With these changes, we can access the REST API equally well fromhttp://:9999 and https://:9998.

I have recently started the Coursera Functional Programming with Scala course (taught by Martin Odersky - the creator of Scala) - which is actually serving as an introduction to both FP and Scala at the same time having done neither before. The course itself is great, however, trying to watch the videos and take in both the new Scala syntax and the FP concepts at the same time can take a bit of effort. I wanted to work through some of the core FP concepts in a more familiar context, so am going to apply some of the lessons/principles/exercises in Groovy. Functions: If you have done any Groovy programming then you will have come across Groovy functions/closures. As Groovy is dynamically typed (compared to Scala's static typing), you can play it fairly fast and loose. For example, if we take the square root function that is demonstrated in the Scala course, it is defined as follows: def sqrt(x: Double): Double = ... As you can see, Scala expects the values to be typed (aside, you don't actually always need to provide a return type in Scala). But in the Groovy function it is: def sqrt = {x-> ... } A groovy function can be defined and assigned to any variable, thereby allowing it to be passed around as a first class object. If we look at the complete solution for calculating the square root of a number (using Newton's method - To compute the square root of "x" we start with an estimate of the square root, "y" and continue to improve the the guess by taking the mean of x and x/y ) def sqrtIter(guess: Double, x: Double): Double = if (isGoodEnough(guess, x)) guess else sqrtIter(improve(guess, x), x) def improve(guess: Double, x: Double) = (guess + x / guess) / 2 def isGoodEnough(guess: Double, x: Double) = abs(guess * guess - x) < 0.001 def sqrt(x: Double) = srqtIter(1.0, x) So that's in Scala, if we try Groovy we will see we can achieve pretty much the same thing easily: //Improve the guess using Newton's method def improve = { guess, x -> (guess + x / guess) / 2 } //Check if our guess is good enough, within a chosen threshold def isGoodEnough = {guess, x -> abs(guess * guess - x) < 0.001 } //iterate over guesses until our guess is good enough def sqrtIter = { guess, x -> if (isGoodEnough(guess, x)) guess else sqrtIter(improve(guess, x), x) } //wrap everythin in the square root function call def sqrt = {x -> srqtIter(1.0, x)} Recursion (and tail-recursion): As FP avoids having mutable state, the most common approach to solve problems is to break the problem down in to simple functions and call them recursively - This avoids having to maintain state whilst iterating through loops, and each function call is given its input and produces an output. If we again consider an example from the Scala course, with the example of a simple function that calculates the factorial for a given number. def factorial ={ n -> if (n == 0) 1 else n * factorial(n - 1) } factorial(4) This simple function recursively calculates the factorial, continuing to call itself until all numbers to zero have been considered. As you can see, there is no mutable state - every call to the factorial function simply takes the input and returns an output (the value of n is never changed or re-assigned, n is simply used to calculate output values) There is a problem here, and that is as soon as you attempt to calculate the factorial of a significantly large enough number you will encounter a StackOverflow exception - this is because in the JVM every time a function is called, a frame is added to the stack, so working recursively its pretty easy to hit upon the limit of the stack and encounter this problem. The common way to solve this is by using Tail-Call recursion. This trick is simply to have the last code that is evaluated in the function to be the recursive call - normally in FP languages the compiler/interpreter will recognise this pattern and under the hood, it will really just run the code as a loop (e.g. if we know the very last piece of code in the block of code is calling itself, its really not that different to just having the block of code/function inside a loop construct) In the previous factorial example, it might look like the last code to be executed is the recursive callfactorial(n-1) - however, the value of that call is actually returned to the function and THENmultiplied by n - so actually the last piece of code to be evaluated in the function call is actually n * return value of factorial(n-1). Let's have a look at re-writing the function so it is tail-recursive. def factorial ={ n, accumulator=1 -> if (n == 1) accumulator else factorial(n-1, n*accumulator) } factorial(4) Now, using an accumulator, the last code to be evaluated in the function is our recursive function call. In most FP languages, including Scala, this is enough - however, the JVM doesn't automatically support tail-call recursion, so you actually need to use a rather clunkier approach in Groovy: def factorial ={ n, accumulator=1 -> if (n == 1) accumulator else factorial.trampoline(n-1, n*accumulator) }.trampoline() factorial(4) The use of the trampoline() method means that the function will now be called using tail-call recursion, so there should never be a StackOverflow exception. It's not as nice as in Scala or other languages, but the support is there so we can continue. Currying: This is like function composition - the idea being you take a generic function, and then you curry it with some value to make a more specific application of the function. For example, if we look at a function that given values x and y, it returns z which is the value x percent of y (e.g. given x=10, y=100, it returns the 10 percent of 100, z=10) def percentage = { percentage, x -> x/100 * percentage } The above simple function is a generic mechanism to get a percentage value of another, but if we consider that we wanted a common application of this function was to always calculate 10% of a given value - rather than write a slightly modified version of the function we can simply curry the function as follows: def tenPercent = percentage.curry(10) Now, if the function tenPercent(x) is called, it uses the original percentage() function, but curries the value 10 as the first argument. (If you need to curry other argument positions you can also use the rcurry() function to curry the right most argument, or ncurry() which also takes an argument position - check the Groovy docs on currying for more info) Immutability: Immutability is partially supported in Java normally with use of the final keyword (meaning variables can't be changed after being initially set on object instantiation). Groovy also provides a quick and easy @Immutable annotation that can be added to a class to easily make it immutable. But really, there is more to avoiding immutable state than just having classes as immutable - As we have functions as first class objects, we can easily assign variables and mutate them within a function - so this is more of a mindset or philosophy that you have to get used to. For example: def list = ['groovy', 'functional'] //This mutates the original list list.add('programming') //This creates a new list leaving the original unchanged def newList = list.plus(2, 'programming') The first example is probably more like the Groovy/Java code we are used to writing, but that is mutating the state of the list - where as the second approach leaves the original list unchanged. Map Reduce: As a final note, there are some functions in FP that are pretty common techniques - the most famous of which these days (in part thanks to Google) is Map-Reduce, but the trio of functions are actually Map, Reduce(also known as Fold) & Filter - you can read more about the functions here (or just google them!), but these functions actually correlate pretty nicely to core Groovy functions that you probably use a lot of (assuming you are groovy programmers). Map map is the easiest to understand of the three. It takes in two inputs - a function, and a list. It then applies this function to every element in the list. You can basically do the same thing with a list comprehension however. Sound familiar? This is basically the .collect{} function in Groovy Reduce/Fold fold takes in a function and folds it in between the elements of a list. It's a bit hard to understand at first This one is a bit more complicated to descibe, but is the same as the .inject{} function in groovy Filter filter is easy. It takes in a 'test' and a list, and it chucks out any elements of the list which don't satisfy that test. And another simple one - filtering out a list for desired elements, this is Groovy's .findAll{} function As I said at the start, I am new to FP and coming from an OO background, but hopefully the above isn't too far from the truth! As I get further through the Coursera course I will try to post again, maybe with some of the assignments attempted in Groovy to see how it really stands up. Some useful references: Groovy docs on FP: http://groovy.codehaus.org/Functional+Programming+with+Groovy Coursera Scala course: https://www.coursera.org/course/progfun

The Windows Explorer (shell) provides extended file property information which can be quite valuable. The challenge was how to extract this information, given that the .NET Framework has somewhat limited support for this type of extraction?

