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Unified Cloud automated deployment and testing

The unified Cloud test automation toolchain can be used to deploy and run both Crowbar and Ardana OpenStack Cloud flavors on top of an OpenStack virtual cloud infrastructure, or one of the available Cloud bare-metal infrastructure clusters, with sources and SLE disk images coming from the internal build service.

The hosting OpenStack cloud platforms that are currently set up for the toolchain are the the engineering cloud and the SUSE cloud.

The toolchain is largely implemented by ansible playbooks located in this repository, but the process of deploying Ardana and Crowbar clouds is also automated via Jenkins pipeline jobs running in the SUSE Jenkins service.

The easiest way to experiment with an automated Cloud deployment is by triggering a customized Cloud Jenkins job build. Alternatively, the toolchain can be used to deploy and test a Crowbar or Ardana cloud independently from Jenkins, by running the ansible playbooks locally, from any host that has access to the target cloud or bare-metal infrastructure.

Deploying and testing a SUSE OpenStack Cloud through Jenkins

The Jenkins pipeline jobs called openstack-ardana and openstack-crowbar defined in the automation git repo can be used to deploy a virtual Ardana or Crowbar cloud environment in the engineering cloud or the SUSE cloud or to deploy a bare-metal Ardana environment on one of the Ardana QA hardware clusters. The deployment can then be accessed via the IP address allocated to the admin/deployer node and used for debugging.

Prerequisites

To be able to use the Jenkins job and login to a deployed environment, the following are needed:

  • the cloud team login credentials for https://ci.nue.suse.com (ask in the cloud-team RocketChat channel if you don't know what they are, and someone will PM you)
  • your public ssh key added to the list of keys(via normal github pull requests)

Deploying a Jenkins Cloud environment

An Ardana deployment can be started by launching a custom openstack-ardana build. Similarly, a Crowbar deployment can be started by launching a custom openstack-crowbar build. The available parameters should be self-descriptive, but the Customized deployments section will provide more information on the provided options. Here are some best practices:

  • For a virtual cloud deployment:
    • use your Rocket/irc nickname as cloud_env. If you need to deploy several Cloud setups, use a unique cloud_env value for each one
    • use the os_cloud parameter to choose between the available OpenStack cloud platforms that can host the virtual environment. Currently only the engineering cloud (engcloud) and the SUSE cloud (susecloud) are available
    • by default, the OpenStack virtual resources from the shared cloud project will be used. To choose another project, use the os_project_name parameter
  • For a bare-metal cloud deployment, use one of the cloud_env values starting with qe (e.g qe101, qe102) that are reserved for QA bare-metal deployments (ask in the cloud-qe RocketChat channel for one that is available).
  • The reserve_env option should not be selected unless the cloud_env value corresponds to one of the shared environments.
  • Select a cleanup value to decide what happens with a virtual cloud deployment after the job completes
  • To test a different software media, use another cloudsource value. The available values and their meaning are documented here
  • Select one of the supported deployment scenarios (i.e. the way services are grouped together and deployed onto nodes and/or HA clusters, number of nodes in each cluster, etc) by choosing one of the available scenario_name values and changing the values of related parameters (e.g. clm_model, controllers, computes etc.) to control the number of nodes for each service group. standard and entry-scale-kvm are the scenarios most widely used for Ardana. Note that only a subset of the available scenarios work with bare-metal environments (entry-scale-kvm and mid-scale-kvm currently), and the number of nodes is limited to the nodes available in the target bare-metal cluster.
  • SES is the recommended storage back-end for Glance, Cinder and Nova. Selecting ses_enabled will also deploy a one-node virtual SES cluster to be used as a storage back-end by your cloud environment (or will use the shared bare-metal SES cluster, for QA bare-metal deployments). Note that the SES back-end isn't available for Crowbar versions 7 and 8 yet.
  • To test a custom automation repository, push to your fork and adjust the git_automation_repo and git_automation_branch parameter values

