This is the kind of question where you can spiral fast if you don't anchor on requirements first.
Start by clarifying requirements and scale, then design a high-level architecture with decoupled components for ingestion, processing, and delivery. Focus on scalability, reliability, and trade-offs, and dive deep into one or two critical areas like fan-out or channel-specific delivery.
Pro tip: Emphasize idempotency and deduplication to handle retries and at-least-once delivery, and discuss how to prioritize notifications (e.g., urgent vs. promotional) to avoid overwhelming users.
Ask about notification types, user preferences, delivery guarantees, latency, and scale (e.g., millions per minute). Define functional and non-functional requirements.
Propose a decoupled system with an ingestion API, message queue (e.g., Kafka), processing workers, and channel-specific delivery services. Include a user preference service and a template service.
Detail how to handle fan-out (e.g., per-channel queues), rate limiting, retries, and failure handling. Discuss storage for user preferences and notification logs.
Explain how to scale horizontally (partitioning, sharding), ensure high availability (replication, multi-region), and achieve at-least-once delivery with idempotency.
Discuss trade-offs like push vs. pull, synchronous vs. asynchronous, and cost vs. latency. Mention monitoring, alerting, and analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a REST ingest endpoint with idempotency keys on the producer side, then a common adapter interface that each channel (push, email, SMS) implements.
Start by clarifying requirements: what channels (email, push, SMS, in-app), expected throughput, latency, and reliability needs. Then propose a producer ingest API that accepts notification requests asynchronously with idempotency and validation, and a channel adapter interface that abstracts provider-specific details behind a common contract. Emphasize extensibility, fault tolerance, and observability.
Pro tip: Show you understand the difference between the ingest API (public-facing, high-volume, must be fast and reliable) and the adapter interface (internal, provider-specific, must be pluggable). Mention that you'd version the API and use a schema like CloudEvents for standardization.
Ask about scale (notifications per second), channels, delivery guarantees, latency, and whether producers need synchronous responses. This shapes the API design and adapter contract.
Define a RESTful or gRPC endpoint that accepts a notification request with fields like recipient, template, channel preferences, and metadata. Include idempotency keys, validation, rate limiting, and async processing via a queue.
Create an interface with methods like send(notification), getStatus(messageId), and validateConfig(). Ensure it's provider-agnostic, supports retries, and allows easy addition of new channels.
Discuss error handling, retries with exponential backoff, dead-letter queues, observability (logging, metrics, tracing), and security (auth, PII encryption).
Explain how to add new channels without changing the ingest API, versioning strategies, and how to handle provider-specific features via adapter capabilities.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The dedup piece I felt decent about: idempotency keys stored in a fast key-value store, TTL-based expiry.
Start by clarifying the pipeline's requirements and constraints, then walk through each component—deduplication, retries with exponential backoff, and DLQ management—in the order events flow. For each, explain the mechanism, trade-offs, and how you'd monitor and handle failures, tying choices back to Reddit's scale and reliability needs.
Pro tip: Emphasize idempotency and observability: deduplication and retries are only safe if downstream consumers are idempotent, and you need metrics and alerts to know when retries or DLQs are spiking. Mention that DLQ messages should be replayable after fixes, not just parked forever.
Ask about notification types, volume, latency tolerance, delivery guarantees (at-least-once vs exactly-once), and existing infrastructure. This shows you design for context rather than reciting a generic pattern.
Explain using a unique idempotency key per notification (e.g., event ID + recipient) stored in a fast lookup store like Redis or a database with TTL. Discuss trade-offs: storage cost vs. duplicate prevention window, and how to handle race conditions with atomic checks.
Describe a retry policy with exponential backoff and jitter, capped at a max delay and max attempts. Mention using a message queue with delayed retry (e.g., SQS visibility timeout, RabbitMQ dead-letter exchanges, or a scheduler) and distinguishing transient vs. permanent failures.
Explain routing messages that exceed retry limits to a DLQ, with metadata (error reason, attempt count, original payload). Cover alerting, manual inspection, and a replay mechanism to reprocess after fixing the root cause.
Highlight metrics (retry rate, DLQ depth, dedup hit rate), idempotent consumers, and trade-offs like at-least-once vs exactly-once, storage costs, and complexity. Tie back to Reddit's scale and user experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Token bucket per provider, tracked in Redis.
Start by clarifying requirements and constraints, then propose a layered rate limiting strategy that combines per-provider quotas, global limits, and adaptive throttling. Emphasize monitoring, backpressure, and graceful degradation to protect downstream SMS gateways and other channels.
Pro tip: Mention that rate limits should be dynamic and based on provider feedback (e.g., error rates, latency) to avoid static limits that either underutilize or overwhelm providers. Also, highlight the importance of idempotency and deduplication to prevent duplicate notifications during retries.
Ask about scale (notifications per second), provider SLAs, failure modes, and whether limits are per user, per provider, or global. Understand the criticality of different notification types (e.g., urgent vs. marketing).
Propose a combination of client-side throttling, a centralized rate limiter (e.g., token bucket or sliding window) per provider, and a global circuit breaker. Use a distributed cache like Redis for shared state across instances.
Incorporate feedback loops: monitor provider response times, error rates, and queue depths. Dynamically adjust limits and use exponential backoff with jitter for retries. Apply backpressure to upstream producers when queues fill.
Instrument metrics (e.g., rate limit hits, provider latency, error rates) and set up alerts for threshold breaches. Use dashboards to visualize per-provider throughput and saturation.
Define fallback strategies: queue and retry with delays, downgrade to alternative channels (e.g., email instead of SMS), or drop non-critical notifications. Ensure idempotency to avoid duplicates during retries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with delivery latency per channel, error rates by provider, DLQ depth, and end-to-end notification age.
Start by framing observability around the system's key user journeys and business goals, then propose a layered approach covering metrics, logs, and traces. Prioritize signals that directly impact user experience and system reliability, such as latency, error rates, and saturation, and tie them to actionable alerts.
Pro tip: Emphasize that observability should be driven by SLOs and error budgets, not just raw metrics. Mention that you'd start with the 'four golden signals' and then add domain-specific metrics like engagement or content freshness for Reddit's use case.
Clarify what you want to achieve: reduce MTTD/MTTR, ensure SLOs, and understand user impact. Align with business metrics like daily active users or content freshness.
Select metrics (e.g., latency, traffic, errors, saturation), logs (structured, with context), and traces (distributed tracing for critical paths). Prioritize based on user impact and system criticality.
Use standard tools (Prometheus, OpenTelemetry, etc.) to collect data. Ensure high cardinality and context are captured without overwhelming storage or cost.
Create actionable alerts based on SLOs and error budgets. Build dashboards for different audiences: on-call engineers, product managers, and executives.
Continuously review incidents and adjust metrics and alerts. Use post-mortems to identify missing signals and refine observability coverage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.