Cloud Architecture Patterns

There are various patterns defined in cloud space based on our requirements as below :

Horizontally Scaling Compute Pattern. This simple pattern implies that an application ready to handle auto-of-the-box increase or decrease in compute capacity. The Horizontal Scaling Compute Pattern architecturally aligns applications with the most cloud-native approach for resource allocation.

Database Sharding. When using the Database Sharding Pattern, workloads can be distributed over many database nodes rather than concentrated in one. This helps overcome size, query performance, and transaction throughput limits of the traditional single-node database.

Queue-Centric Workflow Pattern. Please use queues/exchanges/streams to decouple components and increase elasticity. You can have multiple queuer-s and de-queuer-s to work with queues that don’t know anything about each other and can horizontally scale.

Auto-Scaling Pattern. The Auto-Scaling Pattern is an essential operations pattern for automating cloud administration. By automating routine scaling activities, cost optimisation becomes more efficient with less effort.

Busy Signal Pattern. Handling transient failures is essential for building reliable cloud-native applications. Using the Busy Signal Pattern, your application can detect and handle transient failures appropriately.

CDN Pattern. Adding CDN support to a cloud application is a great example of a low-friction adoption of a cloud service. Enabling a CDN can be accomplished either programmatically or through a one-time manual configuration via the cloud vendor’s web-hosted management tool.

All above patterns are always first choose for any cloud architect to design a solution for given requirement.

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