Introduction
System-design interviews are essential for backend, full-stack, and engineering roles. They test your ability to architect scalable, efficient, and maintainable systems under real-world constraints.
This article captures 100 commonly asked system design questions and answers, spanning from fundamentals to advanced designs and real-world problem statements — suitable for freshers and experienced candidates alike.
Section 1: Basics / Fundamental Concepts (Good for Freshers & Beginners)
Q1. What is System Design?
A: System design is the process of defining the architecture, components, modules, interfaces, and data flow of a system to satisfy given requirements. It ensures scalability, performance, maintainability, and reliability.
Q2. What is the difference between High-Level Design (HLD) and Low-Level Design (LLD)?
A: HLD outlines the overall system architecture — major components, services, and their interactions. LLD dives into implementation details: data models, classes/modules, API specifications, interfaces, and internal logic needed to build the components.
Q3. What is the CAP Theorem? What does it imply for distributed systems?
A: CAP Theorem states that in a distributed data-store you can only guarantee two out of these three: Consistency, Availability, and Partition Tolerance. This forces trade-offs: you must prioritize which two properties your system needs depending on requirements (e.g. availability over strong consistency or vice versa).
Q4. What is a Load Balancer and why is it used?
A: A load balancer distributes incoming traffic across multiple server instances to avoid overloading a single server, enabling horizontal scaling, improved availability, fault tolerance, and better resource utilization.
Q5. What is caching and why is it important in system design?
A: Caching stores frequently accessed data in a fast-access layer (memory or in-memory store) to reduce latency, decrease load on main databases, and serve high read throughput — essential for high-performance systems under heavy load.
Q6. What is an API Gateway in microservices architecture?
A: An API Gateway acts as a single entry point for clients: routing requests to appropriate backend services, handling authentication/authorization, rate limiting, request/response transformations, versioning — simplifying client-server interactions and hiding internal service complexity.
Q7. What are message queues and why are they used in distributed systems?
A: Message queues provide asynchronous communication between services — decoupling components, buffering tasks, handling load spikes, ensuring reliability and eventual processing without blocking clients. Useful for tasks like background processing, event-driven workflows, and rate smoothing.
Q8. What is the difference between monolithic and microservices architecture?
A: Monolithic architecture bundles all components as a single deployable unit. Microservices architecture divides the system into small, independent services — each responsible for a specific functionality and deployable independently — enhancing scalability, maintainability, and team autonomy.
Q9. What are common challenges when designing large-scale systems?
A: Challenges include handling high traffic & concurrency, data consistency vs availability trade-offs, distributed state management, fault tolerance, load balancing, caching strategy, data partitioning/sharding, latency, network failures, scalability, and maintainability over time.
Q10. What is redundancy and how does it help in system design?
A: Redundancy involves duplicate components (servers, databases, data centers) or replicated data so that if one fails, others take over — ensuring high availability and fault tolerance, and minimizing single points of failure.
Section 2: Common Design Problems & Intermediate Questions
Q11. How would you design a URL shortener service (like tinyURL / bit.ly)?
A: Outline requirements — read-heavy with occasional writes. Use a service that generates unique short keys (e.g. base62), map key → original URL in persistent storage (DB), cache frequent lookups, add rate limiting and analytics, handle aliasing/expiry, and set up redirect service with cache fallback for performance and scalability.
Q12. How to design a Rate Limiter service / API rate limiting for clients?
A: Implement token-bucket or leaky-bucket algorithm using a fast in-memory store (e.g. Redis) for per-user or per-API-key counters. Enforce limits per time window, manage distributed counter if multiple instances, block or throttle when limit exceeded.
Q13. How to design a Chat Service (one-to-one or group messaging)?
A: Major components: user service, message service, real-time delivery (via WebSocket or push), message storage (DB), caching for recent chats, message queue for reliability, presence & notification service. Address scalability, message ordering, offline storage, sync across devices.
Q14. How to design a News-Feed / Social Feed system (like social media timeline)?
A: Components: user service, post service, feed generation engine (push-based or pull-based), storage (DB + cache), feed ranking logic, pagination, caching layers, update queues, horizontal scaling — ensure efficient feed delivery for millions of users.
