I started with the schema and worked outward, which felt right.
Start by clarifying requirements and defining a clear order schema with states and transitions. Then design the in-memory data structures and algorithms for each operation, emphasizing idempotency and concurrency. Finally, discuss trade-offs, edge cases, and how you would test and scale the service.
Pro tip: Explicitly define idempotency keys and state transition rules upfront; this shows you understand the importance of correctness in financial systems. Also, mention how you would handle concurrent operations to avoid race conditions, as Coinbase deals with high-throughput trading.
Ask questions to understand expected throughput, latency, consistency requirements, and whether operations are per-order or global. Clarify what 'pause' and 'resume' mean (e.g., pausing processing of new events for an order).
Specify fields like orderId, userId, symbol, side, price, quantity, status, timestamps, and version. Define valid states (e.g., NEW, OPEN, PAUSED, FILLED, CANCELLED, DELETED) and allowed transitions, ensuring operations like delete and pause are only valid from certain states.
Choose in-memory structures (e.g., concurrent hash map for orders, queues for streams) and incorporate idempotency keys or operation IDs to deduplicate requests. Explain how you track processed operations to ensure add, delete, pause, resume are idempotent.
Describe algorithms for each operation, using locks or atomic operations to handle concurrent access. Ensure state transitions are atomic and idempotent, and discuss how to handle out-of-order or duplicate events.
Cover trade-offs like memory vs. durability, latency vs. consistency, and how to handle failures. Mention edge cases (e.g., deleting a paused order, resuming a deleted order) and how you would test idempotency and state transitions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Two hashmaps: one keyed on order_id for O(1) direct access, another keyed on user_id mapping to a set of order_ids.
Start by clarifying the requirements: are lookups read-heavy, what are the data sizes, and are updates frequent? Then propose a primary hash map keyed by order_id for O(1) lookups, plus a secondary index (hash map from user_id to a list/set of order_ids) for user-based lookups, and discuss the O(n) space overhead and O(1) average time for both. Finally, compare with alternatives like balanced BSTs (O(log n) time, ordered traversal) and mention real-world considerations like database indexing and caching.
Pro tip: Don't just list data structures—tie your choice to Coinbase's likely read-heavy, low-latency trading environment, and mention that in practice you'd use a database with secondary indexes rather than in-memory structures for persistence and scale.
Ask about data volume, read/write ratio, latency needs, and whether ordering or range queries are required. This shows you avoid premature optimization.
Use a hash map (e.g., unordered_map) keyed by order_id for primary lookup, and a second hash map from user_id to a collection of order_ids (list or set) for user-based lookup.
Both lookups are O(1) average time (O(n) worst-case for hash collisions). Space is O(n) for the primary map plus O(n) for the secondary index, effectively doubling memory.
Compare with balanced BSTs (O(log n) time but ordered traversal) and mention that hash maps don't support range queries. Also note update costs: inserting/deleting requires updating both indexes.
Explain that in production, you'd likely use a database with secondary indexes (e.g., PostgreSQL B-tree indexes) or a distributed store like Cassandra, and mention caching for hot data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the user_id index really pays off.
First, clarify the requirements: is this a one-time batch deletion or a frequent operation? Then, discuss how the design changes: you need an index on user_id for efficient lookup, and you must consider atomicity, concurrency, and performance implications. Finally, propose a solution that balances correctness and efficiency, such as using a transaction with a batched delete or a background job for large datasets.
Pro tip: Mention that deleting a large number of orders in a single transaction can cause lock contention and replication lag; propose a batched approach with monitoring to avoid impacting production.
Ask about the expected frequency, volume of orders per user, and whether the deletion must be atomic or can be eventually consistent. Also consider if soft delete is acceptable.
Ensure there is an index on user_id to avoid full table scans. If orders are stored in multiple tables (e.g., order items), consider cascading deletes or separate operations.
For small volumes, a single DELETE with WHERE user_id = ? in a transaction works. For large volumes, use batched deletes (e.g., LIMIT 1000) in a loop, or a background job to avoid long locks and replication lag.
Consider using SELECT ... FOR UPDATE or optimistic locking to prevent new orders from being inserted during deletion. If eventual consistency is acceptable, use a soft delete flag and a cleanup job.
Add metrics for deletion time and count, and consider archiving old orders instead of hard deletes to improve performance and maintain audit trails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with the obvious answers: write-ahead log or event sourcing for persistence, locks or a message queue for concurrency.
Start by clarifying the current service architecture and requirements (e.g., data volume, read/write patterns, consistency needs) before proposing a persistence layer. Then discuss trade-offs between different storage options (SQL vs NoSQL, caching) and concurrency control mechanisms (optimistic vs pessimistic locking), tying choices back to Coinbase's needs for scalability, security, and low latency.
Pro tip: Emphasize idempotency and exactly-once processing for financial transactions, and mention how you'd handle failures and retries without double-spending. This shows you understand the domain's critical constraints.
Ask about data volume, read/write ratio, latency requirements, consistency needs, and existing infrastructure. This ensures your solution aligns with actual needs.
Evaluate SQL vs NoSQL, sharding, replication, and caching based on requirements. Discuss trade-offs like consistency vs availability and cost vs performance.
Select concurrency control mechanisms (optimistic/pessimistic locking, MVCC, distributed locks) and explain how they prevent race conditions and ensure data integrity.
Plan for horizontal scaling, partitioning, failover, and monitoring. Include strategies for handling hot spots and ensuring high availability.
Summarize key decisions, acknowledge potential drawbacks, and define success metrics (e.g., throughput, latency, error rates) to measure the solution's effectiveness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered the basics: add an order and verify it appears, delete a nonexistent order and confirm no error, pause an already-paused order and confirm idempotency, resume after delete and confirm it's rejected.
Start by restating the problem and clarifying assumptions, then systematically walk through test cases from basic to complex, covering normal, edge, and error cases. For each case, explain the input, expected output, and why it validates correctness, tying back to the implementation's logic.
Pro tip: Proactively mention how you would automate these tests and integrate them into a CI/CD pipeline, showing you think about maintainability and production readiness—qualities valued at Coinbase.
Restate the problem and confirm constraints, input/output formats, and any assumptions about data ranges or system behavior.
Walk through basic valid inputs that exercise the core functionality, ensuring the implementation produces correct outputs.
Discuss boundary conditions such as empty inputs, minimum/maximum values, duplicates, and large inputs that could break the implementation.
Explain how the implementation handles invalid inputs, exceptions, or unexpected states, and what the expected behavior should be.
Conclude by mapping each test case to specific parts of the code, demonstrating how the tests validate correctness and robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.