Start by clarifying requirements and constraints, then propose a distributed architecture that combines consistent hashing for room-to-server assignment with a per-room state store that supports versioning and idempotent updates. Address out-of-order events using logical clocks or sequence numbers, and ensure the 50ms admission decision is met by caching limits and using fast local checks with asynchronous reconciliation.
Pro tip: Emphasize that the 50ms latency budget forces a trade-off between strong consistency and availability; propose a hybrid approach where admission decisions are made locally with cached limits and eventually consistent global state, and discuss how to handle limit changes mid-call without disrupting ongoing sessions.
Ask questions to understand scale (number of rooms, participants, edge servers), consistency requirements, and failure modes. Confirm that the 50ms deadline is strict and applies to the admission decision.
Use consistent hashing to assign each room to a primary edge server (or a set of replicas) that owns the room's participant count and limit. Store state in a distributed store (e.g., Redis or a custom replicated log) with versioning to handle out-of-order events.
Attach sequence numbers or timestamps to join/leave events. Use idempotent operations and conflict-free replicated data types (CRDTs) or last-writer-wins with version vectors to reconcile state across servers.
Cache the current limit and participant count locally on each edge server. On a join request, check the local cache; if under limit, admit and asynchronously update the global state. If near limit, query the primary server with a timeout to avoid exceeding the 50ms budget.
When a host's subscription tier changes, propagate the new limit to all relevant edge servers via a pub/sub mechanism. Use a two-phase approach: first update the limit in the global store, then notify servers to refresh their caches. Handle in-flight joins by checking the latest limit at the primary server if local cache is stale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Fairness criteria for eviction was the part I fumbled.
Start by clarifying the system architecture and constraints, then propose a pub/sub mechanism for instant propagation and a fair eviction policy based on participant attributes. Emphasize trade-offs between consistency, latency, and user experience.
Pro tip: Consider using a deterministic, attribute-based eviction rule (e.g., longest tenure or lowest priority) to ensure fairness and predictability, and mention the importance of graceful degradation and user notifications.
Ask about the scale (number of edges, participants), latency requirements, and consistency needs. Understand what 'instantly' means and the impact of eviction on user experience.
Propose a publish-subscribe system (e.g., Kafka, Redis Pub/Sub) where the host's upgrade triggers an event that all edges subscribe to. Ensure low-latency delivery and idempotent handling.
Outline a fair eviction strategy: e.g., prioritize by role (host, co-host), tenure, or contribution. Use a deterministic algorithm to avoid race conditions and ensure consistency across edges.
Address potential issues like network partitions, message loss, and concurrent changes. Suggest using versioning or consensus protocols (e.g., Raft) for strong consistency if needed.
Propose monitoring propagation latency and eviction fairness, and suggest A/B testing or feedback loops to refine the policy based on user impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's purpose and the cost of errors in both directions, then propose a hybrid strategy that fails open for non-critical paths and fails closed for critical ones. Emphasize monitoring, graceful degradation, and data-driven thresholds to decide the failure mode.
Pro tip: Frame the decision as a business trade-off: quantify the cost of false positives (blocking legitimate users) versus false negatives (allowing abuse or serving stale data), and suggest A/B testing or canary deployments to validate the chosen policy.
Ask about the specific use case, SLAs, and what happens if the cache or network fails. Identify which operations are revenue-critical versus non-critical.
Evaluate the consequences of failing open (e.g., stale data, abuse) versus failing closed (e.g., downtime, user frustration). Consider both short-term and long-term effects.
Propose a tiered approach: fail open for read-heavy, non-critical paths (e.g., recommendations) and fail closed for write or security-sensitive paths (e.g., payments). Use circuit breakers and fallbacks.
Set up metrics to detect partitions and cache failures, and trigger alerts. Use these signals to dynamically adjust failure modes or thresholds.
Suggest testing strategies like chaos engineering, A/B tests, or canary releases to measure the impact of the chosen policy and refine over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The observability piece I actually felt okay about.
Start by clarifying the system's architecture and the host bypass mechanism, then propose a layered approach: enforce limits at admission time with atomic counters, and continuously reconcile counted occupancy against actual media session telemetry. Emphasize detection of drift through monitoring and automated remediation, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Frame the problem as a distributed systems consistency challenge: use idempotent operations and a reconciliation loop (like a control plane) to self-heal, and mention that you'd measure drift as a key health metric with alerting thresholds.
Ask about the host bypass mechanism, global limit definition, expected scale, and consistency requirements (e.g., strict vs eventual). This ensures your solution aligns with business and technical constraints.
Propose a centralized or distributed counter with atomic operations (e.g., Redis INCR, DynamoDB conditional writes) to enforce limits at join time. Include fallback and idempotency to handle retries and failures.
Periodically compare the counted occupancy (from the admission system) with actual active media sessions (from media servers or WebRTC stats). Use a reconciliation job that logs discrepancies and triggers alerts.
Automatically correct drift by adjusting counters or terminating excess sessions, and ensure the process is safe (e.g., only adjust when confident). Include manual override and audit logs.
Track drift rate, limit violations, and reconciliation latency as SLIs. Set up dashboards and alerts, and use A/B tests or simulations to refine thresholds and mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.