Start by clarifying requirements and constraints, then design a system that uses idempotency, asynchronous processing, and reconciliation to guarantee exactly-once donation recording. Walk through failure scenarios and explain how each component handles retries, duplicates, and spikes, emphasizing trade-offs and monitoring.
Pro tip: Emphasize idempotency keys and a reconciliation process with the payment provider; this shows you understand real-world payment integration pitfalls beyond just happy-path design.
Ask about expected traffic volume, payment provider capabilities (e.g., idempotency support, webhook reliability), and consistency requirements (e.g., strong vs. eventual).
Outline a flow where each donation attempt generates a unique idempotency key, and the system records donation intent before calling the payment provider, ensuring retries don't duplicate charges.
Explain how to use exponential backoff with jitter for retries, deduplicate callbacks via idempotency keys, and handle partial outages by queuing and processing asynchronously.
Describe horizontal scaling, load shedding, rate limiting, and using a message queue to buffer spikes, ensuring the system remains responsive during major fundraising events.
Implement a reconciliation job that periodically compares internal records with the payment provider's reports to detect and correct discrepancies, and set up monitoring and alerts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The cents thing I knew cold, floating point rounding errors in financial systems is pretty standard knowledge.
Start by outlining the core entities and their relationships: campaigns, donations, ledger entries, recurring donations, and payouts. Then explain the rationale for using integer cents to avoid floating-point precision issues and ensure financial accuracy. Finally, discuss how the ledger serves as the source of truth and how recurring donations and payouts integrate with it.
Pro tip: Emphasize that the ledger should be append-only and immutable for auditability, and mention that using integer cents is a common practice in financial systems to prevent rounding errors and ensure exact arithmetic.
Define the main entities: campaigns, donations, ledger entries, recurring donations, and payouts. Describe how they relate, e.g., a campaign has many donations, each donation creates ledger entries, and payouts are derived from campaign balances.
Explain that the ledger is an append-only, immutable record of all financial transactions. Each entry should have a type (debit/credit), amount in cents, timestamp, and references to related entities.
Describe how recurring donations are stored with a schedule (e.g., frequency, next occurrence) and how they generate individual donation records and ledger entries upon execution.
Explain that payouts are calculated based on the net balance of a campaign (donations minus fees) and are recorded as ledger entries to reflect the transfer of funds.
Discuss why monetary values are stored as integer cents: to avoid floating-point precision errors, ensure exact arithmetic, and simplify rounding and currency handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the payment flow at a high level, then drill into each component: client, server, payment SDK, and external processor. Emphasize idempotency, error handling, and security at each step to show production readiness.
Pro tip: Highlight the importance of idempotency keys and webhook reconciliation to prevent double charges and ensure consistency, as these are common pitfalls in payment systems.
User submits donation form; client validates input and invokes the payment SDK (e.g., Stripe SDK) to tokenize card details, avoiding raw card data on your servers.
Client sends token and donation details to your backend; server creates a payment intent with the payment provider, including idempotency key and metadata.
Client confirms the payment intent via the SDK, which handles 3D Secure if needed; SDK returns a result to the client and triggers a webhook to your server.
Server receives webhook events (e.g., payment_intent.succeeded), verifies signature, updates donation status, and reconciles with the payment intent to ensure consistency.
On success, trigger receipt email, update donor records, and handle failures with retries or user notifications; ensure idempotent processing to avoid duplicates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The double-click case I handled fine, idempotency key tied to the session and donation intent.
Start by framing the problem as achieving exactly-once semantics in a distributed system, then propose idempotency keys as the primary solution. Explain how to generate, store, and validate these keys to handle both double-clicks and crash-retry scenarios, and discuss trade-offs like storage overhead and key expiration.
Pro tip: Mention that idempotency keys should be generated client-side and that the server should store the key with the charge result in a single atomic transaction. This shows you understand the importance of atomicity and client-server coordination in preventing duplicates.
Restate the issue: duplicate charges can occur from user double-clicks or retries after a crash. Emphasize the need for exactly-once processing and discuss the impact on user trust and financial reconciliation.
Explain that each donation request should include a unique idempotency key generated by the client. The server uses this key to detect and ignore duplicate requests.
Describe how the server checks the idempotency key against a persistent store (e.g., database) before processing. If the key exists, return the stored result; otherwise, process the charge and store the key with the result atomically.