The IP address allocated to the admin/deployer node will be available in several places:

  • the Jenkins job build name will be updated automatically to include it, when it becomes available

  • it will be printed in the Jenkins job log in several places, e.g.:

      10:49:22 [Prepare virtual cloud] ******************************************************************************
  10:49:22 [Prepare virtual cloud] ** The admin for the 'cloud-ardana-ci-slot1' virtual environment is reachable at:
  10:49:22 [Prepare virtual cloud] **
  10:49:22 [Prepare virtual cloud] **        ssh root@10.86.1.146
  10:49:22 [Prepare virtual cloud] **
  10:49:22 [Prepare virtual cloud] ******************************************************************************

  • for virtual cloud deployments, it will also be available as output as the variable admin-floating-ip in the Heat stack instantiated in the engineering cloud or the SUSE cloud

The environment can then accessed by logging in as root or ardana and using the floating IP.

IMPORTANT:

  • if you are unable to access the environment via SSH, it is probably because you didn't add your public SSH key to the repository (see Prerequisites).

  • PLEASE DELETE YOUR VIRTUAL CLOUD ENVIRONMENT AFTER YOU ARE DONE otherwise we'll run into quota limits in our project. Instructions on how to clean up the environment will be printed in the Jenkins job log at the end of the run, e.g.:

    13:34:20 ****************************************************************************** 13:34:20 ** The admin for the 'alan-turing' virtual environment is reachable at: 13:34:20 ** 13:34:20 ** ssh root@10.86.1.235 13:34:20 ** 13:34:20 ** Please delete the 'alan-turing-cloud' stack when you're done, 13:34:20 ** by using one of the following methods: 13:34:20 ** 13:34:20 ** 1. log into the ECP at https://engcloud.prv.suse.net/project/stacks/ 13:34:20 ** and delete the stack manually, or 13:34:20 ** 13:34:20 ** 2. (preferred) trigger a manual build for the openstack-cloud-heat-engcloud 13:34:20 ** job at https://ci.nue.suse.com/job/openstack-cloud-heat-engcloud/build and 13:34:20 ** use the same 'alan-turing' cloud_env value and the 'delete' action for the 13:34:20 ** parameters 13:34:20 ** 13:34:20 ******************************************************************************

Testing a predeployed Jenkins Ardana environment

Aside from manual testing, any of the following pipeline Jenkins jobs can be manually triggered via Build with Parameters to run on an Ardana environment that was previously deployed using the Ardana Jenkins jobs. The target Ardana virtual or bare-metal environment must be indicated via the cloud_env parameter value:

Customized deployments

TBD:

  • custom cloudsource
  • different input model
  • gerrit changes
  • extra repositories
  • MUs
  • tempest run filter
  • QA test cases
    • TODO: link to adding new test cases
  • automation repo PR
  • generated input model
    • different scenario (standard, split, mid-size)
    • control number of nodes allocated to each cluster
    • integrated/standalone deployer
    • TODO: disabled services
    • TODO: link to adding new scenarios
  • QA physical deployment
  • custom automation repo fork/branch

Deploying and testing a SUSE OpenStack Cloud from your host

The Ansible playbooks used by the Jenkins job can alternatively be executed manually from any host, without involving Jenkins at all, to deploy and test either a virtual Ardana or Crowbar environment hosted in the engineering cloud or SUSE cloud, or an Ardana QA bare-metal environment.

Prerequisites

To be able to deploy a Cloud environment using the toolchain on your host, the following are needed:

  • for virtual Cloud deployments, an account for either the engineering cloud or SUSE cloud with access to the one of the OpenStack projects that are already maintained by the CI automated Jenkins jobs that regularly update SLES images in the ECP and SUSE clouds (currently, only cloud and support)
  • your public ssh key added to the list of keys (via normal github pull requests)

Test environment setup

Setting up a local Cloud test environment:

  • clone the automation repository locally

  • for building test packages from gerrit changes, the osc utility needs to be correctly installed and configured on the local host.

