Top 50 System Design Interview Questions

System design interviews have become a cornerstone of technical hiring at major tech companies. These interviews assess your ability to architect scalable, reliable systems that can handle real-world challenges. Understanding the foundational questions is essential for any software engineer looking to advance their career. This article explores the critical system design questions that form the bedrock of interview preparation.

Also Read: How to Prepare for a System Design Interview?

Mastering Core System Design Concepts

1. Design a URL Shortening Service (like bit.ly)

A URL shortening service transforms long URLs into compact, shareable links. This seemingly simple problem reveals deep architectural considerations.

Core Requirements: The system must generate unique short codes for long URLs, redirect users efficiently, and track analytics. The primary challenge lies in generating collision-free short codes while maintaining high performance under massive traffic.

Architecture Approach: Start with a REST API that accepts long URLs and returns shortened versions. For short code generation, consider base62 encoding (using characters a-z, A-Z, 0-9) which provides 62^7 or approximately 3.5 trillion combinations with a 7-character code.

The database schema needs two main tables: one mapping short codes to long URLs with metadata like creation timestamp and expiration date, and another for analytics tracking click counts and user data. Use a NoSQL database like Cassandra or DynamoDB for horizontal scalability and fast reads.

Scalability Considerations: Implement distributed caching using Redis to store frequently accessed URL mappings, reducing database load. For high availability, deploy the application across multiple regions with a global load balancer. Consider using a CDN for serving redirect responses closer to users geographically.

Handle uniqueness through a distributed ID generation system like Twitter's Snowflake or by using a counter service with range allocation to different application servers. This prevents duplicate short codes across distributed systems.

Advanced Features: Include custom alias support where users can choose their short code, rate limiting to prevent abuse, and expiration policies for temporary links. Implement analytics to track click sources, geographic distribution, and time-based patterns.

2. Design a Distributed Cache System

Caching is fundamental to building performant systems. A distributed cache must provide fast data access while maintaining consistency across multiple nodes.

Key Design Elements: The cache should support standard operations: get, set, delete, and update with configurable time-to-live (TTL) values. Design considerations include eviction policies, data distribution, and consistency guarantees.

Data Distribution Strategy: Implement consistent hashing to distribute keys across cache nodes. This minimizes data movement when nodes are added or removed. Each cache server holds a portion of the keyspace, determined by hashing the key and mapping it to a position on a hash ring.

Use virtual nodes to improve load distribution. Each physical server manages multiple positions on the hash ring, ensuring more even data distribution and reducing the impact of individual server failures.

Eviction Policies: Implement LRU (Least Recently Used) as the primary eviction policy. Maintain a doubly linked list with a hash map for O(1) access and updates. When the cache reaches capacity, remove the least recently used item.

Consider offering multiple eviction strategies: LFU (Least Frequently Used) for workloads where access frequency matters more than recency, and TTL-based expiration for time-sensitive data.

Replication and Consistency: For high availability, replicate data across multiple nodes. Choose between synchronous replication (strong consistency, higher latency) and asynchronous replication (eventual consistency, lower latency) based on use case requirements.

Implement write-through or write-behind caching strategies. Write-through ensures data consistency by writing to both cache and database simultaneously, while write-behind improves performance by buffering writes and persisting them asynchronously.

Monitoring and Observability: Track cache hit rates, latency percentiles, memory usage, and eviction rates. Set up alerts for degraded performance or approaching capacity limits.

3. Design a Rate Limiter

Rate limiting protects services from abuse and ensures fair resource allocation. This system controls the number of requests a client can make within a time window.

Algorithm Selection: Choose from several algorithms based on requirements. The Token Bucket algorithm allows burst traffic while maintaining average rate limits. Tokens are added to a bucket at a fixed rate, and requests consume tokens. When the bucket is empty, requests are throttled.

The Sliding Window Log algorithm provides precise rate limiting by tracking timestamps of all requests within the window. While accurate, it consumes more memory.

The Sliding Window Counter combines the benefits of both, using fixed window counters with weighted calculations for the current window, balancing accuracy with memory efficiency.

Implementation Architecture: Store rate limit counters in Redis for fast access and atomic operations. Use Redis sorted sets for sliding window logs or simple counters with TTL for fixed windows.

The rate limiter should run as middleware in your application or as a separate service. When a request arrives, check the counter against the limit. If under the limit, increment the counter and allow the request. Otherwise, reject with a 429 status code.

Distributed Considerations: In a distributed system, rate limiting becomes challenging due to multiple application servers. Centralize rate limit state in Redis or a similar distributed store.

For high-traffic scenarios, implement local counters with periodic synchronization to reduce Redis load. This trades perfect accuracy for better performance.

Configuration and Flexibility: Support different rate limits for various user tiers, API endpoints, or client types. Store limits in a configuration service for dynamic updates without deployment.

Provide clear feedback in responses, including headers like X-RateLimit-Remaining and X-RateLimit-Reset to help clients manage their request patterns.

4. Design a Content Delivery Network (CDN)

A CDN distributes content across geographically dispersed servers, reducing latency and improving user experience for global audiences.

Core Components: The CDN architecture includes origin servers (hosting original content), edge servers (distributed globally to cache content), and a routing system that directs users to the nearest edge server.

Content Distribution Strategy: Implement a pull-based model where edge servers fetch content from origin servers on first request, then cache it locally. This reduces origin server load and storage requirements at edge locations.

Alternatively, use a push-based model for predictable, high-demand content where you proactively distribute files to edge servers. This ensures immediate availability but requires more sophisticated content management.

Routing and Traffic Management: Use GeoDNS to route users to the nearest edge server based on geographic location. Implement anycast routing where multiple edge servers share the same IP address, and network routing automatically directs traffic to the closest one.

Include health checking to detect failed or degraded edge servers and route traffic away from them automatically.

Caching Strategy: Implement intelligent caching policies based on content type. Static assets like images and CSS files can be cached for extended periods, while dynamic content requires shorter TTLs or cache invalidation mechanisms.

Support cache control headers from origin servers, allowing content creators to specify caching behavior. Implement purging mechanisms to invalidate cached content when updates occur.

Performance Optimization: Compress content using gzip or Brotli before transmission. Support HTTP/2 or HTTP/3 for multiplexed connections and reduced latency.

Implement edge computing capabilities, allowing simple computations at edge servers to personalize content without origin server involvement.

5. Design a Notification System

A notification system delivers messages to users through multiple channels including push notifications, email, and SMS. This system must be reliable, scalable, and support different priority levels.

Architecture Overview: Design a service-oriented architecture with distinct components: notification API for receiving requests, channel handlers for different delivery methods, a queuing system for reliability, and a tracking system for analytics.

Message Queue Integration: Use message queues like Kafka or RabbitMQ to decouple notification creation from delivery. When a service needs to send a notification, it publishes to the queue. Worker processes consume messages and handle delivery through appropriate channels.

This approach provides reliability through persistent queues, scalability by adding workers, and fault tolerance through automatic retry mechanisms.

Multi-Channel Support: Implement separate handlers for each notification channel. The push notification handler integrates with services like Firebase Cloud Messaging for Android and APNs for iOS. The email handler connects to SMTP servers or email APIs like SendGrid. The SMS handler interfaces with providers like Twilio.

Allow users to configure channel preferences and support fallback chains where failed delivery attempts on one channel trigger attempts on alternative channels.

Priority and Scheduling: Support multiple priority levels for notifications. Critical alerts bypass rate limits and retry aggressively, while low-priority notifications can be batched and sent during off-peak hours.

Implement scheduling capabilities for future delivery and support for time zone awareness to send notifications at appropriate local times.

Template Management: Create a template system for notification content supporting multiple languages and personalization variables. Templates should be versioned and support A/B testing for optimization.

Delivery Tracking: Track notification lifecycle events: queued, sent, delivered, opened, and clicked. Store this data for analytics and debugging. Implement idempotency to prevent duplicate notifications and deduplication logic to collapse multiple similar notifications within a time window.

6. Design a Web Crawler

A web crawler systematically browses the internet to discover and index content. This distributed system must efficiently crawl billions of pages while respecting website policies and avoiding duplicates.

Crawling Strategy: Implement breadth-first search (BFS) starting from seed URLs. Maintain a URL frontier queue containing URLs to crawl. Workers fetch pages from the queue, extract new URLs, and add them to the frontier.

Use politeness policies to avoid overwhelming websites. Implement delays between requests to the same domain and respect robots.txt files that specify crawling rules.

URL Management: Store discovered URLs in a distributed queue system. Use a bloom filter or distributed hash table to quickly check for duplicate URLs before adding them to the frontier, preventing infinite loops.

Implement URL normalization to treat equivalent URLs as identical: handle trailing slashes, default ports, and parameter ordering consistently.

Content Processing: Parse HTML to extract links, text content, and metadata. Implement domain-specific parsers for JavaScript-heavy sites, requiring headless browser rendering.

Store crawled content in a distributed file system like HDFS or object storage like S3. Index extracted data in a search system for querying.

Distributed Architecture: Deploy multiple crawler workers across different machines and regions. Use a coordinator service to distribute work and track progress. Implement checkpointing to resume crawling after failures without losing progress.

Freshness and Recrawling: Different content has different change frequencies. News sites update frequently while documentation pages remain static. Implement priority-based crawling where important or frequently changing pages are crawled more often.

7. Design a Search Autocomplete System

Autocomplete provides real-time search suggestions as users type, improving search experience and discoverability. This system must respond in milliseconds while handling millions of concurrent users.

Data Structure Selection: Implement a trie (prefix tree) data structure for efficient prefix matching. Each node represents a character, and paths from root to nodes form complete words. Store popular search queries in the trie with associated weights for ranking.

For massive scale, partition tries by prefix: the first character determines which trie to query, enabling horizontal scaling.

Real-Time Updates: Collect search queries continuously and update suggestion rankings based on search popularity. Use a streaming processing framework like Apache Flink to compute trending searches in real-time.

Implement a two-tier system: an offline batch process that builds comprehensive tries from historical data, and an online component that incorporates recent trending queries.

Ranking Algorithm: Rank suggestions based on multiple factors: historical search frequency, click-through rates, query freshness, and user context like location or search history.

Apply personalization by weighting suggestions based on individual user behavior while maintaining privacy by processing user data with differential privacy techniques.

Caching and Performance: Cache popular prefix results in Redis. Since a small set of prefixes accounts for most queries, aggressive caching dramatically reduces computation.

Implement client-side debouncing to limit server requests while users type. Send requests only after typing pauses or at character intervals.

Handling Scale: Replicate autocomplete services across multiple data centers. Use GeoDNS to route users to nearby servers. Implement eventual consistency for suggestion updates; slight delays in propagating trending queries are acceptable.

8. Design a Ride-Sharing Service (like Uber)

A ride-sharing platform connects riders with drivers in real-time, requiring sophisticated location services, matching algorithms, and payment processing.

Core Components: The system includes rider and driver mobile applications, a backend API for business logic, a location service for tracking vehicles, a matching engine for connecting riders with drivers, and a payment system.

Location Services: Implement real-time location tracking using GPS data from driver phones. Drivers send location updates every few seconds. Store this data with timestamps in a geospatial database supporting efficient proximity queries.

Use geohashing to partition the map into grid cells. Index drivers by their geohash, enabling quick queries for nearby drivers without complex distance calculations.

Matching Algorithm: When a rider requests a ride, query for available drivers within a radius. Calculate expected pickup times considering traffic and distance. Rank drivers by pickup time, ratings, and acceptance rates.

Send ride requests to the optimal driver. If declined, cascade to the next best match. Implement timeout mechanisms to prevent riders from waiting indefinitely.

Dynamic Pricing: Calculate ride prices based on distance, time, vehicle type, and current demand. Implement surge pricing during high demand periods to incentivize more drivers to come online.

Use predictive models to forecast demand and pre-position drivers in anticipated hotspots.

Real-Time Communication: Implement WebSocket connections for real-time updates between riders and drivers. Send location updates, status changes, and messages through persistent connections.

Use push notifications for important events like driver assignment, arrival, and trip completion.

Payment Integration: Support multiple payment methods including credit cards, digital wallets, and ride credits. Process payments asynchronously after trip completion. Implement split payment capabilities for shared rides.

Handle payment failures gracefully with retry logic and alternative payment method prompts.

9. Design a Video Streaming Service (like YouTube)

A video platform must handle massive files, support various devices and bandwidths, and provide smooth playback experiences for millions of concurrent users.

Video Upload Pipeline: When users upload videos, store original files in object storage. Queue transcoding jobs to convert videos into multiple formats and resolutions (360p, 720p, 1080p, 4K).

Use distributed transcoding workers processing jobs in parallel. Apply video compression algorithms to reduce file sizes while maintaining quality. Generate thumbnails at multiple timestamps for preview.

Adaptive Bitrate Streaming: Implement HTTP Live Streaming (HLS) or Dynamic Adaptive Streaming over HTTP (DASH). Segment videos into small chunks (2-10 seconds) at different quality levels.

The video player monitors available bandwidth and automatically switches between quality levels, ensuring smooth playback without buffering.

Content Delivery: Distribute video segments through a CDN to reduce latency and origin server load. Cache popular videos at edge locations worldwide.

Implement predictive prefetching where the CDN anticipates which video segments users will request next and preloads them.

Metadata and Search: Store video metadata (title, description, tags, duration) in a distributed database. Build a search index supporting full-text search across titles, descriptions, and transcriptions.

Implement recommendation algorithms analyzing user watch history, engagement metrics, and content similarity to suggest relevant videos.

Scalability Considerations: Store video files in distributed object storage partitioned by video ID. Use sharding for metadata databases to handle billions of videos.

