Magestore use RAIL model to evaluate the performance based on user experience:
First Content Full Paint: < 0.37s
Largest Contentful Paint: < 2.39s
Time to Interactive: < 1.00s
Fully Loaded Time: < 3.00s
Front-End: Base on User Experience - we load cache on the browser to make sure the user experience feels smooth and it will not be affected by the sizing of systems such as the number of orders, customers or SKUs.
Back-End: Scaling for system sizing - we design infrastructure architecture scaling according to system sizing such as number of order, customer or SKU; so that the system response and processing time is not affected as well.
According to POS Local Architecture, each POS will have Local Data to store Master Data and Transaction Data which is synchronized every 5 minutes with Magento. Therefore the speed of data uploading from their Local Data is quite high even when the network is disrupted. This is to avoid any risks that could happen in the operation process when the store’s internet is unstable.
This architecture is designed to make sure each POS has a smooth-feeling user experience, which, again, will not be affected by the size of systems such as number of orders, customers or SKUs.
We implement Magento based on AWS recommend model. So that when the system has a bigger volume in terms of number of orders, customers or SKUs; the infrastructure instance will be scaled up at the same time to adapt and enhance system processing speed.
We’ve performed some test cases on the testing environment so that we can simulate some stressful situations, such as when 300 POS users log in and make orders at the peak time of store opening. With this test case, we can observe that 1 server instance can handle concurrent 300 POS users to make 1,000 orders. The more POS and users you have, the more server instance (according to AWS recommend model ) needs to be implemented for scaling!!!
For Magestore Solution, we understand the struggle of keeping track of your system, so we come up with a feature set so you can easily monitor and check the performance of each POS instantly, detect if any errors or low-speed POS is there in order to find a solution and resolve it quickly.