Q15. How to design a File Storage / File Sharing service (like Dropbox / Google Drive)?
A: Use object storage for files, metadata DB for file info, support uploads/downloads, versioning, permissions, sharing links. Employ CDN or distributed storage for scalability, implement concurrency control, redundancy/backups, and sync clients for multiple devices.
Q16. How to design a Video Streaming Service (like Netflix / YouTube)?
A: Architecture: object storage for video files, encoding/transcoding pipeline, streaming servers with chunked & adaptive-bitrate streaming, CDN for edge delivery, caching at edge, load balancing, metadata service, user & subscription service, logging & analytics, redundancy & global distribution for scalability.
Q17. How to design a Ride-Sharing / Taxi-Booking service (like Uber / Lyft)?
A: Components: user & driver services, ride-matching service, real-time location tracking, geospatial indexing, dispatch algorithm, booking/payment service, notification system, database for rides/history, caching, concurrency handling, scaling, and fault tolerance.
Q18. How to design a Search Autocomplete / Typeahead service?
A: Use in-memory index (trie or prefix tree) or search engine (e.g. Elasticsearch), pre-compute frequent prefixes, cache results, handle concurrency and latency constraints, shard or distribute index if large, and implement efficient query handling.
Q19. How to design a Notification / Pub-Sub system for real-time updates?
A: Use message broker/event queue (e.g. Kafka, RabbitMQ), subscription registry, delivery service via WebSocket or push, fan-out mechanism, retry logic, offline storage for missed notifications, scale consumers, and manage back-pressure.
Q20. How to design a CDN (Content Delivery Network) to serve static resources globally?
A: Use globally distributed edge servers, cache static assets, geo-DNS routing to nearest edge, origin server fallback, cache invalidation, compression, proper TTL settings — for low latency and high availability across regions.
Section 3: Advanced & Scalable System Design Questions (Senior / Complex Systems)
Q21. How would you design a globally distributed system with multi-region deployment?
A: Use geo-distributed data centers, database replication (multi-region or master-slave), global load balancer / DNS-based routing, redundancy & failover, data synchronization strategies, cache/CDN layers, latency vs consistency trade-offs — to ensure availability and low latency worldwide.
Q22. How to choose between consistency vs availability in a distributed system — what strategy to adopt?
A: It depends on use-case: for systems demanding strong correctness (e.g. banking), prefer consistency + partition tolerance; for services where availability matters (e.g. social feeds), opt for eventual consistency with replication. Always explain trade-offs and design decisions.
Q23. How to design a distributed cache or in-memory data store for scalability?
A: Use distributed cache solutions (e.g. Redis cluster, Memcached), partition (shard) data, implement replication for fault tolerance, eviction/invalidation strategy, cache-miss fallback to persistent store, and handle cache coherence / consistency when underlying data changes.
Q24. How to implement data partitioning / sharding for large datasets?
A: Partition data by key (user ID, region, hash), distribute across multiple DB instances, handle routing logic in application layer, plan for re-sharding when data grows, replicate shards for redundancy, and ensure balanced load and fault-tolerance.
Q25. How to implement rate-limiting and throttling in a distributed environment?
A: Use distributed counters (e.g. in Redis), implement token-bucket or sliding-window algorithm, maintain shared state across instances, use TTL or sliding window for resets, handle concurrency & race conditions — ensures consistent throttling across servers.
Q26. How to design API versioning and backward compatibility?
A: Use API gateway or versioned endpoints (v1, v2), maintain deprecated endpoints, schema versioning, backward-compatible data formats, feature toggles, deprecation policy — helps clients migrate smoothly without disruption.
Q27. How to ensure fault tolerance and graceful degradation under failures?
A: Use redundancy, failover mechanisms, circuit-breakers, retries with back-off, fallback services, read-replicas, health checks, monitoring, degrade non-critical features gracefully — design system to remain partially functional even under failures.
Q28. How to design logging, monitoring and observability for large-scale systems?
A: Use centralized logging (e.g. ELK/EFK), distributed tracing, metrics/monitoring (e.g. Prometheus), health checks, alerting dashboards — collect logs, error rates, latency, resource usage; enable tracing across services for debugging, capacity planning and system insights.
Q29. How to design scalable database schema for high-traffic systems (relational or NoSQL)?
A: Analyze access patterns (read vs write), choose appropriate DB (SQL vs NoSQL), define indexing/sharding strategy, normalize/denormalize based on use-case, add caching layer, use replication & backups, and design for scalability and consistency requirements.
Q30. How to design authentication, authorization and API security in a scalable system?
A: Use secure authentication methods (OAuth, JWT), encrypt data in transit and at rest, enforce access controls, rate limiting, input validation, use API gateway, secure communication (HTTPS/TLS), proper error handling — ensure security and compliance while scaling.
Section 4: Scenario-based & Real-World Questions (LeetCode-/ Interview-style Prompts)
Q31. Design a system like LeetCode — an online judge & code-submission platform.
A: Components: submission API server, code-execution sandbox, job-queue for submissions, result service, storage for user data & submissions, database, caching for problem data, rate-limiting to avoid abuse, result retrieval API, scalable infrastructure for many concurrent submissions, security/sandboxing, version control, and real-time status updates.
Q32. Design a URL shortener service (with analytics, expiry, custom alias etc.).
A: Build on basic URL shortener design: key-generation service (base-62), map short-key to long URL, metadata DB, caching layer for fast redirects, analytics tracking, expiry / alias support, rate limiting, monitoring, backup & redundancy, and scalable infrastructure to handle large request volume.
Q33. Design a Rate-Limiter / API-Gateway for a multitenant web service.
A: Use distributed counters or token-bucket per tenant, shared cache (Redis) for counters, implement sliding-window algorithm, support per-client / per-tenant limits, dynamic configuration, fallback / error handling, logging for rate-limit events, and scalable gateway instances for high availability.
Q34. Design a Chat / Messaging service for real-time or group chat.
A: Use WebSocket or real-time message broker, user & connection service, message storage (DB), message queue for reliability, caching for recent messages, presence & status service, support offline storage & sync, retry/fallback, scaling across multiple servers, load balancing, and secure communication.
Q35. Design a Social Feed / News Feed system for millions of users.
A: Feed generation engine (push vs pull), storage (DB + cache), pre-compute feeds for active users, caching popular feeds, pagination, feed ranking logic, queue-based updates, fan-out/fan-in strategies, rate-limiting, storage sharding, horizontal scaling for load handling.
Q36. Design a File Storage & Sharing service (like cloud-drive storage).
A: Use distributed object storage, metadata database, upload/download APIs, versioning, permissions/sharing logic, CDN or edge caching for downloads, backup & redundancy, concurrency & conflict resolution, user-quota management, and scalable architecture for storage growth.
Q37. Design a Video Streaming / CDN-backed Streaming Platform (like YouTube / Netflix).
A: Use object storage for video files, transcoding pipeline, streaming servers using chunked & adaptive streaming, CDN or edge servers for distribution, metadata & user-service, caching & load balancing, regional replication, analytics & logging, redundancy and global distribution for low latency and high availability.
Q38. Design a Ride-Sharing / Taxi Booking System (with real-time matching, surge pricing etc.).
A: Components: user & driver service, real-time location tracking, geospatial index & matching service, dispatch service, booking/payment service, notification system, ride-history DB, caching, load balancing, concurrency control, failure handling, scaling for high user base.
Q39. Design a Search Autocomplete / Search Suggestion system for a large user base.
A: Use fast prefix-based search index (trie or search engine), frequent prefix caching, distributed indexing, sharding for scalability, debounce & rate-limiting for high load, efficient query handling, fallback mechanisms for cache misses, and horizontal scaling across nodes.
Q40. Design a Notification / Pub-Sub / Real-Time Push Notification service.
A: Use message broker or event queue, subscription registry, delivery service via WebSocket/push, fallback for offline users (store notifications), fan-out across subscribers, retries & error handling, scalable consumers, rate limiting, and monitoring/alerting for delivery metrics.
Q41. Design a Collaborative Editing platform (like document editing with real-time collaboration).
A: Use operational transformation (OT) or CRDT for concurrent edits, real-time sync via WebSocket or WebRTC, version control, conflict resolution, user/permission service, document storage, change history, offline syncing, scalability across sessions and users, and data consistency mechanisms.
