Scalable application Design - Example app EasyWin

 

In this application design important design consideration discussed mainly to make sure at least 1k concurrent users can use this app and also required reporting regarding review of content and dashboards. Major design objective is to scale this app to work with more users and more content.

Request Queuing and processing:

BFF  end point can handle 1k requests which saves meta data to DB. upload file content needs queue and function app to process queue. advanced File upload component can add some scalable featured which will be discussed later.

Data synchronization

Review of content can be handled by multiple admin and needs process to schedule content and update dashboards . signal R will make sure review data reflecting properly to multiple admins reviewing same content.

Analytics and Reporting:

 Component BUZZ will promote content to different channels based on predefined rules. Rules r stored in DB but can be moved to rule engine in future.

Reports r generated using data bricks and power BI, dashboards will read data from Databricks processed analytics data.

Advanced AI capabilities

Content review can be automated by using azure AI capabilities like cognitive services and others. spam detection and adult content  filtering , duplicate content detection and other useful review parameters can be automated using AI capacities. which intern improve review process and accuracy of review.