Recently I needed to quickly create some temporary JSON objects from some preexisting data. So, I turned to Json.NET, a very popular JSON framework for .NET. Since I got my data on the fly (think parsing some other data structures and you have lots of anonymous results), I didn’t want to create classes for such instances. Luckily, Json.NET supports serializing anonymous objects. First add the library via NuGet – it is called Newtonsoft.Json. For example: var obj = new { user = new { name = "John", age = 21, data = new[] { 1, 2, 3, 4 } } }; var result = JsonConvert.SerializeObject(obj, Formatting.Indented); If we did that in a console application, we could print out the result. The printout is: { "user": { "name": "John", "age": 21, "data": [ 1, 2, 3, 4 ] } } The code above works on Windows 8, Windows Phone 7 and 8. This way it is dead simple to create JSON representation of your anonymous data. Think REST services.

this post comes from ernie souhrada at the mysql performance blog. note: this is part 1 of what will be a two-part series on the performance implications of using in-flight data encryption. some of you may recall my security webinar from back in mid-august; one of the follow-up questions that i was asked was about the performance impact of enabling ssl connections. my answer was 25%, based on some 2011 data that i had seen over on yassl’s website, but i included the caveat that it is workload-dependent, because the most expensive part of using ssl is establishing the connection. not long thereafter, i received a request to conduct some more specific benchmarks surrounding ssl usage in mysql, and today i’m going to show the results. first, the testing environment. all tests were performed on an intel core i7-2600k 3.4ghz cpu (8 cores, ht included) with 32gb of ram and centos 6.4. the disk subsystem is a 2-disk raid-0 of samsung 830 ssds, although since we’re only concerned with measuring the overhead added by using ssl connections, we’ll only be conducting read-only tests with a dataset that fits completely in the buffer pool. the version of mysql used for this experiment is community edition 5.6.13, and the testing tools are sysbench 0.5 and perl. we conduct two tests, each one designed to simulate one of the most common mysql usage patterns. first, we examine connection pooling, often seen in the java world, where some small set of connections are established by, for example, the servlet container and then just passed around to the application as needed, and one-request-per-connection, typical in the lamp world, where the script that displays a given page might connect to the database, run a couple of queries, and then disconnect. test 1: connection pool for the first test, i ran sysbench in read-only mode at concurrency levels of 1, 2, 4, 8, 16, and 32 threads, first with no encryption and then with ssl enabled and key lengths of 1024, 2048, and 4096 bits. 8 sysbench tables were prepared, each containing 100,000 rows, resulting in a total data size of approximately 256mb. the size of my innodb buffer pool was 4gb, and before conducting each official measurement run, i ran a warm-up run to prime the buffer pool. each official test run lasted 10 minutes; this might seem short, but unlike, say, a pcie flash storage device, i would not expect the variable under observation to really change that much over time or need time to stabilize. the basic sysbench syntax used is shown below. #!/bin/bash for ssl in on off ; do for threads in 1 2 4 8 16 32 ; do sysbench --test=/usr/share/sysbench/oltp.lua --mysql-user=msandbox$ssl --mysql-password=msandbox \ --mysql-host=127.0.0.1 --mysql-port=5613 --mysql-db=sbtest --mysql-ssl=$ssl \ --oltp-tables-count=8 --num-threads=$threads --oltp-dist-type=uniform --oltp-read-only=on \ --report-interval=10 --max-time=600 --max-requests=0 run > sb-ssl_${ssl}-threads-${threads}.out done done if you’re not familiar with sysbench, the important thing to know about it for our purposes is that it does not connect and disconnect after each query or after each transaction. it establishes n connections to the database (where n is the number of threads) and runs queries though them until the test is over. this behavior provides our connection-pool simulation. the assumption, given what we know about where ssl is the slowest, is that the performance penalty here should be the lowest. first, let’s look at raw throughput, measured in queries per second: the average throughput and standard deviation (both measured in queries per second) for each test configuration is shown below in tabular format: # of threads ssl key size 1 2 4 8 16 32 ssl off 9250.18 (1005.82) 18297.61 (689.22) 33910.31 (446.02) 50077.60 (1525.37) 49844.49 (934.86) 49651.09 (498.68) 1024-bit 2406.53 (288.53) 4650.56 (558.58) 9183.33 (1565.41) 26007.11 (345.79) 25959.61 (343.55) 25913.69 (192.90) 2048-bit 2448.43 (290.02) 4641.61 (510.91) 8951.67 (1043.99) 26143.25 (360.84) 25872.10 (324.48) 25764.48 (370.33) 4096-bit 2427.95 (289.00) 4641.32 (547.57) 8991.37 (1005.89) 26058.09 (432.86) 25990.13 (439.53) 26041.27 (780.71) so, given that this is an 8-core machine and io isn’t a factor, we would expect throughput to max out at 8 threads, so the levelling-off of performance is expected. what we also see is that it doesn’t seem to make much difference what key length is used, which is also largely expected. however, i definitely didn’t think the encryption overhead would be so high. the next graph here is 95th-percentile latency from the same test: and in tabular format, the raw numbers (average and standard deviation): # of threads ssl key size 1 2 4 8 16 32 ssl off 1.882 (0.522) 1.728 (0.167) 1.764 (0.145) 2.459 (0.523) 6.616 (0.251) 27.307 (0.817) 1024-bit 6.151 (0.241) 6.442 (0.180) 6.677 (0.289) 4.535 (0.507) 11.481 (1.403) 37.152 (0.393) 2048-bit 6.083 (0.277) 6.510 (0.081) 6.693 (0.043) 4.498 (0.503) 11.222 (1.502) 37.387 (0.393) 4096-bit 6.120 (0.268) 6.454 (0.119) 6.690 (0.043) 4.571 (0.727) 11.194 (1.395) 37.26 (0.307) with the exception of 8 and 32 threads, the latency introduced by the use of ssl is constant at right around 5ms, regardless of the key length or the number of threads. i’m not surprised that there’s a large jump in latency at 32 threads, but i don’t have an immediate explanation for the improvement in the ssl latency numbers at 8 threads. test 2: connection time for the second test, i wrote a simple perl script to just connect and disconnect from the database as fast as possible. we know that it’s the connection setup which is the slowest part of ssl, and the previous test already shows us roughly what we can expect for ssl encryption overhead for sending data once the connection has been established, so let’s see just how much overhead ssl adds to connection time. the basic script to do this is quite simple (non-ssl version shown): #!/usr/bin/perl use dbi; use time::hires qw(time); $start = time; for (my $i=0; $i<100; $i++) { my $dbh = dbi->connect("dbi:mysql:host=127.0.0.1;port=5613", "msandbox","msandbox",undef); $dbh->disconnect; undef $dbh; } printf "%.6f\n", time - $start; as with test #1, i ran test #2 with no encryption and ssl encryption of 1024, 2048, and 4098 bits, and i conducted 10 trials of each configuration. then i took the elapsed time for each test and converted it to connections per second. the graph below shows the results from each run: here are the averages and standard deviations: encryption average connections per second standard deviation none 2701.75 165.54 1024-bit 77.04 6.14 2048-bit 28.183 1.713 4096-bit 5.45 0.015 yes, that’s right, 4096-bit ssl connections are 3 orders of magnitude slower to establish than unencrypted connections. really, the connection overhead for any level of ssl usage is quite high when compared to the unencrypted test, and it’s certainly much higher than my original quoted number of 25%. analysis and parting thoughts so, what do we take away from this? the first thing is, of course, is that ssl overhead is a lot higher than 25%, particularly if your application uses anything close to the one-connection-per-request pattern. for a system which establishes and maintains long-running connections, the initial connection overhead becomes a non-factor, regardless of the encryption strength, but there’s still a rather large performance penalty compared to the unencrypted connection. this leads directly into the second point, which is that connection pooling is by far a more efficient method of using ssl if your application can support it. but what if connection pooling isn’t an option, mysql’s ssl performance is insufficient, and you still need full encryption of data in-flight? run the encryption component of your system at a lower layer – a vpn with hardware crypto would be the fastest approach, but even something as simple as an ssh tunnel or openvpn *might* be faster than ssl within mysql. i’ll be exploring some of these solutions in a follow-up post. and finally… when in doubt, run your own benchmarks. i don’t have an explanation for why the yassl numbers are so different from these (maybe yassl is a faster ssl library than openssl, or maybe they used a different cipher – if you’re curious, the original 25% number came from slides 56-58 of this presentation ), but in any event, this does illustrate why it’s important to run tests on your own hardware and with your own workload when you’re interested in finding out how well something will perform rather than taking someone else’s word for it.

