I went straight to hash maps and felt pretty good about it.
Start by clarifying requirements (e.g., coupon uniqueness, redemption limits, concurrency). Then propose a design using a hash map for coupon metadata and a min-heap or balanced BST for expiration, achieving O(1) average-case for add and redeem, and O(log n) for getRemaining if needed. Analyze time and space complexity for each operation.
Pro tip: Mention that in a real system, you'd need to handle concurrency and persistence, but for an in-memory service, you can use thread-safe data structures or locks. Also, discuss trade-offs between O(1) and O(log n) for getRemaining.
Ask about coupon uniqueness, redemption limits, expiration, and concurrency. Confirm expected operations and performance goals.
Select a hash map for O(1) coupon lookup and a priority queue (min-heap) for expiration. Consider a counter for remaining redemptions.
Implement addCoupon to insert into map and heap; redeem to check validity and decrement count; getRemaining to return count or compute from heap.
State time and space complexity for each operation, highlighting average-case O(1) for add and redeem, and O(log n) for getRemaining if using heap.
Compare alternatives (e.g., balanced BST for getRemaining in O(log n) vs. O(1) with extra space) and mention concurrency handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that edge cases are critical for correctness and trust in a payments system like Plaid. Then systematically walk through each edge case, explaining how you would detect, prevent, and handle it at the API and data layers, emphasizing idempotency, atomicity, and clear error responses.
Pro tip: Emphasize that handling edge cases is not just about validation but also about designing idempotent operations and transactional consistency to avoid race conditions in distributed systems.
Ask clarifying questions about coupon usage, such as whether expired coupons should be rejected outright or allow grace periods, and how limits are defined (per user, per coupon, global).
For each edge case, specify validation rules and appropriate error responses (e.g., 400 for invalid input, 409 for conflicts, 410 for expired). Ensure errors are descriptive and actionable.
Use database transactions or distributed locks to atomically check and update redemption counts, and design idempotent endpoints to handle retries without double-applying discounts.
Log edge case occurrences and set up alerts for unusual patterns (e.g., spikes in expired coupon attempts) to detect abuse or bugs.
Write unit and integration tests covering all edge cases, including race conditions, and consider property-based testing for robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining idempotency in the context of coupon redemption: the same request should have the same effect whether executed once or multiple times. Then propose a concrete mechanism, such as using a client-generated idempotency key stored server-side with the redemption result, and discuss how to handle concurrent requests and failures. Finally, mention trade-offs like storage overhead and key expiration.
Pro tip: Emphasize that idempotency should be enforced at the API layer, not just the database, and that the idempotency key must be unique per logical operation, not per HTTP request. Also, consider using a database transaction with a unique constraint on the key to atomically record the redemption and prevent duplicates.
Confirm what 'redeem' means (e.g., applying a coupon to an order) and what failure modes exist (network timeouts, server errors). Identify the need for idempotency to avoid double-application.
Propose using a client-supplied idempotency key (e.g., UUID) that uniquely identifies the redemption attempt. The server stores this key along with the result of the operation.
Use a database table with a unique constraint on the idempotency key. On request, attempt to insert the key; if it already exists, return the stored result. Wrap the redemption logic and key insertion in a transaction to ensure atomicity.
For concurrent requests with the same key, the unique constraint ensures only one succeeds; others can wait or return the existing result. For retries after failure, the key ensures the operation is not repeated if it already succeeded.
Discuss key expiration (e.g., 24 hours), storage costs, and what to do if the first request is still in progress (e.g., return 409 Conflict). Mention monitoring and logging for idempotency key usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's time model and the coupon's expiration semantics, then systematically walk through edge cases like clock skew, DST transitions, and timezone handling. For each, explain the potential failure mode and propose a concrete mitigation, emphasizing trade-offs between correctness, simplicity, and performance.
Pro tip: Mention that you would store all timestamps in UTC and perform expiration checks using a trusted time source like NTP, but also consider adding a grace period to handle minor clock skew gracefully. This shows you balance correctness with real-world resilience.
Ask whether the coupon expiration is absolute (e.g., a specific UTC instant) or relative (e.g., 24 hours after issuance), and whether it's tied to a user's local timezone. Confirm the system's source of truth for time.
Enumerate scenarios: clock skew between servers, DST transitions (spring forward/fall back), timezone conversions, leap seconds, and system clock adjustments. Consider both client and server perspectives.
For each edge case, determine what could go wrong: premature expiration, extended validity, inconsistent behavior across users, or security vulnerabilities. Assess the severity and likelihood.
Suggest solutions like using UTC, NTP synchronization, grace periods, idempotent checks, and client-side validation with server-side enforcement. Discuss trade-offs between strictness and user experience.
Conclude with a recommended approach that balances correctness, simplicity, and resilience, and mention monitoring/alerting for time anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the follow-up that killed me on time.
Start by clarifying the requirements and constraints, then propose a data model that supports category restrictions and minimum spend thresholds, and finally design the bulk coupon input parsing with validation and error handling. Emphasize extensibility, performance, and backward compatibility.
Pro tip: Discuss how you would handle partial failures in bulk parsing and ensure idempotency, showing you think about real-world reliability. Also, mention trade-offs between storing restrictions as structured fields vs. a flexible JSON schema.
Ask questions to understand the scope: Are categories hierarchical? Can coupons have multiple categories? What is the expected volume for bulk input? This ensures you design the right solution.
Propose extending the coupon schema with fields like applicable_categories (array or reference), min_spend (amount and currency), and consider indexing for efficient queries. Discuss normalization vs. denormalization.
Outline how the service will validate coupon applicability at redemption time, checking category and spend thresholds. Consider caching and performance implications.
Describe the parsing pipeline: accept CSV/JSON, validate each entry, handle errors gracefully (e.g., return per-row status), and ensure atomicity or partial success semantics.
Discuss how to make the system extensible for future restriction types (e.g., using a rules engine or flexible schema) and trade-offs between simplicity and flexibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.