This is a single prompt but it's really two different problems stapled together, and the interviewers clearly care that you notice the tension.
Start by clarifying requirements and scale for both surfaces, then design each independently before discussing integration points. For the food item detail page, focus on read-heavy optimization with caching and denormalization; for the settlement pipeline, emphasize correctness, idempotency, and batch processing. Conclude by discussing trade-offs and how you would validate the design.
Pro tip: Explicitly call out the tension between consistency and availability in the settlement pipeline—drivers care about accurate payouts, so favor strong consistency and idempotent operations over eventual consistency. Also, mention how you would handle late-arriving events (e.g., a delivery completed after the monthly cutoff) to show you understand real-world edge cases.
Ask about expected QPS, data volume, latency requirements, and consistency needs for each surface. Confirm functional requirements like what data appears on the food item page and how settlements are calculated.
Propose a read-optimized architecture: a primary datastore (e.g., PostgreSQL) for food items, a separate store for user-generated content (posts, likes, ratings), and a caching layer (e.g., Redis) for hot items. Discuss denormalization and precomputed aggregates for ratings.
Outline a batch-oriented pipeline: ingest delivery events into a message queue (e.g., Kafka), process them in a stream or batch job to compute earnings, store settlement records in a transactional database, and trigger payouts via a payment provider. Emphasize idempotency and exactly-once processing.
Discuss how to handle failures, retries, and data consistency across both systems. Compare SQL vs NoSQL, batch vs stream processing, and caching strategies. Explain how you would monitor and alert on pipeline health.
Recap the key design decisions and how they meet the requirements. Suggest ways to test the system, such as load testing for the read path and reconciliation checks for the settlement pipeline.
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The incremental approach clicked for me pretty fast.
Propose maintaining a denormalized aggregate (count and sum of ratings) alongside the item, updated atomically on each rating write. For updates, adjust the sum by the delta between old and new rating, and handle concurrency with optimistic locking or atomic increments. Discuss trade-offs like eventual consistency and idempotency.
Pro tip: Mention that you'd store the sum and count as separate fields rather than just the average, because you can't derive the new average from the old average alone when a rating changes. Also, highlight the importance of idempotent updates to handle retries safely.
Ask about read/write patterns, expected QPS, consistency needs, and whether ratings can be updated or deleted. This shows you tailor the solution to the problem.
Store rating_count and rating_sum (or average and count) on the item record or a separate table. This avoids full recomputation on reads.
On new rating: increment count and add rating to sum. On update: adjust sum by (new_rating - old_rating). On delete: decrement count and subtract rating.
Use atomic operations (e.g., Redis INCR, SQL UPDATE with WHERE version) or optimistic locking to prevent lost updates. Consider eventual consistency and idempotency for retries.
Mention caching, batch updates, or event-driven updates via message queues. Compare with periodic recomputation for accuracy vs. performance.
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Blanked for a second on the contention angle.
Start by acknowledging the write contention problem on a single counter row for hot posts, then propose a sharded counter approach where increments are distributed across multiple rows or partitions. Discuss how to aggregate counts for reads, handle eventual consistency, and trade-offs between accuracy and scalability.
Pro tip: Mention that you would use a combination of sharding and asynchronous aggregation, and that you'd consider using a write-optimized store like Redis or a distributed counter service to absorb the write load, while periodically flushing to a durable store.
Explain that a single counter row per post creates a hotspot for hot posts, leading to lock contention and reduced write throughput.
Propose splitting the counter into N shards (e.g., using a hash of user ID or random shard key) so increments are spread across multiple rows, reducing contention.
Describe how to sum the shards on read, either on-demand or via a background job that periodically computes the total and caches it.
Discuss trade-offs: eventual consistency for reads, using a fast store like Redis for writes with periodic persistence, and ensuring no lost updates.
Mention additional techniques like batching writes, using a queue to decouple, or employing a CRDT-based counter for distributed environments.
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Cursor-based pagination on created_at or post id, not offset.
Start by clarifying the scale and requirements (e.g., QPS, latency, consistency). Then propose a cursor-based pagination strategy using a composite index on (food_item_id, created_at, post_id) to efficiently retrieve posts in order. Discuss how to handle real-time updates, caching, and sharding to ensure scalability and low latency.
Pro tip: Emphasize the importance of avoiding offset-based pagination due to performance degradation at scale, and highlight how cursor-based pagination with a stable sort key (like created_at + post_id) prevents duplicates and missing items when new posts are added.
Ask about expected traffic (QPS, number of food items, posts per item), latency SLAs, and consistency requirements. This shows you understand the problem context before diving into solutions.
Compare offset-based vs. cursor-based pagination. Recommend cursor-based (keyset) pagination using a composite key (e.g., created_at, post_id) to ensure efficient and stable pagination at scale.
Propose a schema where posts are stored with food_item_id, created_at, and post_id. Create a composite index on (food_item_id, created_at DESC, post_id DESC) to support efficient range queries.
Discuss sharding by food_item_id or using a distributed database like Cassandra or DynamoDB. Mention caching hot feeds and using read replicas to handle high read throughput.
Explain how to handle new posts, deleted posts, and updates. Use a stable sort key to avoid duplicates/skips, and consider eventual consistency trade-offs with caching.
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This was the part I found most interesting and also most stressful.
Start by clarifying the requirements: exactly-once payout semantics, idempotency, and failure recovery. Then propose a design that uses idempotency keys, a state machine for payout status, and transactional guarantees to ensure each driver is paid once. Finally, discuss how to handle retries and reruns, including reconciliation and monitoring.
Pro tip: Emphasize that exactly-once is achieved through idempotent operations and at-least-once delivery, not by trying to prevent retries. Mention that you would use a unique idempotency key per driver per pay period and store it in a database with a unique constraint.
Ask about scale (number of drivers, payouts per month), latency requirements, and existing infrastructure. Confirm that exactly-once means no duplicate payments even with retries.
Use a unique idempotency key (e.g., driver_id + pay_period) for each payout. Before processing, check if the key exists; if so, return the previous result. Store keys in a database with a unique constraint.
Model payout as states: PENDING, PROCESSING, PAID, FAILED. Use a transactional outbox to atomically update state and publish events. Ensure state transitions are idempotent.
On retry, the idempotency key prevents duplicate payments. For reruns, use the same keys. Implement reconciliation to detect and resolve discrepancies between internal records and payment provider.
Track metrics like duplicate attempts, payout failures, and reconciliation mismatches. Set up alerts for any duplicate payment attempts or state inconsistencies.
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Start by acknowledging the ambiguity and the need for idempotency to safely retry. Then describe a policy that combines client-side retries with exponential backoff, server-side idempotency keys, and reconciliation via webhooks or polling. Finally, discuss trade-offs between consistency and availability, and how to handle edge cases like duplicate charges.
Pro tip: Emphasize that the policy must be idempotent end-to-end, not just at the API level—include database transactions and external calls. Also mention that you'd log and monitor timeout rates to detect systemic issues.
Ask clarifying questions about the payment processor's behavior, timeout duration, and whether the operation is idempotent. Understand the business impact of duplicate charges vs. missed payments.
Use idempotency keys generated by the client and stored server-side to ensure that retries don't result in duplicate charges. Ensure the payment processor supports idempotency or implement a deduplication layer.
On timeout, retry the request with exponential backoff and jitter, but only if the operation is idempotent. Set a maximum retry limit to avoid infinite loops.
If retries fail or are not possible, use webhooks or a polling mechanism to check the payment status later. Update the order state based on the final outcome.
Define fallback actions for when reconciliation fails (e.g., manual review, customer notification). Log all timeouts and monitor for patterns to improve system reliability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The answer that preserves immutability is to never rewrite a settled run.
Start by clarifying that the month-end cutoff is a financial reporting concept, while the ledger model must handle late-arriving tip edits without corrupting historical periods. Propose an event-sourced or append-only ledger with effective dates, and explain how to reconcile the cutoff using accruals and adjustments.
Pro tip: Mention that you would treat tip edits as separate adjustment entries rather than mutating original transactions, and that you'd use a 'tip payable' liability account to handle the delay between order completion and final tip settlement.
Ask whether the month-end cutoff is for revenue recognition, driver payouts, or both, and confirm the exact edit window and any regulatory constraints. This ensures you design for the right invariants.
Design a double-entry ledger where each tip edit creates a new adjustment entry with its own timestamp and effective date, never overwriting the original. This preserves auditability and allows point-in-time reconstruction.
At month-end, accrue estimated tips for orders still within the edit window, and post subsequent edits as adjustments to the current period or as prior-period corrections if material. Use a clearing or suspense account to track pending edits.
Make tip edit events idempotent and provide reconciliation jobs that compare the sum of original tips plus adjustments against expected totals. This catches discrepancies and ensures the ledger remains balanced.
Explain how you would index by order ID and effective date to efficiently compute balances for any period, and how you would handle high write volume from tip edits without locking the ledger.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Drift happens when a write to the aggregate fails after the rating row was already committed, or vice versa.
Start by hypothesizing common causes of cache drift (e.g., non-atomic updates, race conditions, missed events) and explain how you would diagnose the issue using logs, metrics, and data reconciliation. Then outline a remediation plan that includes a backfill or repair job and preventive measures like transactional updates or periodic reconciliation, all while ensuring the page remains available.
Pro tip: Emphasize the importance of idempotent repair jobs and monitoring to detect drift early, and mention that you would communicate with stakeholders about the fix timeline and potential temporary inconsistencies.
Analyze recent code changes, system logs, and data patterns to determine why the cache drifted, such as a bug in the update logic, race conditions, or failed event processing.
Quantify how many items are affected and the magnitude of drift, and determine if the issue is ongoing or a one-time occurrence.
Create a repair job that recalculates aggregates from source data and updates the cache idempotently, running in the background without affecting page availability.
Introduce safeguards like transactional updates, event sourcing with idempotent consumers, or periodic reconciliation to prevent future drift.
Set up alerts for cache drift and validate that the fix resolved the issue and that preventive measures are working.
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You can do this cheaply by writing the user's new rating to a short-lived per-user cache entry and computing their personal view of the average client-side or at the edge using that cached value plus the stale aggregate.
First, clarify the requirements: the user should see their own rating reflected immediately in the average, while other users can tolerate eventual consistency. Then propose a read-time merge strategy where the user's own rating is combined with the eventually consistent aggregate, ensuring correctness without sacrificing scalability.
Pro tip: Mention that you would avoid updating the global aggregate synchronously to prevent hot-key contention and instead handle read-your-writes at the query layer, which is a common pattern at scale.
Confirm that only the submitting user needs immediate consistency, while others can see stale data. This scopes the problem and avoids over-engineering.
Describe how ratings are written asynchronously to an aggregate store (e.g., via a queue) and how the user's own rating is stored separately for quick retrieval.
On read, fetch the eventually consistent aggregate and the user's own rating, then compute the adjusted average on the fly. This ensures the user sees their write immediately.
Address scenarios like multiple rapid submissions, deletions, or updates to the user's rating, ensuring the merge logic remains correct and idempotent.
Compare with other approaches (e.g., synchronous update, session stickiness) and explain why read-time merge is preferable for scalability and simplicity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.