Implement rate limiting on uploads and concurrent streaming connections to prevent abuse and ensure fair resource allocation.

10. Design a Social Media News Feed

A news feed aggregates content from followed users, displaying relevant posts in a personalized, timely manner. This system must balance real-time updates with algorithmic ranking at massive scale.

Feed Generation Approaches: Implement fan-out on write: when a user posts, write the post to all followers' feeds immediately. This provides fast read performance since feeds are pre-computed but struggles with users having millions of followers.

Alternatively, use fan-out on read: query followed users' posts when loading the feed. This scales better for popular users but requires more computation at read time.

A hybrid approach combines both: fan-out on write for regular users, fan-out on read for celebrities, optimizing for the common case while handling outliers efficiently.

Ranking Algorithm: Sort posts using multiple signals: post recency, content type (videos rank higher than text), engagement metrics (likes, comments, shares), relationship strength with the poster, and user preferences.

Implement machine learning models trained on user behavior to predict post relevance. Features include historical engagement patterns, content similarity to previously liked posts, and trending topics.

Real-Time Updates: Use WebSockets or Server-Sent Events to push new posts to active users. Implement a notification system alerting users about posts from close connections.

Cache feeds in Redis for fast access. Invalidate cache entries when new posts arrive or when users follow/unfollow accounts.

Data Storage: Store posts in a distributed database partitioned by user ID. Use Cassandra or similar systems supporting high write throughput and horizontal scaling.

Maintain separate tables for different content types: text posts, photos, videos, and links. Use object storage for media files.

Performance Optimization: Paginate feeds to load posts incrementally. Implement infinite scrolling with lazy loading of images and videos.

Precompute feeds for active users during off-peak hours. Cache feed generation results and refresh periodically rather than computing on every request.

Distributed Systems and Data Management

11. Design a Distributed Message Queue

A message queue enables asynchronous communication between services, decoupling producers from consumers. Building a distributed queue requires careful consideration of ordering guarantees, fault tolerance, and throughput.

Core Architecture: Design the system with three main components: brokers that store and serve messages, producers that publish messages to topics, and consumers that subscribe to topics and process messages.

Organize messages into topics, which can be further divided into partitions for parallel processing. Each partition is an ordered sequence of messages, enabling horizontal scaling while maintaining order within partitions.

Message Storage: Store messages on disk using sequential writes for optimal performance. Organize files by partition with each message containing an offset (position in the partition), timestamp, key, and payload.

Implement a configurable retention policy: time-based (delete messages older than seven days) or size-based (maintain a maximum queue size). This prevents unbounded storage growth while ensuring consumers have reasonable processing windows.

Delivery Guarantees: Support three delivery semantics based on use case requirements. At-most-once delivery offers the lowest latency by not confirming message receipt, risking message loss during failures. At-least-once delivery confirms receipt but may deliver duplicates during retries. Exactly-once delivery provides the strongest guarantee through transactional processing and deduplication but with higher complexity and latency.

Implement idempotency tokens allowing consumers to safely retry message processing without duplicating side effects.

Consumer Groups: Enable multiple consumers to process messages from the same topic through consumer groups. Each partition is assigned to exactly one consumer within a group, allowing parallel processing while maintaining order.

Implement partition rebalancing when consumers join or leave groups. Use a coordination service like ZooKeeper to manage consumer group membership and partition assignments.

High Availability: Replicate partitions across multiple brokers using leader-follower replication. The leader handles all reads and writes for a partition, while followers replicate data asynchronously.

Configure replication factors based on durability requirements. A factor of three ensures data survives two broker failures. Implement automatic leader election when the current leader fails.

Performance Optimization: Batch messages during publishing and consumption to reduce network overhead. Allow consumers to fetch multiple messages per request.

Implement zero-copy transfers using sendfile system calls to move data from disk to network without intermediate buffers, significantly improving throughput.

Use compression for message payloads to reduce network bandwidth and storage requirements. Support multiple compression algorithms with configurable compression levels.

12. Design a Key-Value Store

A distributed key-value store provides simple get/put operations with high availability and partition tolerance, making it a fundamental building block for many systems.

Partitioning Strategy: Use consistent hashing to distribute keys across storage nodes. Hash each key and map it to a position on a hash ring. Nodes are also positioned on the ring, and each key is assigned to the next node in clockwise order.

This approach minimizes data movement when adding or removing nodes. Only keys between the new node and its predecessor need relocation.

Implement virtual nodes where each physical server manages multiple positions on the ring. This improves load distribution and reduces the impact of heterogeneous hardware.

Replication Model: For each key, replicate data to N successive nodes on the hash ring. Common configurations use N=3 for balance between durability and overhead.

Implement tunable consistency where clients specify read and write consistency levels. Require W nodes to acknowledge writes and R nodes to respond to reads. Setting W + R > N ensures strong consistency, while lower values trade consistency for availability and performance.

Conflict Resolution: In distributed systems with eventual consistency, concurrent updates to the same key create conflicts. Implement vector clocks to track causality between versions. Each update increments the writer's counter in the vector clock.

When reading a key with multiple versions, return all conflicting versions to the client for resolution. Support last-write-wins as a default strategy using timestamps, though this risks data loss with clock drift.

Hinted Handoff: When a target node is temporarily unavailable, write data to another node with a hint indicating the intended recipient. When the original node recovers, transfer hinted data to it.

This technique improves write availability during transient failures without compromising eventual consistency.

Anti-Entropy and Repair: Implement periodic Merkle tree comparisons between replicas to detect inconsistencies. Partition the key space into ranges and compute a hash tree where leaf nodes represent key ranges and internal nodes hash their children.

Comparing tree roots quickly identifies divergent ranges requiring synchronization, minimizing data transfer during repairs.

Performance Considerations: Use SSD storage for low-latency access. Implement a write-ahead log for durability, allowing in-memory data structures for fast reads while ensuring recovery after crashes.

Employ bloom filters to quickly determine if a key exists before checking disk, reducing unnecessary I/O operations.

13. Design a Distributed File System

A distributed file system stores large files across multiple machines, providing fault tolerance, scalability, and efficient data processing capabilities similar to HDFS.

Architecture Components: Separate metadata management from data storage. The NameNode maintains the file system namespace: directory structure, file metadata, and block locations. DataNodes store actual file data in fixed-size blocks (typically 64MB or 128MB).

This architecture allows the NameNode to handle metadata operations quickly while DataNodes scale independently to provide massive storage capacity.

File Organization: Split large files into blocks and distribute them across DataNodes. Store multiple replicas of each block (default three) on different nodes for fault tolerance.

Place replicas intelligently: one on the same rack as the client, another on a different node in the same rack for locality, and the third on a different rack for rack-level fault tolerance.

Read Operations: When clients request file reads, query the NameNode for block locations. The NameNode returns a list of DataNodes holding replicas for each block, sorted by proximity to the client.

Clients directly read data from DataNodes, avoiding NameNode involvement in data transfer and preventing it from becoming a bottleneck.

Write Operations: For writes, the NameNode allocates new blocks and assigns DataNodes to store replicas. Clients write data to the first DataNode, which forwards it to the second, which forwards to the third in a pipeline fashion.

Each DataNode acknowledges successful writes back through the pipeline. Only when all replicas confirm does the write complete, ensuring consistency.

Fault Tolerance: Monitor DataNode health through periodic heartbeats to the NameNode. Missing heartbeats indicate node failure, triggering replication of under-replicated blocks.

Implement checkpoint and edit log mechanisms for NameNode metadata persistence. Regular snapshots combined with incremental edit logs enable recovery after NameNode failures.

For NameNode high availability, maintain a standby NameNode that replicates metadata changes. Automatic failover switches clients to the standby when the primary fails.

Data Integrity: Compute checksums for each block during writes. Verify checksums during reads, detecting data corruption from hardware failures or network errors. When corruption is detected, read from alternate replicas and replace the corrupted block.

14. Design a Real-Time Analytics System

Real-time analytics processes streaming data and computes metrics with minimal latency, enabling immediate insights and responsive decision-making for applications like fraud detection or performance monitoring.

Stream Processing Architecture: Implement a lambda or kappa architecture. Lambda architecture maintains both batch and stream processing pipelines, while kappa simplifies to only stream processing with replayable event logs.

Use Apache Kafka or similar systems as the data backbone, ingesting events from various sources. Stream processors like Apache Flink or Kafka Streams consume events, perform computations, and output results.

Data Ingestion: Accept events through REST APIs, SDKs embedded in applications, or agents collecting system metrics. Buffer incoming events in Kafka partitions for durability and backpressure management.

Implement schema validation to ensure data quality. Use a schema registry to version and enforce event schemas across producers and consumers.

Processing Patterns: Support various computation patterns including filtering, transformation, aggregation, and joining streams. Implement windowing operations for time-based analytics: tumbling windows (non-overlapping), sliding windows (overlapping), and session windows (activity-based).

Handle late-arriving data through configurable watermarks defining how long to wait for delayed events before computing results. Provide mechanisms to update previous window results when late data arrives.

State Management: Maintain state for stateful operations like counting or aggregation. Store state in embedded key-value stores within stream processors with periodic checkpoints to distributed storage for fault tolerance.

Implement state partitioning to distribute state across processing instances, enabling horizontal scaling while maintaining correctness.

Output and Visualization: Write computed metrics to time-series databases like InfluxDB or Prometheus for efficient storage and querying of timestamped data. Support real-time dashboards querying these databases to display current and historical trends.

Implement alerting rules evaluating metrics against thresholds, triggering notifications when anomalies occur.

Exactly-Once Processing: Achieve exactly-once semantics through transactional processing coordinating state updates and output writes. Use two-phase commit or similar protocols ensuring both succeed or fail together.

Assign unique IDs to events enabling deduplication of replayed events during failure recovery.

15. Design a Recommendation System

Recommendation systems suggest relevant content, products, or connections to users based on their preferences and behavior, driving engagement and satisfaction in platforms from e-commerce to social media.

Recommendation Approaches: Implement collaborative filtering analyzing user-item interaction patterns. User-based collaborative filtering finds similar users and recommends items they liked. Item-based collaborative filtering recommends items similar to those the user has liked.

Content-based filtering recommends items with features similar to those the user has preferred historically. Hybrid approaches combine multiple techniques for more accurate recommendations.

Data Collection: Capture explicit feedback like ratings and reviews alongside implicit signals such as clicks, views, purchases, and time spent on items. Implicit data is more abundant but noisier than explicit feedback.

Store interaction data in a data warehouse partitioned by user or item for efficient batch processing. Stream recent interactions to enable real-time recommendation updates.

Model Training: For collaborative filtering, build user-item matrices where cells contain interaction strengths. Apply matrix factorization techniques like Singular Value Decomposition to discover latent features representing user preferences and item characteristics.

Train deep learning models like neural collaborative filtering or transformer-based architectures on interaction sequences to capture complex patterns.

Periodically retrain models on updated data. Use A/B testing to evaluate new models against production models before full deployment.

Real-Time Serving: Precompute recommendations for active users during offline batch processing. Store results in a fast key-value store for quick retrieval.

Implement online recommendation generation for new users or real-time personalization based on current session activity. Use cached item embeddings and efficient nearest neighbor search algorithms for low-latency serving.

Cold Start Problem: For new users without interaction history, recommend popular items or content trending in their demographic or geographic region. Prompt users for explicit preferences during onboarding.

For new items, use content-based features for initial recommendations until sufficient interaction data accumulates. Implement exploration strategies exposing new items to diverse users to gather feedback quickly.

Evaluation Metrics: Measure recommendation quality using precision, recall, and normalized discounted cumulative gain (NDCG) on historical data. Track online metrics like click-through rate, conversion rate, and user engagement time.

Monitor diversity to ensure recommendations aren't overly similar, and fairness to avoid discriminatory patterns.

16. Design a Location-Based Service

Location-based services help users discover nearby places, navigate efficiently, and connect with others based on geographic proximity. These systems handle spatial queries at massive scale.

Spatial Indexing: Implement geospatial indexing using R-trees, QuadTrees, or geohashing. Geohashing encodes latitude-longitude pairs into strings where shared prefixes indicate proximity, enabling efficient range queries.

Store location data in databases supporting geospatial indexes like PostgreSQL with PostGIS extension or MongoDB with geospatial queries. Index points of interest and user locations for fast proximity searches.

Proximity Queries: Support "find nearby" queries returning entities within a radius of a location. Implement efficient range queries using spatial indexes rather than scanning all records calculating distances.

For polygon-based queries (entities within a boundary), use PostGIS or similar spatial databases with built-in geometric operations.

Real-Time Location Tracking: Accept location updates from mobile devices via REST API or WebSocket connections. Rate-limit updates to balance accuracy with battery consumption and server load.

Update user locations in the spatial index efficiently. Use in-memory data structures for active users with periodic persistence to durable storage.

Map Data Management: Store map data including roads, buildings, and geographic features. Use hierarchical storage with different detail levels for various zoom levels, serving low-detail tiles for zoomed-out views and high-detail tiles for close-ups.

Integrate with third-party map providers like Google Maps or OpenStreetMap, caching tiles to reduce API costs and improve performance.

Routing and Navigation: Implement shortest path algorithms like Dijkstra's or A* for route calculation. Preprocess road networks with contraction hierarchies or other speedup techniques enabling millisecond query response times despite millions of road segments.

Incorporate real-time traffic data to provide accurate travel time estimates and suggest optimal routes avoiding congestion.

Privacy Considerations: Implement location privacy controls allowing users to share precise locations only with trusted contacts. Support approximate location sharing for reduced privacy exposure.

