I started with the obvious stuff: exact size match first, then upsize if nothing's available, small to medium to large in that order.
Start by clarifying requirements and defining 'best fit' as minimizing wasted space (e.g., smallest locker that fits the package) while considering operational constraints like locker availability and courier speed. Then outline core operations (assign, release, query) and choose data structures (e.g., balanced BST or segment tree) to efficiently find the best-fit locker. Finally, analyze time and space complexity and discuss trade-offs.
Pro tip: Emphasize that 'best fit' should be configurable based on business priorities (e.g., minimizing wasted space vs. maximizing locker utilization) and mention real-world constraints like package dimensions, locker sizes, and concurrent access.
Ask clarifying questions about locker sizes, package dimensions, assignment criteria, and system constraints. Define 'best fit' as the smallest locker that can accommodate the package, possibly with tie-breakers like proximity to entrance.
Specify operations: assignLocker(package), releaseLocker(lockerId), and possibly queryAvailability. Choose data structures like a segment tree or balanced BST keyed by locker size to efficiently find the best-fit locker.
Describe how to find the smallest available locker that fits the package. For example, use a segment tree to query the minimum size >= package size, or maintain sorted sets of available lockers by size.
Analyze the complexity of each operation: assignment O(log n) with a balanced BST or segment tree, release O(log n), and space O(n) for storing lockers. Discuss trade-offs between different data structures.
Address concurrency, distributed lockers, and potential optimizations like caching or sharding. Discuss trade-offs between optimal best-fit and simpler approaches like first-fit.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I spent most of my time and still felt shaky.
Start by clarifying the requirements and constraints, then propose a layered solution: use database transactions with row-level locking or optimistic concurrency control to prevent race conditions, design idempotent APIs to handle retries safely, and implement a fair queuing mechanism (e.g., FIFO with timestamps) to ensure couriers are served in order. Discuss trade-offs between consistency, latency, and scalability, and mention how you would test for concurrency issues.
Pro tip: Emphasize idempotency keys and conditional writes (e.g., 'assign locker only if status is available') to avoid double-assignment, and mention that fairness can be achieved with a distributed lock or a queue service like Amazon SQS with FIFO semantics.
Ask about expected concurrency level, consistency requirements, and whether fairness is strictly FIFO or just no starvation. This shows you don't jump to solutions prematurely.
Propose using database transactions with SELECT FOR UPDATE or optimistic locking (version numbers) to ensure only one courier can assign a locker at a time. Discuss trade-offs like contention and retries.
Design the assignment API to be idempotent using idempotency keys, so duplicate requests (e.g., from network retries) don't result in multiple assignments. Mention storing the key with a unique constraint.
Use a FIFO queue (e.g., SQS FIFO) or timestamp-based ordering to process requests in the order received, preventing starvation. Alternatively, use a distributed lock with fair queuing.
Compare approaches (e.g., pessimistic vs optimistic locking, centralized vs distributed locks) and explain how you would test concurrency (load tests, chaos engineering).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a relational schema pretty quickly: lockers table with status and size, assignments table with locker ID, package ID, timestamps.
Start by defining the data model and storage choices for lockers and assignments, emphasizing durability and consistency. Then explain the assignment flow with transactional guarantees and idempotency, and finally describe failure recovery mechanisms like write-ahead logging, retries, and reconciliation. Use a concrete example of a crash mid-assignment to illustrate how the system recovers without double-assignment or data loss.
Pro tip: Tie your design to Amazon's leadership principles by highlighting customer trust (no lost packages) and operational excellence (automated recovery, minimal manual intervention). Also, mention how you'd measure and monitor recovery success with metrics like assignment latency and reconciliation errors.
Describe the entities (locker, package, assignment) and choose a durable, transactional store like a relational database or DynamoDB with strong consistency. Explain how you model assignment state (e.g., pending, confirmed, failed) and use unique constraints to prevent double-booking.
Walk through the steps: reserve locker, write assignment record, update locker status, and notify user. Emphasize atomicity via a single transaction or saga with compensating actions, and idempotency keys to handle retries safely.
Detail what happens if the service crashes after reserving but before confirming: the transaction either commits fully or rolls back. If using a saga, describe how the orchestrator or choreography detects the incomplete state and triggers compensation or retry.
Explain mechanisms like write-ahead logging, periodic reconciliation jobs that scan for stuck assignments, and timeouts that release reserved lockers. Mention how you ensure exactly-once semantics and avoid duplicate assignments.
Cover metrics (e.g., assignment success rate, recovery time), alarms, and automated remediation. Highlight how you'd test failure scenarios with chaos engineering and ensure the system meets SLAs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem requirements first, including the data structures for lockers and packages, concurrency needs, and edge cases. Then design the pseudocode for assign and release, focusing on efficient lookup, thread safety, and error handling. Walk through the logic step-by-step, explaining your choices and trade-offs.
Pro tip: Demonstrate awareness of real-world constraints like race conditions and scalability by mentioning locking mechanisms or lock-free approaches, and discuss how your design would handle high concurrency.
Ask questions to understand the system: What data structures are available? Are lockers of fixed size? Is concurrency a concern? What should happen if no locker is available or if an invalid lockerId is released?
Specify the data structures for lockers and packages, such as a hash map for lockerId to locker status, a queue for available lockers, and a map for packageSize to available lockers of that size.
Outline steps: find an available locker that fits the package size, mark it as occupied, associate the package with the locker, and return the lockerId. Handle cases where no locker is available.
Outline steps: validate lockerId, mark the locker as available, remove the package association, and update available locker structures. Handle invalid lockerId or already released locker.
Discuss how to make the functions thread-safe using locks or atomic operations, and mention edge cases like concurrent assign/release, locker size mismatches, and error handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came at the end and I was running low on time.
Start by clarifying the current system architecture and requirements, then propose a high-level design that addresses each extension (reservations, sharding, SLA tiers, scaling) while discussing trade-offs. Emphasize incremental changes, data modeling, and operational considerations, aligning with Amazon's leadership principles like Customer Obsession and Think Big.
Pro tip: Frame your answer around customer impact and business value, and proactively discuss failure modes and mitigation strategies—Amazon values operational excellence and ownership.
Ask questions to understand the current system, expected scale, consistency needs, and SLA definitions. State your assumptions explicitly to guide the design.
Introduce a reservation service with a data model (e.g., resource, time slot, user) and handle concurrency via optimistic locking or distributed transactions. Discuss idempotency and conflict resolution.
Shard data by location to distribute load and enable horizontal scaling. Choose a sharding key (e.g., location ID) and discuss routing, rebalancing, and cross-shard queries.
Add a tier attribute to requests and implement prioritization via separate queues, rate limiting, or weighted scheduling. Ensure isolation to prevent lower tiers from impacting higher ones.
Address scaling challenges: automate shard management, use a control plane for metadata, and consider geo-distribution for latency. Discuss monitoring, autoscaling, and cost optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.