This was the core question and it ate most of the session.
Start by clarifying requirements and scale, then propose a distributed architecture using a centralized store like Redis with atomic Lua scripts to enforce all three limits in a single operation. Emphasize exact rolling window semantics via sorted sets or token buckets with timestamps, and discuss trade-offs around consistency, latency, and failure modes.
Pro tip: Highlight the all-or-nothing atomicity requirement: a rejection must not consume quota in any scope. This means you need a single atomic check-and-consume operation across all three limits, not sequential checks that could partially consume quota.
Ask about expected QPS, number of users/teams/companies, latency SLA, and whether limits are static or dynamic. Confirm that the window is exact rolling 10 minutes and that rejection must not count against any scope.
Choose a centralized store like Redis for atomic operations. Represent each scope's window as a sorted set of timestamps or a ring buffer, keyed by scope ID (user, team, company).
Use a Lua script to atomically: 1) prune expired entries, 2) check counts against all three limits, 3) if all pass, add the new timestamp to all three sets; otherwise reject without modifying any set.
Discuss sharding by user/team/company to scale, and how to ensure atomicity across shards if needed (e.g., single Redis instance per shard or distributed transactions). Address replication and failover.
Cover memory usage, clock skew, hot keys, and failure modes (e.g., Redis down). Propose fallbacks like local rate limiting or graceful degradation, and discuss monitoring and alerting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The interviewer pushed hard on this after I described the naive sequential check.
Start by clarifying the requirements: rate limits must be enforced atomically across stateless servers, covering user, team, and company scopes. Then propose a centralized atomic counter store (e.g., Redis with Lua scripts) that performs multi-scope checks and increments in a single atomic operation, and discuss trade-offs like latency, consistency, and failure modes.
Pro tip: Mention that you would use a single Lua script in Redis to atomically check and increment all three counters, and that you'd handle the case where one limit is exceeded by rolling back or using a two-phase approach. This shows you understand both atomicity and practical failure handling.
Ask about the expected scale, latency requirements, and consistency needs. Confirm that the system is stateless and that rate limits must be enforced globally.
Propose using a centralized data store like Redis that supports atomic operations. Explain why a distributed lock or database transaction might be too slow or complex.
Describe using a Lua script or Redis transaction to atomically check and increment counters for user, team, and company in one round trip. Ensure that if any limit is exceeded, no counters are incremented.
Discuss what happens if the central store is unavailable: fallback to local limits, fail open/closed, or degrade gracefully. Also mention latency impact and potential hot keys.
Talk about sharding counters, using sliding windows vs fixed windows, and how to handle high cardinality (e.g., many users) without overwhelming the store.
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Went through the obvious ones: exact vs approximate window, fail-open vs fail-closed on storage outage, latency budget, who owns the timestamp.
Demonstrate a structured, hypothesis-driven approach by categorizing clarifying questions into functional requirements, non-functional requirements, constraints, and scope. Prioritize questions that most impact architectural decisions, and explain how each answer would change your design.
Pro tip: Ask about the expected scale and latency requirements early, as they often dictate the choice between simple and complex architectures. Also, clarify what 'designing this system' means in the context of the interview—whether it's a high-level architecture or a detailed API design.
Ask questions to understand the core problem, the system's boundaries, and what is explicitly out of scope. This ensures you focus on the right aspects.
Determine the key features and use cases the system must support, such as user interactions, data flows, and integrations.
Ask about performance, scalability, availability, consistency, and security requirements to guide technology choices.
Inquire about technical, budgetary, or timeline constraints, as well as any existing systems or technologies that must be integrated.
Summarize the key questions and explain how the answers would influence your design decisions, showing a clear link between requirements and architecture.
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Talked about caching the mapping close to the app servers with a short TTL and accepting brief staleness during transfers.
Start by clarifying the domain model: users belong to teams, teams belong to companies, and relationships can change over time. Then discuss how to handle transfers mid-operation by using temporal data modeling (e.g., valid_from/valid_to timestamps) and event-driven updates to ensure consistency across services. Emphasize trade-offs between consistency, latency, and complexity, and how you would validate the design with real-world scenarios.
Pro tip: Mention that transfers should be treated as immutable events with effective dates, and that downstream systems should consume these events asynchronously to avoid tight coupling. Also, highlight the importance of idempotency and replayability in event processing to handle failures gracefully.
Ask about scale, consistency needs, and whether historical mappings must be preserved. Clarify if transfers are immediate or scheduled, and what systems depend on the mapping.
Propose a temporal model with separate tables for users, teams, companies, and a mapping table with valid_from/valid_to timestamps. Discuss indexing and query patterns for efficient lookups.
Describe how to process a transfer: create a new mapping record with an effective date, close the previous record, and emit an event. Ensure atomicity or eventual consistency depending on requirements.
Explain how to notify other services (e.g., via events, CDC, or API calls) and handle idempotency, ordering, and retries. Discuss caching strategies and invalidation.
Discuss trade-offs between strong vs. eventual consistency, latency, and complexity. Cover edge cases like concurrent transfers, backdated changes, and data reconciliation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that an exact sliding window log becomes impractical at thousands per window due to memory and performance overhead. Then propose approximate or hybrid alternatives like sliding window counters or token buckets, explaining the trade-offs between accuracy and scalability. Finally, tie your choice to the specific requirements of the system, such as whether strict accuracy is necessary or if slight over/under-counting is acceptable.
