Deep Dive into Building Cloud Native Software for the Future
What is Cloud Native Software?
It refers to applications designed for a cloud computing architecture. Specifically, cloud native applications are developed to run on and take advantage of cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud.
The Key Characteristics of Software
This software has some key defining characteristics including:
- Scalability: Applications should be able to automatically scale up or down to handle varying amounts of traffic and workloads. This Cloud Native Software application to efficiently use compute resources.
- Containers: Its applications are packaged and run in lightweight containers rather than being deployed directly onto servers. This improves portability across clouds.
- Microservices: Large monolithic applications are broken down into independently deployable services that can be updated independently. This increases agility and provides fault isolation.
- Continuous Delivery: Changes to its applications should be able to be continuously and reliably deployed through automated pipelines in order to rapidly deploy features and fixes.
- Kubernetes: Kubernetes is an orchestrator that manages containers and has become the de facto standard for automating deployment and management of containers at scale.
- 12-factor App Principles: Cloud native applications adhere to 12-factor principles including storing config in environment variables and backing services with a stateless, front-end web server.
Designing a Application Architecture
When designing an application , the focus is on creating modular, independent services that communicate through well-defined APIs. Some key aspects to consider in the application architecture include:
- Identifying logical services: Break the application down into distinct services based on the problem domain, each with a specific purpose and business capability.
- Programming model: Cloud native apps typically use programming languages/frameworks well-suited for microservices like Java, Node.js, Python, Go.
- Data management: Services should not tightly couple to specific databases and instead connect to data sources through abstracted interfaces. Aim for eventual data consistency.
- APIs: Services communicate through REST APIs documented with OpenAPI/Swagger. Version APIs to maintain backwards compatibility when services change internally.
- Containers: Package each service into containers for portability and managing them as units.
- Automated deployments: Use tools like Kubernetes to automate deploying and scaling containerized services based on demand or failures.
- Observability: Monitor and collect logs, metrics, and traces across services for visibility into performance and troubleshooting.
- Resiliency: Build retry and timeout mechanisms into services to handle partial failures rather than failing open.
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