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Fault Tolerance and System Resilience Questions

Designing systems to anticipate, tolerate, contain, and recover from component and network failures while minimizing customer impact and preserving correctness. Topics include identifying common failure modes and single points of failure, redundancy and isolation patterns at hardware, service, and geographic levels, and failover strategies including active active and active passive. Cover retry policies with exponential backoff, timeouts, circuit breaker and bulkhead patterns, graceful degradation, rate limiting, and backpressure techniques to protect systems during overload. Discuss orchestration of node rejoin and state rebuild, replication strategies and consistency trade offs, leader election and consensus implications, and techniques to avoid and mitigate split brain. Explain monitoring, health checks, alerting, and metrics such as mean time to recovery and mean time between failures to guide operational improvements. Include testing for resilience through chaos engineering and fault injection, handling flaky components in test environments, analysis of past failures and refactoring for resiliency, and operational practices that reduce blast radius and speed recovery.

MediumSystem Design
71 practiced
Design a leader election mechanism for a distributed job scheduler (similar to Airflow) that must tolerate node failures and avoid split-brain. Compare using ZooKeeper, etcd (Raft), or a cloud-managed leader election service. Explain membership changes, lease durations, and how to minimize downtime during leader failover.
MediumSystem Design
75 practiced
Describe the orchestration and technical steps required when a node running a stateful stream processing task (e.g., Flink operator or Kafka Streams instance) rejoins after a failure. Include how state is rebuilt from checkpoints/changelogs, how partition rebalancing is coordinated, and ways to minimize processing lag and duplicate outputs.
EasyTechnical
82 practiced
Write Python pseudocode for a retry helper used by an ingestion job to call an idempotent external API. Requirements: configurable max_retries, base_delay, max_delay, full jitter, abort on non-retriable 4xx errors, and a simple usage example showing how to wrap a request. You may use pseudocode / comments rather than exact library calls.
MediumTechnical
62 practiced
Explain mechanisms to implement backpressure in a streaming ingestion pipeline that uses Kafka producers feeding a Spark/Flink consumer cluster, and downstream services that can be slower. Cover producer throttling, consumer pause/resume, windowing, admission control, and architectural patterns (e.g., buffers, bounded queues) to prevent overload.
HardSystem Design
102 practiced
You must implement a distributed metadata store for an ETL orchestrator that requires strong consistency and leader-based writes. Compare Raft (etcd) vs Paxos (ZooKeeper) for consensus and leader election. Discuss implementation complexity, handling membership changes, performance, and failure recovery implications for each choice.

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