Store location history with appropriate retention policies and encryption. Provide clear user controls for data deletion and opt-out from location tracking.

17. Design an Online Judge System

An online judge system evaluates code submissions for correctness and efficiency, supporting programming competitions, educational platforms, and technical interviews.

Submission Processing: Accept code submissions through a web interface, storing source code, programming language, and problem ID. Queue submissions for evaluation, prioritizing contest submissions or premium users.

Code Execution Environment: Run submitted code in isolated sandboxes to prevent malicious code from affecting the system. Use Docker containers or virtual machines with resource limits: CPU time, memory usage, and disk space.

Implement secure execution by restricting system calls, network access, and file system operations. Only allow reading input files and writing output files.

Test Case Management: Store problem test cases securely, preventing unauthorized access. Include sample test cases for users to verify basic correctness and hidden test cases for thorough evaluation.

Support multiple test case types: standard input-output tests, special judges for problems with multiple valid solutions, and interactive problems requiring multiple rounds of communication.

Evaluation Process: Compile submitted code with the appropriate compiler or interpreter. Capture compilation errors and return them to users immediately.

Execute compiled programs against each test case, capturing output and measuring execution time and memory usage. Compare output with expected results, supporting exact matching or custom validators.

Verdict Determination: Return verdicts including Accepted (all test cases passed), Wrong Answer, Time Limit Exceeded, Memory Limit Exceeded, Runtime Error, or Compilation Error. Provide feedback on which test case failed and the execution metrics.

Implement partial scoring for problems supporting multiple subtasks or graduated test cases.

Scalability: Deploy multiple judge machines to handle concurrent submissions. Implement a distributed task queue with workers pulling evaluation jobs.

Cache compilation results for identical submissions during contests, reducing evaluation time for repeated submissions.

Plagiarism Detection: Analyze submissions for similarity using techniques like token-based comparison, AST matching, or machine learning models. Flag suspicious pairs for manual review.

18. Design a Job Scheduler

A job scheduler executes tasks at specified times or intervals, handling dependencies, retries, and distributed execution for workflow automation and data pipelines.

Job Definition: Define jobs with execution schedules using cron expressions, intervals, or specific timestamps. Support one-time jobs and recurring jobs with configurable recurrence patterns.

Specify job parameters, execution environments (Docker images or language runtimes), resource requirements, and timeout limits.

Scheduling Engine: Implement a priority queue ordered by next execution time. A scheduler daemon continuously checks for due jobs and dispatches them to execution workers.

Support different scheduling modes: fixed delay (wait after completion before next run) and fixed rate (execute at regular intervals regardless of previous run duration).

Dependency Management: Model job dependencies as a directed acyclic graph (DAG). Before executing a job, verify all upstream dependencies have completed successfully.

Implement topological sorting to determine valid execution orders respecting all dependencies. Parallelize execution of independent jobs within a workflow.

Distributed Execution: Run jobs on a cluster of worker nodes. Implement a work-stealing algorithm where idle workers pull jobs from a shared queue, balancing load dynamically.

Use containerization to isolate job executions and support diverse runtime requirements. Implement resource allocation ensuring workers don't overcommit CPU or memory.

Fault Tolerance: Implement automatic retries for failed jobs with exponential backoff. Configure maximum retry attempts and backoff multipliers per job.

Store job state in a distributed database for recovery after scheduler failures. Implement leader election ensuring only one scheduler instance dispatches jobs, preventing duplicate execution.

Monitoring and Alerting: Track job execution history: start time, end time, status, and logs. Compute metrics like success rates, average duration, and failure frequency.

Alert on missed schedules, jobs exceeding duration thresholds, or consecutive failures. Provide dashboards visualizing job execution timelines and resource utilization.

19. Design a Real-Time Collaboration System

Real-time collaboration tools enable multiple users to simultaneously edit documents, spreadsheets, or whiteboards with immediate synchronization across all participants, similar to Google Docs or Figma.

Operational Transformation: Implement operational transformation (OT) to handle concurrent edits. Transform operations based on concurrent changes to maintain document consistency. When two users edit simultaneously, apply one operation, then transform the second operation relative to the first before applying it.

This ensures all users converge to the same final state regardless of operation arrival order.

Conflict-Free Replicated Data Types: Alternatively, use CRDTs which guarantee eventual consistency without coordination. CRDT operations are commutative and idempotent, allowing application in any order with identical results.

Implement a CRDT sequence for collaborative text editing, where each character has a unique identifier enabling deterministic merging of concurrent insertions.

Real-Time Communication: Establish WebSocket connections between clients and servers for bidirectional communication. Clients send local edits to the server immediately, and the server broadcasts transformed operations to other participants.

Implement presence awareness showing active users, their cursor positions, and current selections. Use separate channels for document edits and presence updates.

History and Versioning: Store all operations in an append-only log, enabling complete edit history reconstruction. Support time-travel features viewing document state at any historical point.

Implement periodic snapshots capturing document state, enabling efficient loading without replaying the entire operation log.

Offline Support: Allow users to continue editing offline, queuing operations locally. Upon reconnection, sync queued operations with the server, resolving conflicts through OT or CRDT mechanisms.

Implement optimistic updates reflecting local edits immediately while waiting for server confirmation, with rollback capabilities if operations are rejected.

Access Control: Support different permission levels: viewer (read-only), commenter (read plus annotations), and editor (full edit access). Implement real-time permission changes affecting active sessions immediately.

Use JWT tokens for authentication and authorization, validating permissions before applying operations.

20. Design a Logging and Monitoring System

A comprehensive logging and monitoring system collects, stores, and analyzes application logs and metrics, enabling observability for debugging, performance optimization, and incident response.

Log Collection: Deploy agents on application servers collecting logs from files, standard output, or direct API calls. Support structured logging with JSON format for easier parsing and analysis.

Implement log buffering and batching to reduce network overhead. Use compression before transmission to minimize bandwidth usage.

Log Aggregation Pipeline: Ingest logs into a message queue like Kafka for durability and backpressure management. Stream processing workers consume logs, parse them, enrich with metadata (hostname, service name, environment), and filter based on severity or content.

Write processed logs to a distributed storage system like Elasticsearch for full-text search or object storage like S3 for archival.

Metrics Collection: Collect time-series metrics using push (applications send metrics) or pull (scraping endpoints) models. Store metrics in specialized time-series databases optimized for timestamp-based queries.

Support different metric types: counters (monotonically increasing), gauges (point-in-time values), histograms (distribution of values), and summaries (quantiles).

Querying and Visualization: Provide a query language for filtering and aggregating logs. Support full-text search, field-based filtering, and regular expressions.

Build visualization dashboards displaying key metrics as time-series graphs, heat maps, or tables. Support custom time ranges and automatic refresh for real-time monitoring.

Alerting: Define alert rules evaluating metrics or log patterns against thresholds. Support complex conditions using logical operators and aggregation functions.

Implement alert routing sending notifications through appropriate channels (email, Slack, PagerDuty) based on severity and on-call schedules. Include alert grouping and deduplication to prevent notification storms.

Data Retention: Implement tiered storage moving older data to cheaper storage mediums. Keep recent logs (last 7 days) in fast storage for active debugging, archive older logs (8-90 days) to object storage with lower access speed, and delete very old logs beyond retention policies.

Downsample high-resolution metrics to coarser granularity over time, reducing storage requirements while maintaining historical trends.

E-Commerce and Financial Systems

21. Design an E-Commerce Platform

An e-commerce platform connects buyers with sellers, managing product catalogs, shopping carts, orders, and payments while ensuring reliability and scalability during peak shopping periods.

Product Catalog Management: Design a flexible product schema supporting diverse product types with varying attributes. Use a document database like MongoDB or a relational database with JSONB columns for attribute storage.

Implement hierarchical categories enabling product organization and faceted search. Store product images in object storage with CDN distribution for fast global access. Support multiple images per product with zoom capabilities.

Index products using Elasticsearch for full-text search across names, descriptions, and attributes. Implement autocomplete suggestions and spell correction to improve search experience.

Shopping Cart System: Store shopping carts in Redis for fast access with automatic expiration after inactivity periods. Key carts by session ID for guest users or user ID for authenticated users.

Handle concurrent modifications to carts using optimistic locking with version numbers. Allow adding items, updating quantities, removing items, and applying discount codes.

Implement cart persistence for authenticated users across sessions and devices. Merge guest carts with user carts upon login.

Inventory Management: Track inventory levels per product and warehouse location. Implement reserved inventory concept where adding items to carts temporarily reserves stock, preventing overselling.

Release reservations after cart expiration or checkout completion. Use distributed locks during checkout to ensure atomic inventory checks and updates.

Implement inventory forecasting based on sales velocity and reorder points triggering restocking workflows. Support inventory synchronization across multiple warehouses with real-time updates.

Order Processing Workflow: Model orders with states: created, payment pending, payment confirmed, processing, shipped, delivered, and cancelled. Implement state machine transitions with validation rules preventing invalid state changes.

Generate unique order IDs using distributed ID generation. Store orders in a transactional database ensuring consistency between order records, order items, and inventory updates.

Integrate with warehouse management systems for order fulfillment, triggering picking and packing workflows. Support split shipments when items come from multiple warehouses.

Payment Integration: Integrate with payment gateways like Stripe or PayPal using their SDKs. Implement PCI compliance requirements if handling credit card data directly, or use tokenization to avoid storing sensitive information.

Support multiple payment methods: credit cards, debit cards, digital wallets, and buy-now-pay-later options. Implement fraud detection checking for suspicious patterns like unusual purchase amounts, mismatched billing addresses, or high-risk geographic locations.

Use idempotency keys ensuring payment operations execute exactly once despite retries. Implement webhook handlers receiving payment status updates asynchronously.

Scalability for Peak Traffic: Expect traffic spikes during sales events. Implement horizontal scaling with auto-scaling groups adding capacity based on load metrics.

Cache aggressively: product details, category pages, and search results in CDN and application caches. Invalidate caches when products update.

Use asynchronous processing for non-critical operations like sending confirmation emails or updating analytics, preventing them from blocking checkout completion.

22. Design a Payment Processing System

A payment processing system handles financial transactions securely and reliably, serving as the backbone for e-commerce platforms, subscription services, and peer-to-peer payment applications.

Transaction Processing: Accept payment requests containing amount, currency, payment method, and merchant information. Validate request integrity using HMAC signatures preventing tampering.

Implement the two-phase commit pattern: first authorize the transaction verifying funds availability and fraud checks, then capture funds if the merchant fulfills the order. Support authorization expiration and cancellation before capture.

Store transactions in a database with ACID properties ensuring consistency. Use write-ahead logging guaranteeing durability even during crashes.

Payment Method Management: Securely store payment methods using tokenization. Replace sensitive card numbers with tokens, storing actual card data in PCI-compliant vaults operated by payment processors.

Support payment method verification through small charge-and-refund cycles or address verification systems. Implement card update services automatically refreshing expired cards.

Multi-Currency Support: Handle payments in multiple currencies with real-time exchange rate fetching from reliable sources. Store exchange rates with timestamps enabling historical transaction analysis.

Support currency conversion during checkout allowing customers to pay in their preferred currency while merchants receive funds in their local currency. Display clear conversion rates and fees.

Fraud Prevention: Implement rule-based fraud detection checking transaction patterns, velocity limits, geographic mismatches, and unusual amounts. Flag high-risk transactions for manual review.

Use machine learning models analyzing historical fraud patterns to score transactions. Integrate with third-party fraud detection services providing device fingerprinting and behavioral analytics.

Implement 3D Secure authentication adding an extra verification step for online card transactions, shifting liability from merchants to card issuers.

Reconciliation and Settlement: Implement daily reconciliation processes comparing internal transaction records with payment processor statements. Detect and investigate discrepancies automatically.

Calculate net settlement amounts considering transaction fees, refunds, and chargebacks. Generate settlement reports for merchant payouts.

Support different settlement frequencies based on merchant risk profiles: daily settlements for low-risk established merchants, weekly or monthly for higher-risk or new merchants.

23. Design a Stock Trading Platform

A stock trading platform enables users to buy and sell securities, requiring ultra-low latency, strong consistency, and regulatory compliance for fair and transparent markets.

Order Management: Support various order types: market orders (execute immediately at current price), limit orders (execute only at specified price or better), stop-loss orders (trigger market orders when price reaches threshold), and advanced types like iceberg orders or good-till-cancelled orders.

Validate orders checking for sufficient buying power or shares to sell. Generate unique order IDs and timestamp orders with nanosecond precision for accurate sequencing.

Order Matching Engine: Implement an order book maintaining buy orders (bids) and sell orders (asks) sorted by price and time priority. Use efficient data structures like heaps or trees for fast insertion and matching.

Match orders following price-time priority: the best price gets filled first, and among orders at the same price, earlier orders execute first. This ensures fairness and market integrity.

Execute matches atomically, updating buyer and seller accounts, transferring shares and funds, and recording trade details. Generate trade confirmations sent to both parties.

Real-Time Market Data: Stream real-time quotes including current bid price, ask price, last trade price, and volume. Update quotes as new orders arrive or trades execute.

Implement ticker plants broadcasting market data to thousands of concurrent subscribers through WebSocket connections or UDP multicast for minimal latency.

Provide market depth showing the order book with aggregated quantities at each price level. Support historical tick data for backtesting trading strategies.

Account Management: Maintain user accounts tracking cash balances, securities positions, pending orders, and transaction history. Implement double-entry bookkeeping ensuring account balances always reconcile.

Calculate buying power considering cash, marginable securities, and leverage ratios. Enforce margin requirements preventing users from taking excessive leveraged positions.

