System Design Components and Trade Offs

Large Scale (1B users)API Gateway• Simple API server with basic rate limiting
• Single deployment in one region
• Direct connection to backend services
• Minimal authentication• Dedicated API gateway with caching capabilities
• Advanced authentication/authorization (OAuth, JWT)
• Custom rate limiting and throttling
• Basic request validation and transformation
• Health checks and circuit breakers• Global API gateway mesh with edge locations
• Advanced traffic management with canary deployments
• API versioning and lifecycle management
• Sophisticated rate limiting with ML-based anomaly detection
• Automated documentation and developer portal
• GraphQL federation for complex data requirementsDatabases: Key-Value Store• Single-instance Redis/DynamoDB for caching and session management
• In-memory operation with basic persistence
• Simple key patterns for lookups• Redis cluster with read replicas
• Cache invalidation strategies
• Persistence for resilience
• More complex key patterns
• Basic monitoring• Globally distributed key-value store (Redis Enterprise, DynamoDB global tables)
• Multi-region replication with conflict resolution
• Hierarchical caching (local, regional, global)
• Auto-scaling based on traffic patterns
• Advanced performance optimizationDatabases: Blob Store• Single S3 bucket or Azure Blob Storage
• Basic organization with folders/prefixes
• Manual backup procedures
• Simple access patterns• Regional blob storage with CDN integration
• Automated lifecycle policies (archiving, deletion)
• Access patterns optimization
• Backup and disaster recovery procedures
• Basic data tiering• Multi-region blob storage with automatic replication
• Content-aware routing and caching
• Tiered storage for hot/warm/cold data
• Encryption and compliance controls
• Custom metadata indexing for faster searches
• Intelligent data placement based on access patternsDatabases: OLTP• Single instance PostgreSQL/MySQL
• Basic indexing for common queries
• Regular backup schedule
• Simple schema design• Primary-replica setup with read replicas
• Connection pooling and query optimization
• Advanced indexing strategies
• Regular performance tuning
• Automated backups and point-in-time recovery• Horizontally sharded database clusters
• Multi-region deployment with cross-region replication
• Automated schema management
• Query routing based on data locality
• Automated performance optimization
• Zero-downtime schema changes
• Multi-tenant isolation where neededDatabases: OLAP• Analytics queries run directly on OLTP database during off-peak
• Basic reporting using SQL queries
• Manual data exports for analysis
• Simple aggregations• Dedicated columnar database (Redshift, BigQuery, Snowflake)
• ETL pipelines for data loading
• Pre-aggregated views for common queries
• Basic data modeling and schema design
• Scheduled reporting• Distributed data warehouse with compute/storage separation
• Multi-region deployment for global analysis
• Materialized views and advanced indexing
• Query federation across data sources
• ML-powered query optimization
• Real-time analytics capabilities
• Self-service analytics with governancePub/Sub Systems• Simple message queue (RabbitMQ, SQS)
• Basic producer/consumer patterns
• Retry logic for failed operations
• Single instance deployment• Managed Kafka/EventHub with multiple partitions
• Topic-based routing
• Consumer groups for parallel processing
• Dead-letter queues for failed messages
• Message schema management
• Regional deployment• Global event mesh with multi-region Kafka clusters
• Cross-region replication with conflict resolution
• Schema registry and evolution management
• Stream processing integration (Kafka Streams, KSQL)
• Event sourcing patterns for system state
• Real-time analytics on message streams
• Sophisticated failure handling and disaster recoveryML Training Infrastructure• Local training or single-instance VM
• Manual experiment tracking
• Basic version control for models
• Single GPU/CPU for training
• Simple data preprocessing• Dedicated training cluster with auto-scaling
• Experiment tracking platform (MLflow, Weights & Biases)
• Hyperparameter optimization
• Model registry and versioning
• Pipeline scheduling
• Preemptible instances for cost savings• Distributed training platform
• Specialized hardware acceleration (TPUs/GPUs)
• Automated neural architecture search
• Continuous training pipelines
• Multi-tenant training environment
• Hardware-aware scheduling
• Cost-optimized resource allocation
• Advanced experimentation frameworksFeature Store• Feature computation within application code
• Manual feature engineering
• Static feature definitions
• Limited feature reuse• Dedicated feature store with offline/online storage
• Feature versioning and lineage tracking
• Batch feature computation
• Feature sharing across models
• Basic feature documentation• Distributed feature platform
• Real-time feature computation
• Time-travel capabilities for point-in-time lookups
• Automated feature discovery
• Feature monitoring and drift detection
• Access control and governance
• Feature importance tracking across models
• Self-service feature creation with quality checksModel Serving• Direct model inference in application
• Pre-computed predictions for common cases
• Manual model updates
• Single model version in production• Dedicated model serving with basic A/B testing
• Model versioning and canary deployments
• Batching for higher throughput
• Basic monitoring of prediction quality
• Model rollback capabilities• Multi-region inference service with auto-scaling
• Model versioning and shadow deployments
• Sophisticated A/B testing framework
• Hardware acceleration for inference
• Dynamic batching and adaptive scaling
• Feature importance analysis in production
• Automated model rollbacks
• Explainability for predictions
• Prediction caching where appropriateETL/Data Processing• Batch jobs on application server
• Scheduled scripts for data processing
• Manual monitoring of job completion
• Simple error handling• Dedicated data processing service (Apache Spark)
• Workflow orchestration (Airflow, Prefect)
• Data quality validation
• Job monitoring and alerting
• Error handling and retry logic• Distributed stream processing platform (Spark, Flink, Beam)
• Real-time and batch processing unified
• Data lineage and impact analysis
• Automated data quality assurance
• Self-service data pipelines
• Metadata management and data discovery
• Cost-optimized processing strategies
• Multi-regional data processingMonitoring & Observability• Basic logging and metrics
• Manual alert checking
• Simple dashboards
• Periodic model evaluation• Comprehensive monitoring with alerts
• Centralized logging
• Distributed tracing
• Model performance monitoring
• SLO/SLA tracking
• Automated performance reporting• End-to-end observability with automated anomaly detection
• ML-powered root cause analysis
• Prediction explainability tracking
• Correlation between model and business metrics
• Automated capacity planning
• Real-time visualization of system health
• Drift detection across all system components
• Self-healing systems where possible