An Autonomic Manager is a core component of the self-healing design pattern. It implements the MAPE-K loop (Monitor, Analyze, Plan, Execute, Knowledge) to enable a system to automatically detect issues (like traffic spikes) and execute pre-defined remediation plans (like scaling resources) without human intervention. This directly addresses the requirement for the service to handle spikes autonomously and remain responsive.

The Circuit Breaker pattern is designed to prevent cascading failures. When Service A becomes slow or unresponsive, the circuit breaker in Service B trips after a failure threshold is met. This prevents Service B from making further calls to the faulty service, allowing it to fail fast, use fallback logic, and protect its own resources (like connection pools), thereby containing the failure.

The Orchestrated Compensation pattern is used to reverse the effects of a series of operations in a long-running, distributed transaction when a later step fails. It involves defining a compensating transaction (like a ‘reverse debit’) for each forward operation. An orchestrator is responsible for executing these compensations in reverse order to roll back the workflow, ensuring eventual consistency.

While orchestration centralizes control logic, the orchestrator service itself becomes a critical central point. This creates a risk of tight coupling, as the orchestrator must have intimate knowledge of the participant services and their interfaces. If the orchestrator fails, the entire coordinated process can be halted, making it a potential single point of failure for that workflow.

In Choreography, there is no central coordinator. Instead, each service in the saga publishes domain events when it completes its local transaction. Other services subscribe to these events and react by executing their own local transactions and publishing subsequent events. The business process emerges from this decentralized, event-driven collaboration.

The Bulkhead pattern isolates elements of an application into pools (like separate connection pools for different subsystems), so that if one fails, the others continue to function. By giving the reporting engine and order processing system their own dedicated resource pools, a failure in one cannot exhaust the resources of the other, containing the damage and improving overall system resilience.

The Half-Open state is a probationary period. After the circuit has been Open for a configured timeout, it moves to Half-Open. In this state, it allows one or a few trial requests to pass through. If these requests succeed, the circuit assumes the fault is fixed and resets to Closed. If they fail, it returns to the Open state for another timeout period.

High availability focuses on redundancy and minimizing downtime. Self-healing is a proactive capability that goes further. It involves an autonomic manager that continuously monitors the system’s health, analyzes metrics to detect anomalies or failures, plans a corrective action (like restarting a container or failing over to a standby), and executes that plan—all with minimal to no human intervention.

The sidecar proxy is a fundamental component of a service mesh. It is deployed as a separate container/process alongside the service instance (the ‘maincar’). Its role is to intercept all network traffic to and from the service, offloading cross-cutting operational concerns such as service-to-service authentication, TLS encryption, load balancing, retries, and collecting metrics, without requiring changes to the service’s own code.

An Autonomic Manager is a core component of the self-healing design pattern. It implements the MAPE-K loop (Monitor, Analyze, Plan, Execute, Knowledge) to enable a system to automatically detect issues (like traffic spikes) and execute pre-defined remediation plans (like scaling resources) without human intervention. This directly addresses the requirement for the service to handle spikes autonomously and remain responsive.0