in today’s tutorial, i’m going to show you the process of creating nearly an entire website with a new library – angularjs. however, i would like to introduce to you to angularjs first. angularjs is a magnificent framework by google for creating web applications. this framework lets you extend html’s syntax to express your application’s components clearly and succinctly, and lets you use standard html as your main template language. plus, it automatically synchronizes data from your ui with your js objects through 2-way data binding. if you’ve ever worked with jquery, the first thing to understand about angular is that this is a completely different instrument. jquery is a library, but angularjs is framework. when your code works with the library, it decides when to call a particular function or operator. in the case of the framework, you implement event handlers, and the framework decides at what moment it needs to invoke them. using this framework allows us to clearly distinguish between templates (dom), models, and functionality (in controllers). let’s come back to our template, take a look at our result: live demo download in package description this template is perfect for business sites. it consists of several static pages: projects, privacy, and about pages. each product has its own page. there is also a contact form for communication. that is all that is necessary for any small website. moreover, it is also a responsive template, thus it looks good on any device. i hope you liked the demo, so if you’re ready – let’s start making this application. please prepare a new folder for our project, and then create new folders in this directory: css – for stylesheet files images – for image files js – for javascript files (libraries, models, and controllers) pages – for internal pages stage 1. html the main layout consists of four main sections: a header with navigation, a hidden ‘contact us’ form, a main content section, and a footer. first we have to prepare a proper header: index.html as you can see, it’s an ordinary header. now – the header with the navigation: our projectsprivacy & termsaboutcontact us it's an ordinary logo, and the menu is the usual ul-li menu. the next section is more interesting – the ‘contact us’ form: contact us send message your message has been sent, thank you. finally, the last key element is the main content section: have you noticed the numerous ‘ng-’ directives? all these directives allow us to do various actions directly in the dom, for example: ng-class – the ngclass allows you to set css classes on an html element dynamically by databinding an expression that represents all classes to be added. ng-click – the ngclick allows you to specify custom behavior when element is clicked. ng-hide – the nghide directive shows and hides the given html element conditionally based on the expression provided to the nghide attribute. ng-include – fetches, compiles, and includes an external html fragment. ng-model – is a directive that tells angular to do two-way data binding. ng-show – the ngshow directive shows and hides the given html element conditionally based on the expression provided to the ngshow attribute. ng-submit – enables binding angular expressions to onsubmit events. stage 2. css in this rather large section, you can find all the styles used: css/style.css /* general settings */ html { min-height:100%; overflow-x:hidden; overflow-y:scroll; position:relative; width:100%; } body { background-color:#e6e6e6; color:#fff; font-weight:100; margin:0; min-height:100%; width:100%; } a { text-decoration:none; } a img { border:none; } h1 { font-size:3.5em; font-weight:100; } p { font-size:1.5em; } input,textarea { -webkit-appearance:none; background-color:#f7f7f7; border:none; border-radius:3px; font-size:1em; font-weight:100; } input:focus,textarea:focus { border:none; outline:2px solid #7ed7b9; } .left { float:left; } .right { float:right; } .btn { background-color:#fff; border-radius:24px; color:#595959; display:inline-block; font-size:1.4em; font-weight:400; margin:30px 0; padding:10px 30px; text-decoration:none; } .btn:hover { opacity:0.8; } .wrap { -moz-box-sizing:border-box; -webkit-box-sizing:border-box; box-sizing:border-box; margin:0 auto; max-width:1420px; overflow:hidden; padding:0 50px; position:relative; width:100%; } .wrap:before { content:''; display:inline-block; height:100%; margin-right:-0.25em; vertical-align:middle; } /* header section */ header { height:110px; } header .wrap { height:100%; } header .logo { margin-top:1px; } header nav { float:right; margin-top:17px; } header nav ul { margin:1em 0; padding:0; } header nav ul li { display:block; float:left; margin-right:20px; } header nav ul li a { border-radius:24px; color:#aaa; font-size:1.4em; font-weight:400; padding:10px 27px; text-decoration:none; } header nav ul li a.active { background-color:#c33c3a; color:#fff; } header nav ul li a.active:hover { background-color:#d2413f; color:#fff; } header nav ul li a:hover,header nav ul li a.activesmall { color:#c33c3a; } /* footer section */ footer .copyright { color:#adadad; margin-bottom:50px; margin-top:50px; text-align:center; } /* other objects */ .projectobj { color:#fff; display:block; } .projectobj .name { float:left; font-size:4em; font-weight:100; position:absolute; width:42%; } .projectobj .img { float:right; margin-bottom:5%; margin-top:5%; width:30%; } .paddrow { background-color:#dadada; color:#818181; display:none; padding-bottom:40px; } .paddrow.aboutrow { background-color:#78c2d4; color:#fff !important; display:block; } .paddrow .head { font-size:4em; font-weight:100; margin:40px 0; } .paddrow .close { cursor:pointer; position:absolute; right:50px; top:80px; width:38px; } .about { color:#818181; } .about section { margin:0 0 10%; } .about .head { font-size:4em; font-weight:100; margin:3% 0; } .about .subhead { font-size:2.5em; font-weight:100; margin:0 0 3%; } .about .txt { width:60%; } .about .image { width:26%; } .about .flleft { float:left; } .about .flright { float:right; } .projecthead.product { background-color:#87b822; } .projecthead .picture { margin-bottom:6%; margin-top:6%; } .projecthead .picture.right { margin-right:-3.5%; } .projecthead .text { position:absolute; width:49%; } .projecthead .centertext { margin:0 auto; padding-bottom:24%; padding-top:6%; text-align:center; width:55%; } .image { text-align:center; } .image img { vertical-align:top; width:100%; } .contactform { width:50%; } .input { -moz-box-sizing:border-box; -webkit-box-sizing:border-box; box-sizing:border-box; margin:1% 0; padding:12px 14px; width:47%; } .input.email { float:right; } button { border:none; cursor:pointer; } .textarea { -moz-box-sizing:border-box; -webkit-box-sizing:border-box; box-sizing:border-box; height:200px; margin:1% 0; overflow:auto; padding:12px 14px; resize:none; width:100%; } ::-webkit-input-placeholder { color:#a7a7a7; } :-moz-placeholder { color:#a7a7a7; } ::-moz-placeholder { /* ff18+ */ color:#a7a7a7; } :-ms-input-placeholder { color:#a7a7a7; } .loader { -moz-animation:loader_rot 1.3s linear infinite; -o-animation:loader_rot 1.3s linear infinite; -webkit-animation:loader_rot 1.3s linear infinite; animation:loader_rot 1.3s linear infinite; height:80px; width:80px; } @-moz-keyframes loader_rot { from { -moz-transform:rotate(0deg); } to { -moz-transform:rotate(360deg); } } @-webkit-keyframes loader_rot { from { -webkit-transform:rotate(0deg); } to { -webkit-transform:rotate(360deg); } } @keyframes loader_rot { from { transform:rotate(0deg); } to { transform:rotate(360deg); } } .view-enter,.view-leave { -moz-transition:all .5s; -o-transition:all .5s; -webkit-transition:all .5s; transition:all .5s; } .view-enter { left:20px; opacity:0; position:absolute; top:0; } .view-enter.view-enter-active { left:0; opacity:1; } .view-leave { left:0; opacity:1; position:absolute; top:0; } .view-leave.view-leave-active { left:-20px; opacity:0; } please note that css3 transitions are used, which means that our demonstration will only work modern browsers (ff, chrome, ie10+ etc) stage 3. javascript as i mentioned before, our main controller and the model are separated. the navigation menu can be handled here, and we also can operate with the contact form. js/app.js 'use strict'; // angular.js main app initialization var app = angular.module('example359', []). config(['$routeprovider', function ($routeprovider) { $routeprovider. when('/', { templateurl: 'pages/index.html', activetab: 'projects', controller: homectrl }). when('/project/:projectid', { templateurl: function (params) { return 'pages/' + params.projectid + '.html'; }, controller: projectctrl, activetab: 'projects' }). when('/privacy', { templateurl: 'pages/privacy.html', controller: privacyctrl, activetab: 'privacy' }). when('/about', { templateurl: 'pages/about.html', controller: aboutctrl, activetab: 'about' }). otherwise({ redirectto: '/' }); }]).run(['$rootscope', '$http', '$browser', '$timeout', "$route", function ($scope, $http, $browser, $timeout, $route) { $scope.$on("$routechangesuccess", function (scope, next, current) { $scope.part = $route.current.activetab; }); // onclick event handlers $scope.showform = function () { $('.contactrow').slidetoggle(); }; $scope.closeform = function () { $('.contactrow').slideup(); }; // save the 'contact us' form $scope.save = function () { $scope.loaded = true; $scope.process = true; $http.post('sendemail.php', $scope.message).success(function () { $scope.success = true; $scope.process = false; }); }; }]); app.config(['$locationprovider', function($location) { $location.hashprefix('!'); }]); pay attention here. when we request a page, it loads an appropriate page from the ‘pages’ folder: about.html, privacy.html, index.html. depending on the selected product, it opens one of the product pages: product1.html, product2.html, product3.html or product4.html in the second half, there are functions to slide the contact form and to handle its submit process (to the sendemail.php page). next is the controller file: js/controllers.js 'use strict'; // optional controllers function homectrl($scope, $http) { } function projectctrl($scope, $http) { } function privacyctrl($scope, $http, $timeout) { } function aboutctrl($scope, $http, $timeout) { } it is empty, because we have nothing to use here at the moment. stage 4. additional pages angularjs loads pages asynchronously, thereby increasing the speed. here are templates of all additional pages used in our project: pages/about.html about us script tutorials is one of the largest web development communities. we provide high quality content (articles and tutorials) which covers all the web development technologies including html5, css3, javascript (and jquery), php and so on. our audience are web designers and web developers who work with web technologies. additional information promo 1 lorem ipsum dolor sit amet, consectetur adipiscing elit. nunc et ligula accumsan, pharetra nibh nec, facilisis nulla. in pretium semper venenatis. in adipiscing augue elit, at venenatis enim suscipit a. fusce vitae justo tristique, ultrices mi metus. ..... pages/privacy.html privacy & terms by accessing this web site, you are agreeing to be bound by these web site terms and conditions of use, all applicable laws and regulations, and agree that you are responsible for compliance with any applicable local laws. if you do not agree with any of these terms, you are prohibited from using or accessing this site. the materials contained in this web site are protected by applicable copyright and trade mark law. other information header 1 lorem ipsum dolor sit amet, consectetur adipiscing elit. nunc et ligula accumsan, pharetra nibh nec, facilisis nulla. in pretium semper venenatis. in adipiscing augue elit, at venenatis enim suscipit a. fusce vitae justo tristique, ultrices mi metus. ..... pages/footer.html copyright © 2013 script tutorials pages/index.html product #1 product #2 product #3 product #4 finally, the product pages. all of them are prototypes, so i decided to publish only one of them. pages/index.html product 1 page lorem ipsum dolor sit amet, consectetur adipiscing elit. nunc et ligula accumsan, pharetra nibh nec, facilisis nulla. in pretium semper venenatis. in adipiscing augue elit, at venenatis enim suscipit a. fusce vitae justo tristique, ultrices mi metus. lorem ipsum dolor sit amet, consectetur adipiscing elit. nunc et ligula accumsan, pharetra nibh nec, facilisis nulla. in pretium semper venenatis. in adipiscing augue elit, at venenatis enim suscipit a. fusce vitae justo tristique, ultrices mi metus. download the app finishing touches – responsive styles all of these styles are needed to make our results look equally good on all possible mobile devices and monitors: @media (max-width: 1200px) { body { font-size:90%; } h1 { font-size:4.3em; } p { font-size:1.3em; } header { height:80px; } header .logo { margin-top:12px; width:200px; } header nav { margin-top:11px; } header nav ul li { margin-right:12px; } header nav ul li a { border-radius:23px; font-size: 1.3em; padding:10px 12px; } .wrap { padding:0 30px; } .paddrow .close { right:30px; } } @media (max-width: 900px) { .contactform { width:100%; } } @media (max-width: 768px) { body { font-size:80%; margin:0; } h1 { font-size:4em; } header { height:70px; } header .logo { margin-top:20px; width:70px; } header nav { margin-top:8px; } header nav ul li { margin-right:5px; } header nav ul li a { border-radius:20px; font-size:1.1em; padding:8px; } .wrap { padding:0 15px; } .projectobj .name { font-size:3em; } .paddrow { padding-bottom:30px; } .paddrow .head { font-size:3em; margin:30px 0; } .paddrow .close { right:20px; top:60px; width:30px; } .projecthead .picture { width:67%; } .projecthead .picture.right { margin-right:16.5%; } .projecthead .text { position:static; width:100%; } .projecthead .centertext { width:70%; } .view-enter,.view-leave { -webkit-transform:translate3d(0,0,0); transform:translate3d(0,0,0); } } @media (max-width: 480px) { body { font-size:70%; margin:0; } header { height:50px; } header .logo { display:none; } header nav { margin-top:3px; } header nav ul li { margin-right:3px; } header nav ul li a { border-radius:20px; font-size:1.3em; padding:5px 14px; } #contactbtn { display:none; } .wrap { padding:0 10px; } .paddrow { padding-bottom:20px; } .paddrow .head { margin:20px 0; } .paddrow .close { right:10px; top:45px; width:20px; } .about .image { margin:10% auto; width:60%; } .about .abicon { display:inline; } .projecthead .centertext { width:90%; } .about .txt,.input { width:100%; } .about .flleft,.about .flright,.input.email { float:none; } } live demo download in package conclusion that’s all for today. thanks for your patient attention, and if you really like what we did today, share it with all your friends in your social networks.

