speaking in the context of angularjs, as we see below, we have the view layer represented by the html pages. These pages communicate with the controller layer through policies explained in the beginning of our article, invoking the controllers of the framework, consisting of a series of javascript functions encoded by the developer. These functions use a binding layer, provided by the framework, called $ scope, which is populated by the controller and displayed by the view, representing the model layer. Typically, in the architecture of a angularjs system, we have the model layer being filled through REST services. The figure below illustrates the interaction between these layers: Hands-on For this hands-on, we will use the Wildfly 8.1.0 server, angularjs and the bootstrap. At the time of this article, the latest version (stable) of angularjs is 1.2.26 and for the bootstrap is 3.2.0. As IDE, I am using Eclipse Luna. First, the reader must create a project of type “Dynamic Web Project” by selecting the wizard image below. In fact, it would be perfectly possible to use static web project as a project, but we’ll use the dynamic only to facilitate the deploy on the server. Following the wizard, remember to select the runtime Wildfly, as indicated below, and select the checkbox that calls for the creation of the descriptor “web.xml” on the last page of the wizard. At the end, we will have a project structure like the one below: As a last step of project configuration, we will add the project in the automatically deployed application list on our Wildfly server. To do this, select the server from the perspective of servers, select the “add or remove” and move the application to the list of configured applications, as follows: Including the angularjs and bootstrap on the application To include the angular and the bootstrap in our application, simply include the contents thereof, consisting of js files, css, etc inside the folder “webcontent” generating the project structure below: PS: This structure aims only to provide a quick and easy way to set the environment for the realization of our learning. In a real application, the developer has the freedom to not include all angular files, including only those whose resources will be used in the project. Starting our development, we will create a simple page containing a single page application that consists of a list of tasks, with the option of new tasks and filtering tasks already completed. Keep in mind that the goal of this small application is only to show the basic structure of angularjs. In a real application, the js code should be structured in directories, ensuring readability and maintainability of the code. Thus, we come to the implementation. To deploy it, we will create an html page called “index.html”, and put the code below: Tarefas de {{todo.user} Add DescriptionDone{{item.action} Show Complete As we can see, we have a simple html page. First, insert the policy “ng-app”, which initializes the framework on the page, and import the files needed for the operation of the angular and the bootstrap: Following is the creation of a module. A angularjs module consists of other components such as filters and controllers, constituting a unit similar to a package that can be imported into other application modules. For the initial data source, we put a JSON structure in a JavaScript variable, which is inserted into the binding layer later. In addition, we also have to create a filter. Filters, as the name implies, are created to provide a means of filtering the data of a listing. Later on we will see that use in practice: Once we have our populated binding, it is easy for us to display its values, as in the example below, where we show the value of “user”. We also define a form to our inclusion of tasks: Tarefas de {{todo.user} Add Finally, we have the proper view of the tasks. To do this, we use the “ng-repeat” policy. The reader will notice that we have made use of our filter, and we ask the angularjs to order our list by the “action” value. In addition, we can also see the binding for changing the filter, through the checkboxes to enable / disable filtering, in addition to the marking of completed tasks: DescriptionDone{{item.action} Show Complete The final screen in operation can be seen below: Conclusion And so we conclude our article on angularjs. With design flexibility and an easy learning structure, its adoption as well as the single page applications model is a powerful tool that every web developer should have in his pocket knife options. Thanks to everyone who supported me in this article, until next time. Links Source-code Angularjs Bootstrap Wildfly

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

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

Since Groovy 2.2 we can subtract a part of a String value using a regular expression pattern. The first match found is replaced with an empty String. In the following sample code we see how the first match of the pattern is removed from the String: // Define regex pattern to find words starting with gr (case-insensitive). def wordStartsWithGr = ~/(?i)\s+Gr\w+/ assert ('Hello Groovy world!' - wordStartsWithGr) == 'Hello world!' assert ('Hi Grails users' - wordStartsWithGr) == 'Hi users' // Remove first match of a word with 5 characters. assert ('Remove first match of 5 letter word' - ~/\b\w{5}\b/) == 'Remove match of 5 letter word' // Remove first found numbers followed by a whitespace character. assert ('Line contains 20 characters' - ~/\d+\s+/) == 'Line contains characters' Code written with Groovy 2.2.

In this post I’m going to explain you about Linq AddRange method. This method is quite useful when you want to add multiple elements to a end of list. Following is a method signature for this. public void AddRange( IEnumerable collection ) To Understand let’s take simple example like following. using System; using System.Collections.Generic; namespace Linq { class Program { static void Main(string[] args) { List names=new List {"Jalpesh"}; string[] newnames=new string[]{"Vishal","Tushar","Vikas","Himanshu"}; foreach (var newname in newnames) { names.Add(newname); } foreach (var n in names) { Console.WriteLine(n); } } } } Here in the above code I am adding content of array to a already created list via foreach loop. You can use AddRange method instead of for loop like following.It will same output as above. using System; using System.Collections.Generic; namespace Linq { class Program { static void Main(string[] args) { List names=new List {"Jalpesh"}; string[] newnames=new string[]{"Vishal","Tushar","Vikas","Himanshu"}; names.AddRange(newnames); foreach (var n in names) { Console.WriteLine(n); } } } } Now when you run that example output is like following. Add Range in more complex scenario: You can also use add range to more complex scenarios also like following.You can use other operator with add range as following. using System; using System.Collections.Generic; using System.Linq; namespace Linq { class Program { static void Main(string[] args) { List names=new List {"Jalpesh"}; string[] newnames=new string[]{"Vishal","Tushar","Vikas","Himanshu"}; names.AddRange(newnames.Where(nn=>nn.StartsWith("Vi"))); foreach (var n in names) { Console.WriteLine(n); } } } } Here in the above code I have created array with string and filter it with where operator while adding it to an existing list. Following is output as expected. That’s it. Hope you like it. Stay tuned for more..