  • Install openstack cli client:

      sudo zypper install python-openstackclient

  • set up an OpenStack cloud configuration reflecting your engineering or SUSE cloud credentials (for virtual Cloud environments). To do that, create an ~/.config/openstack/clouds.yaml file reflecting your OpenStack cloud accounts:

      clouds:
    engcloud:
      region_name: CustomRegion
      auth:
        auth_url: https://engcloud.prv.suse.net:5000/v3
        username: <your ldap user name>
        password: <your ldap password>
        project_domain_name: default
        user_domain_name: ldap_users
      identity_api_version: 3
      cacert: /usr/share/pki/trust/anchors/SUSE_Trust_Root.crt.pem
    susecloud:
      region_name: CustomRegion
      auth:
        auth_url: https://cloud.suse.com:5000/v3
        username: <your ldap user name>
        password: <your ldap password>
        project_domain_name: Default
        user_domain_name: ldap_users
      identity_api_version: 3
      cacert: /usr/share/pki/trust/anchors/SUSE_Trust_Root.crt.pem

Note the following:

  • the names used for the possible cloud configuration entries (engcloud and susecloud) is also the list of values that can be configured for the os_cloud option in the input.yml file
  • the project_name option is explicitly left out of the configuration, because it is controlled separately, through the os_project_cloud option in the input.yml file

To verify that the test environment is properly set up:

  • run an openstack CLI command, e.g.:

      openstack --os-cloud engcloud stack list

Manual Cloud deployment

A bash script is provided to help deploying Ardana or Crowbar manually. The script sets up a virtual environment with ansible and all requirements and mimics the steps of the openstack-ardana and openstack-crowbar jobs by calling the ansible playbooks in the appropriate sequence with the appropriate parameters, which are taken from input.yml.

Before running the script you need to configure the parameters in the input.yml file to fit how you want the cloud to be deployed. The options available are basically a subset of the inputs from the openstack-ardana and openstack-crowbar jenkins jobs.

Deploying Ardana:

  • Go to scripts/jenkins/cloud/manual directory

  • Edit the input.yml file

  • Run deploy-ardana.sh or deploy-crowbar.sh

      cd scripts/jenkins/cloud/manual
  vim input.yml
  ./deploy-ardana.sh

Alternatively you can also call each step individually (after configuring input.yml) by sourcing the script library. E.g.:

cd scripts/jenkins/cloud/manual
source lib.sh
setup_ansible_venv
mitogen_enable
prepare_input_model
prepare_infra
...

Alternatively there is a containerized manager that is set up so that the above commands can be run directly without additional setup.

$ ./run-containerized-manager.sh [--python2|--python3]

This script will build a docker image based on SLES-12-SP4 and start a container with this image. When the container is started, the virtualenv is active. The Python version can be switched between Python 2 and 3 via the command line switch when starting the container.

Custom Ardana Gerrit CI builds

The default integration test job launched by the Gerrit Jenkins CI uses a standard generated input model, with a standalone deployer, 2 controller nodes and one SLES compute node. It also runs against a develcloud cloud media, on top of which it applies the most recent Ardana ansible changes and, of course, the target Gerrit change under test and all its Gerrit change dependencies.

This is sufficient for most cases, but sometimes there are special circumstances that require one or more additional, customized openstack-ardana type integration jobs to be executed against the same Gerrit change and to have them report their results back to Gerrit, where they can be tracked and verified by all stake holders.

This is now supported by allowing users to trigger manual builds for the parameterized Jenkins job that runs the Gerrit CI for either cloud 8 or cloud 9, and supplying custom values for its parameters, to achieve different results:

  • GERRIT_CHANGE_NUMBER - this parameter must be set to the target Gerrit change number value

  • voting - this flag controls whether the Gerrit job posts Verify+2 or Verify-2 label values on the Gerrit change. Non-voting jobs also post progress and outcome messages, but do not alter the Verify label value. There can be at most one voting job running for a Gerrit change at any given time. The most recent voting job automatically cancels older voting jobs running for the same Gerrit change.

