🏗️ Architecture & System Design

Tight coupling between services, synchronous communication causing cascade failures. Design event-driven system: 1) Message bus (Kafka or RabbitMQ) 2) Event schema registry 3) Producers and consumers 4) Event sourcing for audit 5) CQRS pattern. Events: OrderPlaced, UserRegistered, PaymentProcessed. Benefits: decoupling, async processing, scalability, resilience. Patterns: publish-subscribe, event sourcing, CQRS. Challenges: eventual consistency, event ordering, duplicate handling (idempotency), debugging distributed flows. Use: Kafka for high throughput, dead letter queues for failures. Est: 21 story points. Architectural shift.

Implement event-driven architecture with message bus

Monolith at 200K LOC, deployment takes 30min, can't scale individual components, team stepping on each other. Plan microservices migration: 1) Identify service boundaries (domain-driven design) 2) Strangler fig pattern (gradual extraction) 3) Service mesh for communication 4) Shared data challenges. Proposed services: User Service, Order Service, Payment Service, Notification Service, Search Service, Analytics Service. Challenges: distributed transactions (saga pattern), data consistency (eventual consistency), network latency, operational complexity. Benefits: independent scaling, faster deploys, team autonomy. Timeline: 12-18 months. Write RFC document. Est: 55 story points. Major undertaking.

Design microservices architecture migration strategy from monolith

Cache strategy ad-hoc, inconsistent, cache invalidation problems. Design comprehensive caching: 1) Multi-level caching (CDN, Redis, in-memory) 2) Cache-aside vs write-through patterns 3) Invalidation strategy 4) Cache warming 5) Cache monitoring. Layers: CDN (CloudFront) for static assets, Redis for data cache, application in-memory for hot data. Invalidation: time-based (TTL), event-based (pub/sub), manual. Handle: cache stampede (locking), thundering herd (jittered TTL), stale data. Metrics: hit rate (target 80%+), latency improvement. Document: what to cache, TTL guidelines. Est: 13 story points.

Design distributed caching strategy for improved performance

Business metrics delayed by 24 hours (batch processing), need real-time dashboards. Design streaming analytics: 1) Event ingestion (Kafka) 2) Stream processing (Flink or Spark Streaming) 3) Real-time aggregations 4) Time-series database (InfluxDB) 5) Visualization (Grafana). Metrics: active users, revenue, conversion rates, error rates, all real-time. Architecture: events → Kafka → Flink (windowing, aggregations) → InfluxDB → Grafana. Challenges: late arriving data, out-of-order events, stateful processing, exactly-once semantics. Benefits: real-time insights, faster decisions. Est: 21 story points. Data infrastructure.

Design real-time analytics pipeline for business intelligence

Microservices growing (12 services), observability nightmare, manual configuration for each service. Implement service mesh (Istio or Linkerd): 1) Automatic service discovery 2) Load balancing 3) Circuit breaking 4) Distributed tracing 5) mTLS between services 6) Traffic management. Benefits: centralized observability, security by default, canary deployments, A/B testing. Features: retry policies, timeouts, rate limiting per service. Observability: Jaeger for tracing, Prometheus for metrics, Kiali for visualization. Challenges: complexity, learning curve, resource overhead. Est: 21 story points. Advanced infrastructure.

Implement service mesh for microservices observability and control

Each microservice handling: auth, rate limiting, logging, CORS independently. Code duplication, inconsistent behavior. Implement API Gateway: 1) Single entry point 2) Authentication/authorization 3) Rate limiting 4) Request/response transformation 5) Routing 6) Caching. Use: Kong, AWS API Gateway, or custom (Express). Features: load balancing, circuit breaking, API versioning, request aggregation (GraphQL). Benefits: centralized concerns, simplified clients, consistent behavior. Challenges: single point of failure (mitigate with redundancy), latency. Pattern: Backend for Frontend (BFF) per client type. Est: 13 story points. Microservices pattern.

Implement API Gateway pattern for centralized cross-cutting concerns

Complex domain logic mixed with reporting queries, slow reads affecting writes, difficulty scaling. Implement CQRS: separate read and write models. Write side: commands, domain logic, event sourcing. Read side: queries, denormalized views, eventual consistency. Sync: events from write side update read models. Benefits: optimized for each use case, independent scaling, complex reads don't impact writes. Challenges: complexity, eventual consistency, data duplication. Use cases: analytics, reporting, search. Technologies: write DB (PostgreSQL), read DB (Elasticsearch), event store. Est: 21 story points. Advanced pattern.

