Start by clarifying requirements and scale, then design a high-level architecture with decoupled components (ingestion, processing, delivery). Focus on reliability, scalability, and trade-offs, and discuss how to handle failures and ensure delivery guarantees.
Pro tip: Emphasize idempotency and deduplication to handle retries without spamming users, and discuss how to prioritize notifications (e.g., critical alerts vs. marketing) to avoid overwhelming recipients.
Ask about expected event volume, user base size, delivery guarantees (at-least-once vs. exactly-once), latency requirements, and supported channels. This sets the stage for design decisions.
Propose a decoupled system: ingestion service receives events and publishes to a message queue (e.g., Kafka). Processing service consumes events, applies user preferences and deduplication, then enqueues delivery tasks per channel.
Design channel-specific workers (push, email, SMS) that handle provider integrations, retries with exponential backoff, and dead-letter queues. Ensure idempotency to avoid duplicate notifications.
Discuss horizontal scaling of services, partitioning of queues, and database sharding for user preferences. Address monitoring, alerting, and graceful degradation when providers fail.
Compare push vs. pull models, synchronous vs. asynchronous processing, and storage choices. Mention potential extensions like rate limiting, user batching, and analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the notification pipeline's requirements and constraints, then systematically address reliability concerns: retries with backoff, dead-letter queues for failed messages, and idempotency to prevent duplicate notifications. Emphasize trade-offs between reliability and latency, and how you would monitor and alert on failures.
Pro tip: Mention that idempotency keys should be generated at the source and propagated through the pipeline, and that dead-letter queues should be monitored and have a process for manual intervention or automated replay.
Ask about the expected volume, latency requirements, and tolerance for duplicate or lost notifications. Understand the criticality of different notification types (e.g., order updates vs. marketing).
Implement retries with exponential backoff and jitter to handle transient failures. Set a maximum retry limit to avoid infinite loops and consider circuit breakers to prevent cascading failures.
After retries are exhausted, route failed messages to a DLQ for later analysis and reprocessing. Ensure DLQs are monitored and have alerting to detect systemic issues.
Use idempotency keys to deduplicate notifications, ensuring that retries or multiple deliveries do not result in duplicate notifications. Store keys with a TTL to manage storage.
Set up metrics for retry rates, DLQ size, and duplicate detection. Use these to continuously improve the pipeline and adjust parameters like retry limits and backoff intervals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the quiet hours piece caught me a bit flat-footed.
Start by clarifying the requirements and scale, then propose a multi-layered architecture that enforces rate limits at the API gateway and application levels while respecting user preferences through a preference service. Emphasize idempotency, distributed counters, and asynchronous processing to handle high throughput and ensure compliance.
Pro tip: Mention the importance of idempotency keys and dead-letter queues to handle retries and failures gracefully, showing you understand production reliability concerns.
Ask questions to understand the expected traffic volume, types of notifications (e.g., push, SMS, email), and regulatory constraints like GDPR or TCPA. This ensures your design meets business and legal needs.
Propose a centralized service that stores user opt-outs, quiet hours, and channel preferences. It should be highly available and support fast reads, possibly using a cache like Redis.
Use a distributed rate limiter (e.g., token bucket or sliding window) at the API gateway and per-user level. Leverage Redis or a similar in-memory store for atomic counters and low latency.
Before sending, check the preference service and apply quiet hours logic (considering user time zones). If a notification is blocked, queue it for later or drop it based on priority.
Use idempotency keys to prevent duplicate sends, and implement retries with exponential backoff. Monitor rate limit hits, preference violations, and system latency with alerts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer from me: delivery success rates per channel, latency percentiles, DLQ depth as an alert signal, and per-alert-type SLA tracking.
Start by clarifying the system's critical user journeys and failure modes, then propose a layered observability strategy covering metrics, logs, traces, and alerts. Tie each tool to a concrete operational need, such as detecting order failures or latency spikes, and explain how you'd iterate based on incidents.
Pro tip: Emphasize that observability should be designed alongside the system, not bolted on later, and mention how you'd balance signal vs. noise to avoid alert fatigue. Show you understand the cost and complexity trade-offs of different tools.
Map the system's key user journeys (e.g., order placement, payment, delivery tracking) and list potential failure points. This ensures your observability covers what matters most to the business.
Propose specific service-level indicators (e.g., order success rate, p99 latency) and objectives. Explain how error budgets guide prioritization and alerting thresholds.
Recommend tools for metrics (Prometheus, Datadog), logging (ELK, Splunk), tracing (Jaeger, OpenTelemetry), and dashboards (Grafana). Justify choices based on scale, cost, and integration.
Describe how alerts are routed (PagerDuty), escalated, and tied to runbooks. Include strategies to reduce noise, such as multi-condition alerts and suppression during deploys.
Explain how you'd use post-mortems and regular reviews to refine metrics, add missing instrumentation, and adjust SLOs. Highlight the importance of continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.