Q42. Design a Payment Gateway / Payment Processing System — handling security, concurrency and reliability.
A: Use transaction service, secure payment APIs, idempotent request handling, queue based processing for requests, retries & rollback, secure storage of payment data, encryption (in-transit and at rest), fraud detection, logging & audit trail, failover support and scalability for high volume transactions.
Q43. Design a Distributed Cache / Cache-Cluster for high throughput low-latency data access.
A: Use distributed cache cluster (e.g. Redis cluster), partition data via sharding, replication for redundancy, eviction/invalidation strategy, fallback to main DB, connection pooling, consistent hashing for distribution, and cache coherence / synchronization for data updates.
Q44. Design a Global CDN / Content Delivery Network for static & media assets with high availability and low latency.
A: Use globally distributed edge servers, geo-DNS to route clients to nearest edge, origin server fallback, caching & versioning, cache invalidation strategy, compression, TLS/HTTPS, replication, and scalability to handle traffic bursts.
Q45. Design a Scalable Logging & Monitoring System for microservices architecture.
A: Use centralized logging (log collector), distributed tracing, metrics/monitoring (prometheus / metrics store), alerting dashboards, storage for logs/traces, aggregation, health-checks, scalable ingestion pipeline, and retention/archival policies.
Q46. Design a Distributed Database / Sharding & Partitioning scheme for high-scale datastore.
A: Determine partition key (userID / region / hash), shard data across DB nodes, replicate shards for redundancy, routing layer to direct queries, handle re-sharding on growth, balance load, ensure data availability and consistency trade-offs, design fallback/recovery strategies.
Q47. Design a Search & Recommendation Engine (for e-commerce or content platform) supporting personalization and scalability.
A: Use indexing/search engine, user profiling & behavioral data store, caching for frequent queries, recommendation algorithms (collaborative filtering / content-based / hybrid), batch or real-time processing pipelines, horizontal scaling, latency optimization, and A/B testing support.
Q48. Design a Multi-Tenant API Gateway with rate-limiting and per-tenant isolation (for SaaS model).
A: Gateway routes requests to tenant-specific services, enforces rate-limiting per tenant, uses per-tenant data partitioning/sharding, logging/auditing per tenant, tenant isolation, versioning support, authentication/authorization, and scalable infrastructure for many tenants.
Q49. Design a High-Availability, Multi-Region Deployment Architecture with redundancy and disaster recovery.
A: Use geo-distributed data centers, active-passive or active-active deployment, data replication across regions, global load-balancing, failover routing, backup/restore strategies, disaster recovery plan, latency-optimized routing, and monitoring/health checks.
Q50. Design a Scalable Analytics / Event-Processing Pipeline (for logs, metrics or user events) for large traffic.
A: Use event queue/broker, stream processing framework, batch processing for heavy workloads, data store for processed data, scalable ingestion, back-pressure handling, monitoring, partitioning, horizontal scaling, fault tolerance, and data retention/archival policy.
Section 5: Additional Concepts, Patterns & Design-Principles Questions
Q51. What is a Design Pattern? Why are design patterns relevant to system design?
A: A design pattern is a reusable solution to a commonly recurring problem in software/design — it’s not a finished design but a template you can adapt. Using design patterns helps structure systems in a maintainable, scalable, and extensible way. Examples include Strategy, Singleton, Facade, Observer, etc. :contentReference[oaicite:0]{index=0}
Q52. What is the difference between synchronous and asynchronous communication in distributed systems? When would you use each?
A: Synchronous communication waits for a response (request → response), making the caller block until completion — useful when you need immediate results or strong consistency. Asynchronous uses message queues or events, decoupling caller & callee — better for scalability, resilience, load-handling, and non-blocking workflows (e.g. background jobs, notifications, event-driven services).
Q53. What is the “circuit breaker” pattern and why is it useful in distributed systems?
A: The circuit breaker pattern helps prevent cascading failures: if a downstream service is failing repeatedly or is too slow, the circuit “opens” to stop requests, return fallback/default responses or errors — protecting the system and improving resilience. :contentReference[oaicite:1]{index=1}
Q54. What is horizontal scaling vs vertical scaling? Pros/cons of each?
