All posts by Tommy Tynjä

Twitter: @tommysdk

Next week is conference week!

Next week will be a conference heavy one in Stockholm! The inaugural CoDe Continuous Delivery & DevOps conference takes place on June 2nd while Agila Sverige (Agile Sweden) occupies June 3rd and 4th. Both conferences target agile software development practices and Diabol is very well represented on these events. We will have one speaker on each conference presenting talks related to Continuous Delivery.

Marcus Philip will present “Continuous Delivery for databases” on the CoDe conference while Tommy Tynjä will give his talk “Next Generation Continuous Delivery” on Agila Sverige where he takes a look on the software of tomorrow can be built and delivered.

So if you’re interested in learning more about Continuous Delivery and what we do, don’t hesitate to come by!

See you on a conference very soon!

AWS Cloudformation introduction

AWS Cloudformation is a concept for modelling and setting up your Amazon Web Services resources in an automatic and unified way. You create templates that describe your AWS resoruces, e.g. EC2 instances, EBS volumes, load balancers etc. and Cloudformation takes care of all provisioning and configuration of those resources. When Cloudformation provisiones resources, they are grouped into something called a stack. A stack is typically represented by one template.

The templates are specified in json, which allows you to easily version control your templates describing your AWS infrastrucutre along side your application code.

Cloudformation fits perfectly in Continuous Delivery ways of working, where the whole AWS infrastructure can be automatically setup and maintained by a delivery pipeline. There is no need for a human to provision instances, setting up security groups, DNS record sets etc, as long as the Cloudformation templates are developed and maintained properly.

You use the AWS CLI to execute the Cloudformation operations, e.g. to create or delete a stack. What we’ve done at my current client is to put the AWS CLI calls into certain bash scripts, which are version controlled along side the templates. This allows us to not having to remember all the options and arguments necessary to perform the Cloudformation operations. We can also use those scripts both on developer workstations and in the delivery pipeline.

A drawback of Cloudformation is that not all features of AWS are available through that interface, which might force you to create workarounds depending on your needs. For instance Cloudformation does not currently support the creation of private DNS hosted zones within a Virtual Private Cloud. We solved this by using the AWS CLI to create that private DNS hosted zone in the bash script responsible for setting up our DNS configuration, prior to performing the actual Cloudformation operation which makes use of that private DNS hosted zone.

As Cloudformation is a superb way for setting up resources in AWS, in contrast of managing those resources manually e.g. through the web UI, you can actually enforce restrictions on your account so that resources only can be created through Cloudformation. This is something that we currently use at my current client for our production environment setup to assure that the proper ways of workings are followed.

I’ve created a basic Cloudformation template example which can be found here.

Tommy Tynjä
@tommysdk

Jfokus 2015 main takeaways

The biggest Java conference in Sweden, Jfokus, wrapped up a three day conference on Wednesday. This was my fifth consecutive one and I thought it was the best in recent years. In this blog post I’ll summarize my main takeaways.

During the first day, the tutorial day, I truly enjoyed Ken Sipe from Mesosphere and his talk “Docker at Production Scale”. It was supposed to be a tutorial but it wasn’t focused on one subject, which one might expect from the title. The talk was divided in several parts where he first started talking about infrastructure in general, what’s problematic in today’s datacenters regarding resource consumption etc. He then gave a good intro to Docker, where even I who have been using Docker on a day-to-day basis during the last two months learned a couple of new things. The last part of the talk focused on Apache Mesos, which problems it solves and how. He briefly also touched on subjects such as Service Discovery. For me, the main takeaway from this talk is that the way we structure our datacenters and our applications are definitely changing and that there is a lot of exciting technologies taking form in this area at this point of time.

Christian Heilmann held an interesting opening keynote where he talked a lot about mobile apps and their profitability. He pinpointed that many think apps are sure way to make good money, but in reality, that is not the case. Very few apps out of all apps in the marketplace are actually that profitable. Your company will have a greater chance of making money out of apps if targeting the enterprise customers instead of individuals. Enterprises are constantly looking for ways for their employees to be more efficient and certain apps could possible help them achieve that. Individuals are also very conservative now days when it comes to installing new apps. Most people just go with the apps they have and install one or no new app per month. He had some interesting figures backing up those statements.

