1. Introduction

.
├── common
├── concourse
├── dist
├── docs
├── docs-sources
└── jenkins

In the common folder you can find all the Bash scripts containing the pipeline logic. These scripts are reused by both Concourse and Jenkins pipelines.

In the concourse folder you can find all the necessary scripts and setup to run Concourse demo.

In the dist folder you can find the packaged sources of the project. Since the package contains no tests or documentation it’s extremely small and can be used in the pipelines.

In the docs folder you have the whole generated documentation of the project.

In the docs-source folder you have the sources required to generate the documentation.

In the jenkins folder you can find all the necessary scripts and setup to run Jenkins demo.

This repository can be treated as a template for your pipeline. We provide some opinionated implementation that you can alter to suit your needs. The best approach to use it to build your production projects would be to download the Spring Cloud Pipelines repository as ZIP, then init a Git project there and modify it as you wish.

$ # pass the branch (e.g. master) or a particular tag (e.g. v1.0.0.RELEASE)
$ SC_PIPELINES_RELEASE=...
$ curl -LOk https://github.com/spring-cloud/spring-cloud-pipelines/archive/${SC_PIPELINES_RELEASE}.zip
$ unzip ${SC_PIPELINES_RELEASE}.zip
$ cd spring-cloud-pipelines-${SC_PIPELINES_RELEASE}
$ git init
$ # modify the pipelines to suit your needs
$ git add .
$ git commit -m "Initial commit"
$ git remote add origin ${YOUR_REPOSITORY_URL}
$ git push origin master

You can also clone the repository in case you would like to keep aligned with the changes in the upstream repository. In order not to have many merge conflicts it’s encouraged to use the custom folder hooks to override functions.

Let’s take a look at the flow of the opinionated pipeline

Figure 1.1. Flow in Concourse

Figure 1.2. Flow in Jenkins

We’ll first describe the overall concept behind the flow and then we’ll split it into pieces and describe every piece independently.

So we’re on the same page let’s define some common vocabulary. We discern 4 typical environments in terms of running the pipeline.

Unit tests - tests that are executed on the application during the build phase. No integrations with databases / HTTP server stubs etc. take place. Generally speaking your application should have plenty of these to have fast feedback if your features are working fine.

Integration tests - tests that are executed on the built application during the build phase. Integrations with in memory databases / HTTP server stubs take place. According to the test pyramid, in most cases you should have not too many of these kind of tests.

Smoke tests - tests that are executed on a deployed application. The concept of these tests is to check the crucial parts of your application are working properly. If you have 100 features in your application but you gain most money from e.g. 5 features then you could write smoke tests for those 5 features. As you can see we’re talking about smoke tests of an application, not of the whole system. In our understanding inside the opinionated pipeline, these tests are executed against an application that is surrounded with stubs.

End to end tests - tests that are executed on a system composing of multiple applications. The idea of these tests is to check if the tested feature works when the whole system is set up. Due to the fact that it takes a lot of time, effort, resources to maintain such an environment and that often those tests are unreliable (due to many different moving pieces like network database etc.) you should have a handful of those tests. Only for critical parts of your business. Since only production is the key verifier of whether your feature works, some companies don’t even want to do those and move directly to deployment to production. When your system contains KPI monitoring and alerting you can quickly react when your deployed application is not behaving properly.

Performance testing - tests executed on an application or set of applications to check if your system can handle big load of input. In case of our opinionated pipeline these tests could be executed either on test (against stubbed environment) or stage (against the whole system)

1.5.1 Testing against stubs

Before we go into details of the flow let’s take a look at the following example.

Figure 1.3. Two monolithic applications deployed for end to end testing

When having only a handful of applications, performing end to end testing is beneficial. From the operations perspective it’s maintainable for a finite number of deployed instances. From the developers perspective it’s nice to verify the whole flow in the system for a feature.

In case of microservices the scale starts to be a problem:

Figure 1.4. Many microservices deployed in different versions

The questions arise:

• Should I queue deployments of microservices on one testing environment or should I have an environment per microservice?

  • If I queue deployments people will have to wait for hours to have their tests ran - that can be a problem

• To remove that issue I can have an environment per microservice

  • Who will pay the bills (imagine 100 microservices - each having each own environment).
  • Who will support each of those environments?
  • Should we spawn a new environment each time we execute a new pipeline and then wrap it up or should we have them up and running for the whole day?

• In which versions should I deploy the dependent microservices - development or production versions?

  • If I have development versions then I can test my application against a feature that is not yet on production. That can lead to exceptions on production
  • If I test against production versions then I’ll never be able to test against a feature under development anytime before deployment to production.

