Start by clarifying requirements and constraints, then design a simple in-memory data model with accounts and a transaction log for idempotency. Walk through the transfer algorithm step-by-step, emphasizing validation, atomicity, and idempotency, and discuss trade-offs like locking granularity and failure scenarios.
Pro tip: Demonstrate awareness of concurrency by discussing how you would prevent race conditions (e.g., per-account locks or a global lock) and how you would ensure idempotency even if the same transaction ID is retried concurrently.
Ask about expected scale, concurrency, persistence, and error handling. Confirm that amounts are nonnegative, accounts must exist, and transfers must be atomic and idempotent.
Propose an in-memory structure: a map of account IDs to balances, and a map of transaction IDs to transfer records (or a set of processed transaction IDs) for idempotency.
Define CreateAccount (initialize balance to zero) and Transfer (validate, check funds, update balances atomically). Use a lock or transactional mechanism to ensure atomicity.
Before processing a transfer, check if the transaction ID has already been processed. If so, return the previous result (or success) without reapplying the transfer.
Talk about locking strategies (global vs per-account), memory usage, failure recovery, and how to handle concurrent requests with the same transaction ID.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went straight to a sorted set and talked through maintaining a running aggregate per account.
Start by clarifying the requirements: are updates frequent, and do we need real-time top K? Then propose a data structure like a hash map for account balances combined with a heap or balanced BST for ordering. Discuss the tradeoffs between update and query time, and consider if approximate or batch processing is acceptable.
Pro tip: Mention that in a real system like Coinbase, you might use a streaming approach with a min-heap of size K to maintain top K efficiently, and that you'd consider persistence and concurrency. Also, note that if updates are extremely frequent, a heap might not be optimal due to O(log n) updates, and you might use a bucket or count-based approach if volumes are bounded.
Ask about update frequency, query frequency, K size, and whether exact top K is needed. Determine if data fits in memory and if real-time updates are required.
Suggest a hash map for account balances and a min-heap of size K for top K, or a balanced BST for ordered access. Alternatively, consider a combination like a hash map with a sorted list or a Fenwick tree for prefix sums.
For each structure, detail update and query time/space. For example, hash map + heap: update O(log K) if in top K, query O(K log K) to extract sorted; balanced BST: update O(log n), query O(K log n).
Compare tradeoffs: heap is space-efficient but query requires sorting; BST allows ordered traversal but higher update cost. Consider if K is small, heap is better; if K is large, BST or sorted array might be better.
Mention distributed or streaming approaches if data is large, like using a priority queue per shard and merging, or approximate algorithms like count-min sketch for heavy hitters.
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 with a pending rewards table and a scheduled job that processes matured rewards. Emphasize idempotency via unique transaction IDs and crash safety through transactional writes and retries.
Pro tip: Mention that you would use a database transaction to atomically mark the reward as processed and credit the account, and that you would use a unique constraint on the reward ID to prevent double crediting. Also, discuss monitoring and alerting for failed jobs.
Ask about scale, consistency requirements, and whether the 24-hour delay is exact or approximate. Confirm that cashback is 2% of purchase amount and that crediting should happen exactly 24 hours later.
Propose a pending_rewards table with fields: id, user_id, purchase_id, amount, status (pending/credited), created_at, scheduled_at, and a unique constraint on purchase_id to prevent duplicates. Also, consider a ledger table for credits.
Use a scheduled job (e.g., cron or a queue with delayed messages) that runs periodically to find pending rewards where scheduled_at <= now. Process each reward by crediting the user's account and updating the reward status.
Wrap the credit operation in a database transaction: update reward status to 'processing' with a unique transaction ID, then credit the account, then mark as 'credited'. Use idempotency keys to handle retries. If crash occurs, the transaction rolls back, and the job can retry.
Implement retries with exponential backoff for transient failures. Log errors and set up alerts for failed rewards. Consider a dead-letter queue for persistent failures and manual intervention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
By this point I was pretty mentally cooked.
Start by clarifying requirements and constraints, then design the operation as an idempotent, transactional workflow that ensures atomicity and consistency. Break down the problem into data migration, ID redirection, and reconciliation of pending cashback and overlapping transaction IDs, addressing edge cases and failure recovery.
Pro tip: Emphasize idempotency and auditability: design the operation to be safely retryable and log every step for reconciliation and rollback. This shows you understand production-grade financial systems where correctness and traceability are critical.
Ask about data volume, consistency requirements, downtime tolerance, and regulatory constraints. Confirm whether the operation should be synchronous or asynchronous, and how to handle failures.
Plan how to move balances and transaction history to the primary account, ensuring atomicity and consistency. Consider using a two-phase commit or saga pattern if multiple services are involved.
Create a mapping from the duplicate ID to the primary ID, and update all references (e.g., in user profiles, payment methods) to redirect future operations. Ensure the mapping is persisted and used by all relevant services.
Identify pending cashback tied to the duplicate account and transfer or re-issue it to the primary account. For overlapping transaction IDs, deduplicate and resolve conflicts based on timestamps or business rules.
Make the operation idempotent using a unique operation ID, log all changes for auditing, and design a rollback plan in case of partial failure. Consider using a state machine to track progress.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.