Chapter 2
Gatekeeper Architecture and Core Concepts
At the intersection of extensibility and control, Gatekeeper emerges as the flagship policy engine built for Kubernetes-scale realities. This chapter illuminates the internal mechanics and strategic design choices behind Gatekeeper's architecture, revealing how its modular components, declarative policy modeling, and data-driven workflows combine to deliver robust policy enforcement. Prepare to uncover the inner workings that make Gatekeeper both powerful and operationally sound.
2.1 Core Components: Controller, Webhook, and Audit
Gatekeeper operates through a synergistic combination of three principal components: the controller, the admission webhook, and the audit process. Each component plays a distinct role in enforcing policy compliance within Kubernetes clusters, yet they collaborate tightly to ensure both immediate and continuous governance. Understanding their individual responsibilities, communication paths, and architectural foundations is essential for appreciating Gatekeeper's operational reliability, scalability, and extensibility.
The controller lies at the heart of Gatekeeper's control plane, tasked primarily with continuous reconciliation of constraint and configuration resources. It maintains an internal cache of constraint templates and constraints defined by users, periodically verifying that all cluster resources conform to these constraints. The reconciliation loop implemented by the controller is designed following Kubernetes controller patterns: it watches for changes on relevant resources and queues reconciliation requests to ensure eventual consistency. By continuously validating the live state of cluster objects against policies, the controller provides retrospective enforcement-a crucial mechanism for detecting drift in long-lived resources or inconsistencies arising from external modifications.
At the core of the controller's operation is its reconciliation loop, which can be summarized algorithmically as:
- wait for change event on constraints or relevant resources
- fetch current desired state (constraints, templates)
- fetch current live state of target resources
- evaluate constraints against live state resources
- enforce or report violations as needed
- update status and metrics accordingly
This loop ensures that Gatekeeper eventually detects any non-compliant resource and marks it as violating a policy, even if the resource was created or modified prior to policy deployment. The controller's scalability hinges on the efficiency of resource watchers, the use of indexing on informers, and the batching of constraint validation-architectural choices chosen to minimize latency and CPU utilization in clusters of varying scale.
The admission webhook component, conversely, addresses real-time enforcement. It integrates tightly with the Kubernetes API server admission pipeline by intercepting create and update requests for API objects, applying constraint validation during the admission phase before objects are persisted. This synchronous validation prevents the introduction of policy violations proactively, serving as a gatekeeper that rejects operations conflicting with established constraints.
From an architectural standpoint, the webhook operates as a Mutating and Validating Admission Webhook server, although Gatekeeper primarily uses validating admission webhooks to enforce policies. Incoming requests trigger a series of constraint evaluations against the submitted resource. The response decision-admit or reject-is based on whether any constraint violation is detected. Gatekeeper's webhook leverages cached constraint data updated by the controller to avoid querying the Kubernetes API server directly at every admission decision, improving response time and throughput.
The communication path within Gatekeeper from the API server to the webhook can be described as:
This diagram highlights how the controller and webhook are decoupled but communicate asynchronously, with the controller pushing constraint updates that the webhook uses along the admission path. This design enables Gatekeeper to minimize latency in admission processing while ensuring up-to-date policy knowledge.
The third key component, the audit process, serves as a background enforcement mechanism that periodically scans existing resources in the cluster asynchronously. Its primary purpose is to detect violations that escaped admission control-due either to policy introduction after resource creation, administrative overrides, or failure of the webhook component. Audit provides eventual consistency by reconciling cluster state against constraints defined by users, reporting violations, and performing remediation actions if so configured.
Gatekeeper's audit process is implemented as a scheduled job within the controller manager, capable of scanning resource sets defined by constraint selectors. The audit execution respects rate limits and resource footprint, making use of Kubernetes informers and efficient indexing strategies to scale across large clusters. Results of audit scans populate constraint violation statuses and event logs, feeding into compliance dashboards or alerting systems. This retrospective enforcement complements the webhook's preventive model by providing a persistent safety net.
Collectively, these three components enable Gatekeeper to implement a multi-layered control plane for policy enforcement. The controller ensures consistent, up-to-date enforcement state; the webhook provides immediate request-level prevention; and the audit process guarantees compliance over time. This architectural partitioning enhances fault tolerance and extensibility, making it possible to plug in new constraint types, expand the scope of audit capabilities, or improve webhook efficiency independently.
From a reliability perspective, the use of Kubernetes-native mechanisms-such as informers, leader election, and API aggregation-permits Gatekeeper components to gracefully handle cluster scaling, failover scenarios, and incremental updates. Scalability is further supported by the controller's ability to batch process validations and by webhook caching to reduce overhead on admission latency. Extensibility is ensured through the constraint template CRD abstraction, which allows definition of custom constraints using Rego policies without modifying Gatekeeper core logic.
The interplay of Gatekeeper's controller, admission webhook, and audit components constitutes a robust architecture balancing the tension between proactive and reactive enforcement. This design enables Kubernetes administrators to confidently implement fine-grained governance controls while maintaining cluster operability and performance.
2.2 ConstraintTemplates and Rego Policy Design
At the core of Gatekeeper's policy framework lies the ConstraintTemplate, a critical abstraction that encapsulates reusable policy logic expressed in the Rego policy language. ConstraintTemplates function as blueprints for defining flexible, parameterized policies, enabling a clear demarcation between the policy logic itself and its operational instantiation within a Kubernetes environment.
A ConstraintTemplate consists primarily of two components: a CRD definition and an embedded Rego policy module. The CustomResourceDefinition (CRD) exposes the template's parameters, allowing runtime customization of policy attributes when instantiated as constraints, without necessitating changes to the underlying logic. The Rego policy within the ConstraintTemplate explicitly enforces the checks and constraints that govern resource admission decisions.
Structurally, a typical ConstraintTemplate YAML manifest includes a spec.crd.spec subsection that defines the schema of the user-configurable parameters. This schema is specified using OpenAPI v3 schema language, detailing required fields, types, and validation patterns. The schema serves as the interface for cluster operators or developers to inject specific policy requirements tailored to their operational context. By enforcing strong typing and validation rules, ConstraintTemplates promote robustness and guard against misconfiguration.
Complementing this schema is the Rego policy module under spec.targets, which implements the actual evaluation logic. Within this module, the Rego violation rule is the conventional point of failure declaration. It produces descriptive messages when policy violations are detected. Importantly, Rego programs in ConstraintTemplates leverage input variables mapped from the parameterized CRD as well as the Kubernetes resource's admission request context, permitting dynamic and context-sensitive policy enforcement.
For example, a ConstraintTemplate designed to restrict image registries might define parameters such as permitted registries or forbidden tags in the CRD schema. The Rego logic then iterates over container specs in...
