GitOps was started as a cloud-native movement to apply many of the things we had learned in Infrastructure as Code (IaC) environments to Kubernetes cluster and multi-cluster management. GitOps finds it purest expression in the upstream projects ArgoCD and FluxCD, which are designed and implemented to work with the Kubernetes API and represent the clearest expression of the GitOps principles.
And yet, while Kubernetes is one of the most dramatically impactful and exciting things to happen in the IT landscape in quite some time, it is not entirely pervasive; and there are key components of technology solutions that do not have a clear path to being managed under Kubernetes or Kubernetes APIs (consider, for example, network gear and sensors like IP cameras - it is hard to picture a scenario where network switches, routers, and access points would respond to the Kubernetes API, or where edge devices like IP Cameras would). This is far from a criticism or indictment of Kubernetes and its approach - rather, it is an endorsement. We see the value of what Kubernetes brings in the form of useful abstractions and approaches to common problems. The question we ask here is, how do we bring those benefits to environments where it is impossible or impractical to run Kubernetes? And how do we think about systems with mixed Kubernetes and non-Kubernetes components? Can we, in fact, extend GitOps outside the Kubernetes native space?
We believe that the answer to that question is "yes", and the following will propose a framework for doing just that. The concepts here are all expressed in terms of Ansible, but could conceptually be adapted to other frameworks as well.
We propose this approach when the user is not in control of any OpenShift (Kubernetes) infrastructure. Clearly, if no OpenShift is available at all, there needs to be a different approach than one that starts with and assumes the presence of OpenShift (which existing hybrid cloud patterns do).
An entirely valid question might be, "what about when we have limited access to OpenShift?" (That is, access to a limited set of APIs or resources in one or more OpenShift clusters, but no administrative access to it or the ability to deploy operators and pods.) Since Ansible can also manage resources in Kubernetes, this might seem to be present a dilemma. In general, we recommend Kubernetes-based GitOps solutions to implement and manage GitOps; but we also recommend that hybrid cloud patterns be self-contained. If it is not possible to make a pattern "self-contained" from a well-documented and understood entry point, then it would be appropriate to consume Kubernetes resources with an Ansible-based framework. One such scenario might be using OpenShift Virtualization/Kubevirt for machine virtualization. The user of the pattern is given credentials/access to spin up VMs on the OpenShift Virtualization fabric, but does not have the ability to manage the fabric itself or install other operators or applications on the OpenShift cluster(s). In this (hypothetical, but plausible) situation, it would be acceptable to use this variant of the framework to drive the pattern.
- When starting from OpenShift, we recommend the OpenShift-based patterns framework. (This includes hybrid scenarios where OpenShift is available but elements of the pattern are non-Kubernetes manageable, such as the network gear/sensor types mentioned previously.)
- When a pattern has no administrative access to OpenShift, use the Ansible variant.
- When a pattern has limited access to OpenShift, use the mechanism that allows the best access to a single, self-contained entry point (likely the Ansible variant).
Ansible and the GitOps Principles
Ansible is not an inherently declarative language, or system; but it is quite possible (and recommended) to write Ansible in a declarative fashion.
In an Ansible GitOps pattern, we make every effort to write code that expresses desired state, in preference to code that uses procedures to achieve a desired state. (That is, we prefer "declarative" code over "imperative" code. For example, we would prefer:
- name: "Install some packages"
ansible.builtin.package:
name:
- vim
- systemd
- python
as opposed to something like this:
- name: "Install some packages"
ansible.builtin.command:
dnf install -y vim systemd python
We should avoid "command" types, "shell" types, and other such mechanisms that may introduce configuration side effects
or hide configuration details, in order to maximize the declarativeness of code. While this particular example would
be relatively free of side effects, we would still not prefer it because it is less declarative. Further, the use of
the explicit dnf command might limit the portability of the code from version to version of RHEL.
In some cases, procedural code will be unavoidable, but for the best GitOps results, the more declarative code, the better.