One of the questions that lot of developers ask is – Is there any difference between string andSystem.String and what should be used? Short Answer There is no difference between the two. You can use either of them in your code. Explanation System.String is a class (reference type) defined the mscorlib in the namespace System. In other words, System.String is a type in the CLR. string is a keyword in C# Before we understand the difference, let us understand BCL and FCL terms. BCL is Common Language Infrastructure (CLI) available to languages like C#, A#, Boo, Cobra, F#, IronRuby, IronPython and other CLI languages. It includes common functions such as File Read/Write or IO and database/XML interactions. BCL was first implemented in Microsoft .NET in the form of mscorlib.dll FCL is standard Microsoft .NET specific library containing reusable classes/assets like System, System.CodeDom, System.Collections, System.Diagnostics, System.Globalization, System.IO, System.Resources and System.Text Now in C#, string (keyword in BCL) directly maps to System.String (an FCL type). Similarly, intmaps directly to System.Int32. Here int is mapped to a integer type that is 32 bit. But in other language, you could probably map int (keyword in BCL) to a 64 bit integer (FCL type). So the fact that using string and System.String in C# makes no difference is well established. Is it better to still use string instead of System.String? There is no universally agreed answer to this. But, as per me, even though both string and System.String mean the same and have no difference in performance of the application, it is better to use string. This is because string is a C# language specific keyword. Also C# language specification states, As a matter of style, use of the keyword is favored over use of the complete system type name Following this practice ensures that your code consistently uses keywords wherever possible rather than having a code with BCL and FCL types used.

JavaScript’s prototype-based inheritance is interesting and has its uses, but sometimes one just wants to express classical inheritance, familiar from C++ and Java. This need has been recognized by the ECMAScript committee and classes are being discussed for inclusion in the next version of the standard. It was surprisingly hard for me to find a good and simple code sample that shows how to cleanly and correctly express inheritance with ES5 (a lot of links discuss how to implement the pre-ES5 tools required for that) and explains why the thing works. Mozilla’s Object.Create reference came close, but not quite there because it still left some open questions. Hence this short post. Without further ado, the following code defines a parent class named Shape with a constructor and a method, and a derived class named Circle that has its own method: // Shape - superclass // x,y: location of shape's bounding rectangle function Shape(x, y) { this.x = x; this.y = y; } // Superclass method Shape.prototype.move = function(x, y) { this.x += x; this.y += y; } // Circle - subclass function Circle(x, y, r) { // Call constructor of superclass to initialize superclass-derived members. Shape.call(this, x, y); // Initialize subclass's own members this.r = r; } // Circle derives from Shape Circle.prototype = Object.create(Shape.prototype); Circle.prototype.constructor = Circle; // Subclass methods. Add them after Circle.prototype is created with // Object.create Circle.prototype.area = function() { return this.r * 2 * Math.PI; } The most interesting part here, the one that actually performs the feat of inheritance is these two lines, so I’ll explain them a bit: Circle.prototype = Object.create(Shape.prototype); Circle.prototype.constructor = Circle; The first line is the magic – it sets up the prototype chain. To understand it, you must first understand that "the prototype of an object" and "the .prototype property of an object" are different things. If you don’t, go read on that a bit. The first line, interpreted very technically, says: the prototype of new objects created with the Circle constructor is an object whose prototype is the prototype of objects created by Shape constructor. Yeah, that’s a handful. But it can be simplified as: each Circle has a Shape as its prototype. What about the second line? While not strictly necessary, it’s there to preserve some useful invariants, as we’ll see below. Since the assignment to Circle.prototype kills the existing Circle.prototype.constructor (which was set to Circle when the Circle constructor was created), we restore it. Let’s whip up a JavaScript console and load that code inside, to quickly try some stuff: > var shp = new Shape(1, 2) undefined > [shp.x, shp.y] [1, 2] > shp.move(1, 1) undefined > [shp.x, shp.y] [2, 3] … but we’re here for the circles: > var cir = new Circle(5, 6, 2) undefined > [cir.x, cir.y, cir.r] [5, 6, 2] > cir.move(1, 1) undefined > [cir.x, cir.y, cir.r] [6, 7, 2] > cir.area() 12.566370614359172 So far so good, a Circle initialized itself correctly using the Shape constructor; it responds to the methods inherited from Shape, and to its own area method too. Let’s check that the prototype shenanigans worked as expected: > var shape_proto = Object.getPrototypeOf(shp) undefined > var circle_proto = Object.getPrototypeOf(cir) undefined > Object.getPrototypeOf(circle_proto) === shape_proto true Great. Now let’s see what instanceof has to say: > cir instanceof Shape true > cir instanceof Circle true > shp instanceof Shape true > shp instanceof Circle false Finally, here are some things we can do with the constructor property that wouldn’t have been possible had we not preserved it: > cir.constructor === Circle true // Create a new Circle object based on an existing Circle instance > var new_cir = new cir.constructor(3, 4, 1.5) undefined > new_cir Circle {x: 3, y: 4, r: 1.5, constructor: function, area: function} A lot of existing code (and programmers) expect the constructor property of objects to point back to the constructor function used to create them with new. In addition, it is sometimes useful to be able to create a new object of the same class as an existing object, and here as well the constructor property is useful. So that is how we express classical inheritance in JavaScript. It is very explicit, and hence on the long-ish side. Hopefully the future ES standards will provide nice sugar for succinct class definitions.

I wanted to know: how would Java EE compare to Node.js in this particular case?