This question sprawled way more than I expected.
Start by clarifying requirements and scale, then identify core entities and their relationships, and finally discuss trade-offs and extensions like social features. Use an entity-relationship diagram to visualize the model and explain key design decisions.
Pro tip: Emphasize idempotency and consistency in payment transactions, as these are critical for financial systems. Also, consider how social features can be decoupled from core payment processing to maintain performance and security.
Ask questions to understand expected scale, consistency needs, and key features. This ensures the data model aligns with business goals.
Define main entities like User, Account, Transaction, PaymentMethod, Transfer, and SocialFeature. Specify their attributes and how they relate (e.g., one-to-many, many-to-many).
Propose a relational schema with tables, primary/foreign keys, and indexes. Discuss normalization vs. denormalization for performance.
Explain how to ensure transactional integrity, handle concurrent updates, and prevent duplicate payments using idempotency keys.
Talk about scaling, sharding, and how social features can be integrated without impacting core payment flows. Mention potential NoSQL use for feeds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the exact ledger structure.
Start by explaining the core principle of double-entry accounting: every money movement affects at least two accounts, with debits equaling credits. Then, describe how you would model ledger lines to capture these entries, and emphasize the importance of idempotency keys to ensure exactly-once processing in distributed systems. Finally, discuss trade-offs such as consistency, scalability, and auditability.
Pro tip: Demonstrate awareness of real-world constraints by mentioning how idempotency keys are stored and validated (e.g., with a unique constraint in a database) and how you handle duplicate requests without double-spending. This shows you understand both theory and practical implementation.
Define double-entry accounting: every transaction has equal debits and credits, ensuring the accounting equation (Assets = Liabilities + Equity) holds. Give a simple example like transferring $100 from Account A to Account B.
Describe the schema for ledger lines: each line includes transaction ID, account ID, amount (positive for debit, negative for credit), timestamp, and metadata. Emphasize that the sum of amounts per transaction must be zero.
Explain that each money movement request carries a unique idempotency key. Before processing, check if the key has been used; if so, return the previous result. Store keys with a unique constraint to prevent duplicates.
Discuss how to ensure atomicity: use database transactions to write all ledger lines and the idempotency key together. Mention isolation levels and potential locking strategies to avoid race conditions.
Talk about trade-offs: strong consistency vs. availability, performance impact of idempotency checks, and how to scale (e.g., sharding by account ID, using event sourcing). Highlight auditability and reconciliation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the queries and their access patterns, then propose composite indexes tailored to each use case, explaining how they support the query efficiently. Discuss trade-offs like write overhead and storage, and suggest monitoring and iterating on indexes based on real usage.
Pro tip: Mention that indexes should be designed based on actual query patterns and that you would use the database's query planner to verify index usage, showing a data-driven approach.
Ask for specific query examples, expected data volume, read/write ratio, and latency requirements to understand the workload.
For transaction history, propose a composite index on (user_id, created_at DESC); for friends activity feed, consider (user_id, created_at DESC) on a feed table or a fan-out approach; for balance lookups, a simple index on user_id or account_id.
If queries need additional columns, suggest covering indexes to avoid table lookups, but balance against index size.
Discuss write amplification, storage costs, and maintenance overhead; suggest using partial indexes or filtering if applicable.
Recommend using database monitoring tools to track index usage and query performance, and adjust indexes as access patterns evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said reversals should be new ledger entries rather than deleting or updating existing rows, which felt right.
Start by framing reversals and disputes as first-class events in an append-only ledger, not as mutations to existing records. Then explain how you'd model the original transaction, the reversal/dispute entry, and the linking between them to preserve a complete audit trail. Finally, discuss the trade-offs between simplicity, performance, and regulatory compliance.
Pro tip: Emphasize that auditability isn't just logging—it's about being able to reconstruct the exact state of any account at any point in time, which often requires event sourcing or a ledger-based design. Mention that you'd align the model with financial regulations like SOX or PCI-DSS, showing you understand the business context.
Ask about the types of reversals (full, partial, chargebacks), dispute lifecycle, and regulatory requirements. This shows you don't jump to solutions without understanding the problem.
Propose an immutable, append-only ledger where every financial event is recorded as a new entry. Reversals and disputes are new entries that reference the original transaction, preserving history.
Outline key entities: Transaction, Reversal, Dispute, and their relationships. Include fields like transaction_id, reversal_id, dispute_status, timestamps, and actor IDs to track who did what and when.
Explain how you'd capture metadata (who, what, when, why) for every change, and how you'd enable point-in-time queries. Mention using event sourcing or temporal tables to reconstruct state.
Discuss trade-offs: append-only models can grow large, so consider partitioning, archiving, or snapshots. Also mention consistency vs. availability and how you'd handle idempotency for retries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.