A great talk was “Thinking fast and slow in software development” by Daniel Bryant. His talk was based on the book Thinking fast and slow by Daniel Kahneman, but applied to software development. One good quote from this talk was “look for actual problems instead of solutions”. As developers we tend to start elaborating solutions instead of trying to understand the actual problem. Are we really understanding what the actual problem is? Or do we just think we know what the problem is when in fact do not? He also mentioned that some of the most common factors for failures in software projects (source IEEE) is poor communication among customers, developers and users, the use of immature technology and sloppy development practices. These are things that we definitely could do better at in our industry! There is no reason why we should accept this happening. We should all care more about software craftsmanship.

Jeremy Deane held an interesting presetnation on concurrent processing techniques using e.g. plain java.util.concurrent techniques and actors. He had a few good tips on how to decouple a web service which under the hood depend on a slow responding third party web service by making the communication in between them asynchronous. All of his examples can be found in his GitHub repo.

As usual at Jfokus, Arun Gupta presented what’s to come in the next Java EE version, in this case Java EE 8. Focus will be to improve the new features introduced in Java EE 7 to make them more usable. Support for HTTP 2 and HTML 5 will be added to help those technologies gain traction. Servlet 4.0 will be introduced as well as MVC 1.0. JAX-RS, JMS and JSON APIs will also get facelifts. The Batch processing API will not be tied to Java EE only but will in the future be available in conjunction with Java SE as well. Obviously Java EE 8 will contain improvements for the new language features introduced in Java 8, such as functional interfaces etc.

The second day kicked off with a talk by Jez Humble called “21st Century Software Delivery”. He really put emphasis on how important continuous delivery is along with continuous experimentation. As an industry, we are bad at experimenting. We try to build this big thing that we think our customers want (but which we don’t really know). Instead we should try out the thing we’re building in a small scale first, not only as a protype but a fully functional, nice and shiny feature that the customers will appreciate, but with very limited scope. We can then measure how well this experiment turns out by conducting A/B testing and comparing the analytics for this feature. Should we continue to build on top of that experiment or not? You wouldn’t go out to build a very large complex building without building it in small scale first and to assure that techniques and functionality is matching the expections. We should do that in software development as well.

Jez Humbles second talk, on automated acceptance testing was valuable. For me, who am passionated about delivering quality software, there wasn’t much new in his content but it was still refreshing to get reminded on why we actually want to do testing in a certain way, even though you might do it already. Most people have probably encountered flaky tests. Those tests that for one reason or another goes red with or without an apparant reason, just to go green in a build later containing a totally unrelated code change. Flaky tests leads to less confidence and trust for the tests which eventually leads to people ignoring them or not even running them at all. One interesting technique to battle flaky tests is to move known flaky tests to separate suite, which is to be run separately (preferrably after) your actual suite. Then, you can remain confident about your test suite always being reliable. When a flaky test has become stable again, it should move back into the main suite. It is important to remember that test suite shouldn’t be static. Tests should come and go and be moved appropriately alongside the codebase development. Do also not be afraid to actually delete tests that no longer brings value. We are horrible at identifying and actually removing those tests.

In between talks I also enjoyed meeting a lot of people I’ve worked with in various projects throughout my career. Always a real pleasure to catch up with old friends as well as getting to know a few new ones as well. I also got a brief chat with Jez Humble regarding continuous delivery and strategies for balancing between maintenance and development of new features in highly autonomous teams with end-to-end responsibility, a discussion which was very much appreciated. Even though the vast majority of sessions were awesome, I almost wish there would have been some more breaks as well so I could have had the opportunity for even more socializing.

All in all, a great event. Thanks to everyone involved in organizing this event and to all the speakers and attendees. See you in 2016!

Tommy Tynjä
@tommysdk

Docker on Mac OS X using CoreOS

Docker is on everybodys lips these days. It’s an open-source software project that leverages from Linux kernel resource isolation to allow independent so called containers to run within a single Linux instance, thus avoiding overhead of virtual machines/hypervisors while still offering full container isolation. Docker is therefore a feasible approach for automated and scalable software deployments.