A: Vertical scaling means upgrading a single server (more CPU, RAM, etc.). Horizontal scaling adds more servers (nodes) to share load. Vertical is simpler but hits hardware limits and single-point-of-failure; horizontal gives better redundancy, fault tolerance, scalable capacity — common in large distributed systems.
Q55. What is sharding/partitioning? Why is it important in databases at large scale?
A: Sharding (or partitioning) splits your data across multiple database instances based on a key (user-ID, region, hash, etc.), so that no single DB node holds all data. This enables handling large data volumes, distributing load, avoiding bottlenecks, and improving performance and scalability. :contentReference[oaicite:2]{index=2}
Q56. What is data replication and what are trade-offs when using it?
A: Replication means having copies of data across multiple nodes/regions — helps with availability, fault tolerance, read scalability. Trade-offs: increased storage and complexity, synchronization overhead, and potential consistency issues (depending on replication strategy: synchronous vs asynchronous).
Q57. What is eventual consistency vs strong consistency? When to pick one over the other?
A: Strong consistency ensures that all reads return the latest write — good for critical data (payments, banking). Eventual consistency allows reads to return possibly stale data but guarantees eventual convergence — acceptable for high-throughput systems where speed & availability matter (social feed, cache-heavy services). It’s a trade-off between correctness and performance.
Q58. What is a message queue / pub-sub system? When is event-driven architecture beneficial?</
A: A message queue or pub/sub system decouples senders and receivers: producers enqueue messages/events; consumers process asynchronously. Event-driven architecture helps with decoupling, scaling, resilience, handling spikes, background tasks, and asynchronous workflows (notifications, updates, analytics, etc.). :contentReference[oaicite:3]{index=3}
Q59. What is rate limiting and why is it important in API / distributed systems?
A: Rate limiting restricts the number of requests a user/client/service can make in a timeframe — helps prevent abuse (DDoS), protects backend resources, ensures fair usage, and maintains system stability under heavy load. Commonly implemented using token-bucket, time-window counters, often with distributed support (e.g. Redis). :contentReference[oaicite:4]{index=4}
Q60. How do you handle logging, monitoring, and observability in a large distributed system?
A: Use centralized logging, structured logs, distributed tracing, metrics collection (latency, error rates), health checks, alerting dashboards. This helps debug issues, analyze performance, monitor uptime, trace propagation across services. Observability is crucial for understanding system state in production. :contentReference[oaicite:5]{index=5}
Section 6: More Real-World & Edge-Case / Mixed Questions (Q61–Q100)
Q61. Design a Web Crawler / Web Scraper system — what components would you include?
A: Use distributed crawler workers, URL frontier queue, politeness/rate-limit module, storage for crawled data, deduplication mechanism, scheduler, respect robots.txt/rate limits, failure handling/retries, distributed storage/indexing — scalable, fault-tolerant crawler architecture. :contentReference[oaicite:6]{index=6}
Q62. Design an SSO / Authentication & Authorization service (like OAuth, login-service).
A: Include identity service, token generation & validation (JWT or similar), user & session store, refresh-token mechanism, rate-limiting, token revocation, secure storage (hashed credentials), secure communication (HTTPS), and scalable authentication endpoints for many clients.
Q63. Design a scalable e-commerce order & inventory management system.
A: Components: product catalog service, inventory DB with stock counts + locking/concurrency control, order service, payment gateway, shopping cart service, cache for catalog/stock fast reads, message queue for order processing, notifications, inventory reservation and rollback, horizontal scaling for high load, database sharding/partitioning to handle volume.
Q64. Design a content-delivery and caching layer for a news / blog website to support high read traffic globally.
A: Use global CDN + edge caching, cache invalidation mechanism, origin server fallback, caching static and dynamic content, TTL settings, load balancer, geo-DNS routing, fallback for stale or expired cache, CDN + origin synchronization — ensures fast, scalable global delivery.
Q65. Design a scalable analytics/event-logging pipeline for user events (clicks, actions) at high volume.
