How Redis Simplifies Microservices Design Patterns

How Redis Simplifies Microservices Design Patterns

Microservice architecture can be a game-changer to beat the competition to market and reduce barriers for an organization’s cloud migration.

Microservice architecture continues to grow in popularity, yet it is widely misunderstood. While most conceptually agree that microservices should be fine-grained and business-oriented, there is often a lack of awareness regarding the architecture’s tradeoffs and complexity. For example, it’s common for DevOps architects to associate microservices to Kubernetes, or an application developer to boil implementation down to using Spring Boot. While these technologies are relevant, neither containers nor development frameworks can overcome microservice architecture pitfalls on their own — specifically at the data tier.

Martin FowlerChris Richardson, and fellow thought-leaders have long addressed the trade-offs associated with microservice architecture and defined characteristics that guide successful implementations. These include the tenetsof isolation_empowerment of autonomous team_sembracing eventual consistency, and infrastructure automation. While keeping with these tenets can avoid the pains felt by early adopters and DIYers, the complexity of incorporating them into an architecture amplifies the need for best practices and design patterns — especially as implementations scale to hundreds of microservices.

With Redis rapidly becoming a staple across microservice architecture, it’s worth discussing how it can simplify the implementation of design patterns — such as bounded contexts, asynchronous messaging, choreography-based sagas, event-sourcing, CQRS, telemetry, and more.

At Kafka Summit 2020, I hosted a session on this topic. It detailed how the tenets of successful implementations supported the primary drivers for microservice architecture adoption — including improved time-to-market _and more recently _becoming cloud native. We’ll build on that foundation throughout this article, so feel free to watch the recorded presentation in case you need a refresher, prefer an audio version, or are new to microservices.

Design patterns are best visualized, so let’s start with a diagram…

The following architectural diagram is a composition of microservice design patterns. If it seems busy and complex, don’t be discouraged. We will decompose this mockup of an event-driven payment-processing workflow into its many embedded design patterns. We’ll discuss the challenges solved by each pattern and how their implementation can be simplified by using Redis. Since certain patterns are foundational to the implementation of others, we will cover them in an order that allows them to build upon each other.

By the end of this article, readers will be able to identify microservice design patterns where they once saw chaos. Think of it like Neo eventually seeing the code behind the Matrix. For now:

cloud native data microservices contributed sponsored

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

How native is React Native? | React Native vs Native App Development

Article covers: How native is react native?, React Native vs (Ionic, Cordova), Similarities and difference between React Native and Native App Development.

Multi-cloud Spending: 8 Tips To Lower Cost

Mismanagement of multi-cloud expense costs an arm and leg to business and its management has become a major pain point. Here we break down some crucial tips to take some of the management challenges off your plate and help you optimize your cloud spend.

Building A Cloud-Native, Cloud-Agnostic Data lake

A growing number of organizations now seek flexibility to implement their solutions on any of the widely available public/private cloud platforms.

Applications Of Data Science On 3D Imagery Data

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.

What are the benefits of cloud migration? Reasons you should migrate

To move or not to move? Benefits are multifold when you are migrating to the cloud. Get the correct information to make your decision, with our cloud engineering expertise.