Many developers (including myself) are nowdays developing on Mac OS X, which is not Linux. It is however possible to use Docker on OS X but one should be aware of what this implies. As OS X is not based on Linux and therefore lacks the kernel features which would allow you to run Docker containers natively on your system, you still need to have a Linux host somewhere. Docker provides you with something called boot2docker which essentially is a Linux distribution (based on Tiny Core Linux) built specifically for running Docker containers.

In case you want to use a more general Linux VM, if you want to use it for other tasks than just running Docker containers for instance, an alternative for boot2docker is to use CoreOS as your Docker host VM. CoreOS is quite lightweight (but is obviously bigger than boot2docker) and comes bundled with Docker. Setting up a fresh CoreOS instance to run with Vagrant is easy:

mkdir ~/coreos
cd ~/coreos
echo 'VAGRANTFILE_API_VERSION = "2"
Vagrant.configure(VAGRANTFILE_API_VERSION) do |config|
config.vm.box = "coreos"
config.vm.box_url = "http://storage.core-os.net/coreos/amd64-generic/dev-channel/coreos_production_vagrant.box"
config.vm.network "private_network",
ip: "192.168.0.100"
end' > Vagrantfile
vagrant up
vagrant ssh
core@localhost ~ $ docker --version
Docker version 0.9.0, build 2b3fdf2

Now you have a CoreOS Linux VM available which you can use as your Docker host.

If you want to mount a directory to be shared between OS X and your CoreOS host, just add the following line with the proper existent paths in the Vagrantfile:
config.vm.synced_folder "/Users/tommy/share", "/home/core/share", id: "core", :nfs => true, :mount_options => ['nolock,vers=3,udp']

Happy hacking!

Past and upcoming events

We have been unusually busy at Diabol during the past few months, speaking at various software conferences on a variety of Continuous Delivery related topics. We enjoy sharing our experiences and to meet new and familiar faces to discuss topics that we’re passionate about, such as Continuous Delivery, DevOps and automation.

We have also arranged a Continuous Delivery seminar of our own, which attracted 20 top IT-management professionals from various well known Swedish enterprises. The seminar was a great success, with interesting presentations and good discussions among the attendees.

The next upcoming event where we will be presenting is the first edition of the Continuous Delivery Conference in Bussum, Netherlands on December 4th. Andreas Rehn will present “From dinosaur to unicorn in 12 months: how to push continuous delivery maturity to the next level”.

Past events where we have been presenting lately, together with video recording or presentation material:

If you plan to attend a conference where we’re speaking or just attending, come by and say hi! We look forward talking to you!

Diabol proudly presents Continuous Delivery seminar

Diabol is proud to arrange a seminar completely dedicated to Continuous Delivery, to be kicked off in less than a week on September 30th in Stockholm. This event is an exclusive invite-only event where the top IT-management attendees will learn how Continuous Delivery can help their organization in becoming more efficient in developing and delivering software. Our hand-picked speakers will present how Continuous Delivery and delivery process automation have changed their respective organizations in becoming lean business machines. Instead of dealing with painful manual repetitive tasks which are commonly associated with a traditional release and deploy process, their employees can now focus on innovation and to create business value.

Event speakers:

  • Stefan Berg, former CIO at Com Hem will present: “From average to top performer in less than a year!”
  • Tomas Riha, Agile Architect at Volvo Group Telematics will present: “From hobby project to Continuous Delivery as a Service for the entire organization”

Make sure you keep visiting this channel for more news on Continuous Delivery!

Feature switches in practice

Feature switches (or feature flags, toggles etc) is a programming technique which has gained a lot of attention through the concepts of Trunk Based Development and Continuous Delivery. Feature switches allows you to shield not yet production ready code while still being committed to mainline in version control. This allows you to work on development tasks on mainline and to continuously integrate your code while avoiding the burdens of branching. Another useful benefit is that you can decide which functionality to run in production by switching functionality on/off. The best thing is that this technique is very easy to implement, you basically just need to start doing it! In this blog post I’ll show you how easy it is to do this in Java.

In my current project we are integrating to a third party service which our system depends heavily on. While our system will continue to work if that third party service becomes unavailable, it still means a loss in revenue to the business. Therefore we want to be able to monitor this integration point closely and provide mechanisms to be able to troubleshoot it efficiently. As the communication between these systems are web service based through SOAP, we found it very useful to be able to log the entire payloads sent and received between the two systems. This feature is an ideal candidate for feature switching.

