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Why CasperJS

Being a Java developer is kinda hard these days. Java may not be dead yet, but when keeping in sync with all the hipster JavaScript frameworks could make us feel a bit outside the playground. It’s even hard to list JavaScript frameworks with latest releases on one website.

In my current project, we are using AngularJS. It’a a nice abstraction of MV* pattern in frontend layer of any web application (we use Grails underneath). Here is a nice article with an 8-point Win List of Angular way of handling AJAX calls and updating the view. So it’s not only a funny new framework but a truly helper of keeping your code clean and neat.

But there is also another area when you can put helpful JS framework in place of plan-old-java one - functional tests. Especially when you are dealing with one page app with lots of asynchronous REST/JSON communication.

Selenium and Geb

In Java/JVM project the typical is to use Selenium with some wrapper like Geb. So you start your project, setup your CI-functional testing pipeline and… after 1 month of coding your tests stop working and being maintainable. The frameworks itselves are not bad, but the typical setup is so heavy and has so many points of failure that keeping it working in a real life project is really hard.

Here is my list of common myths about Selenium: * It allows you to record test scripts via handy GUI - maybe some static request/response sites. In modern web applications with asynchronous REST/JSON communication your tests must contain a lot of “waitFor” statements and you cannot automate where these should be included. * It allows you to test your web app against many browsers - don’t try to automate IE tests! You have to manually open your app in IE to see how it actually bahaves! * It integrates well with continuous integration servers like Jenkins - you have to setup Selenium Grid on server with X installed to run tests on Chrome or Firefox and a Windows server for IE. And the headless HtmlUnit driver lacks a lot of JS support.

So I decided to try something different and introduce a bit of JavaScript tooling in our project by using CasperJS.

Introduction

CasperJS is simple but powerful navigation scripting & testing utility for PhantomJS - scritable headless WebKit (which is an rendering engine used by Safari and Chrome). In short - CasperJS allows you to navigate and make assertions about web pages as they’d been rendered in Google Chrome. It is enough for me to automate the functional tests of my application.

If you want a gentle introduction to the world of CasperJS I suggest you to read: * Official website, especially installation guide and API * Introductionary article from CasperJS creator Nicolas Perriault * Highlevel testing with CasperJS by Kevin van Zonneveld * grails-angular-scaffolding plugin by Rob Fletcher with some working CasperJS tests

Full example

I run my test suite via following script:

casperjs test --direct --log-level=debug --testhost=localhost:8080 --includes=test/casper/includes/casper-angular.coffee,test/casper/includes/pages.coffee test/casper/specs/

casper-angular.coffe

casper.test.on "fail", (failure) ->
    casper.capture(screenshot)

testhost   = casper.cli.get "testhost"
screenshot = 'test-fail.png'

casper
    .log("Using testhost: #{testhost}", "info")
    .log("Using screenshot: #{screenshot}", "info")

casper.waitUntilVisible = (selector, message, callback) ->
    @waitFor ->
        @visible selector
    , callback, (timeout) ->
        @log("Selector [#{selector}] not visible, failing")
        withParentSelector selector, (parent) ->
            casper.log("Output of parent selector [#{parent}]")
            casper.debugHTML(parent)
        @echo message, "RED_BAR"
        @capture(screenshot)
        @test.fail(f("Wait timeout occured (%dms)", timeout))

withParentSelector = (selector, callback) ->
    if selector.lastIndexOf(" ") > 0
       parent = selector[0..selector.lastIndexOf(" ")-1]
       callback(parent)

Sample pages.coffee:

x = require('casper').selectXPath

class EditDocumentPage

    assertAt: ->
        casper.test.assertSelectorExists("div.customerAccountInfo", 'at EditDocumentPage')

    templatesTreeFirstCategory: 'ul.tree li label'
    templatesTreeFirstTemplate: 'ul.tree li a'
    closePreview: '.closePreview a'
    smallPreview: '.smallPreviewContent img'
    bigPreview: 'img.previewImage'
    confirmDelete: x("//div[@class='modal-footer']/a[1]")

casper.editDocument = new EditDocumentPage()

End a test script:

testhost = casper.cli.get "testhost" or 'localhost:8080'

casper.start "http://#{testhost}/app", ->
    @test.assertHttpStatus 302
    @test.assertUrlMatch /\/fakeLogin/, 'auto login'
    @test.assert @visible('input#Create'), 'mock login button'
    @click 'input#Create'

casper.then ->
    @test.assertUrlMatch /document#\/edit/, 'new document'
    @editDocument.assertAt()
    @waitUntilVisible @editDocument.templatesTreeFirstCategory, 'template categories not visible', ->
        @click @editDocument.templatesTreeFirstCategory
        @waitUntilVisible @editDocument.templatesTreeFirstTemplate, 'template not visible', ->
            @click @editDocument.templatesTreeFirstTemplate

casper.then ->
    @waitUntilVisible @editDocument.smallPreview, 'small preview not visible', ->
        # could be dblclick / whatever
        @mouseEvent('click', @editDocument.smallPreview)

casper.then ->
    @waitUntilVisible @editDocument.bigPreview, 'big preview should be visible', ->
        @test.assertEvalEquals ->
            $('.pageCounter').text()
        , '1/1', 'page counter should be visible'
        @click @editDocument.closePreview

casper.then ->
    @click 'button.cancel'
    @waitUntilVisible '.modal-footer', 'delete confirmation not visible', ->
        @click @editDocument.confirmDelete

casper.run ->
    @test.done()

Here is a list of CasperJS features/caveats used here:

• Using CoffeeScript is a huge win for your test code to look neat
• When using casper test command, beware of different (than above articles) logging setup. You can pass --direct --log-level=debug from commandline for best results. Logging is essential here since Phantom often exists without any error and you do want to know what just happened.
• Extract your helper code into separate files and include them by using --includes switch.
• When passing server URL as a commandline switch remember that in CoffeeScript variables are not visible between multiple source files (unless getting them via window object)
• It’s good to override standard waitUntilVisible with capting a screenshot and making a proper log statement. In my version I also look for a parent selector and debugHTML the content of it - great for debugging what is actually rendered by the browser.
• Selenium and Geb have a nice concept of Page Objects - an abstract models of pages rendered by your application. Using CoffeeScript you can write your own classes, bind selectors to properties and use then in your code script. Assigning the objects to casper instance will end up with quite nice syntax like @editDocument.assertAt().
• There is some issue with CSS :first and :last selectors. I cannot get them working (but maybe I’m doing something wrong?). But in CasperJS you can also use XPath selectors which are fine for matching n-th child of some element (x("//div[@class='modal-footer']/a[1]")).
  Update: :first and :last are not CSS3 selectors, but JQuery ones. Here is a list of CSS3 selectors, all of these are supported by CasperJS. So you can use nth-child(1) is this case. Thanks Andy and Nicolas for the comments!

Working with CasperJS can lead you to a few hour stall, but after getting things working you have a new, cool tool in your box!

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

• nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

• pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

• implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

• Fliptop is happy with storm - see their presentation here

• topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

• storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

• nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

• pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

• implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

• Fliptop is happy with storm - see their presentation here

• topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

• storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.