The following question will again test your knowledge of the Oracle Weblogic threading model. I’m looking forward for your comments and experience on the same. If you are a Weblogic administrator, I’m certain that you heard of this common problem: stuck threads. This is one of the most common problems you will face when supporting a Weblogic production environment. A Weblogic stuck thread simply means a thread performing the same request for a very long time and more than the configurable Stuck Thread Max Time. Question: How can you detect the presence of STUCK threads during and following a production incident? Answer: As we saw from our last article “Weblogic Thread Monitoring Tips”, Weblogic provides functionalities allowing us to closely monitor its internal self-tuning thread pool. It will also highlight you the presence of any stuck thread. This monitoring view is very useful when you do a live analysis but what about after a production incident? The good news is that Oracle Weblogic will also log any detected stuck thread to the server log. Such information includes details on the request and more importantly, the thread stack trace. This data is crucial and will allow you to potentially better understand the root cause of any slowdown condition that occurred at a certain time. < ExecuteThread: '11' for queue: 'weblogic.kernel.Default (self-tuning)'> <[STUCK] ExecuteThread: '35' for queue: 'weblogic.kernel.Default (self-tuning)' has been busy for "608" seconds working on the request "Workmanager: default, Version: 0, Scheduled=true, Started=true, Started time: 608213 ms POST /App1/jsp/test.jsp HTTP/1.1 Accept: application/x-ms-application... Referer: http://.. Accept-Language: en-US User-Agent: Mozilla/4.0 .. Content-Type: application/x-www-form-urlencoded Accept-Encoding: gzip, deflate Content-Length: 539 Connection: Keep-Alive Cache-Control: no-cache Cookie: JSESSIONID= ]", which is more than the configured time (StuckThreadMaxTime) of "600" seconds. Stack trace: ................................... javax.servlet.http.HttpServlet.service(HttpServlet.java:727) javax.servlet.http.HttpServlet.service(HttpServlet.java:820) weblogic.servlet.internal.StubSecurityHelper$ServletServiceAction.run(StubSecurityHelper.java:227) weblogic.servlet.internal.StubSecurityHelper.invokeServlet(StubSecurityHelper.java:125) weblogic.servlet.internal.ServletStubImpl.execute(ServletStubImpl.java:301) weblogic.servlet.internal.ServletStubImpl.execute(ServletStubImpl.java:184) weblogic.servlet.internal.WebAppServletContext$ServletInvocationAction.... weblogic.servlet.internal.WebAppServletContext$ServletInvocationAction.run() weblogic.security.acl.internal.AuthenticatedSubject.doAs(AuthenticatedSubject.java:321) weblogic.security.service.SecurityManager.runAs(SecurityManager.java:120) weblogic.servlet.internal.WebAppServletContext.securedExecute(WebAppServletContext.java:2281) weblogic.servlet.internal.WebAppServletContext.execute(WebAppServletContext.java:2180) weblogic.servlet.internal.ServletRequestImpl.run(ServletRequestImpl.java:1491) weblogic.work.ExecuteThread.execute(ExecuteThread.java:256) weblogic.work.ExecuteThread.run(ExecuteThread.java:221) Here is one more tip: the generation and analysis of a JVM thread dump will also highlight you stuck threads. As we can see from the snapshot below, the Weblogic thread state is now updated to STUCK, which means that this particular request is being executed since at least 600 seconds or 10 minutes. This is very useful information since the native thread state will typically remain to RUNNABLE. The native thread state will only get updated when dealing with BLOCKED threads etc. You have to keep in mind that RUNNABLE simply means that this thread is healthy from a JVM perspective. However, it does not mean that it truly is from a middleware or Java EE container perspective. This is why Oracle Weblogic has its own internal ExecuteThread state. Finally, if your organization or client is using any commercial monitoring tool, I recommend that you enable some alerting around both hogging thread and stuck thread. This will allow your support team to take some pro-active actions before the affected Weblogic managed server(s) become fully unresponsive.

