10 Must Know Distributed System Patterns

4 min readJun 14, 2023

Distributed patterns can help us design more efficient and scalable systems, so let’s dive right in.

1. Ambassador

Picture yourself as a busy CEO with a personal assistant who handles all your appointments and communication.
That’s precisely what the Ambassador pattern does for our application. It acts as a go between for our app and the services it communicates with, offloading tasks like logging, monitoring, or handling retries.
For instance, Kubernetes uses Envoy as an ambassador to simplify communication between services.
The Ambassador pattern can help reduce latency, enhance security, and improve the overall architecture of your distributed systems.

Imagine a water bite bursting in your house. The first thing you would do is shut off the main valve to prevent further damage.
The circuit breaker pattern works similarly, preventing cascading failures in distributed systems. When a service becomes unavailable, the circuit breaker stops requests allowing it to recover.
Netflix Hystrix library uses this pattern. It ensures a more resilient system.
Now this pattern can be particularly useful when dealing with microservices or cloud based applications where failures are more likely to occur.

In software architecture, the Bulkhead pattern involves dividing the system into separate compartments, or “bulkheads,” where each compartment contains a set of resources or services. By isolating these compartments, failures or overloads in one compartment are contained within that compartment and do not propagate to other parts of the system.
It is especially useful in distributed systems where failures or performance issues in one component can potentially affect other components.

CQRS is having a restaurant with separate lines for ordering food and picking up orders by separating the command or write operations from the query or read operations.
We can scale and optimize each independently. An e-commerce platform might have high read requests for product listings, but fewer write requests for placing orders. CQRS allows each operation to be handled efficiently.
These patterns become especially valuable in systems where read and write operations have different performance characteristics with different latency or resource requirements.

Think of Event Sourcing as keeping a journal of the live events. Instead of updating a record directly, we store events representing changes.
This approach provides a complete history of the system and enables better auditing and debugging. Git Version control is a great example of event sourcing where each commits represents a change now with event sourcing.

Imagine a classroom of students electing a class representative in a distributor system.
The leader election pattern ensures only one node is responsible for a specific task or resource. When the leader node fails, the remaining nodes elect a new leader.
Use this pattern to manage distributed configurations. By having a designated leader, we can avoid conflicts and ensure consistent decision making across the distributed system.

Publisher/Subscriber pattern is like a newspaper delivery service. Publishers emit events without knowing who will receive them, and subscribers listen for events they’re interested in.
This pattern allows for better scalability and modularity.
Complex applications pub/sub systems are well suited for scenarios where we need to propagate changes or updates across multiple components. For example, updating a user’s profile across various services.

Sharding is like dividing a large pizza into smaller slices, making it easier to handle. It’s a technique for distributing data across multiple nodes in a system.
It improves performance and scalability. Each Shard contains a subset of the data, reducing the load on any single node.
Databases like MongoDB and Cassandra use sharding to handle large amounts of data efficiently.
Sharding can also help us achieve better data locality, reducing network latency and speeding up query execution.

This pattern is inspired by the Strangler fig tree, which grows around other trees and eventually replaces them. In software, the Strangler Pattern is a method for gradually replacing legacy systems with new implementations.
Instead of performing a risky Big Bang migration, we can incrementally replace parts of this old system with new components.
This approach can help us manage the risk and complexities associated with system migrations.

Distributes incoming network traffic across multiple servers to improve system performance, scalability, and availability.
The goal is to prevent any single server from becoming overloaded while maintaining smooth and reliable service for users.

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