distributed systems fundamentals⁂

what are distributed systems?⁂

distributed systems are networks of independent computers (nodes) communicating through message passing, collaboratively delivering unified services or achieving shared objectives. each node maintains its own memory, processing capability, and local storage, working concurrently and autonomously. nodes interact through standardized protocols and interfaces, allowing the system to function effectively across geographic distances, network delays, and varied hardware or software platforms.

the primary advantages of distributed systems include:

• scalability: ability to handle growing workloads by adding more resources
• fault tolerance: continuing operation despite component failures
• resilience: recovering from failures automatically
• efficient resource utilization: optimizing use of computing resources across the network

these systems range from small local networks to globe-spanning cloud infrastructures, powering everything from web applications to financial systems and massive data processing pipelines.

the cap theorem⁂

distributed systems embody three essential properties described by the cap theorem, formulated by eric brewer in 1998:

• consistency: ensuring all nodes have a synchronized and accurate view of data. when a write operation completes, all subsequent read operations should reflect that write.
• availability: providing reliable access and responses to user requests. every request to a non-failing node must receive a response, without guaranteeing it contains the most recent write.
• partition tolerance: maintaining operation despite network partitions or node failures. the system continues functioning even when network communication between some nodes is unreliable.

the cap theorem dictates that no system can simultaneously achieve all three properties at full strength. system architects must strategically balance these properties based on specific application requirements:

• cp systems (consistency + partition tolerance): prioritize data consistency at the potential cost of availability during partitions. examples include traditional banking systems and distributed databases like google spanner.
• ap systems (availability + partition tolerance): favor availability over strict consistency. examples include nosql databases like amazon dynamodb and cassandra.
• ca systems (consistency + availability): optimize for both properties but cannot handle network partitions effectively. these systems are theoretical in distributed environments, as partition tolerance is generally required.

distributed system architectures⁂

distributed systems employ diverse architectural patterns to address varying use cases:

client-server architecture⁂

centralizes resource management with dedicated servers responding to client requests. this architecture is ideal for predictable workloads and clear separation of concerns.

characteristics:

• clear separation between service providers (servers) and consumers (clients)
• centralized resource management
• relatively simple to implement and understand

examples: traditional web applications, email services, file servers

peer-to-peer (p2p) architecture⁂

distributes responsibilities evenly among equivalent nodes, improving resilience and scalability. each node can act as both client and server.

characteristics:

• no centralized control
• high resilience to node failures
• excellent scalability
• complex coordination requirements

examples: bittorrent, blockchain networks, distributed file systems

microservices architecture⁂

decomposes applications into loosely coupled, independent services, streamlining development and deployment. each service handles a specific function and can be developed, deployed, and scaled independently.

characteristics:

• service independence
• technology diversity
• focused development teams
• complex orchestration

examples: netflix, amazon, uber applications

event-driven architecture⁂

utilizes asynchronous communication via events or messages, enhancing flexibility and responsiveness. components react to events rather than direct calls.

characteristics:

• loose coupling between components
• asynchronous processing
• enhanced scalability
• complex debugging and testing

examples: iot systems, real-time analytics platforms, financial trading systems

service-oriented architecture (soa)⁂

encapsulates functionalities into reusable, interoperable services with standardized interfaces. this approach emphasizes service reusability and composition.

characteristics:

• business-aligned services
• standardized interfaces
• service reusability
• enterprise service bus (often)

examples: enterprise integration systems, banking platforms

core components of distributed systems⁂

typical distributed system components include:

key design considerations⁂

designing robust distributed systems requires addressing several critical concerns:

performance optimization⁂

• latency: minimize response time through caching, cdns, and optimized data access patterns
• throughput: maximize system capacity through horizontal scaling and efficient resource utilization
• network efficiency: reduce bandwidth consumption with compression, batching, and protocol optimization

consistency models⁂

• strong consistency: all nodes see the same data at the same time (e.g., linearizability)
• eventual consistency: system will become consistent given enough time without updates
• causal consistency: operations that are causally related appear in the same order to all nodes
• session consistency: client operations in a session are consistent with their own operations

data management⁂

• replication strategies: synchronous vs. asynchronous, active-active vs. active-passive
• sharding approaches: range-based, hash-based, directory-based partitioning
• data synchronization: conflict detection and resolution mechanisms

fault tolerance and recovery⁂

• failure detection: heartbeats, gossip protocols, and health checks
• redundancy: multiple instances, geographic distribution, and standby systems
• graceful degradation: maintaining core functionality during partial system failures
• self-healing mechanisms: automated recovery and repair procedures

security considerations⁂

• authentication and authorization: verifying identity and controlling access rights
• encryption: protecting data at rest and in transit
• network segmentation: limiting attack surfaces through isolation
• audit logging: recording security-relevant events for compliance and forensics

monitoring and observability⁂

• metrics collection: gathering performance and health indicators
• distributed tracing: following requests across service boundaries
• log aggregation: centralizing and analyzing system logs
• alerting: detecting and notifying about critical conditions