A: Use producer clients sending events to distributed queue/broker, stream-processing or batch-processing framework, write processed data to data warehouse/storage, indexing for query/aggregation, partitioning, data retention & archival, monitoring/alerting pipelines — capable of handling high throughput and real-time analytics.
Q66. Design a search engine or search-as-a-service component for an e-commerce or content platform (search + ranking + autocomplete).
A: Use inverted index or search engine (Elasticsearch / similar), indexing service, query API, autocomplete/trie or prefix-search module, caching frequent queries, ranking logic, pagination, sharding for scalability, load balancing, fallback mechanisms, indexing updates for new content, fault tolerance, query latency optimization.
Q67. Design a real-time collaborative editing platform (like Google Docs) supporting concurrent edits and conflict resolution.
A: Use CRDT or Operational Transformation (OT) for concurrent edits, real-time sync via WebSocket/WebRTC, versioning, history tracking, conflict resolution, document storage & backups, user permission management, synchronization across clients, offline-support (sync on reconnect), scalability to multiple documents/users concurrently.
Q68. Design a system for real-time notifications / push notifications supporting many users (web + mobile).
A: Use event queue / pub-sub broker, notification service, push gateways (APNs/FCM or WebPush), subscription registry, delivery retry/fallback, user preference store, throttling, rate-limiting, batching, horizontal scaling for large user base, monitoring & analytics for deliveries and failures.
Q69. Design a system to support multi-region deployment with data locality, disaster recovery, and low-latency reads/writes.
A: Use geo-distributed DB replicas, geo-aware routing/load balancing, region-based caching, data replication & synchronization, consistent hashing or partitioning by region/user, fallback/failover, disaster recovery strategy, latency optimization, replication conflict-resolution and eventual consistency or versioning.
Q70. Design a background job processing system where jobs are queued, processed, retried on failure, and durable.
A: Use job-queue broker (message queue), job worker pool, persistent job store, retry mechanism with back-off, idempotent job design, dead-letter queue for failures, monitoring/logging, concurrency control, scheduling (delayed/future jobs), scaling workers horizontally to handle load spikes reliably.
Q71. Design a rate-limited & quota-enforced multi-tenant API platform (SaaS) to handle many clients.
A: Use API gateway with per-tenant configuration, rate limiter (token-bucket/sliding window) per tenant, usage tracking & quota store, authentication/authorization module, logging & billing integration, scalable gateway instances, isolation of tenant data & resources, monitoring & throttling.
Q72. Design a distributed locking / coordination service for shared resource access in distributed systems.
A: Use consensus or coordination algorithm (e.g. Paxos/Raft), lock manager service, lease-based locks, timeout and retry, fallback or queue for lock waiters, fault tolerance (leader election, failover), heartbeat mechanism, safe unlock and dead-lock avoidance.
Q73. Design a service for image/video upload + processing + storage + retrieval (with resizing, thumbnails, streaming support).
A: Use upload API, file storage (object storage), processing pipeline (resize/transcode), CDN or storage + cache, metadata DB, queue-based processing, content delivery with caching/CDN, versioning, permissions/security, scalability and fault tolerance for large file traffic.
Q74. Design a recommendation engine for content or product recommendations (personalization + scalability).
A: Store user profiles & behavior, analytics data store, modeling service (collaborative / content-based / hybrid), scoring/ranking service, cache popular recommendations, recommendation API, periodic recomputation or real-time updates, horizontal scaling, A/B testing support, fallback when data missing.
Q75. Design a logging & audit-trail service for compliance & debugging (write-heavy + read/spread queries).
A: Use append-only write pipeline, partitioned log storage, indexing for queries, archival/retention policies, access control, search/query API, compression, scalable storage (cold + hot), secure storage, ability to handle high write load, data purge/cleanup and audit-logging mechanisms.
Q76. Design a bulk-data import/export system (CSV/JSON upload & processing) for large datasets (millions of rows).
A: Use upload service, chunked file parsing, queue-based processing, batched data insertion with transaction or idempotency, monitoring, validation, error handling/rollback for failures, ability to resume/pause, rate control, data consistency checks, scalability for large imports.
Q77. Design a system for feature-flagging / A/B testing for a large user base.
