This is the whole question, and it's a lot.
Start by clarifying functional and non-functional requirements, then design a scalable, decoupled architecture that ingests events, evaluates routing rules, and delivers notifications reliably. Focus on trade-offs around consistency, latency, and fault tolerance, and discuss how to handle failures and scale each component.
Pro tip: Emphasize idempotency and deduplication at every stage to prevent duplicate notifications, and discuss how to handle user preferences and rate limiting to avoid overwhelming recipients.
Ask questions to understand scale (events per second, number of users), latency expectations, delivery guarantees (at-least-once, exactly-once), and supported channels. Also clarify how routing rules are defined and updated.
Propose a pipeline: ingestion layer (API gateway, message queue), rule evaluation engine, and delivery service per channel. Use a message broker (e.g., Kafka) to decouple producers from consumers and enable scalability.
Design a schema for events, user preferences, and routing rules. Discuss how to store and evaluate rules efficiently, possibly using a rules engine or a DSL, and how to handle dynamic updates.
Explain how to ensure reliable delivery with retries, dead-letter queues, and idempotency. Discuss per-channel adapters, rate limiting, and handling of failures (e.g., third-party outages).
Address scaling bottlenecks (e.g., rule evaluation, delivery throughput) and trade-offs between consistency, latency, and cost. Mention monitoring, alerting, and how to handle spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Silencing windows I got pretty comfortable with, just a time-based filter applied before fan-out.
Start by clarifying the requirements and scale of the notification pipeline, then walk through the three concerns (deduplication, grouping, silencing) in the order events flow through the system. For each, describe the data model, algorithms, and trade-offs, and finish by explaining how they interact and how you would monitor and test the pipeline.
Pro tip: Emphasize idempotency and state management: deduplication and silencing require shared state (e.g., a fast store like Redis) with TTLs, and grouping needs a windowing strategy—discuss how you'd handle failures and ensure exactly-once semantics without blocking the pipeline.
Ask about notification volume, latency tolerance, deduplication window, grouping rules, and silencing scope (user, event type, global). Confirm whether the system is real-time or batch.
Choose a dedup key (e.g., event ID, hash of content) and a store (Redis with TTL, or a database) to track seen events. Discuss idempotency, race conditions, and cleanup.
Define grouping criteria (time window, user, alert type) and a buffering mechanism (e.g., Kafka with windowing, or in-memory with flush). Explain how to aggregate and format digests.
Model silences as rules with start/end times and scope. Check silences before sending, and handle overlapping or conflicting rules. Use a fast lookup store.
Show how the three components fit in the pipeline (e.g., dedup -> silence check -> group -> send). Discuss trade-offs: latency vs. accuracy, storage cost, complexity, and failure modes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with exponential backoff plus a max retry count before routing to a DLQ.
Start by clarifying the notification types and their criticality, then outline a layered retry strategy with exponential backoff and jitter, and explain how dead letter queues (DLQs) capture permanently failed messages for analysis and manual intervention. Emphasize idempotency and deduplication to achieve at-least-once delivery without spamming users, and discuss how you monitor and alert on DLQ depth and retry rates.
Pro tip: Tie your strategy to user experience: for non-critical notifications, consider dropping after a few retries to avoid spam, while for critical ones (e.g., order updates), use persistent retries with a cap and fallback channels. Also, mention that you'd track delivery attempts per user and enforce a global rate limit to prevent notification storms.
Ask about the types of notifications (transactional vs. promotional), expected volume, latency requirements, and tolerance for duplicate or delayed messages. This shapes the retry and DLQ design.
Use exponential backoff with jitter to avoid thundering herd, set a maximum retry count, and differentiate retry policies based on notification criticality. For example, critical alerts might retry for hours, while promotional ones might retry only a few times.
After max retries, move messages to a DLQ for manual inspection or automated reprocessing. Ensure DLQ messages include metadata (failure reason, attempt count) and set up alerts for DLQ depth to detect systemic issues.
Make message processing idempotent using a unique message ID and deduplication store (e.g., Redis or database) to prevent duplicate sends. Additionally, implement user-level rate limiting and suppression lists to avoid overwhelming users.
Track metrics like retry rate, DLQ size, delivery latency, and duplicate rate. Set up alerts for anomalies and use DLQ analysis to improve retry logic and reduce failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Prioritization queue per channel was my answer.
Start by clarifying the requirements: what defines a high-severity alert, what are the latency and delivery guarantees, and what channels are available. Then propose a multi-layered architecture that prioritizes critical alerts through dedicated queues, rate limiting, and fallback mechanisms, while ensuring observability and fairness for lower-priority messages.
Pro tip: Emphasize that rate limits are per-channel and often per-account, so you should design for dynamic limit discovery and backpressure, not just static throttling. Also, mention that you'd measure the impact of throttling on alert delivery SLAs and iterate.
Ask about the definition of high-severity alerts, acceptable latency, delivery guarantees, and whether multiple channels (e.g., SMS, push, email) are available. Understand the provider's rate limit specifics (e.g., per second, burst allowance).
Implement separate queues for different severity levels, with high-severity alerts in a dedicated queue that gets priority access to the rate-limited channel. Use a token bucket or leaky bucket algorithm to enforce the rate limit while allowing bursts up to the cap.
Apply a distributed rate limiter (e.g., using Redis or a dedicated service) that all senders respect. When the limit is reached, high-severity alerts should either wait (if latency allows) or trigger fallback channels; low-severity alerts can be delayed or dropped based on policy.
If the primary channel is saturated, automatically route high-severity alerts to alternative channels (e.g., push notifications, phone calls, or a different SMS provider). Define escalation policies and ensure idempotency to avoid duplicates.
Track metrics like queue depth, rate limit hits, alert delivery latency, and fallback usage. Set up alerts for when high-severity alerts are delayed beyond SLA, and use this data to adjust priorities, limits, or add capacity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scenario and requirements, then propose a multi-layered solution that includes detection, mitigation, and prevention. Emphasize trade-offs between alert fidelity, latency, and system complexity, and tie your answer to DoorDash's scale and reliability needs.
Pro tip: Mention that hot subscribers are often a symptom of misconfigured alerts or a single point of failure, so addressing root causes (e.g., alert deduplication, aggregation) is as important as rate limiting. Also, highlight the importance of observability to detect and debug such issues quickly.
Ask questions to understand the scale, impact, and existing alerting infrastructure. Define what 'disproportionate volume' means and identify the subscriber's criticality.
Propose mechanisms to detect hot subscribers, such as per-subscriber metrics, anomaly detection, and alerting on alert volume. Emphasize the need for observability.
Suggest short-term solutions like rate limiting, throttling, or temporarily disabling the subscriber's alerts. Discuss trade-offs of each approach.
Propose structural changes like alert aggregation, deduplication, batching, or sharding. Consider architectural changes to the alerting pipeline to handle scale.
Discuss how to balance alert delivery guarantees, latency, and system complexity. Suggest monitoring and iterating based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.