Distributed Systems
A distributed system is a collection of independent computers that appear to their users as a single coherent system, coordinating over a network to achieve shared goals such as processing, storage, or availability that no single machine…
Definition
A distributed system is a collection of independent computers that appear to their users as a single coherent system, coordinating over a network to achieve shared goals such as processing, storage, or availability that no single machine could handle alone.
Overview
As applications grow beyond what a single machine can handle, or need to stay available even if individual machines fail, systems are built to span multiple networked nodes that coordinate to behave as one logical whole. This introduces fundamentally different challenges than single-machine programming: network calls can fail or be delayed unpredictably, nodes can crash independently, and there is no single shared clock or memory that all parts can instantly agree on. Much of distributed systems theory revolves around managing these realities. The CAP Theorem (CS) captures a core trade-off: a distributed system facing a network partition must choose between remaining fully consistent or fully available, but cannot guarantee both simultaneously. To keep multiple nodes in agreement despite failures, systems rely on Consensus Algorithms such as Raft Consensus or the classical Paxos Algorithm, which let a cluster of nodes agree on a single value or sequence of operations even when some nodes are slow or unreachable. Other recurring building blocks include data replication for durability and read scalability, sharding to partition data across nodes for write scalability, Load Balancing Algorithms to spread traffic evenly, and caching strategies to reduce load on backend stores. Modern distributed applications are frequently built using Microservices, where independently deployable services communicate over the network, amplifying both the benefits (independent scaling, fault isolation) and the challenges (partial failure, eventual consistency) inherent to distributed computing. Distributed systems concepts are central to virtually all modern large-scale software, and are core prerequisites for working effectively with cloud platforms and courses like Apache Kafka & Messaging and Kubernetes.
Key Concepts
- Coordinates multiple independent machines to act as one system
- Must tolerate partial failures, network delays, and message loss
- Governed by trade-offs described in the CAP theorem
- Relies on consensus algorithms to agree on shared state
- Uses replication for durability and read scalability
- Uses sharding/partitioning to scale writes across nodes
- Underlies modern microservices and cloud-native architectures
- Requires careful handling of consistency versus availability