Implement CQRS pattern for read/write separation

Code not matching business concepts, ubiquitous language missing, anemic domain model. Adopt DDD: 1) Identify bounded contexts 2) Aggregates and entities 3) Value objects 4) Domain events 5) Repositories. Process: event storming workshops, model business domains, define ubiquitous language. Benefits: business logic in domain, testable, maintainable. Patterns: aggregate root, domain services, factories. Refactor: move business logic from controllers to domain. Training: team workshops on DDD. Books: Domain-Driven Design by Eric Evans. Long-term investment. Est: 34 story points. Architectural philosophy.

Implement Domain-Driven Design patterns for better business alignment

Multiple microservices, each with own GraphQL API, clients making multiple requests. Unify with Federation: 1) Gateway aggregates schemas 2) Each service owns part of graph 3) Transparent joins across services 4) Single GraphQL endpoint. Apollo Federation: services define schema, gateway stitches together. Benefits: single API for clients, service autonomy, gradual migration. Challenges: schema coordination, breaking changes, performance (N+1). Features: distributed tracing, caching, batch loading. Alternative: schema stitching. Requires: GraphQL in all services. Est: 21 story points. GraphQL architecture.

Implement GraphQL Federation for unified API across microservices

Database hitting limits: 500GB single instance, queries slowing, vertical scaling expensive. Design sharding: 1) Shard key selection (user ID, tenant ID) 2) Routing logic 3) Re-sharding strategy 4) Cross-shard queries 5) Shard management. Approach: application-level sharding vs database sharding (Vitess). Challenges: shard key choice (hotspots), distributed transactions, joins across shards, rebalancing. Alternative: multi-tenant database per customer. Start: 4 shards, scale to 16-32. Monitor: shard size, query patterns, hotspots. Benefits: horizontal scaling, geographic distribution. Est: 34 story points. Complex database architecture.

Design scalable database sharding strategy for horizontal scaling

SaaS product needs multi-tenancy: enterprises require data isolation, compliance, custom configuration. Design options: 1) Database per tenant (strong isolation, high cost) 2) Schema per tenant (moderate isolation) 3) Shared schema with tenant ID (low cost, careful queries). Choose: shared schema for small customers, dedicated DB for enterprise. Features: tenant identification (JWT claim), query isolation (row-level security), tenant-specific config, resource limits per tenant. Challenges: ensuring isolation, preventing data leakage, performance. Security: test cross-tenant queries, pentest. Document architecture decision. Est: 21 story points. SaaS architecture.

Design multi-tenant architecture with data isolation

No DR plan, single region, data loss risk. Design DR: 1) Multi-region active-passive 2) Data replication 3) Automated failover 4) RPO (Recovery Point Objective: 1 hour) 5) RTO (Recovery Time Objective: 4 hours) 6) DR testing quarterly. Components: database replication, S3 cross-region, DNS failover, runbook. Scenarios: region outage, data corruption, security incident. Process: detect → decide → failover → communicate → recover. Costs: 50-70% additional infrastructure. Document: DR procedures, communication plan, escalation. Required for enterprise. Est: 21 story points. Business continuity.

Design disaster recovery and business continuity plan

Background jobs (emails, reports, data processing) running in API servers causing performance issues. Design dedicated job system: 1) Job queue (Redis Queue, Sidekiq, or Bull) 2) Worker processes 3) Job priorities 4) Retry logic 5) Job monitoring 6) Scheduled jobs. Features: concurrency control, job chaining, progress tracking, failure handling. Workers: auto-scaling based on queue depth. Monitor: queue length, job duration, failure rate, throughput. Use cases: email sending, PDF generation, data import/export, periodic tasks. Benefits: resilience, scalability, observability. Est: 13 story points. Infrastructure pattern.

Design scalable background job processing system

Testing ad-hoc, no strategy, gaps in coverage. Design comprehensive strategy: 1) Unit tests (70% coverage) 2) Integration tests (key flows) 3) E2E tests (critical paths) 4) Performance tests 5) Security tests 6) Contract tests. Pyramid: many unit, some integration, few E2E. Tools: Jest, Playwright, K6, OWASP ZAP. CI/CD: run on every PR, gate deployments. Test environments: dedicated staging, test data management. Metrics: coverage, test duration, flakiness. Culture: TDD, test-first mindset. Investment: test infrastructure, training. Est: 21 story points. Quality strategy.

Design automated testing strategy across all layers

Cascading failures when dependent service down, timeouts causing system-wide slowdown. Implement circuit breaker: 1) Detect failures (error threshold) 2) Open circuit (stop requests) 3) Half-open (test recovery) 4) Close circuit (resume normal) 5) Fallback responses. Use: Hystrix, Resilience4j, or custom. Configuration: failure threshold (50% errors), timeout (3s), retry attempts (3), cooldown period (30s). Benefits: prevent cascade failures, fast failure, graceful degradation. Also: bulkhead pattern (isolate resources), timeout handling, retry with exponential backoff. Monitor: circuit state, failure rates. Est: 8 story points. Resilience pattern.

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🏗️ Architecture & System Design

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