  • gerrit_context - this is a string value that uniquely identifies a job build within the set of builds running against the same Gerrit change. A unique gerrit_context parameter value should be used for each additional job that is manually triggered against a Gerrit change, otherwise it will supersede and automatically cancel the previous jobs running with the same gerrit_context value on the same Gerrit change. This parameter should be set to a short string describing what the job actually achieves, as it is included both in the Gerrit build status reports and the Jenkins build description.

  • integration_test_job - the name of an existing Ardana Jenkins job that is triggered to run integration tests on the target Gerrit change. This parameter may be used to override the default cloud-ardana8-job-std-min-gerrit-x86_64 and cloud-ardana8-job-std-min-gerrit-x86_64 jobs currently employed by the Ardana CI to run integration tests. If the custom integration job also requires its parameters to be overridden, this can be achieved via the extra_params parameter.

  • git_automation_repo and git_automation_branch - can be used to point Jenkins to a different automation repository branch or fork.

  • extra_repos - allows the user to configure custom zypper repositories in addition to those implied by the target cloudsource value.

  • extra_params - this parameter is present for most Ardana Jenkins jobs. It can be used to inject additional parameters into the build, which are reflected in several places:

    • the list of environment variables accessible to shell scripts
    • the list of ansible extra variables, which can override all other variables used by playbooks
    • the extra_params parameter values of jobs triggered downstream, which means the effect is cascaded down to the entire job build hierarchy

Following are a few examples of use-cases where this feature comes in handy.

Custom integration jobs

A Gerrit change may need to be tested against a different input model scenario or cloud configuration. Here are some practical examples of this situation:

  • a Gerrit change that impacts the RHEL compute node support also needs to be tested against a cloud input model that includes RHEL (CentOS) compute nodes. E.g. to have this running for a cloud9 Gerrit change, trigger a manual cloud 9 Gerrit CI build, with the following parameter values:

    • voting unset, to have the new build run in addition to the official voting CI job, as a non-voting job
    • integration_test_job set to point at a job running an input model with RHEL compute nodes (for example cloud-ardana9-job-std-min-centos-gerrit-x86_64)
    • a custom gerrit_context value to indicate what the new job does (e.g. std-min-centos).

  • a Gerrit change that touches on functionality that can only be tested on bare-metal deployments (e.g. cobbler) needs to be tested against a QA bare-metal environment instead of the virtual one. E.g. to have this running for a cloud8 Gerrit change, trigger a manual cloud 8 Gerrit CI build with the following parameter values:

    • cloud_env set to point to one of the available QA bare-metal environments (contact the QA team to find one that is available)
    • reserve_env unchecked (unless the cloud_env QA environment also has an associated Lockable Resource )
    • voting set, to have the new build replace the official voting CI job and determine whether the Gerrit change can be merged or not
    • integration_test_job set to point at a job running an input model that can actually be deployed on bare-metal (for example cloud-ardana8-job-entry-scale-kvm-gerrit-x86_64)
    • a custom gerrit_context value to indicate what the new job does (e.g. entry-scale-bare-metal).

As can be seen, these use-cases are supported by allowing the user to specify a different Ardana integration Jenkins job to be run against the Gerrit change instead of the default one. The custom integration_test_job Jenkins job can either be one of the existing jobs that have already been provided for this purpose - those with their name ending in -gerrit-x86_64 (recommended), or it can even be the fully customizable core openstack-ardana job itself, in which case some or all of its parameters may need to be customized by using the extra_params parameter.

