Distributed Systems
Computing systems composed of multiple independent components located on different networked computers that coordinate to achieve a common goal.
Also known as: Distributed Computing, Distributed Architecture
Category: Software Development
Tags: architecture, software-engineering, systems, scalability, infrastructure, computer-science
Explanation
A distributed system is a collection of autonomous computing elements that appears to users as a single coherent system. Components communicate and coordinate through message passing over a network, working together to accomplish tasks that no single computer could handle alone. Core characteristics include: (1) Concurrency - multiple components execute simultaneously, (2) No global clock - components must coordinate without shared time reference, (3) Independent failures - components can fail independently, (4) Heterogeneity - components may use different hardware, operating systems, and languages. Fundamental challenges include: Network Partitions (components becoming unreachable), Latency (communication delays), Consistency (keeping data synchronized), Ordering (determining event sequences without global clock), and Fault Tolerance (handling component failures gracefully). Key theoretical foundations: CAP Theorem (you can only guarantee two of: Consistency, Availability, Partition tolerance), Byzantine Fault Tolerance (handling malicious nodes), Consensus protocols (Paxos, Raft) for agreement. Common patterns: Replication (copies of data for availability), Sharding/Partitioning (dividing data across nodes), Load Balancing (distributing work), Circuit Breaker (preventing cascade failures), Eventual Consistency (accepting temporary inconsistency for availability). Types include: client-server, peer-to-peer, cluster computing, and cloud computing. Distributed systems power modern infrastructure: databases (Cassandra, CockroachDB), messaging (Kafka), caches (Redis Cluster), container orchestration (Kubernetes), and microservices architectures. While they enable scalability and fault tolerance, the complexity introduced requires careful design and operational expertise.
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