Top 7 Most-Used Distributed System Patterns

In today's video,  we'll  explore the top 7 distributed system  patterns with real-world examples. They can help us design more  efficient and scalable systems. So, let's dive in!

First is 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 your application.

It acts as a "go-between" for your 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.

Next is Circuit Breaker Imagine a water pipe bursting in your house. The first thing you'd 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's Hystrix library uses this pattern. It ensures a more resilient system.

This pattern can be particularly  useful when dealing with microservices or cloud-based applications, where  failures are more likely to occur. The third pattern is CQRS, or Command  Query Responsibility Segregation CQRS is like having a restaurant with separate  lines for ordering food and picking up orders. By separating the write, or command,  and read, or query, 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. This pattern becomes especially valuable in  systems where read and write operations have different performance characteristics, with  different latency or resource requirements.

Next is Event Sourcing Think of Event Sourcing as keeping a  journal of the life 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 commit represents a change. With Event Sourcing, we can also  implement advanced features like time-travel debugging or replaying  events for analytics purposes. Number 5 is Leader Election.

Imagine a classroom of students  electing a class representative. In a distributed system, the Leader  Election pattern ensures only one node is responsible for a  specific task or resource. When a leader node fails, the  remaining nodes elect a new leader.

ZooKeeper and etcd 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. Next is PubSub.

The Publisher/Subscriber pattern is  like a newspaper delivery service. Publishers emit events without  knowing who'll receive them, and subscribers listen for  events they're interested in. This pattern allows for better  scalability and modularity.

For example, Google Cloud Pub/Sub enables  asynchronous messaging between services, making it easier to maintain  and scale 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. Number 7 is Sharding.

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.

Here is a bonus pattern -  the Strangler Fig pattern. This pattern is inspired  by the strangler fig tree, which grows around other trees  and eventually replaces them. In software, the Strangler Fig 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  the old system with new components. This approach can help us manage the risks and  complexities associated with system migrations. And that's it on this topic!

These top 7 distributed system patterns,  along with their real-world examples, can help us build more robust  and scalable applications. Remember, understanding our specific system  needs and applying suitable patterns is crucial. Let’s leverage these patterns in our designs  to create better distributed systems.

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