Pro tip: Mention that you would first clarify the business impact of approximate limits—many systems can tolerate small inaccuracies, and this often leads to a simpler, more scalable solution. Also, note that you might combine approaches, e.g., using a sliding window counter for most cases and falling back to exact logs for critical users.
Explain that storing every timestamp per user becomes memory-intensive and computationally expensive when limits are in the thousands, as it requires O(N) space per user and O(N) time to count events in the window.
Suggest sliding window counters (e.g., using two fixed windows and weighted average) or token bucket/leaky bucket algorithms, which use O(1) space per user and provide approximate rate limiting with tunable accuracy.
Compare the alternatives: sliding window counters are simple and memory-efficient but can over- or under-count near boundaries; token buckets allow bursts but require careful refill logic; exact logs are precise but unscalable.
Mention that you could use approximate methods for most users and exact logs for a small subset (e.g., premium users) or when strict accuracy is required, or dynamically switch based on load.
Conclude by stating that the choice depends on factors like acceptable error margin, latency requirements, and infrastructure (e.g., Redis sorted sets for exact logs vs. in-memory counters).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the sharding scheme and the exact semantics of the company-level limit, then propose a multi-layered solution: split the hot shard, use a distributed rate limiter with a global counter, and add caching or request coalescing. Emphasize that the limit must remain exact, so any approach must avoid approximate counters or eventual consistency for enforcement.
Pro tip: Mention that you would first try to reduce the load on the hot shard by optimizing the query pattern or adding a cache, because sometimes the bottleneck is not the shard itself but inefficient access. This shows you think about root causes before jumping to complex distributed solutions.
Ask questions to understand the sharding key, the definition of 'exact company-level limit', and whether the limit is per-company or global. Confirm if the limit is a hard cap or a target rate.
Identify why the shard is hot: is it due to a single large company, skewed data distribution, or inefficient queries? Determine if the bottleneck is CPU, I/O, or network.
Suggest short-term fixes like caching frequent reads, batching writes, or adding read replicas for that shard. If the limit is enforced by a counter, consider moving the counter to a separate, scalable store like Redis with Lua scripts for atomicity.
For exact enforcement, propose a distributed rate limiter using a global counter with strong consistency (e.g., via a consensus protocol like Raft) or a centralized service that partitions the limit across multiple shards. Alternatively, use a token bucket with a global coordinator.
Discuss trade-offs: added latency vs. exactness, complexity vs. scalability. Propose re-sharding or splitting the hot company's data across multiple shards while maintaining a global limit via a two-phase commit or a dedicated limit service.
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Gave a fail-open answer with a caveat: log every request that was allowed during the outage window, then replay against the recovered state to identify any over-sends and flag them for alerting rather than retroactive blocking.
Structure your answer as a chronological narrative: pre-failure design (circuit breakers, fallbacks), during-failure behavior (fail-open vs fail-closed, response codes, logging), and post-recovery reconciliation (idempotency, eventual consistency). Emphasize trade-offs between availability and consistency, and show you've thought about observability and data integrity.
Pro tip: Mention that you'd use a circuit breaker with a half-open state to probe recovery, and that you'd log a correlation ID for each failed Redis operation to trace reconciliation later. This shows operational maturity beyond just 'we catch exceptions'.
Describe how your service is configured to handle Redis unavailability: timeouts, retries with backoff, circuit breaker thresholds, and fallback strategies (e.g., local cache, fail-open for non-critical reads).
Explain what happens when Redis is down: circuit opens, requests either fail fast with a 503/500 or degrade gracefully (e.g., serve stale data). Specify which operations are critical vs non-critical.
Detail what gets logged (error type, operation, key, correlation ID) and what metrics are emitted (failure count, latency, circuit state). Mention log levels and sampling to avoid log floods.
Explain how the service detects recovery (circuit half-open, health checks) and how it reconciles counts: idempotent writes, compensating transactions, or background jobs that replay missed updates.
Summarize the consistency model chosen (e.g., eventual consistency for counts) and the trade-offs made (availability vs accuracy). Mention how you'd validate reconciliation (e.g., checksums, audits).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: if the new scope's key can be colocated on the same shard, you just add it to the atomic script and it's almost mechanical.
Focus on extending the existing rate limiting system through composition rather than modification. Explain how to introduce a new scope as a pluggable dimension that reuses the atomic core's primitives, such as key construction and token bucket operations. Emphasize that the core remains untouched by abstracting scope-specific logic into a separate layer.
Pro tip: Mention that you'd first check if the existing core already supports multiple scopes via a generic key builder—if so, adding a fourth scope is just configuration. This shows you value simplicity and avoid over-engineering.
Ask about the expected semantics of the per-notification-channel limit (e.g., is it per user per channel, or global per channel?) and whether it should be enforced independently or in combination with existing scopes.
Identify the core's extension points: how keys are generated, how limits are configured, and whether it supports multiple dimensions. Determine if the core is truly atomic and reusable.
Propose adding a new scope as a separate module that constructs a unique key (e.g., combining user ID and channel) and delegates to the same atomic operations. Avoid modifying the core by using dependency injection or a strategy pattern.
Explain how to integrate the new scope into the request pipeline, ensuring it doesn't interfere with existing scopes. Discuss testing strategies, such as unit tests for the new key builder and integration tests for combined limits.
Mention potential trade-offs like increased Redis key cardinality or latency, and alternatives like using a composite key or a separate rate limiter instance. Show awareness of scalability and maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.