Explain that if a crash occurs after the charge but before the database update, the idempotency key ensures that a retry will not re-charge. The key should be stored in the same transaction as the charge record to guarantee atomicity.
Cover considerations like key expiration, storage costs, and handling of concurrent requests. Mention that idempotency keys should be unique per user action and that the system must handle key collisions gracefully.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Deduplication on the webhook event ID stored in a processed-events table, idempotent handler so replaying the same event is safe.
Start by explaining idempotency and event ordering as core design principles, then describe how you'd use unique event IDs and timestamps to deduplicate and reorder. Finally, outline a reconciliation process that periodically compares internal state with the payment provider's records to catch missed webhooks.
Pro tip: Emphasize that webhooks should be treated as untrusted and potentially unreliable; always have a fallback polling mechanism or reconciliation job to ensure data integrity.
Design webhook handlers to be idempotent by using unique event IDs to detect and ignore duplicates. Store processed event IDs with a TTL to prevent reprocessing.
Use event timestamps or sequence numbers to order events correctly. If an event arrives out of order, either buffer it until missing predecessors arrive or apply it with conflict resolution logic.
For transient failures, retry with exponential backoff. After max retries, move events to a dead letter queue for manual inspection and alerting.
Run a periodic job that fetches recent transactions from the payment provider's API and compares them with internal records. Identify and resolve discrepancies by replaying missing events or updating state.
Set up monitoring for webhook failures, latency, and reconciliation mismatches. Alert on anomalies to ensure timely intervention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer I gave: payment records and the ledger need strong consistency, notifications and analytics can lag.
Start by clarifying the system's core entities and user flows, then classify each data operation as requiring strong or eventual consistency based on business impact and user expectations. Explain how these classifications drive choices like synchronous replication, quorum writes, or asynchronous replication with conflict resolution, and tie them to Lyft's scale and latency requirements.
Pro tip: Frame consistency as a spectrum tied to user experience and business risk—e.g., 'A rider seeing a stale driver location is acceptable for a few seconds, but a double charge is not'—and mention how you'd measure and monitor consistency violations in production.
Ask clarifying questions to understand the system's scope, key entities (e.g., rides, payments, driver locations), and user-facing operations. This ensures your consistency analysis targets the right components.
For each entity or operation, decide whether it needs strong consistency (e.g., payments, ride state transitions) or can tolerate eventual consistency (e.g., driver location updates, ratings). Justify with business impact and user expectations.
Translate each classification into concrete architecture choices: strong consistency may use synchronous replication, quorum-based writes (e.g., Paxos/Raft), or transactional databases; eventual consistency may use async replication, CRDTs, or event sourcing with idempotent consumers.
Discuss latency, availability, and partition tolerance trade-offs (CAP theorem). Explain how you'd handle conflicts, retries, and idempotency, and how you'd degrade gracefully during network partitions.
Conclude by describing a pragmatic hybrid: e.g., a strongly consistent core for payments and ride state, with eventually consistent services for location tracking and analytics, connected via events or change data capture.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Reads: caching campaign pages, CDN for static assets, read replicas.
Start by clarifying the dual workload: read-heavy browsing (low latency, high throughput) and write-heavy donation spikes (bursty, high durability). Then propose a decoupled architecture that scales reads and writes independently, using caching, read replicas, and asynchronous processing for writes, while ensuring consistency and fault tolerance.
Pro tip: Emphasize that you would design for the spike as the normal case, not the exception—pre-provision capacity and use backpressure to protect the system. Also, discuss how you'd validate the design with load testing and chaos experiments.
Ask about read/write ratios, latency SLAs, consistency needs, and spike magnitude. Confirm whether donations must be strongly consistent or can be eventually consistent.
Use CQRS to decouple reads (served from caches and read replicas) from writes (handled by a scalable, durable queue and write-optimized store).
Implement multi-layer caching (CDN, application cache, database cache) and horizontal read replicas. Use consistent hashing for cache distribution.
Place a message queue (e.g., Kafka) in front of the write path to buffer spikes. Process donations asynchronously with idempotent consumers and a durable, partitioned datastore.
Add backpressure, rate limiting, and circuit breakers. Monitor queue depth, latency, and error rates. Plan for graceful degradation and auto-scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tokenization via the provider SDK so card data never hits our servers, PCI scope reduction.
