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Version: 1.1.0


Aperture is built on a distributed architecture that provides a unified observability and controllability platform for cloud-native applications. The architecture is designed to ensure high availability, scalability, and reliability.

%% name: architecture_simple flowchart TD blueprints[/"Blueprints"/] policies[/"Policies"/] subgraph controller["Aperture Controller"] circuit["Control Circuit"] end subgraph databases["Aperture Databases"] prometheus[("Prometheus")] etcd[("etcd")] end subgraph worker["Worker Node (Kubernetes/VM/Bare-metal)"] subgraph agent["Aperture Agent"] discovery["Discovery"] telemetry["Telemetry Collector"] flowcontrol["Flow Controller"] autoscaler["Auto Scaler"] end subgraph serviceinstance["Service Instance"] servicelogic["Service Logic"] servicemesh["Service Mesh"] end end subgraph discoverydb["Discovery Databases"] k8sdiscovery["Kubernetes"] consul["Consul"] end subgraph orchestrators["Orchestrators"] k8s["Kubernetes"] end blueprints --> |aperturectl| policies policies --> |Kubernetes Custom Resource| controller controller<--> |Configuration/Telemetry/Flow Control| databases databases<-->|Configuration/Telemetry/Flow Control|agent agent <-->|SDK: Telemetry/Flow Control| servicelogic agent <-->|Mesh: Telemetry/Flow Control| servicemesh orchestrators <-->|Auto Scale| agent discoverydb <-->|Discovery| agent

Aperture Controller

The Aperture Controller is the central component of the platform. The controller monitors the system using an in-built telemetry system and collects metrics on service performance and workloads, including information on customer tiers, request types, and other relevant attributes.

he controller uses declarative policies, expressed as a control circuit, to analyze the collected metrics and make decisions on load shedding, flow control, and auto scaling to ensure that the application operates within the specified SLOs. The controller's policies are based on the principles of Observability-driven closed-loop automation, which continuously track deviations from service-level objectives (SLOs) and calculate recovery or escalation actions.

For example, a gradient control circuit component can be used to implement AIMD (Additive Increase, Multiplicative Decrease) style closed-loop automation that limits the concurrency on a service when response times deteriorate. Advanced control components like PID controller can be used to further tune the concurrency limits based on specific service requirements.

The controller's policies are stored in a policy database and are managed using the Kubernetes Custom Resource Definition (CRD) API, allowing users to easily configure and modify policies as needed. The controller interacts with Aperture Agents, which run alongside service instances as sidecars, to enforce the policies and ensure the reliable operation of cloud-native applications.

Aperture Agents

Aperture Agents are the workhorses of the platform, residing alongside service instances as sidecars. They provide powerful flow control components such as a weighted fair queuing scheduler for prioritized load shedding and a distributed rate-limiter for abuse prevention. A flow is the fundamental unit of work from the perspective of an Aperture Agent. It could be an API call, a feature, or even a database query.

The agents monitor golden signals, such as request latency, error rate, and saturation, using an in-built telemetry system and a programmable, high-fidelity flow classifier used to label requests based on attributes such as customer tier or request type. These metrics are then analyzed by the Aperture Controller.

Graceful degradation of services is achieved by prioritizing critical application features over background workloads. Similar to boarding an aircraft, business class passengers get priority over other passengers; every application has workloads with varying priorities. For example, a video streaming service might prioritize a request to play a movie by a customer over running an internal machine learning workload. A SaaS product might prioritize features used by paid users over those being used by free users. Aperture Agents schedule workloads based on their priorities, helping maximize user experience or revenue even during overload scenarios.

Aperture Agents can be installed on a variety of infrastructure such as Kubernetes, VMs, or bare-metal. They integrate with Service Meshes or can be used with SDKs to provide flow control capabilities. Additionally, agents work with auto-scaling APIs for platforms such as Kubernetes, to help scale infrastructure when needed.

Aperture Databases

Aperture uses two databases to store configuration, telemetry, and flow control information: Prometheus and etcd. Prometheus is a time-series database used to store and query telemetry data collected from Aperture Agents. It enables Aperture to monitor the system and detect deviations from the service-level objectives (SLOs) defined in the declarative policies.

Etcd is a distributed key-value store used to store configuration and flow control information. Aperture Controller uses etcd to store the declarative policies that define the control circuits and their components, as well as the current system state.

Users can optionally reuse their existing etcd or scalable Prometheus installations to minimize operational overhead and leverage their existing monitoring infrastructure.