I implemented a feature which allows us to decide in runtime whether we should log every SOAP message sent and received to a file system. This would also happen asynchronously to not affect application throughput too much. This feature would be switched off in production by default, but would allow us to turn it on if we needed to troubleshoot integration failures.

The most basic feature switch to implement would just be a simple if-statement:

boolean xmlLogFeatureIsEnabled = false;
if (xmlLogFeatureIsEnabled) {
	logToFile(xml);
}

But instead of hardcoding the feature switch state, we want this to be dynamically evaluated so we can change the behavior on a running system without the need for restarts or too much manual labor. To be able to do this we use a small framework called Togglz, which allows you to very easily create feature switches which you then can manage in runtime.

First, we create a feature definition enumeration which implements org.togglz.core.Feature:

public enum FeatureDefinition implements Feature {

    @Label("Log XML to file")
    LOG_XML_TO_FILE;

    public boolean isActive() {
        return FeatureContext.getFeatureManager().isActive(this);
    }
}

Then, we implement org.togglz.core.manager.TogglzConfig which will keep track of the feature states:

@ApplicationScoped
public class FeatureConfiguration implements TogglzConfig {

    @Resource
    private Datasource datasource;

    public Class<? extends Feature> getFeatureClass() {
        return FeatureDefinition.class;
    }

    public StateRepository getStateRepository() {
        return new CachingStateRepository(new JDBCStateRepository(datasource), 10, TimeUnit.MINUTES);
    }

    public UserProvider getUserProvider() {
        return new NoOpUserProvider();
    }
}

We use dependency injection in our project, so this allows us to easily inject a datasource in our feature configuration which Togglz can use to store the feature states in. We then apply a 10 minute cache for the feature state reload so that Togglz won’t have to look up the state in the database for each time a feature state is evaluated. Please note that you might want to implement the configuration a bit more robust than in the example above. When we want to switch a feature on/off it is merely a matter of updating a database column value.

At last, we just change the if-statement encapsulating the feature method call to:

if (FeatureDefinition.LOG_XML_TO_FILE.isActive()) {
    logToFile(xml);
}

And that’s it! This is all we need to do to be able to dynamically switch features on/off in a running Java system. This technique is very useful when exercising Continuous Delivery ways of working where each commit is a potential production release. As you can see, feature switches allows you to commit your changes to version control without necessarily expose them to your end users.

To see this in action, feel free to check out my Togglz example project which uses a simple servlet to demonstrate the behavior.

 

Tommy Tynjä
@tommysdk

Slimmed down immutable infrastructure

Last weekend we had a hackathon at Diabol. The topics somehow related to DevOps and Continuous Delivery. My group of four focused on slim microservices with immutable infrastructure. Since we believe in automated delivery pipelines for software development and infrastructure setup, the next natural step would be to merge these two together. Ideally, one would produce a machine image that contains everything needed to run the current application. The servers would be immutable, since we don’t want anyone doing manual changes to a running environment. Rather, the changes should be checked in to version control and a new server would be created based on the automated build pipeline for the infrastructure.

The problem with traditional machine images running on e.g. VMware or Amazon is that they tend to very large in size, a couple of gigabytes is not an unusual size. Images of that size become cumbersome to work with as they take a long time to create and ship over a network. Therefore it is desirable to keep server images as small as possible, especially since you might create and tear down servers ad-hoc for e.g. test purposes in your delivery pipeline. Linux is a very common server operating system but many Linux distributions are shipped with features that we are very unlikely to ever be using on a server, such as C compilers or utility programs. But since we adopt immutable servers, we don’t even need things as editors, man pages or even ssh!

Docker is an interesting solution for slimmed down infrastructure and full stack machine images which we evaluated during the hackathon. After getting our hands dirty after a couple of hours, we were quite pleased with its capabilities. We’ll definitely keep it on our radar and continue with our evaluation of it.

