Designing Resilient Distributed Systems

Designing Resilient Distributed Systems

Resilience is the ability of a system to absorb failures and continue operating. It goes beyond availability by focusing on degradation, recovery, and fault isolation.

Core Resilience Principles

1. Redundancy and Failure Domains

Replicate critical services across zones and regions, but keep failure domains isolated. Avoid correlated failures by separating networks, data stores, and deployment pipelines.

2. Graceful Degradation

Identify critical vs optional features. For optional features, return partial results or cached data instead of failing the entire request.

3. Backpressure and Load Shedding

Protect the system by rejecting excess load early. Queue depth must be bounded and linked to latency budgets.

4. Automatic Recovery

Automate restarts, traffic shifts, and failover. Avoid manual runbooks for common failures.

Spring Boot Example: Fallback for Optional Dependencies

@Service
public class PricingService {
    private final WebClient webClient;

    public PricingService(WebClient.Builder builder) {
        this.webClient = builder.baseUrl("http://pricing").build();
    }

    public Mono<Price> getPrice(String sku) {
        return webClient.get()
                .uri("/price/{sku}", sku)
                .retrieve()
                .bodyToMono(Price.class)
                .timeout(Duration.ofMillis(150))
                .onErrorReturn(Price.unavailable(sku));
    }
}

Testing for Resilience

• Chaos tests to inject latency and failures.
• Dependency failure drills.
• Game days focused on recovery and observability.

Metrics That Matter

Track SLOs based on success rate and latency, not just uptime. Measure mean time to detect (MTTD) and mean time to recover (MTTR).

Summary

Resilient systems require redundancy, isolation, and controlled degradation. Automating recovery paths and validating them through failure testing is the most reliable way to build durable platforms.