for modern web application development, having dozens of unit tests is not enough anymore. the actual code coverage of those tests would reveal if the application is thoroughly stressed or not. for tests written using the famous jasmine test library, an easy way to have the coverage report is via istanbul and karma . for this example, let’s assume that we have a simple library sqrt.js which contains an alternative implementation of math.sqrt . note also how it will throw an exception instead of returning nan for an invalid input. var my = { sqrt: function(x) { if (x < 0) throw new error("sqrt can't work on negative number"); return math.exp(math.log(x)/2); } }; using jasmine placed under test/lib/jasmine-1.3.1 , we can craft a test runner that includes the following spec: describe("sqrt", function() { it("should compute the square root of 4 as 2", function() { expect(my.sqrt(4)).toequal(2); }); }); opening the spec runner in a web browser will give the expected outcome: so far so good. now let's see how the code coverage of our test setup can be measured. the first order of business is to install karma . if you are not familiar with karma, it is basically a test runner which can launch and connect to a specific set of web browsers, run your tests, and then gather the report. using node.js, what we need to do is: npm install karma karma-coverage before launching karma, we need to specify its configuration . it could be as simple as the following my.conf.js (most entries are self-explained). note that the tests are executed using phantomjs for simplicity, it is however quite trivial to add other web browsers such as chrome and firefox. module.exports = function(config) { config.set({ basepath: '', frameworks: ['jasmine'], files: [ '*.js', 'test/spec/*.js' ], browsers: ['phantomjs'], singlerun: true, reporters: ['progress', 'coverage'], preprocessors: { '*.js': ['coverage'] } }); }; running the tests, as well as performing code coverage at the same time, can be triggered via: node_modules/.bin/karma start my.conf.js which will dump the output like: info [karma]: karma v0.10.2 server started at http://localhost:9876/ info [launcher]: starting browser phantomjs info [phantomjs 1.9.2 (linux)]: connected on socket n9ndnhj0np92ntspgx-x phantomjs 1.9.2 (linux): executed 1 of 1 success (0.029 secs / 0.003 secs) as expected (from the previous manual invocation of the spec runner), the test passed just fine. however, the most particular interesting piece here is the code coverage report, it is stored (in the default location) under the subdirectory coverage . open the report in your favorite browser and there you'll find the coverage analysis report. behind the scene, karma is using istanbul , a comprehensive javascript code coverage tool (read also my previous blog post on javascript code coverage with istanbul ). istanbul parses the source file, in this example sqrt.js , using esprima and then adds some extra instrumentation which will be used to gather the execution statistics. the above report that you see is one of the possible outputs, istanbul can also generate lcov report which is suitable for many continuous integration systems (jenkins, teamcity, etc). an extensive analysis of the coverage data should also prevent any future coverage regression, check out my other post hard thresholds on javascript code coverage . one important thing about code coverage is branch coverage . if you pay attention carefully, our test above is still not exercising the situation where the input to my.sqrt is negative. there is a big "i" marking in the third-line of the code, this is istanbul telling us that the if branch is not taken at all (for the else branch, it will be an "e" marker). once this missing branch is noticed, improving the situation is as easy as adding one more test to the spec: it("should throw an exception if given a negative number", function() { expect(function(){ my.sqrt(-1); }). tothrow(new error("sqrt can't work on negative number")); }); once the test is executed again, the code coverage report looks way better and everyone is happy. if you have some difficulties following the above step-by-step instructions, take a look at a git repository i have prepared: github.com/ariya/coverage-jasmine-istanbul-karma . feel free to play with it and customize it to suit your workflow!

I’m going to start by discussing the Spring WebMVC configuration and move on from there in future posts.

In this article, I will show how to implement auto-completion using Hibernate Search. The same can be achieved using Solr or ElasticSearch. But I decided to use Hibernate Search as its the simplest to get started with, easily integrates with an existing application and leverages the same core - Lucene. And we get all of this without the overhead of managing Solr/ElasticSearch cluster. In all, I found Hibernate Search to be the go-to search engine for simple use cases. For our use case, we build a product title based auto-completion where often, the user queries are searches for product title. While typing, users should immediately see titles matching their requests, and Hibernate Search should do the hard work to filter the relevant documents in near real-time. Lets have the following JPA annotated Product entity class. public class Product { @Id @Column(name = "sku") private String sku; @Column(name = "upc") private String upc; @Column(name = "title") private String title; .... } We are interested in returning suggestions based on the 'title' field. Title will be indexed based on 2 strategies - N-Gram and Edge N-Gram. Edge N-Gram - This will match only from the left edge of the suggestion text. For this we use KeywordTokenizerFactory (emits the entire input as a single token) and EdgeNGramFilterFactory along with some regex cleansing. N-Gram matches from the start of every word, so that you can get right-truncated suggestions for any word in the text, not only from the first word. The main difference from N-gram is the tokenizer which is StandardTokenizerFactory along with NGramFilterFactory. Using these strategies, if the document field is "A brown fox" and the query is a) "A bro"- Will match b) "bro" - Will match Implementation: In the entity defined above, we can map 'title' property twice with the above strategies. Below are the annotations to instruct Hibernate to index 'title' twice. @Entity @Table(name = "item_master") @Indexed(index = "Products") @AnalyzerDefs({ @AnalyzerDef(name = "autocompleteEdgeAnalyzer", // Split input into tokens according to tokenizer tokenizer = @TokenizerDef(factory = KeywordTokenizerFactory.class), filters = { // Normalize token text to lowercase, as the user is unlikely to // care about casing when searching for matches @TokenFilterDef(factory = PatternReplaceFilterFactory.class, params = { @Parameter(name = "pattern",value = "([^a-zA-Z0-9\\.])"), @Parameter(name = "replacement", value = " "), @Parameter(name = "replace", value = "all") }), @TokenFilterDef(factory = LowerCaseFilterFactory.class), @TokenFilterDef(factory = StopFilterFactory.class), // Index partial words starting at the front, so we can provide // Autocomplete functionality @TokenFilterDef(factory = EdgeNGramFilterFactory.class, params = { @Parameter(name = "minGramSize", value = "3"), @Parameter(name = "maxGramSize", value = "50") }) }), @AnalyzerDef(name = "autocompleteNGramAnalyzer", // Split input into tokens according to tokenizer tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class), filters = { // Normalize token text to lowercase, as the user is unlikely to // care about casing when searching for matches @TokenFilterDef(factory = WordDelimiterFilterFactory.class), @TokenFilterDef(factory = LowerCaseFilterFactory.class), @TokenFilterDef(factory = NGramFilterFactory.class, params = { @Parameter(name = "minGramSize", value = "3"), @Parameter(name = "maxGramSize", value = "5") }), @TokenFilterDef(factory = PatternReplaceFilterFactory.class, params = { @Parameter(name = "pattern",value = "([^a-zA-Z0-9\\.])"), @Parameter(name = "replacement", value = " "), @Parameter(name = "replace", value = "all") }) }), @AnalyzerDef(name = "standardAnalyzer", // Split input into tokens according to tokenizer tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class), filters = { // Normalize token text to lowercase, as the user is unlikely to // care about casing when searching for matches @TokenFilterDef(factory = WordDelimiterFilterFactory.class), @TokenFilterDef(factory = LowerCaseFilterFactory.class), @TokenFilterDef(factory = PatternReplaceFilterFactory.class, params = { @Parameter(name = "pattern", value = "([^a-zA-Z0-9\\.])"), @Parameter(name = "replacement", value = " "), @Parameter(name = "replace", value = "all") }) }) // Def }) public class Product { .... } Explanation: 2 custom analyzers - autocompleteEdgeAnalyzer andautocompleteNGramAnalyzer have been defined as per theory in the previous section. Next, we apply these analyzers on the 'title' field to create 2 different indexes. Here is how we do it: @Column(name = "title") @Fields({ @Field(name = "title", index = Index.YES, store = Store.YES, analyze = Analyze.YES, analyzer = @Analyzer(definition = "standardAnalyzer")), @Field(name = "edgeNGramTitle", index = Index.YES, store = Store.NO, analyze = Analyze.YES, analyzer = @Analyzer(definition = "autocompleteEdgeAnalyzer")), @Field(name = "nGramTitle", index = Index.YES, store = Store.NO, analyze = Analyze.YES, analyzer = @Analyzer(definition = "autocompleteNGramAnalyzer")) }) private String title; Start indexing: public void index() throws InterruptedException { getFullTextSession().createIndexer().startAndWait(); } Once indexed, inspect the index using Luke and you should be able to see title analyzed and stored as N-Grams and Edge N-Grams. Search Query: private static final String TITLE_EDGE_NGRAM_INDEX = "edgeNGramTitle"; private static final String TITLE_NGRAM_INDEX = "nGramTitle"; @Transactional(readOnly = true) public synchronized List getSuggestions(final String searchTerm) { QueryBuilder titleQB = getFullTextSession().getSearchFactory() .buildQueryBuilder().forEntity(Product.class).get(); Query query = titleQB.phrase().withSlop(2).onField(TITLE_NGRAM_INDEX) .andField(TITLE_EDGE_NGRAM_INDEX).boostedTo(5) .sentence(searchTerm.toLowerCase()).createQuery(); FullTextQuery fullTextQuery = getFullTextSession().createFullTextQuery( query, Product.class); fullTextQuery.setMaxResults(20); @SuppressWarnings("unchecked") List results = fullTextQuery.list(); return results; } And we have a working suggester. What next? Expose the functionality via a REST API and integrate it with jQuery, examples of which can be easily found. You can also use the same strategy with Solr and ElasticSearch.

