Distributed architecture represents a paradigm shift in the design and deployment of computer systems. It fosters a decentralized approach to computing for enhanced scalability, fault tolerance, and performance. At its core, distributed architecture involves multiple interconnected nodes or computing entities collaborating to achieve a common goal. It distributes computing resources across a network, allowing for more efficient utilization of resources and improved responsiveness.

Understanding Distributed Systems

Distributed systems are a computer science and information technology paradigm where computing resources, data, and processes are spread across multiple interconnected locations. In traditional centralized systems, a single server or processing unit handles all tasks and operations. In contrast, distributed systems distribute the workload across interconnected devices.

In a distributed system, each node (individual device or server) operates independently but collaborates with others to achieve common goals. Communication and coordination between these nodes are crucial for the system to function seamlessly. The decentralization enhances scalability, fault tolerance, and overall system efficiency.

Organizational needs dictate the type of distributed system architecture implementation necessary for a given requirement. Four common types of distributed system architecture are:

• Client-server: In a client-server architecture, client devices request resources or services from centralized, specialized servers. This allows for centralized management while supporting many client devices.

• Peer-to-peer: Peer-to-peer networks allow devices to act as both clients and servers, sharing resources and workload between peers. This avoids centralized points of failure.

• 3-tier: 3-tier architecture separates presentation, application logic, and data storage into layers. This provides separation of concerns for scale and flexibility.

• N-tier: N-tier architecture expands on 3-tier by breaking application components into additional layers for manageability. This adds complexity but allows for very large scale implementations.

The type of architecture utilized depends on the scale, security and performance needs of the application. Loosely coupled architectures allow for more flexibility while tightly integrated architectures optimize performance.

Design Principles of Distributed Systems Architecture

Designing effective distributed systems requires careful consideration of various features and design elements to ensure seamless operation, scalability, and fault tolerance. Here are key features and design elements crucial for distributed systems:

Scalability

Scalability in distributed systems refers to the system's ability to handle an increasing amount of workload or growth in users and data without sacrificing performance. Achieving scalability is crucial for applications and services to meet the demands of a growing user base and handle varying workloads efficiently. There are two common approaches to scalability.

• Horizontal Scalability: Horizontal scalability–or "scaling out"–involves adding more nodes or servers to a distributed system. This approach is most common in cloud environments, where new instances can be added to a cluster automatically to handle increased demand.

• Vertical Scalability: Vertical scalability–or "scaling up"–involves increasing the resources (CPU, RAM, etc.) of existing nodes in the system. This is typically achieved either by adding to the CPU or memory capacity of an existing VM or by purchasing a machine with greater computing resources. Vertical scaling generally requires a much lower initial investment of time and financial resources. However, the primary drawback is that application performance becomes dependent on the resource capacity of a single machine. It introduces a single point of failure, places a hard limit on scalability, and can lead to performance bottlenecks if user traffic exceeds the capabilities of the existing node.

Fault Tolerance

Fault tolerance in distributed systems ensures the system's continued operation despite hardware failures, software errors, or network issues. It removes all single points of failure so the system operates normally even when any individual component malfunctions or becomes unavailable. Implementing fault tolerance requires meticulous architecture design and comprehensive planning for worst-case scenarios.
While the intricacies of such a system vary greatly depending on the nature of the application and its underlying infrastructure, there are several common elements.

Consistency

Consistency in distributed systems refers to the uniformity of data across multiple system nodes at any given time. Achieving consistency is challenging due to the inherent nature of distributed environments, where multiple nodes operate concurrently and independently.

Because of this, different consistency models have emerged:

• Strong consistency: All nodes in the system see the same data simultaneously. This model ensures that any read operation reflects the most recent write. However, this comes at the cost of higher system complexity that can also impact performance.

• Eventual consistency: Guarantees that, given enough time and no further updates, all nodes will converge to the same state. This allows temporary inconsistencies but ensures eventual agreement, which enables higher availability and better performance.

• Causal consistency: Preserves the causal relationship between operations. If operation A causally precedes operation B, all nodes will see B after A. This can be considered a compromise between strong and eventual consistency in terms of performance and complexity.

Linearizability

A stronger form of consistency that combines the principles of atomicity and real-time ordering. It ensures that operations appear to be executed instantaneously at some point between their invocation and response.

Consistency Protocols

To ensure a consistent view of data in distributed systems, various algorithms and mechanisms can be used:

• Two-phase commit (2PC): A protocol that ensures atomicity and consistency in distributed transactions by having a coordinator coordinate the commit or rollback of all participants.
• Paxos and Raft: Distributed consensus algorithms designed to ensure that a group of nodes can agree on a single value or sequence of values, even in the presence of failures.
• Quorum-based systems: Utilize a voting mechanism where a certain number of nodes must agree on an operation to be considered valid. Quorum systems help balance consistency and availability.

Conflict Resolution

In scenarios where concurrent updates lead to conflicts, distributed systems need mechanisms for conflict resolution. Techniques include last-write-wins, vector clocks, and application-level conflict resolution strategies.

What’s next

This is just a brief overview and doesn't include many important implementation details, such as partitioning/sharding, distributed system architecture components and anti-patterns and common traps.

If you are interested in learning the concept more deeply, visit the original Multiplayer guide - Distributed Systems Architecture: Tutorial & Best Practices.