Start by clarifying requirements and scale, then design a data model that captures transfers as immutable events and maintains aggregated views for fast queries. Propose APIs that leverage pre-aggregated hourly and daily rollups, and discuss trade-offs between consistency, latency, and storage.
Pro tip: Emphasize idempotency and exactly-once processing for transfers to prevent double-spending, and consider using a ledger-based approach with append-only logs for auditability and easy rollup computation.
Ask about expected throughput, latency requirements, consistency needs, and data retention. Understand if transfers are between players, to/from system accounts, or both.
Propose a transfer event table (append-only ledger) with fields like transfer_id, from_account, to_account, amount, timestamp, status. Also design aggregated tables for hourly balances and daily inflow/outflow per account.
Specify endpoints: GET /accounts/{id}/flow?start=...&end=... for inflow/outflow over 24h, and GET /accounts/{id}/balance_timeseries?interval=hourly&days=30 for hourly balances. Include pagination and filtering.
Discuss partitioning by account_id or time, using a distributed database like Cassandra or DynamoDB, and pre-computing rollups via stream processing (e.g., Kafka + Flink) to serve queries quickly.
Explain how to ensure atomicity of transfers (e.g., using transactions or saga patterns), idempotency keys, and how to handle failures and retries without double-spending.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.