Feeling good to be back at the blog . Actually, I have been managing GDG Ahmedabad, delivering android talks, and managing workshops locally and outside my region. Last month, I was quite busy in organizing the “DevFest” event for GDG Ahmedabad, and then for the preparation of my two talks for the GDG Kathmandu DevFest. I was invited to deliver two talks at DevFest, which was organized by GDG Kathmandu. I have already published slides on my Speakerdeck. I am not sure whether you have already checked and learned from my speaker deck, but still give me a chance to write about Introduction to Android studio here. What is Android Studio? It’s an Android focused IDE, designed specially for Android development. It was launched on 16th May 2013, during Google's I/O 2013 event. Android studio contains all the Android SDK tools to design, test, debug and profile your app. By looking at the development tools and environment, we can see its similar to Eclipse with the ADT plug-in, but as I have mentioned above, it's an Android focused IDE, and there are many cool features available in Android Studio that can foster and increase your development productivity. One great thing is that it depends on the IntelliJ Idea IDE, which has proved itself to be a great IDE and has been in use by many Android engineers. What is the Difference Between IntelliJ Idea and Android Studio? Nothing, in regards to Android. If you use IntelliJ… Keep using it IntelliJ 13 will have the same stuff EAP of IntelliJ Idea 13 includes all the new stuff If Not… Give Android Studio a try You may have some questions in mind regarding IntelliJ and Android Studio. If so, check the FAQ section: IntelliJ IDEA and Android Studio FAQ. Let’s Download Android Studio You can download Android Studio from the android developer site: http://developer.android.com/sdk/installing/studio.html. Cool Features of Android Studio As I have mentioned, it's similar to Eclipse with the ADT plug-in, but Android Studio has many cool features that can help you to increase development productivity. Here are the cool features: Powerful code editing (smart editing, code re-factoring) Rich layout Editor (As soon as you drag and drop views on the layout, it shows you a preview in all the screens including Nexus 4, Nexus 7, Nexus 10 and many other resolutions. Layout designing can be done much faster way as compared to eclipse.) Gradle-based build support Maven Support Template-based wizards Lint tool analysis (The Android lint tool is a static code analysis tool that checks your Android project source files for potential bugs and optimization improvements for correctness, security, performance, usability, accessibility, and internationalization). You can experience all the cool features by using Android Studio yourself Awesome Stuff Inside Darcula Theme It's actually a black-based theme. While using Android Studio, I enjoy working in Darcula theme environment. By the way, Its Darcula theme, not Dracula. I am correcting this just because I have seen many people on Stackoverflow and Google+ saying Dracula. You can set the Darcula theme in Android Studio by: File > Settings > IDE Settings > Appearance > Theme: Darcula. Preview All the Screens We can consider this is as part of the Rich layout editor feature. With this privilege, users can design layouts and can check layouts by previewing in all the possible screens, such as Nexus 4, Nexus 7, Nexus and many other devices. It helps the user to improve layout designs while providing compatibility to various resolutions available. Device Framed Screen Capture It provides ability to directly generate a screenshot of your application. Yes, it was already included in the SDK, but Android Studio provides something more: Device frame (As frames for many Nexus devices are available, you can capture screenshot in whichever frame you like most) Drop shadow Screen glare Color Preview I like this feature very much and I have found this feature helpful while working on big projects. While using Eclipse, we have to have 3rd party color chooser and picker but this feature gives privilege to select color from in-build color chooser and can also have preview in Colors.xml file. Color Preview – Activity class While using Eclipse, it’s difficult to check which color we have used. Yes, we can imagine the color by its name, but an actual preview is much better. This feature was recently introduced in Android Studio, so you must have latest version installed. Hard Coded Strings Here is another feature I like and have found useful: Whenever you use any string resources from Strings.xml, it displays actual value instead of variable name. This setting comes by default, but in case you aren’t able to get hard coded strings in your activity class, then try any of the below ways. Settings > Editor > Code Folding > Android String References OR Select String and right click on it and then go to Folding > Collapse OR CTRL + Numpad ‘-’ Create Layout Variation This provides the ability to create layout variation directly. For example: layout for the large screen, layout for Xlarge screen, etc. The great thing is that the created variant layout gets stored in particular folders like layout-xlarge, layout-large-land, etc. Should I Use Android Studio? You might have explored all the cool features, or you are ready to explore right now. But questions might have arisen in your mind: “Should I use Android Studio,” or “should we start using Android Studio right now,” or “should I continue with IntelliJ or Eclipse?” My answer is a big NO to use Android Studio as your main IDE for Android development, because currently its EARLY ACCESS PREVIEW and it's maturing over days. Engineers have been working hard to improve this IDE. So, you should wait until the BETA comes out. I agree with Carlos Vega (commented over G+) on this point: “You should at least migrate to Intellij Idea 12 so that you get familiar with the IDE’s workflow and keyboard shortcuts. That way when Android Studio reach a more stable level, you can switch without a major learning curve.” Thanks, Carlos Vega, for the input. By the way, here is the presentation I delivered at the GDG Kathmandu DevFest.

When putting together data sets to play around with, one of the more boring tasks is stripping out characters that you’re not interested in and more often than not those characters are white spaces. Since I’ve been building data sets using Clojure I wanted to write a function that would do this for me. I started out with the following string: (def word " with a little bit of space we can make it through the night ") which I wanted to format in such a way that there would be a maximum of one space between each word. I start out by using the trim function but that only removes white space from the beginning and end of a string: > (clojure.string/trim word) "with a little bit of space we can make it through the night" I wanted to get rid of the space in between ‘a’ and ‘little’ as well so I wrote the following code to split on a space and filter out any excess spaces that still remained before joining the words back together: > (clojure.string/join " " (filter #(not (clojure.string/blank? %)) (clojure.string/split word #" "))) "with a little bit of space we can make it through the night" I wanted to try and make it a bit easier to read by using the thread last (->>) macro but that didn’t work as well as I’d hoped because clojure.string/split doesn’t take the string in as its last parameter: > (->> (clojure.string/split word #" ") (filter #(not (clojure.string/blank? %))) (clojure.string/join " ")) "with a little bit of space we can make it through the night" I worked around it by creating a specific function for splitting on a space: (defn split-on-space [word] (clojure.string/split word #"\s")) which means we can now chain everything together nicely: > (->> word split-on-space (filter #(not (clojure.string/blank? %))) (clojure.string/join " ")) "with a little bit of space we can make it through the night" I couldn’t find a cleaner way to do this but I’m sure there is one and my googling just isn’t up to scratch so do let me know in the comments!

