This is a lot of ground to cover and I think I spent too long on the DNS and ingress layer and then rushed through service discovery.
Start by clarifying the cluster environment (e.g., Kubernetes) and then trace the request path step by step from DNS resolution to the pod, highlighting how each layer (ingress, service, kube-proxy) contributes to routing and load balancing. Emphasize the dynamic nature of service discovery and registration, and mention trade-offs like latency vs. accuracy in load balancing.
Pro tip: Mention that while Kubernetes Services provide basic load balancing, more advanced setups use ingress controllers with custom load balancing algorithms or service meshes for finer control, and that DNS caching can affect failover speed.
Explain how the client resolves the service's DNS name to an IP address, typically the ingress controller's IP or a cloud load balancer's IP, and note that DNS may return multiple IPs for redundancy.
Describe how the ingress controller or external load balancer receives the request, terminates TLS if needed, and routes based on host/path rules to the appropriate backend service.
Explain that the Kubernetes Service provides a stable virtual IP (ClusterIP) and load balances across pods using kube-proxy (iptables/IPVS) or eBPF, abstracting pod IPs.
Detail how the service's endpoint list is populated via label selectors and readiness probes, and how kube-proxy updates routing rules as pods are added/removed.
Describe the final hop: the request is forwarded to a specific pod's IP and port, possibly through a sidecar proxy if a service mesh is used, and the pod processes it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the partial execution angle.
Start by enumerating the failure modes of a mid-hop request failure, then explain how to make operations idempotent and use retries with exponential backoff and jitter. Emphasize the importance of idempotency keys, deduplication, and transactional boundaries to prevent duplicate side effects.
Pro tip: Mention that retries should be paired with idempotency keys and that backoff should include jitter to avoid thundering herd. Also, highlight the need for a dead-letter queue after max retries to avoid infinite loops.
List what can go wrong: timeouts, partial writes, lost responses, network partitions, and duplicate deliveries. Explain how each can lead to inconsistent state or duplicate side effects.
Make operations idempotent using idempotency keys, unique constraints, or deduplication tables. Ensure that retrying the same request does not cause additional side effects.
Use exponential backoff with jitter to space out retries and avoid overwhelming the downstream service. Set a maximum retry limit and consider circuit breakers.
For operations that cannot be made idempotent, use compensating transactions (Sagas) or two-phase commits where appropriate. Discuss trade-offs between consistency and availability.
Log failures, use dead-letter queues for messages that exhaust retries, and set up alerts. Ensure that manual intervention or automated recovery can resolve stuck transactions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing cascading failures as a systemic risk and emphasize a layered defense strategy. Walk through specific patterns like timeouts, circuit breakers, bulkheads, and backpressure, explaining how each prevents failure propagation. Conclude with how you'd monitor and tune these mechanisms in production, tying back to Turo's marketplace reliability needs.
Pro tip: Mention that timeouts must be set based on downstream service latency percentiles (e.g., p99) and that circuit breakers should be paired with fallbacks—this shows you've dealt with real production incidents, not just theory.
Briefly explain what cascading failures are and why they're critical in distributed systems like Turo's, where a single slow service can exhaust resources and bring down the entire platform.
Discuss setting aggressive but realistic timeouts on all network calls, and using retries with exponential backoff and jitter to avoid overwhelming downstream services.
Explain how circuit breakers trip when error rates exceed a threshold, preventing calls to a failing service, and how bulkheads isolate resource pools to contain failures.
Describe mechanisms like rate limiting, queue depth limits, and graceful degradation to shed load when capacity is exceeded, protecting core services.
Emphasize observability (metrics, tracing) and chaos engineering to validate resilience, and tuning thresholds based on real traffic patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.