In order for code to be versioned and immutable, it has to be stored in a version control repository. By convention this is git; we make no assertion about which git service is used. (That is, Github, Gitlab, Gitea or something else. The important characteristic is the ability to store history and return to states at different points in history. Theoretically it would even be possible to use an entirely different version control system - such as Mercurial - but the movement is called GitOps, and the best integrations and "out-of-the-box" experiences come with the Git services.)
Immutability is the property that deploying the same version multiple times should result in the same results on the target. This is related to, but different from idempotence. As used by the GitOps movement, Immutability refers to the ability to deploy any specific revision and get the exact same result for that revision. (This can be challenging in practice, especially when going "back in time;" many solutions have clean forward upgrade paths, but do not test backwards versioning; such transitions in Kubernetes can involve versioned APIs, for example. Theoretically it should all work, in practice...it may not. That is a much longer discussion.)
This kind of immutability is especially hard to achieve in Ansible the more procedural, non-declarative code is used.
There is another situation relative to immutability which places the management of OS instances (as opposed to containers, as is the case in OpenShift/Kubernetes). Specifically, containers have an inherent immutability that OS instances (for examples) do not. Consider the situation of managing a root password (there are many other situations where this sort of situation can occur; this is just an illustration). If our example (declarative) code says:
tasks:
- name: Set root password
user:
name: root
password: "{{ 'foobar' | password_hash('sha512') }}"We could run this code repeatedly (in keeping with GitOps principles); there is nothing that prevents someone else within the system of logging into the system and running something else "out of band" of GitOps, as follows:
echo "helloworld" | passwd root --stdin
chattr +i /etc/shadowThis would result in the state of the system BOTH being out of sync with the expected and desired state, as well as (in
this case) the declared state not being reconcilable to the declared desired state (because the chattr +i would render
that impossible). It would, of course, be possible to add another task to the first example to remove the immutable
attribute from the shadow file; but the point of this example is that it is an unbounded problem to anticipate all such
potential pitfalls in systems that were not designed from the beginning to be immutable and declarative in the way that
OpenShift/Kubernetes is.
Thus, we consider it an inherent limitation of applying GitOps principles outside of the OpenShift/Kubernetes environment in which they were designed to apply, and accept the risk that these kinds of situations are possible in non-container native environments; efforts can be made to minimize these risks but it seems impossible to eliminate them entirely.
In short, this means that changes in Git should be reflected by the software agent without other human intervention. So, change is made to Git repo, after some delay (due to time or delivery), and the "software agents" have that change available.
In an Ansible GitOps pattern, the Ansible code base must be configured to auto-refresh the project repository (where the configuration code is stored); that project should at a minimum be configured to refresh/update on a timer so that new commits to the repository become available on the AAP server. A more elegant way of configuring this would be including an appropriate webhook on the repository, such that the repository refresh triggers an event that updates the repository project in AAP.
This requirement means that the versioned configuration is periodically applied to the managed environment. In general, this means that when the "software agents" (ArgoCD in the case of an OpenShift pattern; AAP in the case of an Ansible Pattern) are responsible for recognizing a new commit and then applying that configuration. In Ansible, that means running units of work like playbooks and roles when the the authoritative git repo has been changed.
Given the definitions on opengitops.dev, we believe the Ansible Automation Platform, when configured with projects connected to Git repos and when configured to repeatedly apply units of work in those repos (that is, playbooks and roles in Ansible terms), meets the strict definition of GitOps.
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Ansible-based Validated Patterns MUST contain at least one version-controlled git repository that is synchronized into AAP with the "Update Revision on Launch" flag turned on. It MAY also be configured with webhooks and/or a polling cycle. This ensures that each time work units defined in that repository run, the work unit and repository will correspond to the latest commit(s) available. This satisfies the "Versioned and immutable" and "Pulled automatically" requirements to be considered GitOps.
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Ansible-based Validated Patterns MUST contain one or more work units that are applied to inventories periodically and/or by webhook or other event driver; this satisfies the "Continuously reconciled" requirement for GitOps.
