DEV Community

Mukesh Kuiry
Mukesh Kuiry

Posted on

Top 5 Distributed System Design Pattern to ace SD interviews

Hey folks! πŸ‘‹ Welcome back to the System Design series by @mukeshkuiry! πŸš€

In today's post, we're delving into essential distributed system design patterns that serve as the building blocks for robust architectures.

Design patterns are like the DNA of a system, providing reusable solutions to common problems. They fall into categories such as Creational, Structural, and Behavioral, each offering a structured approach to specific aspects of system design.

Types of Distributed System Design Patterns

Most distributed system design patterns can be categorized based on their functionality into three main groups:

  • Object Communication: This category encompasses patterns that define messaging protocols and permissions for components to communicate within the system. Efficient communication is crucial for distributed systems to function seamlessly.

  • Security: Security patterns focus on handling concerns related to confidentiality, integrity, and availability. Ensuring the system's protection from unauthorized access and data breaches is paramount in distributed environments.

  • Event-driven: These patterns revolve around the production, detection, consumption, and response to events within the system. Event-driven design is essential for building responsive and scalable distributed architectures.

Top-5 Distributed System Design Patterns

In the intricate realm of distributed system design, selecting the right patterns is akin to choosing the foundational elements of a skyscraper. Among the myriad options, five patterns have emerged as stalwarts, each addressing specific challenges and laying the groundwork for robust and scalable architectures. In this exploration, we delve into the nuances of these patterns, understanding their principles and uncovering the scenarios where their application proves most potent.

1. Command and Query Responsibility Segregation (CQRS) πŸ”„

The CQRS pattern is centered around segregating the read and write operations of a distributed system. It employs commands for writing data and queries for fetching data. This separation enhances scalability and flexibility in handling data-intensive applications.

CQRS is particularly effective in scenarios where the write and read operations have distinct requirements. It finds widespread use in microservices architectures, especially in applications dealing with complex data structures or requiring real-time analytics.

2. Two-Phase Commit (2PC) βš–οΈ

Similar to CQRS, 2PC operates with a transactional approach and a reliance on a central command. It involves two phases: Prepare, instructing services to ready the data, and Commit, signaling services to send the prepared data. This pattern is ideal for high-risk transaction operations that prioritize accuracy.

2PC is best suited for distributed systems where ensuring the consistency of transactions is critical. It's commonly employed in financial applications and systems dealing with mission-critical operations, where data accuracy is paramount even at the expense of some resource efficiency.

3. Saga 🌐

Saga is an asynchronous service that refrains from using central commands for transactions. Instead, it leverages an event bus between microservice objects to perform transactions. Local transactions are created between services, emitting events for others to receive and respond to.

Saga's decentralized approach shines in serverless functions handling numerous parallel requests. It's extensively used in cloud environments, such as AWS, where services like Step and Lambda functions benefit from its event-driven, distributed design.

4. Replicated Load-Balanced Services (RLBS) πŸ”„βš–οΈ

RLBS is a straightforward yet widely used design pattern involving multiple identical services reporting to a central load balancer. Each service can handle tasks and replicate if they fail. The load balancer distributes requests from end-users, ensuring efficiency and fault tolerance.

RLBS is particularly effective for front-facing systems that experience varying workloads throughout the day. Popular platforms like Netflix and Amazon Prime leverage RLBS to maintain low latency and a seamless user experience during peak usage times.

5. Sharded Services πŸ”—

Sharded Services offer an alternative to replica-based designs by creating services that handle specific types of requests. The request flow is split into multiple unequal sections or "shards," each designed for a particular kind of request.

Sharded Services are beneficial when a system experiences a predictable imbalance in request types with varying priorities. For instance, in an e-commerce platform, sharding could be implemented to handle different types of transactions, such as regular purchases and high-priority deliveries.

These distributed system design patterns provide a structured foundation for architects and developers, offering solutions to common challenges in building scalable, reliable, and efficient distributed systems. Stay tuned for more insights in our System Design series! πŸš€πŸ’‘

Top comments (0)