In the previous post, we have seen an example where dataProvider attribute has been used3 to test methods with different sets of input data for the same test method. TestNG provides another attribute dataProviderClass in conjunction with dataProvider to fetch the input data for the test methods from an external class. The actual class that holds input data is set to the dataProviderClass attribute and datProvider by itself holds the method name where the input data is actually fetched. Here is a quick example to show how to use dataProviderClass and dataProvide attribute Code Service Class ? view source print? 01.package com.skilledmonster.example; 02./** 03.* Simple calculator service to demonstrate TestNG Framework 04.* 05.* @author Jagadeesh Motamarri 06.* @version 1.0 07.*/ 08.public interface CalculatorService { 09.int sum(int a, int b); 10.int multiply(int a, int b); 11.int div(int a, int b); 12.int sub(int a, int b); 13.} Service Implementation Class ? view source print? 01.package com.skilledmonster.example; 02./** 03.* Simple calculator service implementation to demonstrate TestNG Framework 04.* 05.* @author Jagadeesh Motamarri 06.* @version 1.0 07.*/ 08.public class SimpleCalculator implements CalculatorService { 09.public int sum(int a, int b) { 10.return a + b; 11.} 12.public int multiply(int a, int b) { 13.return a * b; 14.} 15.public int div(int a, int b) { 16.return a / b; 17.} 18.public int sub(int a, int b) { 19.return a - b; 20.} 21.} Data Provider Class ? view source print? 01.package com.skilledmonster.common; 02.import org.testng.annotations.DataProvider; 03./** 04.* Data Provider class for TestNG test cases 05.* 06.* @author Jagadeesh Motamarri 07.* @version 1.0 08.*/ 09.public class TestNGDataProvider { 10./** 11.* Data Provider for testing sum of 2 numbers 12.* 13.* @return 14.*/ 15.@DataProvider 16.public static Object[][] testSumInput() { 17.return new Object[][] { { 5, 5 }, { 10, 10 }, { 20, 20 } }; 18.} 19./** 20.* Data Provider for testing multiplication of 2 numbers 21.* 22.* @return 23.*/ 24.@DataProvider 25.public static Object[][] testMultipleInput() { 26.return new Object[][] { { 5, 5 }, { 10, 10 }, { 20, 20 } }; 27.} 28.} Finally, test class that uses dataProviderClass attribute to feed the input data for the test methods ? package com.skilledmonster.example; import org.testng.Assert; import org.testng.annotations.BeforeClass; import org.testng.annotations.Test; import com.skilledmonster.common.TestNGDataProvider; /** * Example to demonstrate use of dataProviderClass and dataProvide attributes of TestNG framework * * @author Jagadeesh Motamarri * @version 1.0 */ public class TestNGAnnotationTestDataProviderExample { public CalculatorService service; @BeforeClass public void init() { System.out.println("@BeforeClass: The annotated method will be run before the first test method in the current class is invoked."); System.out.println("init service"); service = new SimpleCalculator(); } @Test(dataProviderClass = TestNGDataProvider.class, dataProvider = "testSumInput") public void testSum(int a, int b) { System.out.println("@Test : testSum()"); int result = service.sum(a, b); Assert.assertEquals(result, a + b); } @Test(dataProviderClass = TestNGDataProvider.class, dataProvider = "testMultipleInput") public void testMultiple(int a, int b) { System.out.println("@Test : testMultiple()"); int result = service.multiply(a, b); Assert.assertEquals(result, a * b); } } Output As shown in the above console output, each of the testSum() and testMutiple() methods are invoked with different sets of input data using an external class with dataProviderClass attribute. Advantage More flexibility and re-usability of commonly used data across several test classes. Download Download TestNG DataProvider Example

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I wanted to do some date manipulation in Clojure recently and figured that since clj-time is a wrapper around Joda Time it’d probably do the trick. The first thing we need to do is add the dependency to our project file and then run lein reps to pull down the appropriate JARs. The project file should look something like this: project.clj (defproject ranking-algorithms "0.1.0-SNAPSHOT" :license {:name "Eclipse Public License" :url "http://www.eclipse.org/legal/epl-v10.html"} :dependencies [[org.clojure/clojure "1.4.0"] [clj-time "0.6.0"]]) Now let’s load the clj-time.format namespace into the REPL since we know we’ll be parsing dates: > (require '(clj-time [format :as f])) The string that I want to convert into a date looks like this: (def string-date "18 September 2012") The first thing we should do is check whether there is an existing formatter that we can use by evaluating the following function: > (f/show-formatters) ... :hour-minute 06:45 :hour-minute-second 06:45:22 :hour-minute-second-fraction 06:45:22.473 :hour-minute-second-ms 06:45:22.473 :mysql 2013-09-20 06:45:22 :ordinal-date 2013-263 :ordinal-date-time 2013-263T06:45:22.473Z :ordinal-date-time-no-ms 2013-263T06:45:22Z :rfc822 Fri, 20 Sep 2013 06:45:22 +0000 ... There are a lot of different built in formatters but unfortunately I couldn’t find one that exactly matched our date format so we’ll have to write our own one. For that we’ll need to refresh our knowledge of Java date formatting: We end up with the following formatter: > (f/parse (f/formatter "dd MMM YYYY") string-date) # It took me much longer than it should have to remember that ‘MMM’ is the pattern to match a short form of a month but it’s just the same as what we’d have to do in Java but with some neat wrapper functions.

This post comes from Alberto Pose at the MuleSoft blog. Introduction Suppose that you have a Maven project and you want to download Node.js modules previously uploaded to NPM. One way of doing that without running node is by using the npm-maven-plugin. It allows the user to download the required Node modules without running node.js: It is completely implemented on the JVM. Getting Started First of all, you will need to add the Mule Maven repo to your pom.xml file: mulesoft-releases MuleSoft Repository https://repository.mulesoft.org/releases/ After doing that, you will need to add the following to the build->plugin section of your pom.xml file: org.mule.tools.javascript npm-maven-plugin 1.0 generate-sources fetch-modules colors:0.5.1 jshint:0.8.1 Then just execute: mvn generate-sources and that’s it! One more thing… By default, the modules can be found in src/main/resources/META-INF but that path can be changed setting the ‘outputDirectory’ parameter. Also, module transitivity is taken into account. That means that it will download all the required dependencies before downloading the specified module. Show me the code! The source code can be found here. Feel free to fork the project and propose changes to it. Happy (Node.js and Maven) hacking!

One of the problems with memory-mapped files is that you can’t actually map beyond the end of the file. So you can’t use that to extend your file. I had a thought about and set out to check out what happens when I create a sparse file, a file that only take space when you write to it, and at the same time, map it. As it turns out, this actually works pretty well in practice. You can do so without any issues. Here is how it works: using (var f = File.Create(path)) { int bytesReturned = 0; var nativeOverlapped = new NativeOverlapped(); if (!NativeMethod.DeviceIoControl(f.SafeFileHandle, EIoControlCode.FsctlSetSparse, IntPtr.Zero, 0, IntPtr.Zero, 0, ref bytesReturned, ref nativeOverlapped)) { throw new Win32Exception(); } f.SetLength(1024*1024*1024*64L); } This creates a sparse file that is 64 GB in size. Then we can map it normally: using (var mmf = MemoryMappedFile.CreateFromFile(path)) using (var memoryMappedViewAccessor = mmf.CreateViewAccessor(0, 1024*1024*1024*64L)) { for (long i = 0; i < memoryMappedViewAccessor.Capacity; i += buffer.Length) { memoryMappedViewAccessor.WriteArray(i, buffer, 0, buffer.Length); } } And then we can do stuff to it. And that includes writing to yet-unallocated parts of the file. This also means that you don’t have to worry about writing past the end of the file, the OS will take care of all of that for you. Happy happy, joy joy, etc. There is one problem with this method, however. It means that you have a 64 GB file, but you don’t have that much allocated. What that means in turn is that you might not have that much space available for the file. Which brings up an interesting question, what happens when you are trying to commit a new page, and the disk is out of space? Using file I/O you would get an I/O error with the right code. But when using memory mapped files, the error would actually turn up during access, which can happen pretty much anywhere. It also means that it is a Standard Exception Handling error in Windows, which requires special treatment. To test this out, I wrote the following so it would write to a disk that had only about 50 GB free. I wanted to know what would happen when it ran out of space. That is actually something that happens, and we need to be able to address this issue robustly. The kicker is that this might actually happen at any time, so that would really result is some… interesting behavior with regards to robustness. In other words, I don’t think that this is a viable option, it is a really cool trick, but I don’t think it is a very well thought out option. By the way, the result of my experiment was that we had an effectively a frozen process. No errors, nothing, just a hung. Also, I am pretty sure that WriteArray() is really slow, but I’ll check this out at another pointer in time.

