Started fine, picked a hashmap keyed by recipeId and just rejected duplicates on add rather than overwriting.
Start by clarifying requirements and defining the data model and API contract, then implement the CRUD operations with explicit handling for edge cases like missing records. Emphasize thread safety and error handling, and discuss trade-offs of in-memory storage.
Pro tip: Mention that you would use a concurrent hash map or locking to ensure thread safety, and define clear error responses (e.g., 404 for missing records) to make the API robust and predictable.
Ask about expected operations, data fields, and concurrency needs. Define a Recipe class with id, name, ingredients, and instructions.
Specify method signatures for add, update, get, and delete, including input parameters and return types. Define error behavior for missing records (e.g., throw exception or return null/optional).
Use a thread-safe data structure like ConcurrentHashMap. For add, generate a unique ID; for update, check existence and replace; for get, return record or indicate not found; for delete, remove and indicate success/failure.
Address missing records, duplicate IDs, null inputs, and concurrent modifications. Use atomic operations or locks to prevent race conditions.
Talk about limitations of in-memory storage (persistence, scalability) and how you might extend to a database or add caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The substring vs exact match decision is where I wasted time second-guessing myself.
Start by clarifying requirements: case-insensitive search by name, sorting by name or size, and tie-breaking rule. Then design data structures and algorithms, discussing trade-offs (e.g., hash map for search, sorting with custom comparator). Finally, implement and test edge cases.
Pro tip: Demonstrate awareness of real-world constraints: mention that case-insensitive search often requires normalization (e.g., Unicode) and that sorting stability matters for tie-breaking. Also, discuss time/space complexity and potential optimizations like indexing.
Ask about search specifics (exact match vs. substring), sorting criteria (ascending/descending), and tie-breaking rule (e.g., by name if sizes equal). Confirm input/output formats.
Choose appropriate structures: for search, a hash map with normalized keys (e.g., lowercase) or a trie; for sorting, a list of recipes with comparators. Consider memory vs. speed trade-offs.
Normalize query and stored names (e.g., to lowercase) for case-insensitive matching. If substring search is needed, consider more complex structures like suffix trees or simply iterate with normalization.
Define a comparator that sorts by the chosen key (name or size) and applies the tie-breaking rule (e.g., if sizes equal, sort by name). Ensure the sort is stable if needed.
Discuss time/space complexity of operations. Test edge cases: empty list, duplicate names/sizes, non-ASCII characters, and large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then propose a data model and API contract for addUser, explicitly defining duplicate userId handling. Discuss trade-offs of different duplicate strategies (e.g., reject, idempotent success, upsert) and justify your choice based on system needs and Coinbase's context.
Pro tip: In fintech, idempotency and auditability are critical. Consider making addUser idempotent for duplicate userIds to avoid duplicate account creation, and always log attempts for compliance.
Ask about expected behavior on duplicates, idempotency needs, and any regulatory constraints. Confirm whether userId is client-supplied or server-generated.
Define the User entity with userId as a unique key. Consider using a database unique constraint to enforce uniqueness at the storage layer.
Specify the addUser endpoint: request/response schema, HTTP status codes, and error format. For duplicates, choose a strategy (e.g., 409 Conflict, 200 OK with existing user, or 201 Created if idempotent).
Implement duplicate detection: check existence before insert or catch unique constraint violation. Decide on the response: reject with error, return existing user, or update (upsert).
Compare strategies: strict rejection ensures data integrity but may frustrate clients; idempotent success simplifies retries but may hide errors. Choose based on use case and document behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where things got interesting and not in a good way.
Start by clarifying requirements: what entities need versioning, expected read/write patterns, and rollback semantics (e.g., full restore vs. selective). Then propose a design that captures each mutation as an immutable version, using a versioned data model (e.g., append-only log or versioned rows) and a rollback mechanism that creates a new version pointing to the old state. Discuss trade-offs around storage, consistency, and performance, and outline how you'd implement and test it.
Pro tip: Emphasize idempotency and auditability: every mutation should be idempotent and produce an immutable version, which is crucial for financial systems like Coinbase. Also, mention that rollback should itself be a versioned operation to maintain a complete history.
Ask questions to understand which entities need versioning, what operations are considered mutations, and what 'restore' means (e.g., full entity state, partial fields). Determine read/write ratios and consistency requirements.
Propose a schema where each version is immutable and identified by a version ID. Options include: append-only log with entity ID and version number, or a versioned table with valid-from/valid-to timestamps. Discuss how to efficiently retrieve the latest version and any version by ID.
Define rollback as creating a new version that replicates the state of the target version. This preserves history and avoids destructive updates. Explain how to handle concurrent mutations and ensure atomicity.
Discuss storage overhead (e.g., full snapshots vs. deltas), read performance (indexing on version ID), and consistency (e.g., using transactions or event sourcing). Consider garbage collection or archival policies for old versions.
Sketch the API changes (e.g., endpoints for listing versions and rolling back), and describe how you'd test correctness (e.g., property-based tests for rollback) and performance under load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.