Software Architecture
Distributed systems are collections of independent computers that appear to users as a single coherent system. These systems consist of multiple nodes (computers) that communicate and coordinate their actions by passing messages to achieve a common goal. Distributed systems have become increasingly important as applications grow in scale, requiring high availability, fault tolerance, and scalability beyond what a single machine can provide.
Modern distributed systems power everything from search engines and social networks to e-commerce platforms and financial systems. They enable organizations to process vast amounts of data, serve millions of users concurrently, and maintain services even when individual components fail.
The CAP theorem states that a distributed data store cannot simultaneously provide more than two out of the following three guarantees:
In practice, since partition tolerance is necessary for distributed systems, architects must choose between consistency and availability when partitions occur.
The most basic distributed architecture where clients request services from centralized servers. Examples include web applications, email services, and file servers.
A decentralized architecture where nodes have equal roles and share resources directly without a central server. Used in file sharing, blockchain, and some messaging systems.
An architectural style that structures an application as a collection of loosely coupled services. Each service is focused on a specific business capability and can be developed, deployed, and scaled independently.
A design pattern where components communicate through events. Producers emit events without knowledge of consumers, allowing for loose coupling and scalability.
An architectural pattern where services provide functionality through a communication protocol over a network. Services are autonomous, self-contained, and discoverable.
Challenge: Maintaining synchronized time across distributed nodes.
Solutions: Network Time Protocol (NTP), Logical clocks (Lamport timestamps), Vector clocks, Google's Spanner TrueTime API.
Challenge: Communication failures between nodes causing system fragmentation.
Solutions: Quorum-based systems, leader election algorithms, optimistic replication with conflict resolution.
Challenge: Balancing consistency, availability, and performance.
Solutions: Strong consistency (linearizability), Eventual consistency, Causal consistency, Session consistency, CRDT (Conflict-free Replicated Data Types).
Challenge: Detecting and resolving deadlocks across multiple systems.
Solutions: Timeout mechanisms, resource ordering, deadlock detection algorithms, deadlock avoidance strategies.
Here are some excellent resources for learning about distributed systems:
Technologies often used with or related to distributed systems: