As a system designer, one of the most important decisions you have to make is choosing the right architecture and design patterns for your system. Different patterns serve different purposes, and selecting the right one can greatly impact the performance, scalability, and maintainability of your system.
In this post, we will explore the top 10 system design patterns and when to use them. This list is not exhaustive, and there are many other patterns that you can use depending on your specific requirements and goals. However, these patterns are among the most commonly used and widely applicable, and they can serve as a useful starting point for your own design decisions.
Client-server:
The client-server pattern is a common architecture for distributed systems, where clients send requests to a server and the server responds with the requested data or services. This pattern is used in many web and mobile applications, where the client (e.g. a web browser or a mobile app) sends requests to a server (e.g. a web server or an API server) and the server provides the data or functionality.
Microservices:
The microservices pattern is a variation of the client-server pattern, where a system is composed of multiple, independent services that communicate with each other through well-defined interfaces. This pattern allows for greater modularity, flexibility, and scalability, and it is often used in large, complex systems where a monolithic architecture would be difficult to maintain and evolve.
Pipeline:
The pipeline pattern is a design pattern for data processing systems, where data is passed through a series of stages or steps, each of which performs a specific transformation or operation on the data. This pattern is used in many ETL (extract, transform, load) systems, where data is extracted from multiple sources, transformed into a common format, and loaded into a target data store.
Publish-subscribe:
The publish-subscribe pattern is a communication pattern for decoupling producers and consumers of data or events. In this pattern, producers send data or events to a message broker, and consumers subscribe to the data or events that they are interested in. This pattern is used in many distributed systems and messaging systems, where it enables asynchronous communication and scalability.
Load balancer:
The load balancer pattern is a design pattern for distributing workloads across multiple servers or instances. In this pattern, a load balancer receives incoming requests and routes them to one of the servers or instances based on various criteria, such as the availability of the servers or the workload they are handling. This pattern is used in many scalable, highly-available systems, where it helps to distribute the workload evenly and avoid overloading any single server or instance.
Caching:
The caching pattern is a design pattern for improving the performance of a system by storing frequently-used data in memory or a fast storage medium. In this pattern, data that is accessed frequently or has a high latency when retrieved from the primary data store is stored in the cache, where it can be accessed more quickly and efficiently. This pattern is used in many systems that require low-latency access to data, such as web applications or real-time data processing systems.
Sharding:
The sharding pattern is a design pattern for partitioning a large dataset or workload across multiple servers or instances. In this pattern, the data or workload is divided into smaller units called shards, which are distributed across the servers or instances. This pattern is used in many systems that have to handle large amounts of data or handle a high volume of requests, where a single server or instance would not be able to handle the workload efficiently.
Event sourcing:
The event sourcing pattern is a design pattern for storing and managing data in a system. In this pattern, data is stored as a sequence of events or changes, rather than the current state of the data. This pattern is used in many systems that need to maintain a history of changes or provide auditing or undo/redo functionality, such as financial systems or content management systems.
Command query responsibility segregation (CQRS):
The CQRS pattern is a design pattern for separating the read and write operations in a system. In this pattern, the system is divided into two parts: the command side, which handles write operations, and the query side, which handles read operations. This pattern is used in many systems that have high read-and-write ratios, or where the read-and-write operations have different performance or consistency requirements, such as e-commerce or social networking systems.
Materialized views:
The materialized views pattern is a design pattern for optimizing the performance of read operations in a system. In this pattern, pre-computed, denormalized views of the data are stored in a separate data store, and the system retrieves the data from the materialized views rather than computing it on the fly. This pattern is used in many systems that have complex or expensive read queries, or where the data is frequently accessed or updated, such as data warehousing or analytics systems.
These are some of the most commonly used and widely applicable system design patterns. You can use them as a starting point for your own design decisions, and you can also combine or adapt them to fit the specific requirements and goals of your system. It’s important to remember that there is no one-size-fits-all solution, and the right pattern for your system will depend on your specific needs and constraints.