One of the possibilities of tackling these problems is to…​ not do end to end tests.

Figure 1.5. Execute tests on a deployed microservice on stubbed dependencies

If we stub out all the dependencies of our application then most of the problems presented above disappear. There is no need to start and setup infrastructure required by the dependant microservices. That way the testing setup looks like this:

Figure 1.6. We’re testing microservices in isolation

Such an approach to testing and deployment gives the following benefits (thanks to the usage of Spring Cloud Contract):

• No need to deploy dependant services
• The stubs used for the tests ran on a deployed microservice are the same as those used during integration tests
• Those stubs have been tested against the application that produces them (check Spring Cloud Contract for more information)
• We don’t have many slow tests running on a deployed application - thus the pipeline gets executed much faster
• We don’t have to queue deployments - we’re testing in isolation thus pipelines don’t interfere with each other
• We don’t have to spawn virtual machines each time for deployment purposes

It brings however the following challenges:

• No end to end tests before production - you don’t have the full certainty that a feature is working
• First time the applications will talk in a real way will be on production

Like every solution it has its benefits and drawbacks. The opinionated pipeline allows you to configure whether you want to follow this flow or not.

Spring Cloud Pipelines uses Bash scripts extensively. Below you can find the list of software that needs to be installed on a CI server worker for the build to pass.

	Tip
	In the demo setup all of these libraries are already installed.

 apt-get -y install \
    bash \
    git \
    tar \
    zip \
    curl \
    ruby \
    wget \
    unzip \
    python \
    jq

	Important
	In the Jenkins case you will also need bats and shellcheck. They are not presented in the list since the installed versions by Linux distributions might be old. That’s why this project’s Gradle tasks will download latest versions of both libraries for you.

Each application can contain a file called sc-pipelines.yml with the following structure:

build:
  main_module: foo/bar
lowercaseEnvironmentName1:
    services:
        - type: service1Type
          name: service1Name
          coordinates: value
        - type: service2Type
          name: service2Name
          key: value
lowercaseEnvironmentName2:
    services:
        - type: service3Type
          name: service3Name
          coordinates: value
        - type: service4Type
          name: service4Name
          key: value

If you have a multi-module project, you should point to the folder, where your module that produces the fat jar lays. In the aforementioned example that module would be present under the foo/bar folder. If you have a single module project, then you don’t have to create this section.

For a given environment we declare a list of infrastructure services that we want to have deployed. Services have

# This file describes which services are required by this application
# in order for the smoke tests on the TEST environment and end to end tests
# on the STAGE environment to pass

# lowercase name of the environment
test:
  # list of required services
  services:
    - name: config-server
      type: broker
      broker: p-config-server
      plan: standard
      params:
        git:
          uri: https://github.com/ciberkleid/app-config
      useExisting: true
    - name: cloud-bus
      type: broker
      broker: cloudamqp
      plan: lemur
      useExisting: true
    - name: service-registry
      type: broker
      broker: p-service-registry
      plan: standard
      useExisting: true
    - name: circuit-breaker-dashboard
      type: broker
      broker: p-circuit-breaker-dashboard
      plan: standard
      useExisting: true
    - name: stubrunner
      type: stubrunner
      coordinates: io.pivotal:cloudfoundry-stub-runner-boot:0.0.1.M1
      manifestPath: sc-pipelines/manifest-stubrunner.yml

stage:
  services:
    - name: config-server
      type: broker
      broker: p-config-server
      plan: standard
      params:
        git:
          uri: https://github.com/ciberkleid/app-config
    - name: cloud-bus
      type: broker
      broker: cloudamqp
      plan: lemur
    - name: service-registry
      type: broker
      broker: p-service-registry
      plan: standard
    - name: circuit-breaker-dashboard
      type: broker
      broker: p-circuit-breaker-dashboard
      plan: standard

Spring Cloud Pipelines supports three main types of project setup - Single Project - Multi Module - Multi Project (aka mono repo)

A Single Project is a project that contains a single module that gets built and package into a single, executable artifact.

A Multi Module project is a project that contains a multiple modules. After building all modules, one gets packaged into a single, executable artifact. You have to point to that module in your pipeline descriptor.

A Multi Project is a project that contains multiple projects. Each of those projects can be in turn a Single Project or a Multi Module project. Spring Cloud Pipelines will assume that if there’s a PROJECT_NAME environment variable that corresponds to a folder with the same name in the root of the repository, that means that this is the project it should build. E.g for PROJECT_NAME=foo, if there’s a folder foo, then Spring Cloud Pipelines will treat the foo directory as the root of the foo project.