Since we’re mostly operating in the Java space, I also spent some time looking at how we could save some size on our machine images by potentially slimming down the JVM. Since a delivery pipeline will be triggered several times a day to deploy, test etc, every megabyte saved will increase the pipeline throughput. But why should you slim down the JVM? Well the JVM also contains features (or libraries) that are highly unlikely to ever be used on a server, such as audio, the awt and Swing UI frameworks, JavaFX, fonts, cursor images etc. The standard installation of the Java 8 JRE is around 150 MB. It didn’t take long to shave off a third of that size by removing libraries such as the aforementioned ones. Unfortunately the core library of Java, rt.jar is 66 MB of size, which is a constraint for the minimal possible size of a working JVM (unless you start removing the class files inside it too). Without too much work, I was able to safely remove a third of the size of the standard JRE installation, landing on a bit under 100 MB of size and still run our application. Although this practice might not be suitable for production use of technical or even legal reasons, it’s still interesting to see how much we typically install on our severs although it’ll never be used. The much anticipated project Jigsaw which will introduce modularity to Java SE has been postponed several times. Hopefully it can be incorporated into Java 9, enabling us to decide which modules we actually want to use for our particular use case.

Our conclusion for the time spent on this topic during the hackathon is that Docker is an interesting alternative to traditional machine image solutions, which not only allows, but also encourages slim servers and immutable infrastructure.

Tommy Tynjä
@tommysdk

Test categorization in deployment pipelines

Have you ever gotten tired of waiting for those long running tests in CI to finish so you can get feedback on your latest code change? Chances are that you have. A common problem is that test suites tend to grow too large, making the feedback loop an enemy instead of a companion. This is a problem when building devilvery pipelines for Continuous Delivery, but also for more traditional approaches to software development. A solution to this problem is to divide your test suite into separate categories, or stages, where tests are grouped according to similarity or type. The categories can then be arranged to execute the quickest and those most likely to fail first, to enable faster feedback to the developers.

An example of a logical grouping of tests in a deployment pipeline:

Commit stage:
* Unit tests
* Component smoke tests
These tests execute fast and will be executed by the developers before commiting changes into version control.

Component tests:
* Component tests
* Integration tests
These tests are to be run in CI and can be further categorized so that e.g. component tests that are most likely to catch failures will execute first, before more thorough testing.

End user tests:
* Functional tests
* User acceptance tests
* Usability/exploratory testing

As development continues, it is important to maintain these test categories so that the feedback loop can be kept as optimal as possible. This might involve moving tests between categories, further splitting up test suites or even grouping categories that might be able to run in parallel.

How is this done in practice? You’ve probably encountered code bases where all these different kind of tests, unit, integration, user acceptance tests have all been scattered throughout the same test source tree. In the Java world, Maven is a commonly used build tool. Generally, its model supports running unit and integration tests separately out of the box, but it still expects tests to be in the same structure, differentiated only with a naming convention. This isn’t practical if you have hundreds or thousands of tests for a single component (or Maven module). To have a maintainable test structure and make effective use of test categorization, splitting up tests in different source trees is desirable, for example such as:

src/test – unit tests
src/test-integration – integration tests
src/test-acceptance – acceptance tests

Gradle is a build tool which makes it easy to leverage from this kind of test categorization. Changing build tool is something that might not be practically possible for many reasons, but it is fully possibile to leverage from Gradles capabilities from your existing build tool. You want to use the right tool for the job, right? Gradle is an excellent tool for this kind of job.

Gradle makes use of source sets to define what source code tree is production code and which is e.g. test code. You can easily define your own source sets, which is something you can use to categorize your tests.

Defining the test categories in the example above can be done in your build.gradle such as:

sourceSets {
  main {
    java {
      srcDir 'src/main/java'
    }
    resources {
      srcDir 'src/main/resources'
    }
  }
  test {
    java {
      srcDir 'src/test/java'
    }
    resources {
      srcDir 'src/test/resources'
    }
  }
  integrationTest {
    java {
      srcDir 'src/test-integration/java'
    }
    resources {
      srcDir 'src/test-integration/resources'
    }
    compileClasspath += sourceSets.main.runtimeClasspath
  }
  acceptanceTest {
    java {
      srcDir 'src/test-acceptance/java'
    }
    resources {
      srcDir 'src/test-acceptance/resources'
    }
    compileClasspath += sourceSets.main.runtimeClasspath
  }
}

