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#
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@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.