Support different account types: cash accounts (no margin), margin accounts (borrow to increase buying power), and retirement accounts with tax advantages and withdrawal restrictions.

24. Design a Digital Wallet

A digital wallet stores value and enables peer-to-peer transfers, bill payments, and merchant purchases through mobile devices, requiring security, convenience, and regulatory compliance.

User Account System: Implement secure registration with multi-factor authentication using SMS codes, email verification, or biometric authentication. Store user credentials with strong hashing algorithms like bcrypt or Argon2.

Support KYC (Know Your Customer) verification collecting identity documents, proof of address, and performing background checks to comply with anti-money laundering regulations.

Implement account tiers with varying limits: unverified users have low transaction limits, while verified users enjoy higher limits enabling larger transfers.

Balance Management: Maintain user balances in a transactional database. Implement operations for loading money from bank accounts or cards, sending money to other users, and withdrawing to external accounts.

Use pessimistic locking during balance updates preventing race conditions where concurrent transactions could violate balance constraints. Validate sufficient balance before authorizing transfers.

Track transaction history with detailed metadata including sender, recipient, amount, timestamp, transaction type, and status.

Peer-to-Peer Transfers: Enable instant money transfers between wallet users. When user A sends money to user B, debit A's balance and credit B's balance atomically within a database transaction.

Support transfer requests where users can request money from others, who approve or reject requests. Implement split bill features dividing amounts among multiple payers.

Notify recipients immediately about incoming transfers through push notifications. Include optional messages or notes with transfers for context.

Merchant Payments: Generate QR codes or NFC tokens for in-store payments. Merchants scan user QR codes or tap NFC-enabled devices to initiate payment requests.

Implement online payment integration with merchant APIs. Support payment links merchants can share with customers for remote transactions.

Provide merchant dashboards showing sales analytics, transaction histories, and settlement schedules. Enable refund processing for returned purchases or service issues.

Security Measures: Implement transaction signing with device-specific keys preventing unauthorized transfers even if credentials are compromised. Require additional authentication for large transfers or sensitive operations.

Monitor for suspicious activities like rapid sequential transfers, unusual geographic patterns, or transfers to known fraudulent accounts. Freeze suspicious accounts pending investigation.

Encrypt sensitive data including balances, transaction details, and personal information. Implement key rotation and secure key management practices.

25. Design an Auction Platform

An auction platform enables buyers to bid on items sold by sellers, requiring real-time bidding, automatic bid management, and winner determination following various auction formats.

Auction Types: Support English auctions where price increases until no higher bids arrive, Dutch auctions where price decreases until a buyer accepts, and sealed-bid auctions where bidders submit secret bids with the highest winning.

Implement timed auctions with specific start and end times plus sniping prevention mechanisms extending auctions when last-minute bids arrive.

Support reserve prices, minimum starting bids, and buy-it-now options allowing immediate purchase at fixed prices.

Bidding System: Accept bids through real-time connections, validating bid amounts exceed current highest bids by minimum increments. Reject invalid bids immediately with clear error messages.

Implement proxy bidding where users specify maximum amounts they're willing to pay. The system automatically increases their bids incrementally as others bid, up to their maximum.

Handle concurrent bids using optimistic locking with version numbers. When multiple bids arrive simultaneously, serialize them ensuring consistent bid history and correct highest bid determination.

Real-Time Updates: Broadcast bid updates to all users watching an auction through WebSocket connections. Update current price, bid count, and leading bidder (anonymized for privacy).

Display countdown timers showing remaining auction time, updating every second. Highlight when auctions are about to end to create urgency.

Send notifications to outbid users encouraging rebidding. Alert users when auctions they're watching are about to close.

Winner Determination: When auctions close, determine winners based on auction rules. For English auctions, the highest bidder wins. For second-price auctions, the highest bidder wins but pays the second-highest bid amount.

Handle ties with predetermined rules like earlier bid time preference. Generate winner notifications and seller notifications simultaneously.

Implement cooling-off periods for high-value auctions allowing winners brief windows to cancel bids under specific conditions.

26. Design a Subscription Management System

A subscription management system handles recurring billing, plan changes, usage tracking, and customer lifecycle management for SaaS applications and subscription-based services.

Subscription Plans: Define flexible plan structures supporting flat-rate pricing, tiered pricing with feature restrictions, usage-based pricing, and hybrid models combining fixed fees with usage charges.

Support billing intervals including monthly, quarterly, annual, or custom periods. Implement discount codes and promotional pricing for trial periods or seasonal offers.

Enable plan addons allowing customers to purchase additional features or capacity beyond their base plans. Track addon usage and billing separately.

Billing Engine: Schedule recurring billing jobs executing at appropriate intervals based on subscription plans. Generate invoices detailing charges for the billing period including base fees, usage charges, and taxes.

Implement payment collection attempting to charge stored payment methods. Handle payment failures through retry logic with exponential backoff, sending dunning emails encouraging payment method updates.

Support proration calculations when customers upgrade or downgrade mid-cycle, crediting unused time from old plans and charging proportionally for new plans.

Usage Tracking: Meter usage for consumption-based pricing models. Track API calls, storage usage, compute hours, or other billable metrics through event streaming systems.

Aggregate usage data daily or hourly providing real-time visibility into current period consumption. Implement usage quotas soft limits warning customers and hard limits blocking usage beyond plan allowances.

Generate usage reports showing historical trends and cost breakdowns helping customers optimize their usage patterns.

Plan Changes: Support immediate upgrades applying new features and pricing instantly with prorated charges. For downgrades, apply changes at current period end preventing service disruption while enforcing lower limits immediately or at renewal.

Validate plan changes ensuring customers meet prerequisites like usage limits or feature dependencies. Preview charges for upcoming changes helping customers make informed decisions.

Track plan change history for analytics and customer support purposes. Enable rollback capabilities for erroneous changes.

27. Design a Ticketing System for Events

An event ticketing system sells tickets for concerts, sports events, and conferences, handling high-concurrency ticket sales, seat selection, and access control at venue entrances.

Event Management: Create events with detailed information including date, time, venue, seating configuration, ticket types (general admission, VIP, reserved seats), and pricing tiers.

Support multi-session events like conferences with different sessions or festival days. Enable package deals combining multiple events or sessions.

Implement capacity limits per ticket type and overall event capacity. Support hold dates for venue bookings before events are publicly announced.

Ticket Inventory: Model seat inventory mapping to physical venue layouts for reserved seating events. Support section, row, and seat number assignments with graphical seat maps for customer selection.

For general admission, track aggregate ticket counts without specific seat assignments. Implement tiered pricing with different zones or access levels.

Use distributed locks during ticket purchasing preventing double booking when multiple customers attempt selecting identical seats simultaneously.

High-Concurrency Sales: Handle traffic spikes during on-sale moments when popular events release tickets. Implement queue systems placing customers in virtual lines, processing orders sequentially to prevent overwhelming backend systems.

Show queue position estimates managing customer expectations. Rate limit ticket selection and checkout steps preventing customers from holding tickets indefinitely.

Implement ticket holding mechanisms giving customers limited time (typically 10-15 minutes) to complete purchases before releasing holds for others.

Purchase Workflow: Support multi-ticket purchases allowing group bookings. Validate ticket quantity against customer account limits preventing scalping.

Calculate total costs including ticket prices, service fees, and taxes. Accept various payment methods with fraud detection for high-risk transactions.

Generate unique ticket IDs with barcodes or QR codes for entry validation. Deliver tickets via email, mobile apps, or physical mail based on customer preferences.

28. Design a Crowdfunding Platform

A crowdfunding platform connects project creators with backers who fund projects through financial contributions, requiring campaign management, payment processing, and milestone tracking.

Campaign Creation: Enable creators to set up campaigns with descriptions, funding goals, duration, and reward tiers for different contribution levels. Support rich media including images, videos, and detailed project descriptions.

Implement campaign approval workflows reviewing projects for policy compliance before public launch. Support draft mode allowing creators to refine campaigns before submission.

Track campaign states: draft, under review, active, successful, unsuccessful, and fulfilled. Each state has specific allowed actions and restrictions.

Funding Models: Support all-or-nothing funding where campaigns must reach goals to collect pledges, encouraging ambitious targets. Implement flexible funding allowing creators to keep funds regardless of goal achievement for less risky projects.

Calculate campaign progress showing current funding percentage, backer count, and remaining time. Display funding momentum through charts showing contribution patterns over time.

Payment Authorization: Authorize payment methods when backers pledge without immediately charging. Hold authorizations until campaigns conclude successfully, then capture funds.

Handle authorization expiration for long-duration campaigns by reauthorizing periodically. Notify backers of authorization failures requiring payment method updates.

Support partial pledges for campaigns with stretch goals, allowing backers to incrementally increase contributions as campaigns progress.

Reward Fulfillment: Track reward tiers with detailed descriptions, delivery timelines, and backer selections. Manage limited reward tiers with maximum backer counts, marking sold out when limits reach.

Generate fulfillment lists for creators showing which backers receive which rewards, including shipping addresses for physical rewards. Support bulk label printing and shipping integration.

Implement update posting allowing creators to share progress, setbacks, and delivery schedules with backers. Send notifications ensuring backers stay informed throughout fulfillment.

29. Design a Loyalty Rewards Program

A loyalty program incentivizes repeat customers by rewarding purchases, engagement, and referrals with points redeemable for discounts, free products, or exclusive experiences.

Points Accrual: Award points based on purchase amounts with configurable earn rates (e.g., 1 point per dollar spent). Support promotional multipliers during special events or for specific product categories.

Grant bonus points for non-purchase activities like social media sharing, writing reviews, or referring friends. Implement birthday bonuses and anniversary rewards celebrating customer relationships.

Track point-earning transactions linking rewards to specific purchases or activities for audit trails and customer service resolution.

Points Redemption: Define redemption options including discount codes, free products, or experiential rewards like exclusive events. Set redemption thresholds and point values ensuring sustainable program economics.

Implement redemption workflows validating sufficient point balances and applying rewards to purchases. Support partial point redemption combining points with regular payments.

Track redemption history showing what customers redeemed and when. Enable redemption reversal for returned purchases crediting points back to customer accounts.

Tier Systems: Implement membership tiers with increasing benefits as customers demonstrate loyalty. Define tier qualification criteria based on annual spending, points earned, or purchase frequency.

Offer tier-specific perks like higher earn rates, exclusive sales access, free shipping, or dedicated customer service. Display tier progress motivating customers toward next tier achievement.

Implement tier maintenance requirements like minimum annual spending to retain status, with grace periods allowing customers brief windows to requalify.

Personalization: Analyze purchase history recommending personalized rewards matching customer preferences. Highlight relevant products eligible for point redemption.

Send targeted offers to dormant members encouraging reengagement. Provide milestone celebrations recognizing significant point achievements or membership anniversaries.

30. Design a Tax Calculation Service

A tax calculation service computes sales tax for e-commerce transactions considering complex jurisdictional rules, product taxability, and customer locations, ensuring compliance with tax regulations.

Tax Rate Management: Maintain comprehensive tax rate databases covering federal, state, county, and city tax rates. Update rates regularly as jurisdictions modify tax policies.

Store rate hierarchies showing how multiple jurisdictional rates combine. Support special tax rates for specific product categories or tax holidays exempting certain purchases.

Implement nexus rules determining where merchants have tax obligations. Track physical presence, economic thresholds, or marketplace facilitator laws establishing tax collection responsibilities.

Product Taxability: Categorize products based on tax treatment: taxable, exempt, or reduced rate. Support complex rules where products have different taxability based on customer location (e.g., groceries taxed in some states but not others).

Maintain product-to-tax-category mappings allowing flexible categorization. Support override capabilities where merchants specify custom taxability for specific products.

Geolocation and Jurisdiction: Resolve customer addresses to precise tax jurisdictions using geocoding services. Handle address validation ensuring accurate tax calculations based on correct locations.

Support multiple address types: shipping address determining destination-based taxes and billing address for origin-based taxes in applicable jurisdictions.

Handle edge cases like PO boxes, military addresses, and international addresses with appropriate tax treatment.

Tax Calculation Engine: Calculate taxes by multiplying taxable amounts by applicable rates considering product taxability, customer location, and seller nexus. Sum rates from all applicable jurisdictions.

Apply exemption certificates for tax-exempt customers like resellers or non-profit organizations. Validate certificate authenticity and expiration dates.

Handle promotional discounts and coupons correctly, calculating tax on discounted amounts or pre-discount amounts based on jurisdiction rules.

Compliance and Reporting: Generate tax collection reports for filing returns in various jurisdictions. Aggregate sales data by jurisdiction showing total sales, taxable sales, tax collected, and exempted sales.

Support multiple filing frequencies: monthly, quarterly, or annual based on jurisdiction requirements and merchant volumes. Remind merchants of upcoming filing deadlines.

Maintain audit trails documenting all tax calculations with timestamps, rates used, and rule versions ensuring reproducibility during audits.

Social and Communication Platforms

31. Design a Live Streaming Platform

A live streaming platform enables content creators to broadcast video in real-time to potentially millions of concurrent viewers, requiring low latency, adaptive quality, and interactive features.

Video Ingestion: Accept video streams from broadcasters through RTMP (Real-Time Messaging Protocol) or WebRTC connections. Deploy ingestion servers in multiple geographic regions allowing creators to connect to nearby endpoints for reduced latency.

Transcode incoming streams into multiple resolutions and bitrates (360p, 480p, 720p, 1080p, 4K) supporting adaptive streaming based on viewer bandwidth. Use GPU-accelerated encoding for efficient processing.

Implement keyframe alignment ensuring all quality levels have synchronized keyframes enabling seamless quality switching without playback interruptions.

