Introduction

System design interviews require understanding various technologies and their trade-offs. This guide provides a comprehensive overview of common technologies used in system design interviews, with detailed pros/cons comparisons to help you make informed decisions during interviews.

Understanding when to use which technology and why is crucial for designing scalable, reliable, and efficient systems.

1. Databases

SQL Databases

PostgreSQL

Pros:

  • ✅ ACID Compliance: Strong consistency guarantees for transactions
  • ✅ Advanced Features: JSON support, full-text search, window functions, CTEs
  • ✅ Extensibility: Custom functions, data types, extensions (PostGIS, etc.)
  • ✅ Performance: Excellent query optimizer, parallel query execution
  • ✅ Open Source: Free and actively developed
  • ✅ Complex Queries: Excellent for joins, aggregations, subqueries
  • ✅ Mature Ecosystem: Rich tooling and community support

Cons:

  • ❌ Scaling: Horizontal scaling requires sharding (complex)
  • ❌ Schema Rigidity: Schema changes can be expensive
  • ❌ Write Performance: Limited by ACID overhead
  • ❌ Memory Usage: Can be memory-intensive for large datasets
  • ❌ Learning Curve: Advanced features require expertise

When to Use:

  • Complex relational data with many relationships
  • Need strong ACID guarantees (financial systems)
  • Advanced query requirements (analytics, reporting)
  • Geospatial data (with PostGIS)
  • Multi-tenant applications

Common Use Cases: Social media platforms, streaming services, content management systems

MySQL

Pros:

  • ✅ Mature: Decades of development and optimization
  • ✅ Wide Adoption: Large community and ecosystem
  • ✅ Performance: Good for read-heavy workloads
  • ✅ Replication: Strong replication support
  • ✅ Ease of Use: Simpler than PostgreSQL for basic use cases
  • ✅ Compatibility: Works well with many frameworks

Cons:

  • ❌ Limited Features: Fewer advanced features than PostgreSQL
  • ❌ Scaling: Similar horizontal scaling challenges
  • ❌ Storage Engines: Multiple engines can be confusing
  • ❌ JSON Support: Less mature than PostgreSQL
  • ❌ Complex Queries: Less optimized for complex queries

When to Use:

  • Web applications with standard requirements
  • Content management systems
  • Simple to moderate complexity queries
  • When team is already familiar with MySQL

Common Use Cases: Social media platforms, video streaming, developer platforms

NoSQL Databases

MongoDB (Document Store)

Pros:

  • ✅ Flexible Schema: Schema-less, easy to evolve
  • ✅ Horizontal Scaling: Built-in sharding
  • ✅ Developer Friendly: JSON-like documents match application objects
  • ✅ Rich Query Language: Complex queries with operators
  • ✅ Replication: Automatic failover with replica sets
  • ✅ Geospatial: Built-in geospatial queries
  • ✅ Aggregation Pipeline: Powerful data processing

Cons:

  • ❌ Document Size Limit: 16MB per document
  • ❌ Joins: Limited join capabilities
  • ❌ Memory Usage: Indexes loaded in memory
  • ❌ Consistency: Eventual consistency (BASE)
  • ❌ Transactions: Multi-document transactions have overhead
  • ❌ Data Duplication: Denormalization can lead to redundancy

When to Use:

  • Content management systems
  • Real-time analytics
  • Mobile applications with offline sync
  • Rapid prototyping
  • Flexible, evolving schemas

Common Use Cases: E-commerce platforms, enterprise applications, content platforms

Cassandra (Column-Family)

Pros:

  • ✅ Write Performance: Excellent write throughput (append-only)
  • ✅ Horizontal Scaling: Linear scalability, no single point of failure
  • ✅ High Availability: Multi-master replication
  • ✅ Time-Series: Excellent for time-series data
  • ✅ No Single Point of Failure: Distributed architecture
  • ✅ Tunable Consistency: Choose consistency level per operation

Cons:

  • ❌ Read Performance: Can be slow for complex queries
  • ❌ Data Modeling: Requires careful data modeling (denormalization)
  • ❌ No Joins: Application-level joins required
  • ❌ Learning Curve: Steep learning curve
  • ❌ Eventual Consistency: Default eventual consistency
  • ❌ Deletes: Tombstones can cause performance issues

When to Use:

  • High write throughput (IoT, logging)
  • Time-series data
  • Global distribution
  • Need linear scalability
  • Eventual consistency acceptable

Common Use Cases: Streaming platforms, social media, mobile applications

Redis (Key-Value)

Pros:

  • ✅ Speed: Sub-millisecond latency (in-memory)
  • ✅ Rich Data Structures: Strings, lists, sets, sorted sets, hashes, streams
  • ✅ Pub/Sub: Built-in publish-subscribe
  • ✅ Atomic Operations: Atomic operations for counters, etc.
  • ✅ Persistence: Optional persistence (RDB, AOF)
  • ✅ Clustering: Redis Cluster for horizontal scaling

Cons:

  • ❌ Memory Limit: Limited by RAM size
  • ❌ Durability: Optional persistence (risk of data loss)
  • ❌ Cost: Expensive for large datasets (RAM costs)
  • ❌ No Complex Queries: Simple key-value lookups
  • ❌ Single-Threaded: CPU-bound operations can block
  • ❌ Data Loss Risk: In-memory data lost on crash (if not persisted)

When to Use:

  • Caching layer
  • Session storage
  • Real-time leaderboards
  • Rate limiting
  • Pub/sub messaging
  • Simple queues

Common Use Cases: Real-time platforms, developer tools, social media

DynamoDB (Managed Key-Value)

Pros:

  • ✅ Fully Managed: No infrastructure management
  • ✅ Auto-Scaling: Automatic scaling based on demand
  • ✅ High Availability: Built-in replication and failover
  • ✅ Global Tables: Multi-region replication
  • ✅ Security: Built-in encryption and access control
  • ✅ Pay-Per-Use: Cost-effective for variable workloads

Cons:

  • ❌ Vendor Lock-in: AWS-specific
  • ❌ Cost: Can be expensive at scale
  • ❌ Item Size Limit: 400KB per item
  • ❌ Query Limitations: Limited query capabilities
  • ❌ Throughput Limits: Need to provision capacity (or use on-demand)
  • ❌ Cold Starts: On-demand can have latency spikes

When to Use:

  • AWS-native applications
  • Serverless applications
  • Variable workloads
  • Need managed service
  • Simple key-value access patterns

Common Use Cases: E-commerce platforms, marketplace applications, IoT devices

2. Caching Systems

Redis

Pros:

  • ✅ Ultra-Fast: Sub-millisecond latency
  • ✅ Rich Data Types: Strings, lists, sets, sorted sets, hashes
  • ✅ Pub/Sub: Real-time messaging
  • ✅ Persistence: Optional RDB snapshots and AOF
  • ✅ Atomic Operations: Atomic increments, etc.
  • ✅ Clustering: Horizontal scaling with Redis Cluster

Cons:

  • ❌ Memory Cost: Expensive for large datasets
  • ❌ Volatility: Data lost if not persisted and server crashes
  • ❌ Single-Threaded: CPU-bound operations block
  • ❌ No Complex Queries: Simple key-value operations only
  • ❌ Eviction: Need to manage memory eviction policies

When to Use:

  • Hot data caching
  • Session storage
  • Real-time features
  • Rate limiting
  • Leaderboards

Memcached

Pros:

  • ✅ Simple: Simple key-value store
  • ✅ Fast: Very fast for simple operations
  • ✅ Multi-Threaded: Better CPU utilization than Redis
  • ✅ Lightweight: Lower memory overhead
  • ✅ Distributed: Built-in distribution across nodes

Cons:

  • ❌ No Persistence: Data lost on restart
  • ❌ Limited Data Types: Only strings
  • ❌ No Replication: No built-in replication
  • ❌ Less Features: Fewer features than Redis
  • ❌ No Pub/Sub: No publish-subscribe

When to Use:

  • Simple caching needs
  • High-performance caching
  • When Redis features not needed
  • Multi-threaded workloads

Common Use Cases: Social media platforms, content platforms, real-time applications

CDN Caching

Pros:

  • ✅ Global Distribution: Content served from edge locations
  • ✅ Low Latency: Reduced latency for end users
  • ✅ Bandwidth Savings: Reduces origin server load
  • ✅ DDoS Protection: Built-in DDoS mitigation
  • ✅ SSL/TLS: Managed SSL certificates

Cons:

  • ❌ Cost: Can be expensive at scale
  • ❌ Cache Invalidation: Complex cache invalidation
  • ❌ Dynamic Content: Less effective for dynamic content
  • ❌ Vendor Lock-in: Cloud provider specific (CloudFront, Cloudflare)

When to Use:

  • Static assets (images, CSS, JS)
  • Video streaming
  • Global user base
  • High traffic websites

Common Use Cases: Streaming platforms, video services, social media, e-commerce

3. Message Queues

Apache Kafka

Pros:

  • ✅ High Throughput: 100K-1M+ messages/second
  • ✅ Durability: Messages persisted to disk
  • ✅ Replayability: Can replay messages from any offset
  • ✅ Multiple Consumers: Multiple consumer groups
  • ✅ Scalability: Horizontal scaling with partitioning
  • ✅ Ordering: Maintains order per partition

Cons:

  • ❌ Complexity: Complex setup and operations
  • ❌ Latency: Higher latency than in-memory queues
  • ❌ Storage: Requires significant disk space
  • ❌ Overkill: Overkill for simple use cases
  • ❌ Learning Curve: Steep learning curve

When to Use:

  • Event streaming
  • Log aggregation
  • High-throughput data pipelines
  • Multiple consumer groups
  • Need message replay

Common Use Cases: Streaming platforms, professional networks, ride-sharing, real-time platforms

RabbitMQ

Pros:

  • ✅ Flexible Routing: Multiple exchange types
  • ✅ Multiple Protocols: AMQP, MQTT, STOMP
  • ✅ Persistence: Configurable message persistence
  • ✅ Management UI: Built-in management interface
  • ✅ Clustering: High availability with clustering
  • ✅ Enterprise Features: Dead-letter queues, priority queues

Cons:

  • ❌ Throughput: Lower throughput than Kafka
  • ❌ Scalability: Less scalable than Kafka
  • ❌ Complexity: More complex than Redis
  • ❌ Performance: Slower than in-memory solutions

When to Use:

  • Complex routing requirements
  • Multiple messaging patterns
  • Enterprise features needed
  • AMQP protocol required

Common Use Cases: Pivotal, Mozilla, Red Hat

AWS SQS

Pros:

  • ✅ Fully Managed: No infrastructure management
  • ✅ Auto-Scaling: Automatic scaling
  • ✅ Unlimited Throughput: Standard queues scale automatically
  • ✅ FIFO Queues: Exactly-once processing
  • ✅ Dead-Letter Queues: Built-in error handling
  • ✅ Long Polling: Reduces empty responses

Cons:

  • ❌ Vendor Lock-in: AWS-specific
  • ❌ Message Size: 256KB limit
  • ❌ No Replay: Cannot replay messages
  • ❌ Latency: Higher latency than Redis
  • ❌ Cost: Can be expensive at scale

When to Use:

  • AWS-native applications
  • Simple queue needs
  • Want managed service
  • Decoupled microservices

Common Use Cases: E-commerce platforms, marketplace applications, streaming services

4. Load Balancers

Application Load Balancer (ALB) - Layer 7

Pros:

  • ✅ Content-Based Routing: Route based on URL path, host, headers
  • ✅ SSL Termination: Handle SSL/TLS at load balancer
  • ✅ Health Checks: Advanced health checking
  • ✅ Sticky Sessions: Session affinity support
  • ✅ WebSocket Support: WebSocket and HTTP/2 support

Cons:

  • ❌ Cost: More expensive than NLB
  • ❌ Latency: Slightly higher latency than NLB
  • ❌ Complexity: More complex configuration
  • ❌ Throughput: Lower throughput than NLB

When to Use:

  • HTTP/HTTPS applications
  • Need content-based routing
  • Microservices architecture
  • WebSocket applications

Network Load Balancer (NLB) - Layer 4

Pros:

  • ✅ High Performance: Very low latency
  • ✅ High Throughput: Handles millions of requests/second
  • ✅ Connection-Based: TCP/UDP load balancing
  • ✅ Static IP: Static IP addresses
  • ✅ Cost: Lower cost than ALB

Cons:

  • ❌ No Content Routing: Cannot route based on content
  • ❌ No SSL Termination: SSL handled by backend
  • ❌ Limited Features: Fewer features than ALB
  • ❌ No HTTP Features: No HTTP-specific features

When to Use:

  • High-performance TCP/UDP applications
  • Need static IP addresses
  • Low latency requirements
  • Gaming or real-time applications

HAProxy

Pros:

  • ✅ Open Source: Free and open source
  • ✅ High Performance: Very fast and efficient
  • ✅ Flexible: Highly configurable
  • ✅ Health Checks: Advanced health checking
  • ✅ SSL Termination: SSL/TLS support
  • ✅ ACL Rules: Advanced access control

Cons:

  • ❌ Self-Managed: Need to manage infrastructure
  • ❌ Configuration: Complex configuration
  • ❌ No Cloud Integration: Less cloud-native
  • ❌ Scaling: Manual scaling required

When to Use:

  • On-premises deployments
  • Need fine-grained control
  • Cost-sensitive deployments
  • Custom requirements

Common Use Cases: Developer platforms, Q&A platforms, community forums

NGINX

Pros:

  • ✅ High Performance: Very fast and efficient
  • ✅ Reverse Proxy: Excellent reverse proxy
  • ✅ Web Server: Can serve static files
  • ✅ SSL Termination: SSL/TLS support
  • ✅ Rate Limiting: Built-in rate limiting
  • ✅ Open Source: Free version available

Cons:

  • ❌ Configuration: Complex configuration
  • ❌ Self-Managed: Need to manage infrastructure
  • ❌ Limited Features: Fewer features than dedicated load balancers
  • ❌ Scaling: Manual scaling required

When to Use:

  • Reverse proxy needs
  • Web server + load balancer
  • Cost-sensitive deployments
  • Need rate limiting

Common Use Cases: Streaming platforms, file storage, content management systems

5. Search Engines

Elasticsearch

Pros:

  • ✅ Full-Text Search: Powerful full-text search capabilities
  • ✅ Distributed: Built-in distributed architecture
  • ✅ Real-Time: Near real-time search
  • ✅ Analytics: Aggregations and analytics
  • ✅ REST API: Easy to use REST API
  • ✅ Scalability: Horizontal scaling

Cons:

  • ❌ Complexity: Complex setup and operations
  • ❌ Resource Intensive: Memory and CPU intensive
  • ❌ Data Loss Risk: Can lose data if not configured properly
  • ❌ Cost: Expensive at scale
  • ❌ Learning Curve: Steep learning curve

When to Use:

  • Full-text search
  • Log analytics
  • Real-time analytics
  • Complex search requirements
  • Need aggregations

Common Use Cases: Streaming platforms, developer tools, Q&A platforms, ride-sharing

Apache Solr

Pros:

  • ✅ Mature: Very mature and stable
  • ✅ Full-Text Search: Powerful search capabilities
  • ✅ Faceted Search: Excellent faceted search
  • ✅ Open Source: Free and open source
  • ✅ Lucene-Based: Built on Apache Lucene

Cons:

  • ❌ Complexity: Complex setup
  • ❌ Less Popular: Less popular than Elasticsearch
  • ❌ Resource Intensive: Memory and CPU intensive
  • ❌ Slower Updates: Slower real-time updates than Elasticsearch

When to Use:

  • Enterprise search
  • E-commerce search
  • Need faceted search
  • Prefer Solr ecosystem

Common Use Cases: Streaming platforms, e-commerce, social media

6. Storage Systems

S3 (Object Storage)

Pros:

  • ✅ Fully Managed: No infrastructure management
  • ✅ Durability: 99.999999999% (11 9’s) durability
  • ✅ Scalability: Virtually unlimited storage
  • ✅ Cost-Effective: Pay for what you use
  • ✅ Versioning: Object versioning support
  • ✅ Lifecycle Policies: Automatic data management

Cons:

  • ❌ Vendor Lock-in: AWS-specific
  • ❌ Latency: Higher latency than local storage
  • ❌ Cost: Can be expensive for frequent access
  • ❌ No File System: Not a traditional file system
  • ❌ Eventual Consistency: Eventual consistency for some operations

When to Use:

  • Object storage
  • Backup and archival
  • Static website hosting
  • Data lakes
  • Media storage

Common Use Cases: Streaming platforms, marketplace applications, file storage

Cloud Storage

Pros:

  • ✅ Fully Managed: No infrastructure management
  • ✅ Multi-Region: Automatic multi-region replication
  • ✅ Integration: Good cloud platform integration
  • ✅ Lifecycle Management: Automatic data management
  • ✅ Versioning: Object versioning

Cons:

  • ❌ Vendor Lock-in: Cloud provider-specific
  • ❌ Less Popular: Less popular than S3
  • ❌ Ecosystem: Smaller ecosystem than AWS

When to Use:

  • Cloud platform applications
  • Need multi-region replication
  • Cloud platform integration

Common Use Cases: Streaming services, social media, payment platforms

Azure Blob Storage

Pros:

  • ✅ Fully Managed: No infrastructure management
  • ✅ Azure Integration: Good Azure integration
  • ✅ Tiers: Hot, cool, archive tiers
  • ✅ CDN Integration: Easy CDN integration
  • ✅ Security: Built-in encryption

Cons:

  • ❌ Vendor Lock-in: Azure-specific
  • ❌ Less Popular: Less popular than S3
  • ❌ Ecosystem: Smaller ecosystem than AWS

When to Use:

  • Azure-native applications
  • Need Azure integration
  • Enterprise ecosystems

Common Use Cases: Enterprise software, creative tools, automotive industry

7. API Gateways

AWS API Gateway

Pros:

  • ✅ Fully Managed: No infrastructure management
  • ✅ Serverless: Pay per request
  • ✅ Integration: Easy AWS service integration
  • ✅ Caching: Built-in response caching
  • ✅ Rate Limiting: Built-in rate limiting
  • ✅ Authentication: Built-in authentication

Cons:

  • ❌ Vendor Lock-in: AWS-specific
  • ❌ Cost: Can be expensive at scale
  • ❌ Latency: Cold starts can add latency
  • ❌ Limitations: Request/response size limits

When to Use:

  • Serverless architectures
  • AWS Lambda integration
  • Need managed API gateway
  • Microservices API management

Common Use Cases: E-commerce platforms, streaming services, marketplace applications

Kong

Pros:

  • ✅ Open Source: Free and open source
  • ✅ Plugin Ecosystem: Rich plugin ecosystem
  • ✅ Performance: High performance
  • ✅ Flexible: Highly configurable
  • ✅ Multi-Cloud: Works across cloud providers

Cons:

  • ❌ Self-Managed: Need to manage infrastructure
  • ❌ Complexity: Complex setup
  • ❌ Scaling: Manual scaling required

When to Use:

  • Need open-source solution
  • Multi-cloud deployments
  • Custom requirements
  • Cost-sensitive deployments

Common Use Cases: Glovo, Vox Media, Rakuten

NGINX Plus

Pros:

  • ✅ High Performance: Very fast
  • ✅ Advanced Features: Load balancing, caching, rate limiting
  • ✅ API Management: API gateway features
  • ✅ Monitoring: Built-in monitoring
  • ✅ Support: Commercial support available

Cons:

  • ❌ Cost: Paid license required
  • ❌ Self-Managed: Need to manage infrastructure
  • ❌ Configuration: Complex configuration

When to Use:

  • Need high performance
  • Already using NGINX
  • Need commercial support
  • Enterprise requirements

Common Use Cases: Streaming platforms, file storage, content management systems

8. Monitoring & Observability

Prometheus

Pros:

  • ✅ Open Source: Free and open source
  • ✅ Time-Series: Optimized for time-series data
  • ✅ Pull Model: Pull-based metrics collection
  • ✅ PromQL: Powerful query language
  • ✅ Ecosystem: Large ecosystem (Grafana, etc.)
  • ✅ Service Discovery: Built-in service discovery

Cons:

  • ❌ Storage: Limited long-term storage
  • ❌ Cardinality: High cardinality can cause issues
  • ❌ Self-Managed: Need to manage infrastructure
  • ❌ No Logging: Metrics only, not logs

When to Use:

  • Kubernetes monitoring
  • Time-series metrics
  • Need pull-based collection
  • Cost-sensitive deployments

Common Use Cases: SoundCloud, DigitalOcean, Docker

Grafana

Pros:

  • ✅ Visualization: Excellent visualization capabilities
  • ✅ Multiple Data Sources: Supports many data sources
  • ✅ Dashboards: Rich dashboard features
  • ✅ Alerts: Built-in alerting
  • ✅ Open Source: Free version available
  • ✅ Community: Large community

Cons:

  • ❌ Not a Database: Visualization tool, not storage
  • ❌ Configuration: Can be complex to configure
  • ❌ Resource Usage: Can be resource-intensive

When to Use:

  • Need visualization
  • Multiple data sources
  • Dashboard requirements
  • Metrics visualization

Common Use Cases: Payment platforms, e-commerce, financial services

Datadog

Pros:

  • ✅ Fully Managed: No infrastructure management
  • ✅ All-in-One: Metrics, logs, traces
  • ✅ Easy Setup: Easy to set up
  • ✅ APM: Application performance monitoring
  • ✅ Integrations: Many integrations

Cons:

  • ❌ Cost: Expensive at scale
  • ❌ Vendor Lock-in: Vendor-specific
  • ❌ Data Retention: Limited data retention

When to Use:

  • Need managed solution
  • All-in-one observability
  • Quick setup required
  • Budget allows

Common Use Cases: Marketplace applications, streaming services, fitness platforms

9. Web Servers

NGINX

Pros:

  • ✅ High Performance: Very fast and efficient
  • ✅ Low Memory: Low memory footprint
  • ✅ Concurrent Connections: Handles many concurrent connections
  • ✅ Reverse Proxy: Excellent reverse proxy
  • ✅ Load Balancing: Built-in load balancing
  • ✅ Open Source: Free version available

Cons:

  • ❌ Configuration: Complex configuration
  • ❌ Dynamic Content: Less efficient for dynamic content
  • ❌ Modules: Limited module ecosystem compared to Apache

When to Use:

  • High-traffic websites
  • Reverse proxy
  • Static content serving
  • Load balancing

Common Use Cases: Streaming platforms, file storage, content management systems, developer tools

Apache HTTP Server

Pros:

  • ✅ Mature: Very mature and stable
  • ✅ Modules: Rich module ecosystem
  • ✅ Flexibility: Highly configurable
  • ✅ .htaccess: Per-directory configuration
  • ✅ Open Source: Free and open source

Cons:

  • ❌ Performance: Lower performance than NGINX
  • ❌ Memory: Higher memory usage
  • ❌ Concurrency: Less efficient for concurrent connections
  • ❌ Complexity: Complex configuration

When to Use:

  • Need specific Apache modules
  • Legacy applications
  • Per-directory configuration needed
  • Team familiar with Apache

Common Use Cases: Content platforms, mobile applications, enterprise systems

10. Container Orchestration

Kubernetes

Pros:

  • ✅ Industry Standard: De facto standard
  • ✅ Scalability: Excellent scalability
  • ✅ Ecosystem: Large ecosystem
  • ✅ Portability: Works across cloud providers
  • ✅ Features: Rich feature set
  • ✅ Community: Large community

Cons:

  • ❌ Complexity: Very complex
  • ❌ Learning Curve: Steep learning curve
  • ❌ Resource Intensive: Requires significant resources
  • ❌ Operations: Complex operations

When to Use:

  • Large-scale deployments
  • Multi-cloud deployments
  • Need advanced features
  • Microservices architecture

Common Use Cases: Cloud platforms, streaming services, music platforms, ride-sharing

Docker Swarm

Pros:

  • ✅ Simplicity: Simpler than Kubernetes
  • ✅ Docker Native: Built into Docker
  • ✅ Easy Setup: Easier to set up
  • ✅ Lightweight: Lower resource requirements

Cons:

  • ❌ Less Features: Fewer features than Kubernetes
  • ❌ Less Popular: Less popular than Kubernetes
  • ❌ Ecosystem: Smaller ecosystem

When to Use:

  • Simple deployments
  • Docker-only environments
  • Small to medium scale
  • Need simplicity

11. Architectural Patterns

Monolithic Architecture

Description: A single, unified codebase where all components are tightly integrated and deployed together.

Pros:

  • ✅ Simplified Deployment: Single deployment unit, easier to deploy and test
  • ✅ Easier Development: Unified codebase, easier to implement and debug
  • ✅ Performance: No network overhead between components
  • ✅ Transaction Management: Easier to maintain ACID transactions
  • ✅ Simpler Testing: Easier to test entire application
  • ✅ Lower Initial Complexity: Simpler to start with

Cons:

  • ❌ Scaling Challenges: Must scale entire application, cannot scale components independently
  • ❌ Single Point of Failure: Failure in one component can affect entire system
  • ❌ Redeployment: Any change requires redeploying entire application
  • ❌ Technology Lock-in: Difficult to adopt new technologies
  • ❌ Team Coordination: Large teams working on same codebase can conflict
  • ❌ Long Startup Time: Large applications take longer to start

When to Use:

  • Small to medium applications
  • Simple requirements
  • Small development teams
  • Rapid prototyping
  • When performance is critical (no network overhead)

Common Use Cases: Many startups, small applications

Microservices Architecture

Description: Application divided into small, independent services that communicate via APIs (typically REST or gRPC).

Pros:

  • ✅ Independent Scaling: Each service can be scaled independently
  • ✅ Technology Diversity: Different services can use different technologies
  • ✅ Team Autonomy: Teams can work independently on different services
  • ✅ Fault Isolation: Failure in one service doesn’t bring down entire system
  • ✅ Continuous Deployment: Services can be deployed independently
  • ✅ Easier Maintenance: Smaller codebases are easier to maintain

Cons:

  • ❌ Complexity: Increased operational complexity
  • ❌ Network Latency: Inter-service communication adds latency
  • ❌ Distributed Transactions: Difficult to maintain ACID across services
  • ❌ Data Consistency: Eventual consistency challenges
  • ❌ Testing Complexity: Integration testing is more complex
  • ❌ Infrastructure Overhead: Need service discovery, load balancing, etc.

When to Use:

  • Large, complex applications
  • Multiple teams working on different features
  • Need independent scaling
  • Different services have different requirements
  • Microservices expertise available

Common Use Cases: Streaming platforms, e-commerce, ride-sharing, music platforms

Event-Driven Architecture

Description: Components communicate by emitting and responding to events asynchronously through an event bus or message broker.

Pros:

  • ✅ Loose Coupling: Components are decoupled, communicate via events
  • ✅ High Scalability: Can handle high event volumes
  • ✅ Flexibility: Easy to add new event consumers without modifying producers
  • ✅ Real-Time Processing: Supports real-time event processing
  • ✅ Resilience: System can continue operating if some components fail
  • ✅ Event Replay: Can replay events for debugging or reprocessing

Cons:

  • ❌ Debugging Complexity: Tracing events through system is difficult
  • ❌ Event Ordering: Ensuring event order can be challenging
  • ❌ Consistency: Eventual consistency, not immediate
  • ❌ Complexity: More complex than request-response patterns
  • ❌ Testing: Testing event flows is more complex
  • ❌ Message Loss: Risk of losing events if not handled properly

When to Use:

  • Real-time data processing
  • High-volume event streams
  • Need loose coupling
  • Multiple consumers for same events
  • Event sourcing patterns

Common Use Cases: Streaming platforms, ride-sharing, professional networks, social media

Serverless Architecture

Description: Uses cloud services (Functions as a Service) to run code without managing servers or infrastructure.

Pros:

  • ✅ Cost Efficiency: Pay only for compute time used
  • ✅ Auto-Scaling: Automatic scaling based on demand
  • ✅ No Infrastructure Management: Cloud provider manages infrastructure
  • ✅ Rapid Deployment: Fast to deploy and update
  • ✅ High Availability: Built-in redundancy and failover
  • ✅ Event-Driven: Natural fit for event-driven architectures

Cons:

  • ❌ Cold Start Latency: Initial invocation can be slow (cold start)
  • ❌ Limited Control: Less control over execution environment
  • ❌ Vendor Lock-in: Tied to specific cloud provider
  • ❌ Debugging: More difficult to debug distributed functions
  • ❌ Cost at Scale: Can be expensive at high volumes
  • ❌ Time Limits: Functions have execution time limits
  • ❌ State Management: Stateless by design, need external state storage

When to Use:

  • Variable or unpredictable workloads
  • Event-driven processing
  • API backends
  • Scheduled tasks
  • Cost optimization for low-traffic services

Common Use Cases: Streaming platforms, marketplace applications, consumer brands, IoT devices

12. Additional Technologies

Graph Databases

Neo4j

Pros:

  • ✅ Relationship Queries: Excellent for relationship-heavy queries
  • ✅ Graph Traversals: Fast graph traversals (O(depth))
  • ✅ Cypher Query Language: Intuitive graph query language
  • ✅ ACID Compliance: Strong consistency guarantees
  • ✅ Visualization: Built-in visualization tools

Cons:

  • ❌ Scaling: Horizontal scaling is challenging
  • ❌ Cost: Can be expensive
  • ❌ Learning Curve: Requires understanding graph concepts
  • ❌ Limited Use Cases: Not suitable for all data types

When to Use:

  • Social networks
  • Recommendation engines
  • Fraud detection
  • Knowledge graphs
  • Network analysis

Common Use Cases: eBay, Walmart, Cisco, NASA

Time-Series Databases

InfluxDB

Pros:

  • ✅ Time-Series Optimized: Optimized for time-series data
  • ✅ High Write Throughput: Excellent write performance
  • ✅ Data Retention: Automatic data retention policies
  • ✅ Continuous Queries: Built-in continuous queries
  • ✅ Downsampling: Automatic data downsampling

Cons:

  • ❌ Limited Use Cases: Only for time-series data
  • ❌ Query Limitations: Limited query capabilities
  • ❌ Scaling: Scaling can be complex

When to Use:

  • IoT sensor data
  • Metrics and monitoring
  • Financial time-series data
  • Real-time analytics

Common Use Cases: Cisco, IBM, Tesla

TimescaleDB

Pros:

  • ✅ PostgreSQL-Based: Built on PostgreSQL, SQL compatible
  • ✅ Hypertables: Automatic partitioning for time-series
  • ✅ Full SQL: Full SQL support
  • ✅ ACID: ACID compliance
  • ✅ Extensions: PostgreSQL extensions work

Cons:

  • ❌ PostgreSQL Limitations: Inherits PostgreSQL scaling challenges
  • ❌ Less Optimized: Less optimized than InfluxDB for pure time-series

When to Use:

  • Need SQL for time-series data
  • Already using PostgreSQL
  • Need ACID guarantees
  • Complex queries on time-series data

Common Use Cases: Comcast, Microsoft, IBM

Proxies

Pros:

  • ✅ Security: Hide origin server identity, add security layer
  • ✅ Caching: Can cache responses to reduce origin load
  • ✅ Load Balancing: Distribute load across multiple servers
  • ✅ SSL Termination: Handle SSL/TLS encryption
  • ✅ Request Filtering: Filter malicious requests

Cons:

  • ❌ Additional Latency: Extra hop adds latency
  • ❌ Single Point of Failure: Can become bottleneck if not redundant
  • ❌ Complexity: Adds complexity to architecture
  • ❌ Configuration: Requires careful configuration

When to Use:

  • Need to hide backend servers
  • Want to add caching layer
  • Need SSL termination
  • Security requirements

Common Tools: NGINX, HAProxy, Squid, Varnish

Content Delivery Networks (CDNs)

Pros:

  • ✅ Low Latency: Content served from edge locations closer to users
  • ✅ Bandwidth Savings: Reduces origin server bandwidth usage
  • ✅ DDoS Protection: Built-in DDoS mitigation
  • ✅ Global Distribution: Content distributed globally
  • ✅ SSL/TLS: Managed SSL certificates
  • ✅ Caching: Automatic caching of static content

Cons:

  • ❌ Cost: Can be expensive at scale
  • ❌ Cache Invalidation: Complex cache invalidation
  • ❌ Dynamic Content: Less effective for highly dynamic content
  • ❌ Vendor Lock-in: Cloud provider specific
  • ❌ Cache Staleness: Risk of serving stale content

When to Use:

  • Static assets (images, CSS, JS, videos)
  • Global user base
  • High traffic websites
  • Need low latency globally
  • Video streaming

Common Providers: Cloudflare, AWS CloudFront, Fastly, Akamai

Common Use Cases: Streaming platforms, video services, social media, e-commerce

13. CAP Theorem Considerations

CAP Theorem: In a distributed system, you can only guarantee two out of three:

  • Consistency: All nodes see same data simultaneously
  • Availability: System remains operational
  • Partition Tolerance: System continues despite network partitions

CP Systems (Consistency + Partition Tolerance)

  • Examples: MongoDB (with strong consistency), HBase, Redis (with persistence)
  • Trade-off: May sacrifice availability during partitions
  • Use When: Data consistency is critical (financial systems)

AP Systems (Availability + Partition Tolerance)

  • Examples: Cassandra, DynamoDB, CouchDB
  • Trade-off: May serve stale data (eventual consistency)
  • Use When: High availability is critical (social media, content delivery)

CA Systems (Consistency + Availability)

  • Examples: Traditional SQL databases (single node)
  • Trade-off: Not partition tolerant
  • Use When: Single data center, no network partitions

Key Insight: Most distributed systems choose AP or CP, as partition tolerance is usually required in distributed systems.

Decision Matrix by Use Case

High-Throughput Data Ingestion

  • Best: Kafka, Cassandra
  • Avoid: SQL databases (without careful design)

Low-Latency Caching

  • Best: Redis, Memcached
  • Avoid: Disk-based solutions

Full-Text Search

  • Best: Elasticsearch, Solr
  • Avoid: SQL databases (limited search capabilities)

Real-Time Analytics

  • Best: Redis, Kafka + Stream Processing
  • Avoid: Batch-only systems

Object Storage

  • Best: S3, Google Cloud Storage, Azure Blob
  • Avoid: File systems for large-scale

API Management

  • Best: AWS API Gateway, Kong, NGINX
  • Avoid: Direct backend exposure

Monitoring

  • Best: Prometheus + Grafana, Datadog
  • Avoid: Manual monitoring

Technology Selection Framework

1. Identify Requirements

  • Scale: Expected traffic, data volume
  • Latency: Acceptable latency requirements
  • Consistency: Consistency requirements (ACID vs eventual)
  • Durability: Data durability requirements
  • Budget: Cost constraints

2. Evaluate Trade-offs

  • Performance vs Cost: Higher performance usually costs more
  • Simplicity vs Features: More features = more complexity
  • Managed vs Self-Managed: Managed = higher cost, less control
  • Vendor Lock-in vs Portability: Cloud services = lock-in

3. Consider Constraints

  • Team Expertise: Use technologies team knows
  • Infrastructure: On-premises vs cloud
  • Compliance: Regulatory requirements
  • Integration: Existing systems integration

4. Make Decision

  • Start simple, add complexity as needed
  • Use managed services when possible
  • Consider polyglot approach (multiple technologies)
  • Plan for scale from the beginning

Common Patterns

Polyglot Persistence

Use multiple databases for different use cases:

  • PostgreSQL: Core business data
  • Redis: Caching and sessions
  • Elasticsearch: Search
  • Kafka: Event streaming

Caching Strategy

  • L1 Cache: Application-level cache
  • L2 Cache: Distributed cache (Redis)
  • L3 Cache: CDN for static content

Message Queue Patterns

  • Kafka: Event streaming, log aggregation
  • Redis: Real-time pub/sub, simple queues
  • RabbitMQ: Service communication
  • SQS: Cloud-native applications

Best Practices

Database Selection

  1. Start with SQL for structured data
  2. Add NoSQL for specific use cases
  3. Use caching for hot data
  4. Consider read replicas for read-heavy workloads

Caching Strategy

  1. Cache frequently accessed data
  2. Set appropriate TTLs
  3. Implement cache invalidation
  4. Use multi-level caching

Message Queue Selection

  1. Kafka for high-throughput event streaming
  2. Redis for low-latency pub/sub
  3. RabbitMQ for complex routing
  4. Cloud queues for managed services

Load Balancing

  1. ALB for HTTP/HTTPS applications
  2. NLB for high-performance TCP/UDP
  3. NGINX for on-premises deployments
  4. Use health checks for reliability

What Interviewers Look For

Technology Selection Skills

  1. Understanding Trade-offs
    • Pros/cons of each technology
    • When to use what
    • Red Flags: Wrong technology choice, no justification, can’t explain differences
  2. Technology Knowledge
    • Deep understanding of technologies
    • Real-world experience
    • Red Flags: Surface knowledge, no experience, wrong understanding
  3. Decision-Making Framework
    • Requirements analysis
    • Constraint identification
    • Trade-off evaluation
    • Red Flags: No framework, random choices, no analysis

System Design Skills

  1. Appropriate Technology Choice
    • Right tool for the job
    • Justified decisions
    • Red Flags: Wrong choices, no justification, dogmatic
  2. Technology Comparison
    • Can compare alternatives
    • Understands differences
    • Red Flags: Can’t compare, no understanding, vague
  3. Integration Understanding
    • How technologies work together
    • System-level thinking
    • Red Flags: Isolated thinking, no integration, poor system view

Problem-Solving Approach

  1. Requirements Analysis
    • Identifies technology needs
    • Considers constraints
    • Red Flags: No analysis, assumptions, wrong requirements
  2. Trade-off Analysis
    • Technology trade-offs
    • Cost vs performance
    • Red Flags: No trade-offs, dogmatic choices, no justification
  3. Alternative Consideration
    • Considers alternatives
    • Explains why not chosen
    • Red Flags: No alternatives, single solution, no comparison

Communication Skills

  1. Technology Explanation
    • Can explain each technology
    • Understands use cases
    • Red Flags: No understanding, vague explanations, can’t explain
  2. Decision Justification
    • Explains technology choices
    • Discusses alternatives
    • Red Flags: No justification, no alternatives, can’t defend

Meta-Specific Focus

  1. Technology Expertise
    • Deep technology knowledge
    • Practical understanding
    • Key: Show technology expertise
  2. Judgment in Selection
    • Right tool for the job
    • Understanding of trade-offs
    • Key: Demonstrate good judgment

Conclusion

Understanding common technologies and their trade-offs is essential for system design interviews. This guide covers:

  1. Databases: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, Redis, DynamoDB)
  2. Caching Systems: Redis, Memcached, CDN caching
  3. Message Queues: Kafka, RabbitMQ, AWS SQS
  4. Load Balancers: ALB, NLB, HAProxy, NGINX
  5. Search Engines: Elasticsearch, Solr
  6. Storage Systems: S3, Google Cloud Storage, Azure Blob
  7. API Gateways: AWS API Gateway, Kong, NGINX Plus
  8. Monitoring: Prometheus, Grafana, Datadog
  9. Web Servers: NGINX, Apache
  10. Container Orchestration: Kubernetes, Docker Swarm
  11. Architectural Patterns: Monolithic, Microservices, Event-Driven, Serverless
  12. Additional Technologies: Graph databases, Time-series databases, Proxies, CDNs
  13. CAP Theorem: Understanding consistency, availability, and partition tolerance trade-offs

Key Takeaways:

  1. No One-Size-Fits-All: Different technologies for different use cases
  2. Trade-offs Matter: Every choice has pros and cons
  3. Polyglot Approach: Use multiple technologies together
  4. Start Simple: Begin with simple solutions, add complexity as needed
  5. Consider Scale: Design for scale from the beginning
  6. Architecture Matters: Choose appropriate architectural patterns
  7. CAP Theorem: Understand consistency vs availability trade-offs

Interview Tips:

Remember to:

  • Justify Choices: Explain why you chose a technology
  • Discuss Trade-offs: Always mention pros and cons
  • Consider Alternatives: Mention other options you considered
  • Think About Scale: Consider how it scales
  • Consider Cost: Factor in operational costs
  • Mention Patterns: Discuss architectural patterns when relevant
  • Reference CAP: Mention CAP theorem when discussing distributed systems

References:

This guide is based on:

  • Industry best practices and common patterns
  • Real-world implementations at major tech companies
  • System design interview requirements
  • Technology documentation and comparisons
  • Web search results for latest trends (2024-2025)

Good luck with your system design interviews!