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Ansible-based Validated Patterns MAY include one or more work units that can be run on demand to modify (that is, add or remove elements from Ansible inventories, or else to trigger CI/CD-style pipeline runs. Elements added to inventories in this way must then be targeted by the automatically run work units, which are expected to be declarative with no or minimal side effects. In the same way, while the triggering of CI/CD pipelines may be done on demand, the pipelines themselves should be set up declaratively and immutably and be ready to run.
Drawing Some Lines - Ansible-based Validated Patterns will define a "minimal set" as a starting point
In light of this, we draw a distinction between the "infra" component of an Ansible-based Pattern, and the "workload" set of such a Pattern. By making this distinction, we make clear what should be common to all Ansible-based patterns, and hopefully simplify the task of post-pattern cleanup (which is vital for the "demo" aspect of VPs as well as for CI/CD testing). In broad terms, we should be able to tell the infra to clean up the workload components, and then have some special code to clean up the infra components. Thus, the minimal set will be the "infra" as specified below.
- IdM (Identity Management - DNS, PKI, and user management)
IdM is built first, and provides foundational infrastructure requirements, including, particularly:
- DNS management and registration
- Certificate trust and management
- User provisioning and management
- Time synchronization
Because these elements are central to other nodes being able to trust and find each other, this node must be built first. (If the workload requires it, additional replicas can be built as part of the workload.)
- Satellite (Content and Provisioning Lifecycle)
- Content hosting
- Host registration
- Node provisioning and deprovisioning (cloud, on-prem, bare metal)
We build a single-node Satellite next, because it will be used to build all of the rest of the infrastructure and workload. Satellite gives us the ability to define content, including operating system levels and system-level configuration, in an abstract way. That is, we can use Satellite to apply policies to how compute will be built, and let Satellite worry about the differences between different hosting mechanisms (i.e. hyperscaler, on-prem, bare metal). By placing Satellite here in the hierarchy we offload the complexity of building any subsequent infra/workload components onto Satellite, such that if we need to add additional Satellite capacity (in the form of Capsules), or need to build a more resilient or higher capacity AAP system, we can define that capability in Satellite rather than defining custom code per potential provider for it elsewhere in the framework.
- AAP (Ansible Automation Platform - the GitOps Engine)
- GitOps capabilities
- Secrets management
The final component of the minimal set is an AAP instance, which will drive workloads and gitops in both the infra and workload components of the pattern going forward. By default, it will consist of an "all-in-one" controller and a separate automation hub node. The workload components will be completely determined by GitOps flows, and changes to the infra components will also be managed via GitOps using the AAP instance.
To be absolutely crystal clear - this is what we are defining as the miminal set for building a framework with Ansible-based GitOps, such that we can build out additional resources in hybrid cloud environments (including on-prem and bare metal) and continue to test them on an ongoing basis. Many customers and users will already have the capabilities provided by Satellite and IdM already running in some capacity, or may provide those capabilities using different products or projects. But to be successful as a framework for testing reference architectures, the framework needs to provide the ability to bootstrap resources, including aspects of itself, and this is the set of things that is essential to doing that.
To put it a slightly different way, Ansible GitOps is possible without Satellite and IdM - it requires, essentially, the capabilities listed in the Principles section (which AAP by itself provides, in conjunction with a Git hosting service). The Validated Patterns framework for Ansible GitOps uses IdM and Satellite to make such GitOps patterns testable from bootstrap in a number of scenarios (hyperscalers, on-prem, baremetal) that align with the Red Hat hybrid cloud vision.
The goal of this effort is to do more than simply assert that GitOps is possible with Ansible and define how to do it - it also requires the ability to define and execute useful use cases on top of that platform that can have CI/CD applied to them to continually test their interoperability; and to do this in a hybrid multi-cloud environment, which we also plan to include bare metal. There are numerous complications with testing different environments and in different clouds and the defined set of tools above abstracts and manages the lion's share of them. (Not all of them, admittedly, but architecturally, having Satellite to do compute provisioning makes a lot more sense than trying to import a different framework for that, or inventing a completely new one; as Satellite already supports the major hyperscalers as well as all of the major on-prem hosting mechanisms, including bare metal).