Content Distribution: Segment transcoded streams into short chunks (2-6 seconds) compatible with HLS or DASH protocols. Store segments in object storage or CDN edge caches for global distribution.

Deploy an origin server managing the playlist files (manifests) directing players to segment locations. Distribute manifests through CDNs ensuring availability and low-latency access worldwide.

Implement multi-tier caching with hot content cached at edge servers and less popular streams cached at regional locations, optimizing storage costs while maintaining performance.

Low Latency Streaming: Reduce latency through shorter segment durations and optimized chunk delivery. Traditional HLS introduces 10-30 second delays; low-latency variants achieve 2-5 second delays.

Implement chunked transfer encoding delivering segments as they're generated rather than waiting for completion. Use HTTP/2 server push proactively sending segments to viewers.

For ultra-low latency applications like interactive streams, implement WebRTC-based delivery achieving sub-second latency, though at higher infrastructure costs and complexity.

Viewer Experience: Implement adaptive bitrate streaming where video players monitor available bandwidth and automatically select appropriate quality levels. Switch to lower quality when bandwidth drops, preventing buffering.

Provide manual quality selection allowing viewers to override automatic decisions based on their preferences or data constraints.

Support DVR functionality for live streams, allowing viewers to pause, rewind, and fast-forward even during live broadcasts. Store recent stream segments enabling time-shifted viewing.

Interactive Features: Implement live chat systems allowing real-time viewer communication. Use WebSocket connections broadcasting messages to all viewers with spam filtering and moderation tools.

Support reactions, polls, and Q&A features enabling viewer participation. Display real-time viewer counts and engagement metrics to creators.

Enable monetization through subscriptions, tips, and ad insertion. Implement server-side ad insertion (SSAI) where ads are stitched into video streams server-side, preventing ad blockers and ensuring consistent experience.

Scalability: Handle sudden viewership spikes through auto-scaling ingestion and transcoding infrastructure. Use message queues buffering transcoding jobs during peak loads.

Implement viewer connection pooling where CDN edge servers maintain persistent connections to origin servers, reducing connection overhead for popular streams.

Monitor stream health tracking metrics like buffering ratio, startup time, and quality switches. Alert creators and operations teams about degraded performance.

32. Design a Dating Application

A dating application connects people seeking romantic relationships through profile matching, messaging, and discovery features, requiring sophisticated algorithms, privacy protection, and safety measures.

User Profiles: Collect comprehensive profile information including demographics, interests, photos, and preferences. Implement photo verification ensuring profile pictures match identity documents, reducing catfishing.

Support rich profile customization with prompts encouraging personality expression beyond basic demographics. Store profile data in document databases supporting flexible schemas as features evolve.

Implement privacy controls allowing users to hide profile elements from specific users or control profile visibility based on matching status.

Matching Algorithm: Implement multi-factor matching considering location proximity, age preferences, shared interests, and complementary personality traits. Calculate compatibility scores ranking potential matches.

Use machine learning models trained on historical match outcomes (conversations started, dates arranged, relationships formed) to improve match quality over time.

Apply collaborative filtering suggesting profiles similar users found attractive. Balance exploration and exploitation showing both high-probability matches and diverse options.

Discovery Mechanisms: Implement card-based swiping interfaces displaying one profile at a time. Track swipe actions (like, dislike, super like) updating user preference models.

Support filter-based search allowing users to specify required criteria like distance, age range, height, education, or lifestyle choices.

Implement daily recommendations highlighting high-quality matches based on recent activity and preference patterns. Limit daily likes encouraging thoughtful consideration.

Messaging System: Enable messaging only after mutual interest (both users like each other) to prevent unwanted messages and harassment. Implement icebreaker features with conversation starters reducing awkward first messages.

Support rich messaging with text, photos, GIFs, and video messages. Implement message expiration for temporary conversations or time-limited matches creating urgency.

Use AI-powered content moderation scanning messages for inappropriate content, harassment, or scam attempts. Block and report features allowing users to flag problematic behavior.

Safety and Trust: Implement identity verification through government ID uploads, selfie matching, or third-party verification services. Display verification badges on verified profiles increasing trust.

Provide safety resources and tips within the application. Implement in-app video calling allowing users to communicate safely before sharing external contact information.

Partner with background check services offering optional screening for serious relationships. Enable user reporting and blocking with review processes addressing reported accounts promptly.

Monetization Features: Offer freemium model with basic features free and premium subscriptions unlocking unlimited likes, rewinds (undo accidental swipes), passport (search any location), and profile boosts increasing visibility.

Implement microtransactions for super likes (special notifications indicating strong interest) or boosts providing temporary visibility increases during peak usage times.

Display profile impression analytics showing premium users who viewed their profiles and engagement statistics.

33. Design a Question-Answer Platform

A question-answer platform connects people seeking knowledge with experts willing to share expertise, requiring effective content organization, quality moderation, and search optimization.

Content Creation: Enable users to post questions with titles, detailed descriptions, and topic tags categorizing content. Support rich text formatting, code snippets, images, and mathematical notation in questions and answers.

Implement duplicate detection analyzing new questions against existing ones, suggesting potential duplicates before posting to reduce content redundancy.

Allow question editing with revision history tracking changes over time. Display edit timestamps and editor information maintaining content transparency.

Answer System: Allow multiple answers per question encouraging diverse perspectives and comprehensive responses. Implement voting systems where users upvote helpful answers and downvote unhelpful ones.

Enable answer acceptance where question askers mark the most helpful answer, signaling resolution to others. Display accepted answers prominently at the top of answer lists.

Support answer comments for requesting clarifications, suggesting improvements, or providing minor additional information without creating full answers.

Reputation and Gamification: Implement reputation systems awarding points for contributions: asking good questions, providing helpful answers, and receiving upvotes. Deduct reputation for downvotes discouraging low-quality content.

Create achievement badges for milestones like first upvote, reaching reputation thresholds, or expertise in specific topics. Display badges on user profiles as social proof.

Implement privilege levels unlocking capabilities as reputation increases: editing others' posts, reviewing suggested edits, moderating content, or accessing detailed analytics.

Content Moderation: Deploy community moderation tools allowing high-reputation users to review and vote on flagged content. Implement review queues for suggested edits, low-quality posts, and spam reports.

Use machine learning models detecting spam, abusive content, or off-topic posts. Flag suspicious content for manual review while automatically removing obvious spam.

Establish clear community guidelines and provide feedback when posts are closed or deleted, helping users understand expectations and improve future contributions.

Search and Discovery: Index questions and answers using full-text search with support for Boolean operators, tag filtering, and relevance ranking. Consider factors like views, votes, answer count, and recency in ranking.

Implement related questions sections showing similar content based on shared tags, text similarity, or users who viewed both questions. This increases content discoverability and reduces duplicate questions.

Support advanced search syntax enabling precise queries filtering by tags, user, date ranges, or vote thresholds. Display search tips helping users construct effective queries.

Tag System: Organize content through hierarchical tags representing topics and subtopics. Allow synonyms redirecting similar tags to canonical versions reducing fragmentation.

Implement tag creation privileges requiring minimum reputation, maintaining tag quality. Enable tag wikis providing descriptions, usage guidelines, and common subtopics.

Generate trending tags showing currently popular topics and display user expertise scores per tag based on answer quality in those areas.

34. Design a Content Moderation System

A content moderation system identifies and filters inappropriate content including hate speech, explicit material, spam, and misinformation across text, images, and videos, protecting users while respecting free expression.

Multi-Modal Detection: Implement separate detection pipelines for different content types. Text moderation uses natural language processing identifying profanity, hate speech, harassment, and spam patterns.

Image moderation employs computer vision models detecting explicit content, violence, or policy violations. Classify images by severity: safe, suggestive, or explicit.

Video moderation combines frame sampling with audio transcription, analyzing both visual content and speech for violations. Process videos asynchronously given computational intensity.

Machine Learning Models: Train classification models on labeled datasets categorizing content as safe or violating specific policies. Use deep learning architectures like BERT for text and ResNet or EfficientNet for images.

Implement multi-label classification since content may violate multiple policies simultaneously. Generate confidence scores for each violation category guiding downstream decisions.

Continuously retrain models on new data and adversarial examples where moderators disagree with model predictions, improving accuracy over time.

Rule-Based Filtering: Complement ML models with pattern matching for known violations. Maintain blocklists of prohibited terms, phrases, or image hashes (perceptual hashing for image variants).

Implement context-aware rules considering surrounding text, user history, and content type. Same words might be acceptable in educational contexts but violate policies in harassment.

Support regular expression matching for complex patterns like phone numbers, URLs, or spam formatting tricks (unusual spacing or special characters).

Human Review Workflow: Route flagged content to human moderators through queuing systems. Prioritize based on severity, content reach, and confidence scores—high severity with low confidence needs immediate human judgment.

Implement review interfaces presenting content with context (user history, conversation thread, community guidelines). Provide action options: approve, remove, warn user, or escalate to senior moderators.

Track moderator decisions building quality datasets for model retraining. Implement inter-rater reliability checks ensuring consistent policy application across moderators.

User Reporting: Enable users to report problematic content providing violation categories and optional explanations. Aggregate reports identifying content receiving multiple flags for prioritized review.

Implement report abuse detection identifying users filing false reports maliciously. Temporary restrictions on reporting privileges for chronic abusers prevent system gaming.

Provide feedback to reporters about outcomes when appropriate, encouraging continued community participation in content quality.

Appeal Process: Allow users to appeal moderation decisions they believe are incorrect. Route appeals to different moderators than original reviewers ensuring independent evaluation.

Implement expedited appeals for time-sensitive content like news or trending discussions. Provide clear communication about appeal status and decisions.

Track appeal overturn rates by policy category and moderator, identifying areas needing guideline clarification or additional moderator training.

35. Design a Group Chat System

A group chat system enables multiple participants to communicate simultaneously through text messages, media sharing, and presence indicators, requiring efficient message delivery and conversation management.

Group Management: Support group creation with configurable settings: name, description, avatar, privacy (public or private), and maximum size. Implement role-based permissions defining who can add members, modify settings, or send messages.

Enable group discovery for public groups through search and recommendations. Support group invitations for private groups with invite links or direct member additions.

Implement group categories or folders helping users organize numerous group memberships. Support muting notifications for specific groups while staying member.

Message Distribution: Design fan-out architecture where incoming messages are replicated to all group members. For small groups, fan-out on write delivers messages to each member's inbox immediately.

For large groups (thousands of members), hybrid approach stores messages centrally with members fetching recent messages on demand, reducing storage and write amplification.

Implement message ordering guarantees ensuring all members see consistent message sequences. Use vector clocks or logical timestamps establishing causality across distributed systems.

Rich Media Handling: Support sharing images, videos, documents, and voice messages. Upload files to object storage, sending references rather than raw content in messages.

Implement client-side media compression reducing bandwidth and storage requirements. Generate thumbnails for images and videos enabling preview before full content loads.

Support ephemeral media expiring after viewing or specified timeframes, reducing storage costs and supporting privacy-conscious communication.

Read Receipts and Typing Indicators: Track message read status per user, displaying read receipts showing who has seen messages. Aggregate read counts for large groups avoiding overwhelming displays.

Implement typing indicators showing when members are composing messages. Use WebSocket broadcasts informing other members of typing activity with debouncing to reduce noise.

Provide privacy controls allowing users to disable read receipts or typing indicators if they prefer not sharing this information.

Search and History: Index message content supporting full-text search within groups. Filter results by sender, date range, or media type. Highlight matching terms in search results.

Implement message pinning allowing moderators to highlight important announcements or resources. Display pinned messages prominently at the top of conversation views.

Support message bookmarking enabling users to save important messages for personal reference. Organize bookmarks across groups in a unified view.

Scalability Considerations: Shard groups across database servers based on group ID. Use consistent hashing determining which shard stores each group's data.

Cache active group data in Redis including recent messages, member lists, and settings. Invalidate caches when groups are modified.

Implement connection pooling for WebSocket connections to backend services, reducing connection overhead for active users participating in multiple groups simultaneously.

36. Design a Microblogging Platform

A microblogging platform enables users to share short-form content publicly, building followership and engaging through replies, shares, and reactions, requiring real-time updates and content virality support.

Post Creation: Accept text posts up to character limits (e.g., 280 characters) encouraging concise communication. Support media attachments (images, GIFs, videos) and link previews enriching posts.

Implement post threading allowing users to create multi-part posts for longer narratives. Connect threaded posts maintaining context while bypassing length restrictions.

Support post scheduling for planned content publication and draft saving for post refinement before publishing.

Follow Relationships: Implement asymmetric follow model where users follow others without requiring reciprocation, unlike friendship models. Track follower counts and following counts as engagement metrics.

Support follow recommendations based on mutual connections, shared interests, or content engagement patterns. Implement follow limits preventing spam accounts from mass following.

Enable blocking and muting controls where users can prevent specific accounts from interacting with or viewing their content.

Timeline Generation: Construct home timelines aggregating posts from followed accounts. Implement reverse-chronological ordering showing newest posts first, though algorithmic ranking based on engagement and relevance is increasingly common.

Use fan-out on write for most users, pre-computing timelines when posts are created. Switch to fan-out on read for celebrities with millions of followers to avoid overwhelming fan-out operations.

Implement timeline caching in Redis with TTLs balancing freshness with performance. Invalidate caches when users post, follow, or unfollow accounts.

Engagement Mechanisms: Support likes, retweets (sharing posts to followers), and replies (public responses). Track engagement counts displaying popularity metrics on posts.

Implement quote tweets allowing users to share posts with added commentary. This creates conversational depth beyond simple shares.

