Distributed Algorithms
Distributed algorithms are computational procedures designed to run on multiple interconnected processors or nodes in a distributed system, where no single node has complete information about the global state. They enable coordination, communication, and problem-solving across decentralized networks, handling challenges like concurrency, failures, and network delays. Common examples include consensus algorithms (e.g., Paxos, Raft), leader election, and distributed data structures.
Developers should learn distributed algorithms when building scalable, fault-tolerant systems such as cloud services, blockchain networks, or distributed databases, where tasks must be coordinated across multiple machines. They are essential for ensuring consistency, availability, and partition tolerance in distributed environments, as described by the CAP theorem, and are critical in fields like microservices, IoT, and peer-to-peer applications.