Start by clarifying requirements and scale, then design the core components: event ingestion, subscription management, delivery workers, and persistence. Walk through the delivery flow, emphasizing at-least-once semantics, retry logic with exponential backoff, dead-letter queue, and tenant isolation. Finally, discuss trade-offs and operational considerations like monitoring and security.
Pro tip: Highlight idempotency and deduplication strategies for consumers, as at-least-once delivery means duplicates are possible. Also, mention the importance of isolating tenants at every layer (data, compute, network) to prevent noisy neighbor issues.
Ask about expected event volume, number of tenants, latency requirements, and payload sizes. This informs architectural decisions like partitioning and queue choices.
Outline the main services: API for subscription management, event ingestion service, delivery workers, and storage for events, subscriptions, and delivery status. Consider using a message queue for decoupling.
Explain how at-least-once delivery is achieved with persistent queues and acknowledgments. Describe retry mechanism with exponential backoff and jitter, and how failures are moved to a dead-letter queue after max attempts.
Discuss signing payloads with HMAC or asymmetric keys, and how tenants are isolated in data storage, processing, and rate limiting to ensure fairness and security.
Cover trade-offs like synchronous vs asynchronous delivery, push vs pull, and operational aspects: monitoring, alerting, replay capabilities, and scaling workers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tricky because ordering and throughput are basically in tension.
Start by clarifying requirements: throughput targets, ordering scope (per subscription), and failure semantics. Then propose a partitioned queue architecture where each subscription maps to a dedicated partition, and workers process partitions in parallel while preserving order within each partition. Discuss trade-offs between strict ordering and throughput, and how to handle scaling, rebalancing, and fault tolerance.
Pro tip: Emphasize that ordering guarantees are per subscription, so you can shard by subscription ID to achieve parallelism without violating order. Also, mention that you'd use a pull-based model with long polling or streaming to reduce latency and avoid overwhelming workers.
Ask about expected throughput, latency SLAs, ordering scope (per subscription), message size, and failure handling. This ensures your design aligns with actual needs.
Propose a partitioned queue where each subscription is assigned to a partition (e.g., using consistent hashing). This allows parallel processing across subscriptions while maintaining order within each.
Use a pool of workers that pull from partitions. Ensure each partition is processed by only one worker at a time to preserve order. Consider dynamic scaling and rebalancing when workers join/leave.
Discuss checkpointing offsets, idempotent processing, and handling worker failures. Ensure that on failure, the partition is reassigned and processing resumes from the last committed offset.
Compare strict ordering vs. throughput, and mention techniques like batching, pipelining, and backpressure. Also, consider using existing systems (e.g., Kafka, Pulsar) vs. building custom.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about including a unique delivery ID in the payload header so consumers can dedup on their side.
Start by clarifying that idempotency on the consumer side is about ensuring that processing a webhook multiple times has the same effect as processing it once. Then outline a concrete strategy: use a unique idempotency key from the webhook (e.g., event ID) to detect duplicates, store processed keys with a TTL, and make the processing logic idempotent or transactional. Finally, discuss trade-offs like storage cost, race conditions, and failure handling.
Pro tip: Mention that idempotency keys should be stored atomically with the side effects (e.g., in the same database transaction) to avoid race conditions where two retries slip through. Also, highlight the importance of a well-defined retention policy for idempotency keys to balance storage cost and deduplication window.
Explain that the goal is to prevent duplicate side effects from retried webhook deliveries, and note constraints like at-least-once delivery, potential out-of-order events, and the need for scalability.
Identify a unique identifier from the webhook payload (e.g., event ID, delivery ID) or derive one from the payload. Ensure it's stable across retries and unique per logical event.
Use a persistent store (e.g., database, Redis) to record processed keys. Ensure the check-and-set is atomic, ideally within the same transaction as the side effects, to avoid race conditions.
If processing fails, ensure the idempotency key is not marked as processed so that retries can succeed. Consider using a two-phase approach or transactional outbox pattern.
Talk about TTL for idempotency keys, storage costs, monitoring for duplicate rates, and how to handle key collisions or missing keys.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
HMAC signatures with a shared secret per subscription, include a timestamp in the signed payload to prevent replay attacks.
Start by framing webhook security as a multi-layered problem: authentication via HMAC signatures, replay protection, and per-tenant rate limiting. Then walk through a concrete design that covers key management, verification flow, and tenant isolation, emphasizing trade-offs and failure modes.
Pro tip: Mention that you verify signatures using a constant-time comparison and that you store per-tenant secrets in a secure vault with rotation support. Also, highlight that rate limiting should be applied at the edge (e.g., API gateway) and enforced per tenant to prevent noisy neighbors.
Ask about expected payload sizes, latency requirements, and whether tenants can have multiple endpoints. Identify threats: spoofing, replay, DoS, and cross-tenant interference.
Use HMAC-SHA256 with a per-tenant secret. Include timestamp and nonce in the signed payload to prevent replay. Verify signature in constant time and reject if timestamp is outside a tolerance window.
Apply rate limits based on tenant ID, using a sliding window or token bucket algorithm. Enforce at the edge (e.g., API gateway) and consider burst allowances. Return 429 with Retry-After header.
Store secrets securely (e.g., AWS Secrets Manager) with rotation. Ensure that verification and rate limiting are scoped per tenant to avoid cross-tenant impact. Log failures for auditing.
Monitor signature failures and rate limit hits. Implement alerting for anomalies. Gracefully handle failures: return appropriate status codes and avoid leaking information.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.