Payment Gateway 2 - Global & Fraud Detection

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Scale to 50 M transactions/day across 30 countries with real-time fraud detection and multi-currency settlement.
• •Design for a peak load target around 2,894 RPS (including burst headroom).
• •Daily transactions: ~50,000,000
• •Countries: 30
• •Fraud scoring latency: < 100 ms
• •Currencies supported: 25+
• •Settlement frequency: Daily

2) Capacity Planning Method

• •Convert traffic and growth constraints into request rate, storage growth, and concurrency budgets.
• •Keep at least 2-3x safety margin per tier (ingress, compute, storage, async workers).
• •Reserve explicit latency budgets per hop so p95 can be defended in review.

3) Architecture Decisions

• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •Sharding: Choose shard keys around access patterns and growth hotspots, not just data size.
• •Message Queues: Move non-blocking and retry-heavy work to async consumers with explicit retry and DLQ policies.
• •Analytics: Maintain separate OLTP and analytics paths; stream events into a warehouse/time-series layer.
• •Geo Distribution: Route users to nearest region/edge while keeping write-consistency boundaries explicit.
• •Monitoring: Instrument golden signals (latency, traffic, errors, saturation) per tier and per tenant/domain.

4) Reliability and Failure Strategy

• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Support rebalancing and hotspot detection from day one.
• •Guarantee idempotent consumers and trace every message with correlation IDs.
• •Version event schemas and monitor drop/late-event rates.
• •Design region failover and data residency controls as first-class requirements.

5) Validation Plan

• •Run one peak-load test, one dependency-degradation test, and one failover test.
• •Verify idempotency for all retried writes and async consumers.
• •Track user-facing SLOs first: p95 latency, error rate, and successful throughput.

6) Trade-offs to Call Out in Interviews

• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •Sharding: Sharding improves horizontal scale but makes cross-shard queries and transactions harder.
• •Message Queues: Async pipelines absorb spikes well, but increase eventual-consistency complexity.
• •Analytics: Analytics pipeline unlocks insights, but adds eventual consistency and governance overhead.
• •Geo Distribution: Global latency improves, but cross-region consistency and operations become harder.

Practical Notes

• •A streaming pipeline (Kafka + Flink) can compute fraud features in real time and feed an ML scoring service.
• •Shard the ledger by merchant_id - each merchant's financial data is independent.
• •Use an append-only, immutable ledger design - never update or delete entries, only add correcting entries.

Why This Solution Works

Request path: The solution keeps ingress, service logic, and stateful dependencies separated so each layer can scale independently.

Reference flow: Web Clients -> DNS -> Load Balancer -> Core Service -> Primary NoSQL DB -> Replica SQL DB -> Event Bus -> Background Workers

Design strengths

• •Async queue/event bus isolates bursty workloads and supports retries without blocking synchronous requests.
• •Analytics pipeline is separated from OLTP path to avoid reporting workloads impacting transactions.
• •Monitoring and logs are wired in from day one for rapid incident triage.

Interview defense

• •This design makes bottlenecks explicit (ingress, core compute, persistence, async workers).
• •It supports progressive scaling without re-architecting the core request path.
• •It keeps correctness-sensitive state changes in durable systems while offloading background work asynchronously.