12 essential distributed system patterns

 

If you design distributed systems, you cannot ignore these patterns

(Explained in 2 minutes) 👇

Here are 12 essential distributed system patterns every architect should know 👇

1. API Gateway
An API Gateway is a single entry point that sits between clients and your backend services. It acts as a reverse proxy that routes requests to the appropriate microservices.

2. Point To Point Async Integration
Point To Point Async Integration is a communication pattern where one service sends messages to another service through a message queue.

3. Publish/Subscribe
The Publish/Subscribe Pattern is an asynchronous messaging pattern where publishers send messages to a central message broker or event bus without knowing who will consume them.

4. Outbox Pattern
The Outbox Pattern ensures reliable event publishing by storing events in a database table (the outbox) within the same transaction as the business data changes.

5. CQRS
CQRS is a pattern that separates read operations (queries) from write operations (commands) by using different models for each. The write model handles business logic and data modifications, while the read model is optimized for querying and reporting.

6. Saga Pattern
The Saga Pattern manages distributed transactions across multiple services by breaking them into a sequence of local transactions. Each local transaction updates data within a single service and publishes an event or message to trigger the next step.

7. Sidecar Pattern
The Sidecar Pattern deploys a helper component alongside your main application container that provides supporting features like logging, monitoring, configuration, or networking capabilities.

8. Strangler Fig Pattern
The Strangler Fig Pattern is a gradual migration strategy that replaces a legacy system by incrementally building a new system around it.

9. Anti-Corruption Layer
The Anti-Corruption Layer Pattern creates a protective boundary between your domain model and external systems or legacy code.

10. Service Discovery
The Service Discovery Pattern enables services to find and communicate with each other dynamically without hardcoding network locations.

11. Sharding
The Sharding Pattern divides data horizontally across multiple databases or storage nodes, with each shard containing a subset of the total data.

12. Replication
The Replication Pattern creates multiple copies of data across different nodes or databases to improve availability, fault tolerance, and read performance.

Tomorrow, I'll share with 𝟭𝟴,𝟭𝟱𝟬+ people the detailed explanation of these patterns with visuals:
- How they work
- Benefits and Drawbacks
- Use Cases

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