This also helps highlight the strength of OpenShift as a platform. Starting with a blank OpenShift cluster is, architecturally speaking, a stronger starting point than a single blank RHEL server. The OpenShift cluster has much more inherent expandability, better resiliency characteristics, and the key advantage of running on an immutable Operating System that cleanly divides the workload from OS management. This proposed division of labor (into the "minimal set" and "workload" components) for Ansible-based patterns is an attempt at architectural equivalence in that regard, and even so, there is considerable additional effort required in the non-OpenShift space to provide resilience and fault-tolerance equivalent to what OpenShift provides.
It is relevant to this discussion that we are coming into this design/development effort with a model implementation that considers the workload division problem in exactly this way. As of this initial treatment, there is an implementation that bootstraps IdM, Satellite, and AAP (in that order) on vmWare. It will take some work to generalize that, but rather less than it would to write a new framework entirely from scratch, or else integrating many different pieces and parts.
It has always been our goal with Validated Patterns to provide artifacts that are genuinely useful, and adaptable to local requirements, while also being easily testable and verifiable. We believe that this approach gives us what we need to make and fulfill this promise in the hybrid multi-cloud environment that we know we must operate in.
Historically, the first Validated Pattern we delivered was Industial Edge - which was and is not a minimalistic pattern. (Consciously so; the first pattern to demonstrate the framework had to be larger and more complex to show that the framework was useful for building larger artifacts.) The next pattern we delivered was MultiCloud GitOps, which was a conscious effort to be more minimalistic in requirements and scope. We intend to provide paths to make use of the GitOps-ness of patterns in this Ansible-based framework without absolutely requiring the setup of new Satellite and/or IdM infrastructure.
- Tang servers for Network Bound Disk Encyption (NBDE) - workload elements, not part of minimal set
- HMI devices for Ignition Demo - workload elements, not part of minimal set (with proposed setup, could be configured as VMs or even as bare metal devices, using Satellite discovery and provisioning techniques)
One of the key requirements to be a validated pattern is that the solution must lend itself to automated testing. Testing on bare metal is a particularly thorny problem. Given project constraints, we should demonstrate success in testing against virtual machines first, while providing a viable roadmap to testing on bare metal.
The entry points for starting the pattern should be as close to OpenShift GitOps patterns as possible; in a case where there is no OpenShift available the Operator is clearly not an option but the Make mechanism still could be.
In both scenarios we endevour to identify the smallest possible seed from which we can use GitOps principles to bootstrap the solution.
We are also not in the business of writing OpenShift or AAP installers, and assume a pristine deployment of either as a prerequisite. Over time the pristine requirement is expected to be relaxed.
One of Ansible's great strengths is its flexibility in composing workflows; while this can pose certain problems for maximum declarativeness, it also makes Ansible uniquely suited to solving some of the multiple cloud/multiple image problems that are inherent in the hybrid cloud story. Further, since Ansible does have good support for Kubernetes, it can be used to consume Kubernetes-based resources even in scenarios where a full admin environment in Kubernetes is not available.
AAP includes its own secret store. Finding a suitable secret store and handling secrets in a reasonable way was one of the biggest challenges we faced early in the Validated Patterns design effort. In an AAP-based pattern, we would most likely create credential types and credentials in the AAP instance, and use that as our authoritative secret store. IdM also includes a "vault" feature, but this feature is not as flexible or generalized as the secret storage offered by AAP (IdM's vault features store a single "blob" per vault, while AAP's can store larger blobs and complex data types). For these reasons we prefer using the AAP vault for this purpose.
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The framework should provide guidance on how to determine whether code lives in the GitOps repository or in Automation Hub, since both options are possible with Ansible-based GitOps.
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The framework should provide guidance on how to declare code dependencies for the framework. (For CI/CD systems, there is benefit to having "open" dependency declarations, since testing new builds of new roles and collections can indicate regressions; however, running open dependencies in production can lead to surprises and possibly regressions.)
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