In this post we are going to learn how we can use NHibernate in ASP.NET MVC application. What is NHibernate: ORMs(Object Relational Mapper) are quite popular this days. ORM is a mechanism to map database entities to Class entity objects without writing a code for fetching data and write some SQL queries. It automatically generates SQL Query for us and fetch data behalf on us. NHibernate is also a kind of Object Relational Mapper which is a port of popular Java ORM Hibernate. It provides a framework for mapping an domain model classes to a traditional relational databases. Its give us freedom of writing repetitive ADO.NET code as this will be act as our database layer. Let’s get started with NHibernate. How to download: There are two ways you can download this ORM. From nuget package and from the source forge site. Nuget - http://www.nuget.org/packages/NHibernate/ Source Forge-http://sourceforge.net/projects/nhibernate/ Creating a table for CRUD: I am going to use SQL Server 2012 express edition as a database. Following is a table with four fields Id, First Name, Last name, Designation. Creating ASP.NET MVC project for NHibernate: Let’s create a ASP.NET MVC project for NHibernate via click on File-> New Project –> ASP.NET MVC 4 web application. Installing NuGet package for NHibernate: I have installed nuget package from Package Manager console via following Command. It will install like following. NHibertnate configuration file: Nhibernate needs one configuration file for setting database connection and other details. You need to create a file with ‘hibernate.cfg.xml’ in model Nhibernate folder of your application with following details. NHibernate.Connection.DriverConnectionProvider NHibernate.Driver.SqlClientDriver Server=(local);database=LocalDatabase;Integrated Security=SSPI; NHibernate.Dialect.MsSql2012Dialect Here you have got different settings for NHibernate. You need to selected driver class, connection provider as per your database. If you are using other databases like Orcle or MySQL you will have different configuration. ThisNHibernate ORM can work with any databases. Creating a model class for NHibernate: Now it’s time to create model class for our CRUD operations. Following is a code for that. Property name is identical to database table columns. namespace NhibernateMVC.Models { public class Employee { public virtual int Id { get; set; } public virtual string FirstName { get; set; } public virtual string LastName { get; set; } public virtual string Designation { get; set; } } } Creating a mapping file between class and table: Now we need a xml mapping file between class and model with name “Employee.hbm.xml” like following in Nhibernate folder. Creating a class to open session for NHibernate I have created a class in models folder called NHIbernateSession and a static function it to open a session for NHibertnate. using System.Web; using NHibernate; using NHibernate.Cfg; namespace NhibernateMVC.Models { public class NHibertnateSession { public static ISession OpenSession() { var configuration = new Configuration(); var configurationPath = HttpContext.Current.Server.MapPath(@"~\Models\Nhibernate\hibernate.cfg.xml"); configuration.Configure(configurationPath); var employeeConfigurationFile = HttpContext.Current.Server.MapPath(@"~\Models\Nhibernate\Employee.hbm.xml"); configuration.AddFile(employeeConfigurationFile); ISessionFactory sessionFactory = configuration.BuildSessionFactory(); return sessionFactory.OpenSession(); } } } Listing: Now we have our open session method ready its time to write controller code to fetch data from the database. Following is a code for that. using System; using System.Web.Mvc; using NHibernate; using NHibernate.Linq; using System.Linq; using NhibernateMVC.Models; namespace NhibernateMVC.Controllers { public class EmployeeController : Controller { public ActionResult Index() { using (ISession session = NHibertnateSession.OpenSession()) { var employees = session.Query().ToList(); return View(employees); } } } } Here you can see I have get a session via OpenSession method and then I have queried database for fetching employee database. Let’s create a new for this you can create this via right lick on view on above method.We are going to create a strongly typed view for this. Our listing screen is ready once you run project it will fetch data as following. Create/Add: Now its time to write add employee code. Following is a code I have written for that. Here I have used session.save method to save new employee. First method is for returning a blank view and another method with HttpPost attribute will save the data into the database. public ActionResult Create() { return View(); } [HttpPost] public ActionResult Create(Employee emplolyee) { try { using (ISession session = NHibertnateSession.OpenSession()) { using (ITransaction transaction = session.BeginTransaction()) { session.Save(emplolyee); transaction.Commit(); } } return RedirectToAction("Index"); } catch(Exception exception) { return View(); } } Now let’s create a create view strongly typed view via right clicking on view and add view. Once you run this application and click on create new it will load following screen. Edit/Update: Now let’s create a edit functionality with NHibernate and ASP.NET MVC. For that I have written two action result method once for loading edit view and another for save data. Following is a code for that. public ActionResult Edit(int id) { using (ISession session = NHibertnateSession.OpenSession()) { var employee = session.Get(id); return View(employee); } } [HttpPost] public ActionResult Edit(int id, Employee employee) { try { using (ISession session = NHibertnateSession.OpenSession()) { var employeetoUpdate = session.Get(id); employeetoUpdate.Designation = employee.Designation; employeetoUpdate.FirstName = employee.FirstName; employeetoUpdate.LastName = employee.LastName; using (ITransaction transaction = session.BeginTransaction()) { session.Save(employeetoUpdate); transaction.Commit(); } } return RedirectToAction("Index"); } catch { return View(); } } Here in first action result I have fetched existing employee via get method of NHibernate session and in second I have fetched and changed the current employee with update details. You can create view for this via right click –>add view like below. I have created a strongly typed view for edit. Once you run code it will look like following. Details: Now it’s time to create a detail view where user can see the employee detail. I have written following logic for details view. public ActionResult Details(int id) { using (ISession session = NHibertnateSession.OpenSession()) { var employee = session.Get(id); return View(employee); } } You can add view like following via right click on actionresult view. now once you run this in browser it will look like following. Delete: Now its time to write delete functionality code. Following code I have written for that. public ActionResult Delete(int id) { using (ISession session = NHibertnateSession.OpenSession()) { var employee = session.Get(id); return View(employee); } } [HttpPost] public ActionResult Delete(int id, Employee employee) { try { using (ISession session = NHibertnateSession.OpenSession()) { using (ITransaction transaction = session.BeginTransaction()) { session.Delete(employee); transaction.Commit(); } } return RedirectToAction("Index"); } catch(Exception exception) { return View(); } } Here in the above first action result will have the delete confirmation view and another will perform actual delete operation with session delete method. When you run into the browser it will look like following. That’s it. It’s very easy to have crud operation with NHibernate. Stay tuned for more.

If you work with large, gridded datasets, you should probably be using NetCDF, the Network Common Data Form from Unidata: NetCDF is a set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. Lots of high-end analysis software can be made to support NetCDF, and it is indispensable for working with gridded datasets that weigh in at tens of gigabytes or more. This brief post describes the easiest way to install the NetCDF libraries and the R ‘ncdf’ package on our favorite systems: CentOS, Ubuntu and Mac OSX. CentOS 6.x CentOS is the operating system of choice if you want a free, robust, open-source server to host your scientific analysis. It is basically an unbranded clone of Red Hat Enterprise Linux. The following instructions worked on CentOS 6.2. Installing System Libraries First, go to http://fedoraproject.org/wiki/EPEL and check for the latest version of the Extended Packages for Enterprise Linux (which contains NetCDF, HDF and many other useful packages). The latest version should be specified on this page. To download a local copy of EPEL and install NetCDF from it, just execute the following commands: sudo wget http://mirror.metrocast.net/fedora/epel/6/i386/epel-release-6-8.noarch.rpm sudo rpm -Uvh epel-release-6-8.noarch.rpm sudo yum --assumeyes install netcdf sudo yum --assumeyes install netcdf-devel Note that adding EPEL as a package archive in the second line does not automatically install all the packages in EPEL. We had to manually install netcdf and netcdf-dev. A list of other available packages is given at the EPEL wiki given above. Installing R Package ‘ncdf’ With the libraries in place, we can now install the ncdf package for our favorite statistical package — R. sudo wget http://cran.r-project.org/src/contrib/ncdf_1.6.6.tar.gz sudo R CMD INSTALL --configure-args="--with-netcdf-include=/usr/include --with-netcdf-lib=/usr/lib" ncdf_1.6.6.tar.gz Ubuntu 12.04 Ubuntu is easy to install and has a great user interface for linux systems. Ubuntu 12.0.4 is the most recent Long-Term-Stable release. Installing System Libraries The following instructions worked on Ubuntu 12.04 LTS. To install NetCDF libraries that allow reading, writing and manipulation, use apt-get, rather than downloading the source files and installing them yourself. To install, open a terminal and type: sudo apt-get install netcdf Installing R package ‘ncdf’ The base version of R on Ubuntu 12.04 slt is 2.14.1. Unfortunately, clicking the install button in RStudio and typing 'ncdf' will only work at the user level. The package will not be installed for all users or even show up in all of your RStudio projects. To install ncdf tools in the global library you must start up R as root and use the following command: install.packages(repos=c('http://cran.fhcrc.org/'),pkgs=c('ncdf'),lib="/usr/lib/R/site-library/") ‘http://cran.fhcrc.org/’ should be replaced by whichever CRAN mirror is closest to you. OSX 10.8.4 Macs run OSX, which is Unix based. The following instructions worked on OSX 10.8.4 — Mountain Lion. Installing System Libraries The absolute easiest way to install NetCDF on a mac requires Macports. Macports is a software package designed to make installing and compiling software easy. Macports .pkg and installation instructions are available here. Once Macports is installed, building and installing NetCDF libraries is a one-step job. sudo port install netcdf More details and instructions for installing Fortran and Python APIs are available here. Installing R Package ‘ncdf’ Having NetCDF command line tools is not required to use the ncdf R package. Simply download the package from CRAN (link), or by clicking on the “install packages” button in RStudio. This package allows reading, writing and manipulation of existing .nc files. However, the package’s ability to view the content of nc files before loading them into the R workspace is limited. For this reason, installing the NetCDF tools outlined in the first section of this post is extremely important. Command line tools such as “ncdump” are crucial to effectively working with NetCDF files.

ElasticSearch provides Java API, thus it executes all operations asynchronously by using client object.