Start by clarifying functional and non-functional requirements, such as supported order types, expected throughput, latency, and consistency needs. Then propose a high-level architecture with an order router, adapters for each exchange, and a state manager, emphasizing idempotency, fault tolerance, and reconciliation. Finally, dive into trade-offs around consistency, reliability, and scalability, and discuss how to handle exchange-specific protocols and failures.
Pro tip: Demonstrate awareness of real-world crypto exchange quirks like rate limits, partial fills, and API versioning by proposing a normalized internal order model and a circuit breaker pattern per exchange. This shows you understand both system design and practical integration challenges.
Ask questions to understand order types (market, limit), expected volume, latency requirements, consistency guarantees, and supported exchanges. Identify non-functional needs like fault tolerance, auditability, and regulatory compliance.
Outline core components: API gateway, order service, router, exchange adapters, state store, and monitoring. Explain how orders flow from client to exchange and how responses are processed.
Propose an adapter pattern to abstract different exchange protocols (REST, WebSocket, FIX). Discuss normalization of order formats, handling authentication, rate limits, and error codes.
Describe mechanisms for idempotency, retries with exponential backoff, circuit breakers, and reconciliation to handle partial failures and ensure eventual consistency between internal state and exchange state.
Analyze trade-offs between consistency and availability, synchronous vs asynchronous processing, and how to scale horizontally. Mention monitoring, alerting, and testing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the ambiguity: the request timed out, but the order may or may not have been placed. Explain that you would record the order in a 'pending' or 'unknown' state, then use idempotency keys and reconciliation to safely retry without duplication. Emphasize that the client should never assume failure or success without verification.
Pro tip: Mention that you would design the system to be idempotent from the start, using a client-generated idempotency key, so that retries are inherently safe. This shows you think about failure modes proactively, not just reactively.
Acknowledge that a timeout does not mean the request failed; the order may have been processed. State that you cannot assume either outcome.
Persist the order with a 'pending' or 'unknown' status, along with a unique idempotency key. This allows later reconciliation and prevents duplicate processing.
Retry the request using the same idempotency key. The server should detect the duplicate key and return the original result if the order was already placed, or process it if not.
If the retry fails or is inconclusive, query the order status using the idempotency key or a separate status endpoint. Update the local state based on the authoritative response.
Discuss timeouts on retries, exponential backoff, and dead-letter queues for manual intervention. Ensure the system is resilient and observable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem as an event-sourced ledger where every balance change is an immutable entry, then explain how orders create reservations (holds) that are later settled or released. Walk through the lifecycle of a single order to show how the ledger and reservation system together enable full auditability.
Pro tip: Emphasize that the ledger is append-only and that balances are derived from entries, not stored as mutable fields—this is how you guarantee auditability and avoid race conditions. Also mention idempotency keys to handle duplicate order submissions safely.
Represent every balance change as an immutable entry with a unique ID, timestamp, account, amount, and type (credit/debit). Balances are computed by summing entries, ensuring a complete audit trail.
When an order is placed, create a reservation entry that earmarks funds without moving them. This prevents double-spending while keeping the funds in the user's account until settlement.
Outline states: placed → reserved → settled (or cancelled/expired). Each transition generates corresponding ledger entries (e.g., release reservation, debit/credit actual funds) to reflect the change.
Use database transactions or a saga pattern to atomically update the ledger and reservation state. This guarantees that funds are never double-reserved or lost during failures.
Store all entries with sufficient metadata (order ID, user ID, reason) so you can replay the log to reconstruct any historical balance. Provide query APIs to fetch entries by account and time range.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system components and the failure scenario, then walk through the reconciliation process step by step, emphasizing idempotency and state consistency. Highlight how reconciliation uses the order's unique ID and exchange data to detect the filled order, release reserved funds, and prevent duplicate submissions.
Pro tip: Mention that reconciliation should be event-driven and idempotent, and that the user's resubmission should be blocked or deduplicated using a client-generated idempotency key. This shows you think about both correctness and user experience.
Restate the problem: an ACK is lost, the order filled, reserved funds are stuck, and the user resubmits. Identify the key components: order management system, exchange, reconciliation service, and user interface.
Describe how reconciliation periodically queries the exchange for order status using the order's unique ID. When it finds the order is filled, it marks it as such in the internal system, even if the original ACK was lost.
Upon detecting the fill, reconciliation releases the reserved funds and updates the user's balance to reflect the actual trade. It ensures idempotency by checking if the order was already processed before applying changes.