Support bookmarking enabling private saving of interesting posts for later reference without public engagement indicators.

Trending Topics: Analyze post content identifying frequently mentioned hashtags or phrases. Calculate trending scores considering mention frequency, velocity (rate of increase), and recency.

Display trending topics encouraging participation in popular conversations. Personalize trending topics based on user location, followed accounts, or interests.

Implement topic moderation preventing spam or coordinated inauthentic activity from manipulating trending algorithms.

Notifications: Notify users about interactions with their content: likes, retweets, replies, and mentions. Aggregate similar notifications reducing noise (e.g., "10 people liked your post" instead of 10 individual notifications).

Implement real-time notification delivery through WebSockets or push notifications. Support granular notification preferences allowing users to customize notification types and frequency.

37. Design a Photo Sharing Application

A photo sharing application enables users to upload, organize, and share photos with friends or publicly, requiring efficient image processing, social features, and discovery mechanisms.

Image Upload and Processing: Accept image uploads through web browsers or mobile apps. Validate image formats, dimensions, and file sizes preventing malicious or excessively large uploads.

Implement server-side image processing generating multiple sizes: thumbnails for grids, medium resolution for feeds, and full resolution for detailed viewing. Use progressive JPEGs or WebP formats for faster loading.

Extract and store image metadata including dimensions, capture date, location (if available), and camera information. Parse EXIF data while respecting privacy by allowing users to control metadata sharing.

Storage Architecture: Store original images in object storage like S3, organizing by user ID and upload date. Use content-based deduplication preventing multiple storage of identical images.

Distribute images through CDNs for global low-latency access. Implement lazy loading in feeds loading images as users scroll rather than all at once.

Implement image transformation APIs generating specific sizes or formats on-demand, caching transformed versions for subsequent requests.

Album Organization: Enable users to organize photos into albums with titles, descriptions, and privacy settings. Support smart albums with automatic organization based on date, location, or detected people.

Implement face detection and recognition grouping photos by people appearing in them. Allow users to tag people in photos creating searchable photo collections.

Support collaborative albums where multiple users can contribute photos, useful for events or shared experiences.

Social Features: Enable commenting on photos fostering engagement and conversation. Support nested comments creating threaded discussions.

Implement liking or reactions allowing quick appreciation without detailed comments. Display like counts as engagement indicators.

Support photo tagging where users identify others in photos, sending notifications to tagged users and creating links to their profiles.

Discovery and Explore: Create explore feeds showing popular or high-quality photos from public accounts. Implement recommendation algorithms based on user interests and engagement history.

Support hashtag searching allowing users to discover photos by topic. Display trending hashtags surfacing popular current topics.

Implement similar image search using computer vision to find visually related photos. This helps users discover photographers with similar styles or subjects.

Privacy Controls: Provide granular privacy settings: public (anyone can view), followers-only, or private (user only). Allow per-album privacy configuration.

Implement location privacy allowing users to remove or obscure location data from shared photos. Support face hiding or blurring for privacy-sensitive photos.

Enable account privacy modes where users must approve follower requests before others can view non-public content.

38. Design a Music Streaming Service

A music streaming service provides on-demand access to millions of songs, requiring efficient audio delivery, personalized recommendations, and artist-listener connections.

Music Catalog Management: Maintain comprehensive music metadata including songs, albums, artists, genres, release dates, and lyrics. Ingest music from record labels and distributors, validating metadata accuracy.

Store audio files in multiple quality levels (128kbps, 320kbps, lossless) supporting different subscriber tiers and bandwidth constraints. Use object storage for scalable, durable storage.

Implement content protection using digital rights management (DRM) preventing unauthorized copying while enabling legitimate playback. License music respecting artist and label agreements.

Audio Streaming: Stream audio using adaptive bitrate streaming, switching quality based on available bandwidth. Segment audio files into small chunks enabling continuous playback despite varying network conditions.

Implement client-side buffering loading several seconds ahead preventing playback interruptions during brief network degradation.

Support offline playback for premium subscribers, downloading songs for playback without internet connectivity. Implement license validation ensuring offline content remains accessible only while subscriptions are active.

Personalized Recommendations: Build recommendation systems analyzing listening history, liked songs, and playlist additions. Use collaborative filtering suggesting music enjoyed by users with similar tastes.

Implement content-based filtering analyzing audio features (tempo, key, mood) recommending songs acoustically similar to user preferences.

Create personalized playlists like "Discover Weekly" introducing users to new artists based on their listening patterns. Refresh playlists regularly maintaining novelty and relevance.

Playlist Management: Enable users to create custom playlists organizing songs by theme, mood, or occasion. Support collaborative playlists where multiple users can add and remove songs.

Curate editorial playlists created by music experts highlighting genres, moods, or cultural moments. Feature curated playlists prominently in discover sections.

Implement smart shuffle algorithms avoiding repetitive song patterns and considering song transitions for cohesive listening experiences.

Social Features: Allow users to follow friends and artists seeing their listening activity and shared playlists. Display recently played songs and current listening for followed users.

Enable song, album, or playlist sharing to social media or messaging apps. Generate rich previews with cover art and playback snippets.

Implement collaborative playlist creation and comment sections fostering community around music discovery.

Artist Tools: Provide artist dashboards showing streaming statistics, listener demographics, and geographic distribution. Track which playlists feature artist's songs and streaming trends over time.

Enable direct artist-to-listener communication through profile updates, exclusive content, or integrated merchandising. Support artist verification badges distinguishing official accounts.

39. Design a Video Conferencing System

A video conferencing system enables real-time audio-video communication among multiple participants, requiring low latency, high quality, and reliability for remote collaboration.

Media Streaming Architecture: Implement peer-to-peer connections for small meetings (2-4 participants) using WebRTC establishing direct connections between participants for lowest latency.

For larger meetings, use Selective Forwarding Units (SFU) where participants send media to a central server forwarding streams to other participants. This reduces bandwidth requirements compared to full mesh peer-to-peer.

Support adaptive bitrate streaming where video quality adjusts based on participant bandwidth and CPU capabilities. Implement simulcast where senders transmit multiple quality levels, allowing receivers to select appropriate streams.

Audio Processing: Implement echo cancellation preventing feedback loops when participants use speakers and microphones. Apply noise suppression filtering background sounds like keyboard typing or ambient noise.

Use automatic gain control normalizing audio levels across participants. Implement spatial audio in virtual rooms positioning voices based on participant locations for more natural conversation.

Support multiple audio layouts: active speaker (highlight current speaker), gallery (show all participants), or spotlight (focus on specific participant).

Video Quality Optimization: Dynamically adjust video resolution and frame rate based on available bandwidth. Prioritize active speakers with higher quality while reducing resolution for non-speakers in gallery view.

Implement background blur or virtual backgrounds using machine learning segmentation. Allow custom backgrounds or logo overlays for professional branding.

Use efficient video codecs like VP8, VP9, or H.264 optimizing quality-to-bandwidth ratios. Support hardware acceleration for encoding and decoding improving performance on resource-constrained devices.

Scalability: Deploy SFU servers globally allowing participants to connect to nearby servers reducing latency. Implement server cascading where multiple SFUs cooperate for meetings spanning geographic regions.

Support thousands of participants through webinar mode where most attendees watch streams without sending audio or video, reducing infrastructure load.

Implement connection fallback automatically switching between UDP and TCP based on network conditions. Use TURN servers relaying traffic when direct connections fail due to strict firewalls or NATs.

Collaboration Features: Integrate screen sharing allowing participants to present applications or entire screens. Implement remote control enabling presenters to grant control to others for collaborative editing.

Support breakout rooms dividing large meetings into smaller groups for focused discussions. Implement automatic or manual assignment with easy navigation between main room and breakout rooms.

Provide in-meeting chat for text communication, file sharing, and poll creation. Record meetings with participant consent, storing recordings in cloud storage for future reference.

Security and Privacy: Implement end-to-end encryption ensuring only meeting participants can decrypt audio and video streams. Use unique meeting IDs and passwords preventing unauthorized access.

Support waiting rooms where hosts approve participant entry. Implement moderator controls allowing hosts to mute participants, disable cameras, or remove disruptive attendees.

Provide compliance features like recording consent, data retention policies, and audit logs meeting regulatory requirements for healthcare, finance, or education sectors.

40. Design a Podcast Platform

A podcast platform enables creators to publish audio content in episodic series and listeners to discover, subscribe, and stream episodes, requiring content distribution, discovery mechanisms, and creator analytics.

Content Management: Accept podcast RSS feeds or direct uploads from creators. Parse RSS feeds extracting show metadata, episode information, and audio file locations.

Store audio files in object storage with CDN distribution for global availability. Transcode audio to multiple formats and bitrates supporting different devices and network conditions.

Generate podcast artwork thumbnails in multiple sizes for various display contexts. Validate audio quality ensuring consistent volume levels across episodes.

Discovery and Search: Implement full-text search across podcast titles, descriptions, and episode notes. Support filtering by category, language, episode duration, or publication date.

Create curated charts ranking podcasts by popularity, trending shows experiencing rapid growth, and editorial picks highlighting quality content.

Implement personalized recommendations based on listening history, subscribed shows, and user ratings. Use collaborative filtering suggesting podcasts enjoyed by similar listeners.

Playback Experience: Build audio players supporting standard controls: play, pause, skip forward/backward, and speed adjustment. Implement chapter markers allowing navigation to specific segments within episodes.

Support sleep timers automatically pausing playback after specified durations. Implement smart speed algorithms accelerating playback while maintaining natural-sounding audio.

Track playback positions syncing across devices allowing seamless transitions between phone, web, and smart speakers. Resume playback where users left off regardless of device.

Subscription Management: Enable podcast subscriptions with automatic downloading of new episodes based on user preferences. Configure download rules: download latest episode only, all episodes, or custom filters.

Implement notification systems alerting subscribers about new episode releases. Support episode archiving automatically deleting old episodes to manage storage.

Provide OPML import and export enabling users to migrate subscriptions between podcast applications.

Creator Analytics: Track listener metrics including total plays, completion rates, geographic distribution, and listening platforms. Display trends showing audience growth over time.

Provide episode performance analytics comparing episodes and identifying high-performing content. Show drop-off points where listeners stop playback indicating engagement issues.

Implement demographic insights revealing audience characteristics helping creators tailor content and attract sponsors.

Monetization: Support subscription podcasts where listeners pay monthly fees for ad-free or bonus content. Implement paywall systems restricting premium episodes to paying subscribers.

Enable dynamic ad insertion injecting targeted advertisements into episodes based on listener characteristics and episode context. Support various ad formats: pre-roll, mid-roll, and post-roll.

Provide listener support features allowing one-time donations or recurring contributions helping creators fund production.

Advanced Infrastructure and Specialized Systems

41. Design a Machine Learning Platform

A machine learning platform provides infrastructure for training, deploying, and monitoring ML models at scale, democratizing machine learning capabilities across organizations while maintaining operational excellence.

Data Pipeline Management: Implement data ingestion supporting batch and streaming sources. Accept data from databases, APIs, file uploads, and event streams, validating schemas and quality before processing.

Build feature engineering pipelines transforming raw data into model-ready features. Support common transformations: normalization, encoding categorical variables, handling missing values, and creating derived features.

Store processed features in feature stores enabling reuse across multiple models and teams. Version features supporting reproducibility and rollback when feature logic changes.

Implement data versioning tracking dataset versions used for model training. Generate data lineage diagrams showing feature dependencies and transformations aiding debugging and compliance.

Model Training Infrastructure: Provide distributed training supporting frameworks like TensorFlow, PyTorch, and Scikit-learn. Orchestrate training jobs across GPU clusters, allocating resources based on job requirements and priority.

Implement hyperparameter tuning using grid search, random search, or Bayesian optimization. Parallelize tuning experiments utilizing available compute resources efficiently.

Support automated machine learning (AutoML) searching algorithm and hyperparameter spaces to find optimal configurations. Provide model selection guidance comparing performance across candidates.

Track experiments recording hyperparameters, metrics, artifacts, and environment specifications. Enable comparison across experiments identifying improvements and understanding model behavior.

Model Registry: Maintain centralized model registries cataloging trained models with metadata: training date, performance metrics, feature dependencies, and creator information.

Implement model versioning supporting multiple versions simultaneously. Tag versions for different environments: development, staging, production.

Support model lineage tracking which datasets and code versions produced each model. This ensures reproducibility and aids debugging when models behave unexpectedly.

Provide model governance workflows requiring approval before production deployment. Implement model retirement processes deprecating underperforming or outdated models.

Model Serving: Deploy models as REST or gRPC APIs accepting prediction requests and returning results. Support batch prediction for offline scoring of large datasets and real-time prediction for interactive applications.

Implement model caching storing predictions for recently seen inputs reducing computation and latency. Use probabilistic data structures like Bloom filters quickly checking cache membership.

Support A/B testing routing prediction traffic between models comparing performance in production. Implement canary deployments gradually shifting traffic to new models while monitoring metrics.

Enable multi-model serving hosting multiple models within single inference servers, optimizing resource utilization through model sharing and batching.

Monitoring and Observability: Track prediction latency, throughput, and error rates. Alert on degraded performance or increased latency indicating infrastructure issues or model problems.

Implement model performance monitoring comparing predictions against ground truth when available. Detect accuracy drift indicating model staleness requiring retraining.

Monitor feature distributions detecting data drift where input characteristics change over time. Significant drift suggests models may perform poorly requiring investigation or retraining.

Provide explainability tools generating prediction explanations using techniques like SHAP or LIME. This builds trust and aids debugging unexpected predictions.

