Design a Scalable Loyalty Points Service
Design a highly scalable, correct, and auditable service responsible for managing loyalty points. This service will award points for purchase transactions denominated in USD, query user balances, and facilitate point redemptions.
The core conversion rate is fixed at 1 USD = 1.5 points.
Assume that transaction data originates from a card/payments platform. Points should only be awarded for posted or settled transactions, explicitly excluding authorizations. Furthermore, the redemption process must rigorously prevent double-spending of points.
Your design should comprehensively address the following aspects:
API Design
- Define APIs for ingesting transactions to award points, querying current user balances, and initiating point redemptions.
Idempotency and Deduplication
- Detail mechanisms to ensure exactly-once awarding of points and to support idempotent client retries for all operations.
Precision and Rounding
- Specify the units for storing monetary values and points, the conversion mathematics, and the rounding policy to maintain accuracy.
Consistency Model
- Discuss the application of strong versus eventual consistency models across different parts of the service, justifying your choices.
Data Model and Schema
- Propose a detailed data model, including tables, primary and secondary keys, indices, constraints, and how user balances are computed and maintained.
Prevention of Double Counting and Fraud
- Outline controls and strategies to prevent duplicate event processing, transaction replay attacks, fraudulent redemptions, and general system abuse.
Real-time and Batch Processing
- Describe the architecture for both streaming new transactions in real-time and handling large-scale batch backfills for historical data.
Backfill and Correction Flows
- Explain how the system will manage refunds, chargebacks, retroactive adjustments, and potential changes to the point conversion rate.
Scaling and Partitioning Strategy
- Detail your approach to scaling the service, including considerations for per-account ordering, data sharding, and achieving high throughput.
Observability
- Define the metrics, logging, tracing, and auditing capabilities necessary to ensure the correctness, performance, and adherence to SLAs of the service.
Rollout and Testing Plans
- Describe strategies for validating the service, including shadowing, canary deployments, and implementing guardrails during rollout and ongoing operations.
AI interview prep
Core dilemma
Balancing high transactional throughput and eventual consistency for awarding points with strict, immediate consistency and fraud prevention for point redemptions and balance accuracy, all while maintaining auditability and handling complex financial edge cases like corrections and rate changes.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a Scalable Loyalty Points Service
Design a highly scalable, correct, and auditable service responsible for managing loyalty points. This service will award points for purchase transactions denominated in USD, query user balances, and facilitate point redemptions.
The core conversion rate is fixed at 1 USD = 1.5 points.
Assume that transaction data originates from a card/payments platform. Points should only be awarded for posted or settled transactions, explicitly excluding authorizations. Furthermore, the redemption process must rigorously prevent double-spending of points.
Your design should comprehensively address the following aspects:
API Design
- Define APIs for ingesting transactions to award points, querying current user balances, and initiating point redemptions.
Idempotency and Deduplication
- Detail mechanisms to ensure exactly-once awarding of points and to support idempotent client retries for all operations.
Precision and Rounding
- Specify the units for storing monetary values and points, the conversion mathematics, and the rounding policy to maintain accuracy.
Consistency Model
- Discuss the application of strong versus eventual consistency models across different parts of the service, justifying your choices.
Data Model and Schema
- Propose a detailed data model, including tables, primary and secondary keys, indices, constraints, and how user balances are computed and maintained.
Prevention of Double Counting and Fraud
- Outline controls and strategies to prevent duplicate event processing, transaction replay attacks, fraudulent redemptions, and general system abuse.
Real-time and Batch Processing
- Describe the architecture for both streaming new transactions in real-time and handling large-scale batch backfills for historical data.
Backfill and Correction Flows
- Explain how the system will manage refunds, chargebacks, retroactive adjustments, and potential changes to the point conversion rate.
Scaling and Partitioning Strategy
- Detail your approach to scaling the service, including considerations for per-account ordering, data sharding, and achieving high throughput.
Observability
- Define the metrics, logging, tracing, and auditing capabilities necessary to ensure the correctness, performance, and adherence to SLAs of the service.
Rollout and Testing Plans
- Describe strategies for validating the service, including shadowing, canary deployments, and implementing guardrails during rollout and ongoing operations.