To be able to run the different test suites, setup a Gradle task for each test category as appropriate for your component, such as:

task integrationTest(type: Test) {
  description = "Runs integration tests"
  testClassesDir = sourceSets.integrationTest.output.classesDir
  classpath += sourceSets.test.runtimeClasspath + sourceSets.integrationTest.runtimeClasspath
  useJUnit()
  testLogging {
    events "passed", "skipped", "failed"
  }
}

task acceptanceTest(type: Test) {
  description = "Runs acceptance tests"
  testClassesDir = sourceSets.acceptanceTest.output.classesDir
  classpath += sourceSets.test.runtimeClasspath + sourceSets.acceptanceTest.runtimeClasspath
  useJUnit()
  testLogging {
    events "passed", "skipped", "failed"
  }
}

test {
  useJUnit()
  testLogging {
    events "passed", "skipped", "failed"
  }
}

Unit tests in src/test will be run by default. To run integration-tests located in src/test-integration, invoke the integrationTest task by executing “gradle integrationTest”. To run acceptance tests located in src/test-acceptance, invoke the acceptanceTest task by executing “gradle acceptanceTest”. These commands can then be used to tailor your test suite execution throughout your deployment pipeline.

A full build.gradle example file that shows how to setup test categories as described above can be found on GitHub.

The above example shows how tests can be logically grouped to avoid waiting for that one big test suite to run for hours, just to report a test failure on a simple test case that should have been reported instantly during the test execution phase.


Tommy Tynjä
@tommysdk

Continuous Delivery testing levels

This blog post is a summary of thoughts discussed between me, Andreas Rehn (@andreasrehn) and Patrik Boström (@patbos).

A key part of Continuous Delivery is automated testing and even the simplest delivery pipeline will consist of several different testing stages. There is unit tests, integration tests, user acceptance tests etc. But what defines the different test levels?

We realized that we often mean different things regarding each testing level and this was especially true when talking about integration tests. For me, integration tests can be tests that test the integrations within one component, e.g. testing an internal API or integration between a couple of business objects interacting with each other, a database etc. This is how the Arquillian (an integration testing framework for Java) community is referring to integration testing. Another kind of integration tests are those testing an actual integration with e.g. a third party web service. What we’ve been referring to when talking about integration tests in the context of Continuous Delivery, is testing a component in a fully integrated environment and testing the component from the outside, rather than the inside, so called black box testing. These are often more functional by nature.

We came to the conclusion that we would like to redefine the terminology for the latter type of integration testing to avoid confusion and fuzziness. Since these kind of tests are more functional tests, testing the behavior and flows of the component, we decided to start calling these types of tests component tests instead. That leaves us with the following levels of testing in the early stages of a delivery pipeline:

* Unit tests
* Smoke tests
* Component tests
* Integration tests

When should you run the different tests? You want feedback as soon as possible but you don’t want to have a too big test suite too early in the pipeline as this could severely delay the feedback. It’s inefficient to force developers to run a five+ minute build before each commit. Therefore you should divide your test suite into different phases. The first phases typically includes unit tests and smoke tests. The second phase will run the component tests in a fully integrated production like environment. The third phase will execute integration tests, e.g. with Arquillian. Certain integration tests will not need to be run in a fully integrated environment, depending on the context, but there are definitely benefits of running all of them in such an environment. These tests can also test integrations towards databases, third party dependencies etc.

To be fully confident in the quality of your releases you need to make use of these different tests as they all fulfill a specific purpose. It is worth considering though, in what phase certain tests should be placed as you don’t want rerun tests in different phases. If you’re validating an algorithm, the unit test phase is probably the most appropriate phase, while testing your database queries fits well into the integration test phase and user interface and functional tests as component tests. This raises the question, how much should you actually test? As that is a topic on its own, we’ll leave that for another time.

Conclusion:
Unit tests – testing atomic pieces of code on their own. Typically tested with a unit testing framework
Integration tests – putting atomic pieces together to moving parts, testing integration points, internal APIs, database interactions etc. Typically tested with Arquillian and/or with a unit testing framework along with mocks and stubs.
Component tests – functional tests of the component, so called black box testing. Often tested with Selenium, acceptance testing frameworks or through web service calls, depending on the component. Also a subject for testing with Arquillian.

Tommy Tynjä
@tommysdk