A: Use feature-flag service storing flags per user/segment, evaluation & rollout subsystem, dynamic configuration API, caching for flag decisions, analytics/event logging for experiment data, rollout strategies (canary, percentage-based), ability to rollback, scalable flag-evaluation layer with minimal latency.
Q78. Design a real-time analytics dashboard system (e.g. live metrics, user stats) for web application.
A: Use event ingestion pipeline (stream or queue), real-time processing engine (stream processing), data store optimized for aggregations, WebSocket or push for live dashboard updates, caching & aggregation, horizontal scaling, fault tolerance, data summarization, retention policy, user-role based data access.
Q79. Design a search-and-filter service (e.g. for e-commerce or job listings) with pagination, sorting, filter combinations.
A: Use search engine or indexed DB, indexing/search indexes, API for filter + sort + pagination, caching for frequent queries, query planner for optimization, pagination strategy (offset or cursor), support sorting + filtering combinatorics, sharding/index partitioning for scale, fallback when index stale, scalability and performance optimization.
Q80. Design a backup & disaster-recovery system for a distributed database with minimal downtime.
A: Use periodic backups, asynchronous replication to secondary region/storage, point-in-time recovery, redundant data storage (multi-region), automatic failover, data integrity checks, rollback strategies, monitoring/alerts, versioning, minimal performance impact during backups, and regular testing of recovery process.
Q81. Design a metrics-collection and monitoring system for microservices architecture.
A: Use metrics exporter in each service, centralized metrics collector (time-series DB), alerting engine, dashboards, service health check API, distributed tracing, log aggregation, anomaly detection, scalability to handle many microservices and high request rates, retention/archival policy.
Q82. Design a system to support search + geolocation queries (e.g. find nearby restaurants/users) at large scale.
A: Use geo-indexing or geospatial database, spatial partitioning/sharding, caching of frequent queries, load balancing, API with filters (radius, bounding box), fallback for stale data, real-time updates for location data, horizontal scaling, data consistency/accuracy considerations.
Q83. Design a multi-language / multi-locale content delivery and i18n system for a global user base.
A: Use content-storage per locale, locale detection/routing, translation service or static translations, CDN/edge caching for each locale, fallback mechanism, dynamic content rendering based on locale, versioning/migration support, cache invalidation per locale, region-based hosting for latency optimization.
Q84. Design a system for data synchronization across offline clients and server (e.g. mobile app offline + sync when online).
A: Use local storage/cache on client with change tracking, conflict resolution strategy (last write wins, merge, CRDT), sync queue, API endpoints for sync, versioning, conflict detection & resolution, retry logic, change logs, offline-first design, scalability for many users syncing concurrently.
Q85. Design a feature-to-feature rollout system with gradual rollout, canary release and rollback support.
A: Use feature-flagging service, release management module, user segmentation, rollout percentage control, monitoring & metrics (error, performance), rollback mechanism, logging & analytics, versioning, fallback defaults, safe deployment pipeline, ability to target specific user segments or percentages.
Q86. Design a distributed notification scheduling and delivery system (delayed notifications, scheduling, retries).
A: Use scheduler service, queue/timer store for scheduled events, delivery workers, retry/failover logic, persistence for pending tasks, delivery logs, rate limiting, batching, scalable worker pool, failure handling, time-zone / locale support, monitoring and alerting.
Q87. Design a paid-subscription / billing service for SaaS with plan management, usage-based billing and subscription lifecycle.
A: Components: user account service, subscription service, billing & payment gateway integration, usage-tracking service, plan definitions (limits, quotas), invoices, notifications, payment retries, billing cycle scheduler, data storage for subscriptions & usage, scaling for many users, integration with auth & service-access rules, reporting module.
Q88. Design a distributed search & recommendation system supporting personalization, trending, and dynamic updates.
A: Combine search indexing, user-behavior data store, personalization engine (ML-based or rule-based), ranking service, caching popular/trending queries, real-time updates for new content, feedback loop for ranking (user actions), scalability by sharding or distributed architecture, logging & analytics, fallback/default recommendations.
Q89. Design a job-scheduling and task-orchestration system (workflows, dependencies, retries) for complex tasks.