E.g. to have a fully customizable openstack-ardana job running for a cloud9 Gerrit change, one might trigger a manual cloud 9 Gerrit CI build with the following parameter values:

  • cloud_env set to the IRC or LDAP username of the user (i.e. this will be a private Ardana ECP deployment environment that remains running and can be accessed after the job is done)

  • reserve_env unchecked

  • voting set, to have the new build replace the official voting CI job and determine whether the Gerrit change can be merged or not

  • integration_test_job set to openstack-ardana

  • a custom gerrit_context value to indicate what the new job does (e.g. my-very-special-input-model)

  • the multi-line extra_params parameter set to the list of openstack-ardana build parameters that are mandatory (need to be supplied) or need to be overridden (different than their default values). For example, the following set of extra_params parameters can be used to deploy a demo like input model (integrated deployer, one controller, one SLES compute, monasca service disabled):

    scenario_name=standard
clm_model=integrated
controllers=1
computes=1
disabled_services=monasca|logging|ceilometer|cassandra|kafka|spark|storm|octavia
ses_rgw_enabled=false
tempest_filter_list=ci

If a particular customized openstack-ardana scenario needs to be used often, it is recommended that it be added with a GitHub pull-request to the list of -gerrit-x86_64 jobs predefined for cloud8 and/or cloud9, and thus enable other users to point to it by name.

Custom package repositories

The user may need additional zypper repositories to be configured, for example:

  • if there is a two-way dependency between a Gerrit change on one hand, and one or more OBS/IBS package changes on the other hand, meaning that they must be tested together to ensure that neither breaks the CI, before they can be merged together into the main code and package streams
  • when the Gerrit change implements a new piece of functionality that goes hand-in-hand with a new set of packages or package updates that are not yet available in the development cloud build (e.g. they reside in an OBS home project or another non-official project while still being developed and tested).

The recommended approach for this use-case is that test packages be created in an OBS or IBS project with package publishing enabled, then the URL to the .repo file generated in the repository can be supplied as the extra_repos parameter value when triggering custom manual Gerrit CI Jenkins builds.

IMPORTANT: currently, this feature is only supported for packages that need to be installed on the deployer node - Ardana ansible and venv packages (see SCRD-7800). If the packages in the extra repositories also need to be installed on the non-deployer nodes, it will not work until SCRD-7800 is solved.

Custom automation scripts or jobs

While new CI test coverage is still being developed in a separate automation repository fork or branch (or pull-request), that doesn't mean it cannot yet be executed to validate open Gerrit changes.

This is useful, for example, when someone has a set of open Gerrit changes that implement a new piece of functionality or use-case and someone else (or the same person) works in parallel on providing automation scripts that target that same functionality or use-case in the CI.

When triggering manual Gerrit CI builds, an alternative git source can be used via the git_automation_repo and git_automation_branch parameters. There are some limitations to this feature though:

  • if the alternative git branch/fork also creates new JJB Jenkins jobs, these will not be available in Jenkins yet until changes are merged in the master branch or until created manually by the user
  • if the alternative git branch/fork updates the configuration of existing JJB Jenkins jobs (e.g. adds, removes or replaces parameters), these changes will also not be available in Jenkins. In this case, the recommendation is to use the extra_params feature, if possible, to achieve the same effect as would updating the existing Jenkins jobs, or create temporary new jobs that are copies of the existing ones that are updated in the automation git branch/fork

Ardana CI jobs

The majority of Ardana SUSE CI Jenkins jobs are Jenkins pipeline jobs. These offer some advantages over the classical job types, which are heavily exploited by the Ardana Jenkins jobs:

  • better usability: a job run can be broken in stages, their pass/fail status and logs individually visualized, especially useful in the Blue Ocean Jenkins UI
  • more flexible
  • TBD

TBD: describe the existing jobs, their purpose, triggers, link to the detailed pipeline description for every job.

Prepare BM cloud

Prepare virtual cloud

Build test packages

Bootstrap CLM

Bootstrap nodes

Deploy cloud

Tempest

Deploy CaaSP

Ardana Jenkins pipelines

Integration pipeline

Describe the pipeline job, stages, link to detail docs for various features/roles., Diagram with job stages/hierarchy.

Gerrit pipeline

Prepare BM cloud

Automated checks

Some extra checks are automatically run by the Jenkins job: