Types of Distributed Systems #
Distributed systems can be categorized based on their architecture, purpose, and how components interact. Each type has distinct characteristics that make it suitable for specific use cases.
1. Client-Server Systems #
Architecture #
┌─────────┐ ┌─────────┐ ┌─────────┐
│ │ │ │ │ │
│ CLIENT │────►│ SERVER │◄────│ CLIENT │
│ A │ │ │ │ B │
└─────────┘ └─────────┘ └─────────┘
▲
│
▼
┌─────────┐
│ │
│ CLIENT │
│ C │
└─────────┘
Key Characteristics #
- Role Separation: Clear division between service providers (servers) and consumers (clients)
- Centralized Control: Servers manage resources and enforce access policies
- Request-Response Pattern: Communication follows a request-response cycle
- Scalability Challenge: Servers can become bottlenecks under high load
Technical Implementation #
- Communication Protocols: HTTP/HTTPS, WebSockets, gRPC
- Load Distribution: Often uses load balancers to distribute client requests
- State Management: Servers typically maintain session state
Real-World Examples #
- Web Applications: Gmail, online banking portals
- API Services: Twitter API, payment gateways
- Database Clients: SQL clients connecting to database servers
- Enterprise Applications: SAP, Salesforce
2. Peer-to-Peer (P2P) Systems #
Architecture #
┌─────────┐ ┌─────────┐ ┌─────────┐
│ │◄───►│ │◄───►│ │
│ PEER A │ │ PEER B │ │ PEER C │
│ │◄─┐ │ │ ┌─►│ │
└─────────┘ │ └─────────┘ │ └─────────┘
│ │
│ ┌─────────┐ │
│ │ │ │
└─►│ PEER D │◄─┘
│ │
└─────────┘
Key Characteristics #
- Equality: All nodes can function as both clients and servers
- Decentralization: No central coordination point
- Resilience: System continues functioning when nodes join or leave
- Resource Sharing: Computing power, storage, and bandwidth are collectively shared
Technical Implementation #
- Discovery Mechanisms: DHT (Distributed Hash Tables), gossip protocols
- Routing Algorithms: Chord, Kademlia, Pastry
- Data Replication: Content is often replicated across multiple peers
- Security Challenges: Trust establishment without central authority
Real-World Examples #
- File Sharing: BitTorrent, IPFS (InterPlanetary File System)
- Cryptocurrency Networks: Bitcoin, Ethereum blockchain
- Communication Tools: Early versions of Skype
- Distributed Computing: BOINC platform projects
3. Clustered Systems #
Architecture #
┌───────────────────────────────────────┐
│ │
│ CLUSTER MANAGER │
│ │
└───────┬───────────┬───────────┬───────┘
│ │ │
▼ ▼ ▼
┌───────────┐ ┌───────────┐ ┌───────────┐
│ │ │ │ │ │
│ NODE 1 │ │ NODE 2 │ │ NODE 3 │
│ │ │ │ │ │
└───────────┘ └───────────┘ └───────────┘
Key Characteristics #
- Physical Proximity: Nodes typically located in the same data center
- Homogeneous Hardware: Often uses similar or identical machines
- Shared Resources: Common storage, memory, or processing capabilities
- High Availability: Designed for fault tolerance and continuous operation
Technical Implementation #
- Resource Management: Uses cluster managers like Kubernetes, Mesos
- Job Scheduling: Distributes workloads across available nodes
- Shared Storage: Often uses distributed file systems or SANs
- Heartbeat Protocols: Monitors node health and manages failover
Real-World Examples #
- High-Performance Computing: Scientific simulation clusters
- Container Orchestration: Kubernetes clusters running microservices
- Big Data Processing: Hadoop/Spark clusters
- Database Clusters: MySQL Cluster, PostgreSQL with replication
4. Grid Computing Systems #
Architecture #
┌───────────────┐ ┌───────────────┐
│ ORGANIZATION │ │ ORGANIZATION │
│ A │ │ B │
│ ┌─────┐ ┌─────┐ │ ┌─────┐ ┌─────┐
│ │NODE │ │NODE │ │ │NODE │ │NODE │
│ │ 1 │ │ 2 │ │ │ 1 │ │ 2 │
│ └─────┘ └─────┘ │ └─────┘ └─────┘
└───────┬───────┘ └───────┬───────┘
│ │
▼ ▼
┌───────────────────────────────────────┐
│ │
│ GRID MIDDLEWARE │
│ │
└───────────────────┬───────────────────┘
│
▼
┌───────────────────────────────────────┐
│ │
│ RESEARCH PROBLEM │
│ │
└───────────────────────────────────────┘
Key Characteristics #
- Geographic Distribution: Resources spread across multiple locations
- Heterogeneous Resources: Different types of computers and networks
- Virtual Organizations: Collaboration across institutional boundaries
- Resource Sharing: Focuses on sharing computing power for large problems
Technical Implementation #
- Grid Middleware: Software like Globus Toolkit that manages resources
- Job Submission: Uses specialized protocols for submitting computing tasks
- Security Infrastructure: Certificate-based authentication across domains
- Data Management: Tools for moving large datasets between grid sites
Real-World Examples #
- Scientific Computing: SETI@home (search for extraterrestrial intelligence)
- Medical Research: Folding@home (protein folding simulations)
- Particle Physics: LHC Computing Grid (analyzing CERN data)
- Climate Modeling: Earth System Grid (climate simulation data)
5. Distributed Databases #
Architecture #
┌─────────────┐ ┌─────────────┐
│ │ │ │
│ DATABASE │◄────►│ DATABASE │
│ NODE 1 │ │ NODE 2 │
│ (Shard A) │ │ (Shard B) │
└──────┬──────┘ └──────┬──────┘
│ │
│ ┌─────────┐ │
└────►│ │◄───┘
│ QUERY │
│ ROUTER │
│ │
└────┬────┘
│
▼
┌─────────┐
│ │
│ CLIENT │
│ │
└─────────┘
Key Characteristics #
- Data Partitioning: Information divided across multiple nodes (sharding)
- Replication: Data copied to multiple locations for redundancy
- Consistency Models: Various approaches to maintaining data consistency
- Distributed Transactions: Mechanisms for maintaining ACID properties across nodes
Technical Implementation #
- Consensus Algorithms: Paxos, Raft for maintaining consistency
- Partitioning Strategies: Range-based, hash-based, or directory-based sharding
- Query Processing: Distributed query execution and optimization
- CAP Theorem Tradeoffs: Balancing Consistency, Availability, and Partition tolerance
Real-World Examples #
- NoSQL Databases: Cassandra (AP system), MongoDB (CP system)
- NewSQL Databases: Google Spanner, CockroachDB
- Time-Series Databases: InfluxDB, TimescaleDB
- Graph Databases: Neo4j (clustered), Amazon Neptune
6. Distributed File Systems #
Architecture #
┌─────────────┐ ┌─────────────┐
│ │ │ │
│ STORAGE │ │ STORAGE │
│ NODE 1 │ │ NODE 2 │
│ │ │ │
└──────┬──────┘ └──────┬──────┘
│ │
│ ┌──────────┐ │
└───►│ │◄───┘
│ METADATA │
│ SERVER │
│ │
└─────┬────┘
│
▼
┌─────────┐
│ │
│ CLIENT │
│ │
└─────────┘
Key Characteristics #
- Transparent Access: Files appear as if they’re on a local system
- Location Independence: Users don’t need to know physical file locations
- Fault Tolerance: System continues operating despite node failures
- Scalability: Can add storage nodes to increase capacity
Technical Implementation #
- Chunking: Files split into blocks distributed across nodes
- Metadata Management: Separate servers track file locations and attributes
- Caching: Local caching improves performance for frequently accessed files
- Consistency Protocols: Mechanisms to handle concurrent file access
Real-World Examples #
- Cloud Storage: Amazon S3, Google Cloud Storage
- Big Data Storage: Hadoop Distributed File System (HDFS)
- Enterprise Storage: GlusterFS, Ceph
- Research Systems: Google File System (GFS), Andrew File System (AFS)
7. Cloud-Based Distributed Systems #
Architecture #
┌───────────────────────────────────────────────────────┐
│ │
│ CLOUD PROVIDER │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ │ │ │ │ │ │
│ │ COMPUTE │ │ STORAGE │ │ DATABASE │ │
│ │ SERVICES │ │ SERVICES │ │ SERVICES │ │
│ │ │ │ │ │ │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
└───────────────────────────┬───────────────────────────┘
│
▼
┌───────────────┐ ┌─────────────┐ ┌───────────────┐
│ │ │ │ │ │
│ MOBILE │ │ WEB │ │ IOT │
│ CLIENTS │ │ CLIENTS │ │ DEVICES │
│ │ │ │ │ │
└───────────────┘ └─────────────┘ └───────────────┘
Key Characteristics #
- Elasticity: Resources can scale up or down based on demand
- Service-Based: Composed of multiple specialized services
- Multi-Tenancy: Infrastructure shared among multiple customers
- Pay-Per-Use: Resources billed based on actual consumption
Technical Implementation #
- Virtualization: Uses VMs, containers, or serverless functions
- Service Orchestration: Automated provisioning and management
- API Gateways: Manages access to backend services
- Monitoring & Telemetry: Distributed tracing and logging
Real-World Examples #
- IaaS: Amazon EC2, Microsoft Azure VMs
- PaaS: Heroku, Google App Engine
- SaaS: Salesforce, Microsoft 365
- Serverless: AWS Lambda, Azure Functions
8. Microservices Architecture #
Architecture #
┌────────────┐ ┌────────────┐ ┌────────────┐
│ │ │ │ │ │
│ USER │ │ PRODUCT │ │ ORDER │
│ SERVICE │ │ SERVICE │ │ SERVICE │
│ │ │ │ │ │
└─────┬──────┘ └─────┬──────┘ └─────┬──────┘
│ │ │
│ │ │
▼ ▼ ▼
┌────────────────────────────────────────────┐
│ │
│ API GATEWAY │
│ │
└────────────────────┬───────────────────────┘
│
▼
┌─────────────┐
│ │
│ CLIENT │
│ │
└─────────────┘
Key Characteristics #
- Service Independence: Each service can be developed and deployed separately
- Domain-Focused: Services organized around business capabilities
- Decentralized Data: Each service typically manages its own data
- Smart Endpoints, Dumb Pipes: Logic in services, simple communication channels
Technical Implementation #
- Service Discovery: Mechanisms for services to find each other
- API Gateways: Single entry point for clients
- Circuit Breakers: Prevent cascading failures
- Event-Driven Communication: Often uses message queues for asynchronous communication
Real-World Examples #
- E-commerce Platforms: Amazon’s service-oriented architecture
- Streaming Services: Netflix microservices (700+ services)
- Financial Systems: PayPal’s payment processing platform
- Transportation: Uber’s ride-sharing platform