Start by acknowledging that security and compliance are non-negotiable in payments, then walk through each requirement systematically, explaining how you would design the system to meet them. Emphasize a defense-in-depth approach, referencing specific technologies and standards like PCI DSS, tokenization, HMAC, RBAC, and machine learning for fraud detection. Conclude by discussing how you balance security with user experience and operational efficiency.
Pro tip: Demonstrate awareness of the broader ecosystem: mention that compliance is an ongoing process, not a one-time fix, and that you'd leverage Lyft's existing security infrastructure and third-party services (e.g., Stripe, Adyen) where appropriate to avoid reinventing the wheel.
Ask clarifying questions to understand the specific compliance standards (e.g., PCI DSS, GDPR) and the scale of the system. Identify which components handle sensitive data and where the boundaries of responsibility lie.
Explain how to avoid storing raw card data by using tokenization or third-party payment processors. Describe encryption at rest and in transit, and data minimization principles.
Detail role-based access control (RBAC) with least privilege, and webhook signature verification using HMAC to ensure authenticity. Mention audit logging and monitoring for suspicious activities.
Discuss real-time fraud detection using rules, machine learning models, and third-party services. Explain how to balance fraud prevention with false positives and user friction.
Describe ongoing monitoring, regular audits, penetration testing, and staying updated with evolving regulations. Highlight the importance of documentation and training.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Recurring donations stored as a schedule record with the payment method token, a job runs on the cadence and triggers a new charge.
Start by clarifying requirements: recurring donation frequency, payment methods, and tax receipt regulations. Then design a data model with separate entities for donors, recurring donation plans, and individual donation transactions, and outline the flow for generating receipts and donation history via asynchronous jobs and APIs.
Pro tip: Emphasize idempotency and auditability: ensure each donation transaction is uniquely identifiable to prevent duplicate charges and to generate accurate tax receipts, and consider using an event-driven architecture to decouple receipt generation from payment processing.
Ask about supported frequencies (monthly, quarterly), payment methods (credit card, ACH), tax receipt legal requirements (e.g., IRS), and expected scale. This ensures the design meets business and compliance needs.
Propose entities: Donor, RecurringDonationPlan (with schedule, amount, status), DonationTransaction (with timestamp, amount, status, receipt ID), and TaxReceipt (with PDF link, tax year). Establish relationships and indexes for efficient querying.
Describe how a scheduler triggers payment processing at each interval, how payment gateway is integrated, and how successful transactions are recorded. Include retry logic for failures and notifications to donors.
Explain that after a successful transaction, an event is published to generate a tax receipt asynchronously. The receipt service creates a PDF, stores it in object storage, and updates the transaction record with the receipt URL.
Design an API endpoint that returns a donor's donation history, including transaction details and links to receipts. Support filtering by date range and pagination, and ensure authorization so donors only access their own data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Metrics on payment success rate, webhook processing lag, queue depth, donation volume per campaign.
Start by framing observability as a first-class design concern, not an afterthought, and tie it directly to the system's SLOs and failure modes. Then walk through the three pillars (metrics, logs, traces) with concrete examples of what you'd instrument and why, before explicitly discussing the tradeoffs you made—such as cost vs. granularity, sampling vs. completeness, and latency vs. detail. Close by explaining how these choices enable faster debugging and safer deployments.
Pro tip: Anchor your observability choices to specific user-facing SLOs and failure scenarios (e.g., 'if p99 latency spikes on the ride-matching service, what signal tells me first?'), and quantify tradeoffs where possible—like 'head-based sampling at 1% cuts trace storage 100x while still catching tail latency issues.'
State what you need to observe: availability, latency, error rates, and throughput for critical user journeys. Tie each signal to an SLO so instrumentation has a clear purpose.
Specify metrics (RED/USE, business KPIs), structured logs (with correlation IDs), and distributed traces (with context propagation). Give examples of what you'd emit at each layer.
Discuss cost vs. granularity (e.g., high-cardinality metrics), sampling vs. completeness (traces/logs), and latency vs. detail (sync vs. async logging). Justify your choices based on scale and criticality.
Describe alerting, dashboards, and runbooks that turn signals into remediation. Mention how you'd use observability for canary deployments, A/B tests, or incident response.
Recap the most important tradeoffs and what you'd revisit as the system evolves. Highlight any metrics you'd monitor to validate the observability setup itself.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.