Resource Optimization: Implement autoscaling for training and serving infrastructure adapting capacity to workload demands. Scale down during idle periods reducing costs while ensuring capacity during peak usage.

Use spot instances or preemptible VMs for training jobs tolerating interruptions through checkpointing. This significantly reduces training costs for non-urgent workloads.

Optimize model inference through quantization, pruning, or knowledge distillation reducing model size and computation requirements without significantly impacting accuracy.

42. Design a Blockchain Network

A blockchain network maintains a distributed ledger recording transactions in an immutable, decentralized manner, requiring consensus mechanisms, cryptographic security, and fault tolerance without central authorities.

Distributed Ledger: Structure the blockchain as a linked list of blocks where each block contains transactions and references the previous block through cryptographic hashes. This creates an immutable chain where tampering with historical blocks invalidates subsequent blocks.

Store blocks in a distributed database replicated across all network nodes. Each node maintains a complete copy of the blockchain ensuring no single point of failure and enabling verification by any participant.

Implement Merkle trees within blocks organizing transactions hierarchically. Merkle roots provide compact proofs of transaction inclusion enabling lightweight clients to verify transactions without downloading entire blocks.

Consensus Mechanisms: Implement consensus algorithms ensuring all nodes agree on blockchain state despite network delays, node failures, or malicious actors. Proof of Work requires miners to solve computationally intensive puzzles, with the first solution winning the right to add the next block.

Alternatively, implement Proof of Stake where validators are selected based on cryptocurrency holdings to propose blocks. This reduces energy consumption compared to Proof of Work while maintaining security.

Support Byzantine Fault Tolerance protocols like PBFT (Practical Byzantine Fault Tolerance) for permissioned blockchains with known participants. These achieve consensus faster than Proof of Work suitable for enterprise applications.

Transaction Processing: Accept transactions signed with users' private keys proving ownership and authorization. Validate transaction signatures, ensuring sufficient balances and preventing double-spending where the same funds are spent multiple times.

Maintain transaction pools (mempools) storing pending transactions awaiting inclusion in blocks. Miners or validators select transactions from pools based on transaction fees, prioritizing higher-paying transactions.

Implement transaction confirmation tracking how many blocks have been added after a transaction's block. More confirmations increase confidence the transaction won't be reversed due to blockchain reorganizations.

Smart Contracts: Support programmable contracts as code executing on the blockchain with guaranteed execution and immutable results. Implement virtual machines like the Ethereum Virtual Machine interpreting smart contract bytecode.

Validate contract code for safety preventing infinite loops or excessive resource consumption. Implement gas mechanisms where contract execution consumes limited resources paid for by transaction senders.

Store contract state in the blockchain ensuring all nodes can independently verify contract execution results. Support contract interactions allowing contracts to call other contracts creating complex decentralized applications.

Network Communication: Implement peer-to-peer protocols for node discovery and communication. New nodes bootstrap by connecting to known seed nodes, then discover additional peers through gossip protocols.

Propagate new transactions and blocks throughout the network using flooding or optimized gossip protocols. Balance rapid propagation with bandwidth efficiency avoiding network congestion.

Handle network partitions where subsets of nodes become disconnected. Upon reconnection, implement blockchain reconciliation where nodes synchronize by exchanging block information and resolving forks.

Security Measures: Implement cryptographic primitives: SHA-256 or similar for hashing, ECDSA or EdDSA for digital signatures, and public-key cryptography for wallet addresses.

Protect against 51% attacks where attackers controlling majority hash power or stake can manipulate the blockchain. Design economic incentives making attacks prohibitively expensive relative to potential gains.

Implement access controls for permissioned blockchains where only authorized entities can participate. Use identity management systems integrating with existing enterprise authentication infrastructure.

43. Design an IoT Platform

An Internet of Things platform manages millions of connected devices, processing sensor data, enabling remote control, and providing analytics for diverse applications from smart homes to industrial monitoring.

Device Management: Implement device registration associating devices with owners and metadata like device type, capabilities, and location. Generate device credentials for authentication and authorization.

Support over-the-air (OTA) firmware updates enabling remote software upgrades. Implement staged rollouts updating subsets of devices initially, monitoring for issues before full deployment.

Track device lifecycle states: inactive, active, maintenance, or decommissioned. Implement device twin concepts maintaining desired and reported states, synchronizing configuration between cloud and devices.

Data Ingestion: Accept device telemetry through MQTT, CoAP, or HTTP protocols optimized for resource-constrained devices and unreliable networks. Support TLS encryption protecting data in transit.

Implement protocol gateways translating between different IoT protocols enabling interoperability. Accept data from edge gateways aggregating multiple devices' data before cloud transmission.

Handle intermittent connectivity buffering data on devices or edge gateways during network outages, then transmitting when connectivity resumes. Implement data compression reducing bandwidth consumption and costs.

Message Routing and Processing: Route incoming device messages to appropriate processing pipelines based on device type, message content, or business rules. Use message brokers like Kafka or MQTT brokers distributing messages to subscribers.

Implement stream processing analyzing real-time data detecting anomalies, threshold breaches, or patterns requiring immediate action. Generate alerts when critical conditions are detected.

Support batch processing periodically analyzing historical data for trends, predictive maintenance, or reporting. Store raw and processed data in data lakes or time-series databases optimized for IoT workloads.

Device Control: Enable remote device control through cloud-to-device messages. Send commands to adjust settings, trigger actions, or query device status.

Implement device shadowing maintaining virtual representations of devices in the cloud. Update shadows when devices report state changes and propagate desired state changes from shadows to devices.

Support device groups enabling batch operations like configuration updates or commands across multiple devices simultaneously.

Security and Privacy: Implement per-device authentication using unique credentials preventing unauthorized devices from accessing the platform. Support certificate-based authentication or symmetric key authentication.

Use access control policies restricting which entities can read device data or send commands. Implement fine-grained permissions based on device ownership, groups, or attributes.

Encrypt data at rest and in transit protecting sensitive information. Implement secure boot on devices ensuring only authorized firmware executes preventing malware installation.

Analytics and Visualization: Provide dashboards visualizing device metrics, locations on maps, and health status. Support customizable widgets allowing users to build personalized views.

Implement alerting rules based on telemetry data or derived metrics. Send notifications through email, SMS, or integration with incident management systems.

Generate reports showing historical trends, device utilization, and operational efficiency. Support data export for integration with external analytics tools.

44. Design a Gaming Leaderboard System

A gaming leaderboard system tracks player scores and rankings across millions of users, requiring real-time updates, fraud detection, and support for various leaderboard types serving competitive gaming communities.

Leaderboard Types: Implement global leaderboards ranking all players worldwide. Support regional leaderboards partitioned by geographic location or game servers.

Create time-based leaderboards resetting daily, weekly, or seasonally. Maintain historical leaderboards preserving past competitions for posterity and player achievements.

Support friend leaderboards showing rankings among connected players. Implement group or guild leaderboards aggregating scores across team members.

Score Update Processing: Accept score submissions from game servers validating authenticity through signed requests preventing client-side cheating. Verify scores against expected ranges and progression patterns detecting anomalies.

Process score updates in real-time updating leaderboards immediately as players complete matches or achievements. Use message queues buffering score updates during traffic spikes ensuring reliable processing.

Implement idempotency handling duplicate score submissions from network retries. Use unique match or session IDs deduplicating submissions preventing artificial score inflation.

Ranking Algorithms: Use sorted sets in Redis efficiently storing scores with constant-time rank retrieval. Redis sorted sets automatically maintain ordering enabling fast queries for player rank or top N players.

Implement tie-breaking rules for identical scores: earliest achievement time, secondary metrics like completion speed, or random selection maintaining fairness.

Support different scoring systems: highest score wins, lowest time wins, or accumulation of points over multiple matches. Handle negative scores appropriately for games with penalty mechanics.

Scalability Strategies: Partition leaderboards by region or game mode distributing load across multiple Redis instances. Route queries to appropriate partitions based on requested leaderboard type.

Implement caching for leaderboard segments frequently accessed like top 100 players. Refresh caches periodically balancing freshness with query performance.

Use approximate ranking algorithms for massive leaderboards where exact rank is less critical. Return rank ranges (top 1%, top 10%) rather than precise positions reducing computation.

Fraud Detection: Monitor score progression patterns identifying suspicious spikes inconsistent with normal gameplay. Flag impossible scores exceeding theoretical maximums or submission rates indicating automated cheating.

Implement statistical analysis comparing player performance against population distributions. Outliers several standard deviations from the mean warrant investigation.

Use machine learning models trained on confirmed cheating patterns detecting sophisticated fraud attempts. Combine multiple signals like score velocity, session duration, and device fingerprints.

Historical Data and Analytics: Archive leaderboard snapshots preserving rankings at specific points. Enable historical queries showing player rank evolution over time.

Generate analytics reporting player engagement, score distribution, and competition intensity. Identify trends informing game balance adjustments or event scheduling.

Support data export for external analysis or visualization tools. Provide APIs enabling third-party websites displaying leaderboards or building community tools.

45. Design a Food Delivery Platform

A food delivery platform connects restaurants, delivery drivers, and customers orchestrating order placement, preparation, and delivery requiring sophisticated logistics, real-time tracking, and multi-party coordination.

Restaurant Management: Enable restaurants to create menus with items, descriptions, prices, and photos. Support modifiers (size, toppings, cooking preferences) and combos grouping items at discounted prices.

Implement inventory management tracking ingredient availability. Automatically hide menu items when ingredients are out of stock preventing order failures.

Provide restaurant dashboards showing incoming orders, preparation status, and historical sales data. Support printer integration automatically printing order tickets for kitchen staff.

Order Placement: Accept customer orders through mobile apps or websites. Validate orders checking restaurant availability, delivery area coverage, and minimum order requirements.

Calculate order totals including item prices, taxes, delivery fees, and tips. Support promotions and discount codes adjusting final amounts. Estimate delivery times based on restaurant preparation time, driver availability, and distance.

Implement payment processing accepting credit cards, digital wallets, or cash on delivery. Authorize payments immediately but capture after successful delivery.

Dispatch System: Assign orders to delivery drivers using optimization algorithms balancing multiple factors: driver proximity to restaurant, current workload, customer wait time, and delivery route efficiency.

Support batch assignments where single drivers handle multiple orders from nearby restaurants with delivery locations along similar routes. This improves efficiency but requires careful timing ensuring food quality.

Implement real-time reassignment when drivers encounter issues like vehicle breakdowns or traffic delays. Automatically find alternative drivers minimizing delivery delays.

Real-Time Tracking: Track driver locations continuously using GPS data from mobile devices. Update customer apps showing driver position relative to restaurant and delivery location.

Provide estimated time of arrival (ETA) predictions using historical data, current traffic conditions, and actual driver progress. Update ETAs dynamically as conditions change.

Send notifications at key milestones: order accepted by restaurant, food ready for pickup, driver en route, approaching delivery, and delivered.

Driver Management: Implement driver onboarding verifying identity, vehicle registration, insurance, and background checks. Provide driver training materials ensuring consistent service quality.

Use shift-based or on-demand models for driver availability. Support driver scheduling for shift-based models or real-time availability toggling for on-demand models.

Track driver performance metrics including acceptance rate, on-time delivery percentage, customer ratings, and earnings. Reward high-performing drivers with bonus opportunities or priority access to high-value orders.

Rating and Review Systems: Collect ratings for restaurants, drivers, and food quality after delivery completion. Aggregate ratings computing average scores displayed to future customers.

Implement review moderation preventing inappropriate content while preserving authentic feedback. Flag suspicious patterns like coordinated negative reviews or rating manipulation attempts.

Use ratings in ranking algorithms promoting highly-rated restaurants and prioritizing reliable drivers for order assignment.

46. Design a Telemedicine Platform

A telemedicine platform enables remote healthcare consultations through video calls, messaging, and health record sharing, requiring HIPAA compliance, appointment scheduling, and integration with medical systems.

User Management: Separate patient and provider accounts with distinct registration processes. Verify provider credentials checking medical licenses, specializations, and certifications against regulatory databases.

Implement patient onboarding collecting medical history, current medications, allergies, and insurance information. Support secure identity verification complying with healthcare regulations.

Maintain family accounts allowing parents to manage children's healthcare or caregivers to assist elderly patients.

Appointment Scheduling: Display provider availability calendars showing open time slots. Support different appointment types: video consultations, phone calls, or messaging-based consultations with varying durations.

Implement automatic timezone conversion ensuring correct appointment times across geographic regions. Send appointment reminders through email, SMS, or push notifications reducing no-shows.

Support appointment rescheduling and cancellation with configurable policies like minimum advance notice requirements or cancellation fees.

Video Consultation: Implement HIPAA-compliant video conferencing with end-to-end encryption protecting patient privacy. Support screen sharing for providers to show test results, images, or educational materials.

Provide virtual waiting rooms where patients wait before scheduled appointments. Notify providers when patients arrive enabling punctual consultation starts.

Implement consultation recording with patient consent for documentation purposes. Store recordings securely with access limited to authorized healthcare providers.

Electronic Health Records (EHR): Maintain patient health records storing consultation notes, diagnoses, prescriptions, test results, and treatment plans. Structure data using standardized medical terminologies like SNOMED CT or ICD codes.

Integrate with existing EHR systems through HL7 FHIR APIs enabling data exchange with hospitals or clinics. Support document import for lab results, imaging reports, or referral letters.

Implement comprehensive audit trails logging all access to patient records identifying who accessed what information when, meeting regulatory compliance requirements.

E-Prescribing: Enable providers to prescribe medications electronically sending prescriptions directly to patient-selected pharmacies. Integrate with e-prescription networks validating medication availability and insurance coverage.