A: Use workflow engine, task graph definitions, queue or broker for job dispatch, dependency management, retry & back-off, failure handling, logging & monitoring, concurrency control, scheduling (cron / delayed tasks), persistence, scalability, and isolation between tasks.
Q90. Design a real-time data-streaming platform (like log ingestion, analytics stream, events pipeline) at massive scale.
A: Use distributed message broker (Kafka / stream system), producers & consumers, partitioning/sharding for load distribution, stream processing engine, scalable storage, back-pressure handling, fault tolerance, data retention/archival, monitoring/tracing, scalable ingestion & consumption, schema/versioning support.
Q91. Design a system for internationalization (i18n) and localization including content management, translations and versioning.
A: Use content storage per locale, translation management service, locale routing at edge or application level, fallback locale support, translation versioning, caching per locale, dynamic content rendering, support for RTL/LTR, date/number format adaptation, scalable storage/translation retrieval, and update mechanism for new translations.
Q92. Design a document storage & version-control system (like Google Docs backend) supporting version history, collaboration and access control.
A: Use document service, versioning metadata store, storage for versions, diff or full snapshot storage, access control & permissions, collaboration / concurrency handling (locking or merge strategies), audit trail, sharing links, storage optimization (delta storage), scalable storage and retrieval APIs, and robust backup/restore mechanism.
Q93. Design a system for real-time metrics + alerting + auto-scaling infrastructure based on load.
A: Use metrics collection & monitoring service, alerting subsystem, autoscaling controller reacting to metrics (CPU, load, latency), load balancer, dynamic resource provisioning, health checks, graceful shutdown/scale-down, logging & trace, scaling policies (thresholds, cooldown), distributed services supporting scale-up/scale-down seamlessly.
Q94. Design a system to handle bulk email/SMS/notification sending for large user base (batch + rate-limit + scheduling + retry).
A: Use queue/broker, batching service, worker pool, rate-limiting per provider, scheduling for delivery windows, retry logic for failures, monitoring, throttling, user preference & unsubscription management, logging, scalability for millions of recipients, fallback strategies (fallback providers), error handling and reporting.
Q95. Design a user-activity logging & audit-trail system for compliance, analytics and debugging.
A: Use write-optimized storage (append-only), partitioning/sharding by user or time, indexing for queries, retention & archival policy, secure storage, permissioned access, query API for audits, backup, data anonymization if needed (privacy compliance), and scalability/high-throughput support.
Q96. Design a system to throttle or blackout region-wise content based on compliance/regulatory requirements (e.g. GDPR, content-blocking by region).
A: Use geo-location routing, regional configuration store, content-access service checking region & user permissions, caching with regional awareness, fallback/default behavior, logging for compliance, dynamic configuration update, efficient checks to avoid latency penalty, and scalable enforcement across regions.
Q97. Design an audit-logging and change-history system for a collaborative application (tracking edits, versions, permissions changes).
A: Use event store or log of actions, metadata for user/time/action type/version, storage optimized for writes (append-only), ability to query by user/document/version, access control, archival retention, data privacy/compliance, scalability for many events, and efficient retrieval for audit or rollback.
Q98. Design a real-time multiplayer game backend (match-making, real-time updates, concurrency, scalability).
A: Use matchmaking service, session manager, real-time communication (WebSocket/UDP), game state synchronization service, distributed state store, load balancing, shard players into game-instances, latency-optimization, concurrency control, fallback/restart, scalability for many simultaneous games, monitoring, and cheat-prevention mechanisms.
Q99. Design a data-migration / schema-migration service to upgrade database schema without downtime for a large production system.
A: Use migration planning (versioning), backward-compatible schema changes, data copy/migration service, dual-read/write during transition, feature-flag rollout, data validation, incremental migration, rollback capability, testing & verification, minimal downtime strategy, monitoring and logging during migration.
Q100. Design a distributed machine-learning model serving system (model deployment, versioning, inference API, scalability, monitoring).
A: Use model storage/version registry, model serving API, load-balanced inference servers, auto-scaling inference cluster, request queue/back-pressure, caching of predictions (if possible), monitoring for latency/error rates, model version control, rollback support, A/B testing, logging & analytics, scalable infrastructure for high request volume.