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Basic Fault Tolerance Patterns Questions

Understanding common patterns that make systems fault-tolerant: replication (data redundancy across multiple servers), failover (switching to backup when primary fails), circuit breakers (stopping requests to failing services to prevent cascades), retry with exponential backoff (intelligent retrying with delays), timeouts (preventing hanging requests), and graceful degradation (providing partial functionality when components fail). Know when each pattern is appropriate and its trade-offs. Understand that fault tolerance usually involves trade-offs: more replicas cost more but tolerate more failures.

MediumTechnical
47 practiced
Write concise pseudocode showing client-side retry behavior that consults a local circuit-breaker before retrying an HTTP request. Show how the client should decide between retrying, backing off, or fast-failing, and how it records metrics for observability.
MediumTechnical
58 practiced
Compare rate-limiting and circuit-breakers: what problems they solve differently, and how they can be combined to protect a service. Provide a practical configuration example where both are necessary and explain the order of enforcement (client, gateway, server).
MediumSystem Design
86 practiced
You're designing a circuit-breaker library to be used by thousands of microservices instances. Describe the data model and architecture choices: local-only counters, local with periodic aggregation, or central store (e.g., Redis). Discuss trade-offs for latency, false-open vs false-closed behaviour, operational complexity, and single points of failure.
MediumTechnical
48 practiced
Design a graceful degradation plan for a media streaming platform when the recommendation service becomes unavailable. Include fallback UX flows, feature flags, degradation thresholds, and metrics you would track to decide whether degradation is successful or should be rolled back.
HardTechnical
58 practiced
Design a graceful degradation strategy for an online store when the third-party recommendation engine fails. Include the fallback algorithm, feature flag controls, error budget thresholds that trigger degradation, and how you'd communicate degraded state to the product team and users.

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