Distributed Systems
A distributed-systems architecture consists of multiple independent computers that communicate and coordinate their actions by passing messages. This design is fundamental for building modern applications that require high scalability and fault-tolerance. According to Andrew-S.-Tanenbaum, a distributed system is a collection of autonomous computing elements that appears to its users as a single coherent system. For further exploration, the textbook Distributed Systems provides extensive theoretical depth.
Theoretical Frameworks
The CAP-theorem, first proposed by Eric-Brewer, states that a distributed data store can only simultaneously provide two out of three guarantees: consistency, availability, and partition-tolerance. In addition to CAP, the PACELC-theorem describes the trade-offs between latency and consistency even in the absence of partitions. Detailed analysis of these trade-offs can be found via the PACELC Theorem Documentation.
Consensus and Coordination
Maintaining state across multiple nodes requires consensus algorithms. The Paxos protocol and the Raft algorithm are the primary mechanisms used to ensure that all non-faulty nodes agree on a single data value. These algorithms are critical for the operation of distributed-databases such as Apache-Cassandra and Google-Spanner.
Communication and Middleware
Nodes within a distributed-systems environment often interact through Remote-Procedure-Call (RPC) or Message-Queuing systems. These abstractions allow developers to build complex microservices without managing the underlying network complexities manually.