Implement drug interaction checking alerting providers about potential conflicts with existing medications or patient allergies. Support prescription refill requests streamlining ongoing medication management.

Maintain prescription history showing all prescribed medications, dosages, and durations enabling comprehensive medication reviews during consultations.

Payment and Insurance: Process consultation fees through secure payment gateways supporting credit cards, debit cards, or health savings accounts. Calculate patient responsibility amounts after insurance coverage.

Implement insurance claim submission generating appropriate billing codes and documentation. Track claim status providing visibility into payment processing.

Support various billing models: fee-for-service, subscription-based unlimited consultations, or employer-sponsored wellness programs.

Compliance and Security: Ensure HIPAA compliance through comprehensive security controls: encryption, access controls, audit logging, and regular security assessments.

Implement data retention policies archiving old records while maintaining accessibility for minimum required durations. Support patient data export enabling patients to obtain complete health records.

Conduct regular compliance audits reviewing practices, documenting procedures, and training staff ensuring ongoing regulatory adherence.

47. Design an Autonomous Vehicle Platform

An autonomous vehicle platform coordinates perception, decision-making, and control for self-driving cars, requiring real-time processing, safety guarantees, and integration with diverse sensors and actuators.

Sensor Fusion: Integrate data from multiple sensor types: cameras capturing visual information, LiDAR providing 3D point clouds, radar detecting object velocity and distance, GPS/IMU for positioning and orientation.

Implement sensor calibration ensuring accurate spatial relationships between sensors. Perform temporal synchronization aligning sensor data streams despite different sampling rates.

Fuse sensor data creating unified environmental representations combining strengths of each sensor type. Use Kalman filters or particle filters estimating object positions and velocities from noisy measurements.

Perception Systems: Implement object detection identifying vehicles, pedestrians, cyclists, and obstacles using deep learning models like YOLO or Faster R-CNN processing camera images.

Perform semantic segmentation classifying each pixel into categories: road, sidewalk, vegetation, buildings. This creates detailed scene understanding supporting path planning.

Track objects across frames maintaining consistent identities and predicting trajectories. Use techniques like Kalman filters or recurrent neural networks forecasting future positions based on historical motion.

Detect lane markings, traffic signs, and signals extracting semantic meaning from visual data. Recognize road conditions like construction zones or adverse weather affecting driving strategies.

Localization: Determine vehicle position and orientation with centimeter-level accuracy despite GPS limitations. Use high-definition maps comparing sensor observations against expected environments.

Implement particle filters or graph-based SLAM (Simultaneous Localization and Mapping) refining position estimates as the vehicle moves through environments.

Maintain map updates detecting changes from expected maps like new construction, temporary obstacles, or modified traffic patterns. Upload changes to cloud systems benefiting other vehicles.

Path Planning: Generate route plans from origin to destination using road networks and real-time traffic data. Optimize routes balancing travel time, distance, and passenger preferences.

Implement behavioral planning determining high-level maneuvers: lane changes, turns, merges, or parking. Consider traffic rules, social norms, and safety margins.

Perform local path planning generating smooth trajectories avoiding obstacles while respecting kinematic constraints and comfort limits. Use optimization-based planners or sampling-based methods like RRT.

Control Systems: Translate planned trajectories into actuator commands controlling steering, acceleration, and braking. Implement feedback control compensating for disturbances like wind or road slope.

Use cascaded control architectures with high-level planners generating reference trajectories and low-level controllers executing them precisely.

Implement safety monitors continuously verifying planned actions don't violate safety constraints. Override planner decisions if safety violations are detected engaging emergency maneuvers.

V2X Communication: Support Vehicle-to-Everything communication exchanging information with other vehicles, infrastructure, and cloud services. Broadcast vehicle position, velocity, and intended maneuvers using DSRC or C-V2X protocols.

Receive information about traffic signal timing, road hazards, or emergency vehicle approaches. Integrate V2X data into planning improving safety and efficiency.

Simulation and Testing: Develop simulation environments replicating real-world conditions for testing. Generate diverse scenarios including edge cases and rare events difficult to encounter during physical testing.

Implement hardware-in-the-loop testing running actual control systems against simulated sensors and environments. This validates software-hardware integration before road testing.

Track comprehensive metrics including miles driven, disengagements (human takeovers), near-misses, and scenario coverage ensuring thorough validation before deployment.

48. Design a Real-Time Bidding Platform

A real-time bidding platform conducts automated ad auctions in milliseconds as users load web pages, connecting advertisers with publishers while respecting user privacy and maximizing value for all parties.

Ad Request Processing: Receive ad requests from publishers' websites containing page context, user information (when consented), and ad slot specifications. Extract features for targeting: page content, keywords, user demographics, and behavioral segments.

Implement user profiling building interest profiles from browsing history while respecting privacy regulations. Use differential privacy or federated learning protecting individual privacy while enabling effective targeting.

Enrich requests with third-party data joining audience segments, geographic information, or device attributes improving targeting precision.

Auction Mechanics: Conduct second-price auctions where highest bidder wins but pays the second-highest bid amount. This incentivizes truthful bidding since bidders pay market-clearing prices rather than their actual bids.

Process auctions within strict time budgets (typically 100 milliseconds) from request receipt to response. Distribute auctions across servers processing millions of simultaneous auctions.

Support different auction formats: header bidding where multiple exchanges compete simultaneously, or waterfall mediation calling exchanges sequentially until bids exceed thresholds.

Bidder Integration: Implement bidder protocols allowing demand-side platforms (DSPs) to participate in auctions. Send bid requests to multiple DSPs simultaneously, awaiting their bid responses within timeout periods.

Handle bid response parsing validating bid formats, creative specifications, and advertiser budgets. Filter invalid bids before auction execution.

Implement bid price floors provided by publishers rejecting bids below minimum acceptable CPMs ensuring publisher revenue requirements are met.

Targeting and Optimization: Enable sophisticated targeting using combinations of user attributes, contextual signals, and geographic locations. Support inclusion and exclusion rules (bid on mobile users in California excluding users who visited competitor sites).

Implement frequency capping limiting ad exposure per user within time windows preventing annoyance and wasted impressions.

Use pacing algorithms spreading campaign budgets across time periods avoiding premature budget exhaustion. Adjust bidding strategies dynamically based on performance and remaining budgets.

Creative Delivery: Select winning creatives matching ad slot specifications. Validate creative sizes, formats, and technical requirements ensuring proper rendering.

Implement creative caching storing frequently served ads at edge servers reducing latency and bandwidth consumption.

Support dynamic creative optimization assembling ads from components (headlines, images, calls-to-action) tailoring combinations to individual users maximizing engagement.

Reporting and Analytics: Track campaign metrics including impressions, clicks, conversions, and costs. Calculate performance indicators like CTR (click-through rate), CPC (cost per click), and ROAS (return on ad spend).

Provide real-time dashboards showing campaign status, spend pacing, and performance. Alert advertisers when campaigns underperform or budgets are nearly exhausted.

Implement attribution tracking connecting ad exposures to downstream conversions. Support last-click, multi-touch, or custom attribution models crediting appropriate campaigns.

49. Design a Cloud Storage Service

A cloud storage service provides scalable, durable file storage accessible from anywhere, requiring efficient data distribution, synchronization across devices, and collaboration features rivaling services like Dropbox or Google Drive.

File Upload and Storage: Accept file uploads through chunked transfers supporting resumable uploads for large files. Split files into chunks (typically 4-8 MB) uploading independently and retrying failed chunks without restarting entire uploads.

Compute content hashes for deduplication storing only unique chunks even when multiple users upload identical files. Use SHA-256 hashes ensuring negligible collision probability.

Store chunks in distributed object storage systems across multiple data centers. Implement erasure coding splitting data into redundant fragments surviving storage device or data center failures with minimal storage overhead compared to full replication.

Metadata Management: Maintain file system metadata in relational or NoSQL databases: file names, paths, sizes, permissions, version history, and sharing settings.

Implement hierarchical folder structures supporting nested directories. Use path-based or node-based representations balancing query performance with modification complexity.

Index metadata enabling fast file search by name, type, modification date, or contained text after content indexing.

File Synchronization: Implement client applications monitoring local file systems detecting changes through file system watchers or periodic scanning. Upload detected changes to cloud storage maintaining consistency.

Use differential synchronization transferring only modified portions of files reducing bandwidth consumption. Employ block-level diffing algorithms like rsync identifying changed blocks within large files.

Resolve synchronization conflicts when the same file is modified on multiple devices simultaneously. Detect conflicts through version vectors or timestamps, preserving both versions or applying automatic merging for specific file types.

Sharing and Collaboration: Support file and folder sharing with configurable permissions: viewer (read-only), commenter, or editor (full access). Generate sharing links with optional expiration dates and passwords.

Implement organization-wide sharing with hierarchical permissions inheriting from parent folders. Override permissions at individual file or subfolder levels.

Enable real-time collaborative editing for supported file types integrating with document editing services. Lock files during editing preventing conflicting modifications or use operational transformation for conflict-free concurrent editing.

Version History: Maintain version history storing previous file versions enabling recovery from accidental modifications or deletions. Implement configurable retention policies balancing storage costs with recovery needs.

Use incremental snapshots storing only differences between versions minimizing storage requirements. Compress historical versions further reducing costs.

Provide version comparison showing differences between versions. Support restoring entire folders to previous states for comprehensive recovery.

Security and Privacy: Encrypt files at rest using strong encryption algorithms with keys managed through key management services. Support client-side encryption where files are encrypted before upload ensuring service providers cannot access contents.

Implement access logging tracking all file operations for security monitoring and compliance auditing. Alert on suspicious patterns like mass downloads or unauthorized access attempts.

Support compliance certifications required for enterprise adoption: SOC 2, ISO 27001, GDPR compliance. Implement data residency controls storing data within specific geographic regions meeting regulatory requirements.

50. Design a Fraud Detection System

A fraud detection system identifies suspicious transactions, account activities, or behaviors in real-time protecting financial services, e-commerce platforms, and online services from fraudulent activities while minimizing false positives affecting legitimate users.

Data Collection: Gather comprehensive signals for fraud assessment: transaction amount and destination, user location and device fingerprint, behavioral patterns like typing speed or mouse movements, and relationship data like previously interacted merchants or users.

Integrate data from multiple sources: internal transaction databases, third-party fraud intelligence networks sharing known fraud patterns, device intelligence services, and social graph data.

Enrich real-time transactions with historical user data, aggregated statistics (transaction velocity, spending patterns), and risk scores from external providers.

Rule-Based Detection: Implement configurable rule engines evaluating transactions against defined criteria. Rules identify obvious fraud patterns: transactions from blacklisted locations, amounts exceeding typical user spending, or rapid sequential transactions indicating account compromise.

Support complex rule logic combining multiple conditions with Boolean operators and temporal constraints. Enable rapid rule updates responding to emerging fraud tactics without system redeployment.

Maintain rule effectiveness metrics tracking detection rates and false positive rates. Regularly review and refine rules removing ineffective or overly broad rules.

Machine Learning Models: Train supervised learning models on labeled historical data distinguishing fraudulent from legitimate transactions. Use features like transaction characteristics, user behavior deviations, and contextual information.

Implement ensemble methods combining multiple models: gradient boosting machines, random forests, and neural networks. Different models capture complementary patterns improving overall accuracy.

Use online learning techniques continuously updating models with new labeled data adapting to evolving fraud patterns. Retrain batch models periodically with accumulated data.

Implement unsupervised learning detecting anomalies representing unusual patterns potentially indicating novel fraud techniques not present in training data.

Real-Time Scoring: Evaluate transactions within milliseconds computing fraud risk scores. Deploy models as services accessible through APIs processing predictions with low latency.

Implement feature computation pipelines calculating required features from transaction data in real-time. Cache frequently accessed user aggregates like 24-hour spending totals reducing computation.

Use score thresholds determining actions: approve low-risk transactions automatically, challenge medium-risk transactions with additional authentication, or block high-risk transactions pending review.

Challenge Mechanisms: Implement step-up authentication for suspicious transactions requiring additional verification. Send one-time codes via SMS or email, request biometric authentication, or pose security questions.

Use adaptive authentication adjusting verification requirements based on risk scores. Low-risk users experience frictionless flows while high-risk scenarios demand stronger verification.

Balance security and user experience minimizing false positives that frustrate legitimate users. Track challenge completion rates and user feedback optimizing threshold calibration.

Case Management: Route blocked transactions to fraud analysts for manual review through case management systems. Prioritize cases by potential loss amounts, detection confidence, or customer importance.

Provide analysts with comprehensive investigation tools: transaction history, device information, behavioral patterns, and comparison with similar cases. Enable annotations documenting investigation findings.

Support analyst feedback loops where verified decisions (confirmed fraud or false positives) retrain machine learning models improving future predictions.

Network Analysis: Build transaction graphs connecting users, merchants, accounts, and devices. Detect fraud rings where multiple accounts controlled by fraudsters exhibit coordinated behaviors.

Use graph algorithms identifying suspicious patterns: circular money flows, hub accounts receiving payments from many users then redistributing funds, or device sharing across supposedly independent accounts.

Implement community detection clustering related accounts. Flagging one account in a suspicious cluster triggers reviews of connected accounts.

Reporting and Analytics: Generate fraud reports quantifying losses prevented, detection rates, and false positive rates. Track trends showing fraud volume evolution and emerging attack vectors.

Provide merchant or user risk profiles summarizing historical fraud rates, typical transaction patterns, and risk indicators. Use profiles informing risk assessments for future transactions.

Implement feedback loops measuring long-term outcomes. Track chargebacks or confirmed fraud for transactions initially approved, refining models and rules based on actual results.