When the user resubmits, the system should detect the existing order via idempotency key or order ID and reject the duplicate, informing the user that the original order was filled. This prevents double-filling.
Discuss trade-offs: reconciliation frequency vs. latency, and the importance of idempotent operations. Mention safeguards like audit logs, alerts for discrepancies, and manual intervention for edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Genuinely hadn't thought through the multi-leg cancel case before.
Start by clarifying the system's order model: a parent order with child legs per venue, each with independent state machines. Then walk through the cancellation flow: attempt to cancel the open leg, handle the partially filled leg by either canceling the remainder or letting it fill, and ensure atomicity via a saga or two-phase commit. Finally, describe the user-facing state: a 'canceling' status with real-time updates showing filled and canceled quantities per leg.
Pro tip: Emphasize idempotency and reconciliation: cancellation requests must be idempotent to handle retries, and a background reconciler should sync venue states to avoid stuck orders. This shows you think about failure modes and eventual consistency, which is critical in trading systems.
Explain that a split order is a parent order with child legs on each venue, each leg having states like PENDING, OPEN, PARTIALLY_FILLED, FILLED, CANCELED. This sets the foundation for handling cancellation.
When the user requests cancellation, generate a unique cancellation ID and send cancel requests to both venues. Ensure the operation is idempotent so retries don't cause duplicate cancels.
For the partially filled leg, attempt to cancel the remaining open quantity. If the venue supports cancel-replace, you might cancel and replace with a smaller order, but typically you just cancel the remainder. The filled portion is irreversible.
For the still-open leg, send a cancel request. If it fills before cancellation, treat it as a fill and update the order state accordingly. Use a timeout and retry mechanism if the venue doesn't respond.
Show the user a 'canceling' status with real-time updates: filled quantity, canceled quantity, and remaining quantity per leg. Once both legs are resolved (canceled or filled), mark the parent order as CANCELED or PARTIALLY_FILLED/CANCELED. Run a reconciliation job to sync with venue states.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Classic exactly-once processing question dressed up in trading clothes.
Start by framing the problem as achieving exactly-once semantics in a distributed system, which typically requires idempotency and atomicity. Then describe a concrete design: using a unique message ID for deduplication, storing it in the same transaction as the ledger update, and ensuring the consumer acknowledges only after commit. Finally, discuss how to handle crashes and redeliveries with at-least-once delivery and idempotent processing.
Pro tip: Emphasize that exactly-once is achieved through idempotent writes and transactional boundaries, not by trying to prevent redelivery. Mention that you'd also monitor for duplicate attempts and alert on anomalies to catch edge cases.
Confirm the delivery semantics (at-least-once), the need for exactly-once application, and the ledger's consistency requirements. Ask about the message broker and database capabilities.
Use a unique execution report ID to deduplicate. Before applying, check if the ID has been processed; if not, apply the update and record the ID in the same atomic transaction.
Wrap the ledger update and the deduplication record insertion in a single database transaction. This guarantees that either both succeed or both fail, preventing partial updates.
Acknowledge the message only after the transaction commits. If the consumer crashes before commit, the message will be redelivered, but the deduplication check will prevent double application.
Discuss handling of duplicate IDs with different payloads, transaction isolation levels, and monitoring for duplicate processing attempts. Consider using a unique constraint on the deduplication table to enforce idempotency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Last follow-up, felt almost like a bonus question.
Start by clarifying the requirements: what types of conditional orders (stop-loss, take-profit, trailing stop), latency expectations, and consistency guarantees. Then propose a design that separates order submission from trigger evaluation, using a dedicated service that monitors live market data and triggers order placement when conditions are met. Discuss trade-offs around consistency, fault tolerance, and scalability.
Pro tip: Emphasize idempotency and exactly-once triggering to avoid duplicate orders, and consider using a distributed lock or leader election to ensure only one trigger evaluator acts on a condition.
Ask about order types, latency requirements, consistency needs, and failure handling expectations to scope the design appropriately.
Propose a separate trigger service that subscribes to market data, evaluates conditions, and submits orders to the matching engine when triggered.
Describe how conditional orders are stored, how market data flows to the evaluator, and how state is maintained to detect trigger conditions reliably.
Discuss mechanisms to ensure exactly-once triggering, handle failures (e.g., evaluator crashes), and avoid duplicate orders.
Address scaling to many conditional orders and high market data throughput, and discuss trade-offs between latency, consistency, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.