Thanks for the great feedback. We love to hear that response, we spend thousand of hours trying to make it Awesome!
Great question about the performance of loading based upon the size of the store and number of products. We’ve done a lot of optimization work to speed up loading as much as possible. You are correct having a good hosting plan and a good connection of the device to the internet are a very important factor in the loading performance.
You might also consider for such a large store, not showing the photos on tiles, as this will impact the loading. ??
In the end, loading the data does take time for the API to communicate with the server and transfer data, especially for 6,000+ products. Our optimization work has been performed on store sizes of 10,000 products.
A few things we’ve done to speed up the loading of large stores and allow large stores to perform fast and smooth during order transactions.
– Background loading. You might have noticed, during the loading screen we allow the user to switch to background loading. This allows the POS to load, but the rest of the products database load in the background. This allows initial operation of the POS while the product catalog syncs.
The functionality to switch to background loading as shown here:
https://www.loom.com/share/cafbff979c0e492cb46abed3c5ab189b
Note: This is not a large store, so you don’t notice the different in behavior after loading, but larger stores will continue to load after the initial load.
– Browser Indexeddb. During the syncing process, we are temporary storing data in the Indexeddb, this allows for large stores to run fast and smooth, avoiding any new loading operations after the initial loading sync. If you use the disabled logout feature, this will always allow the POS to be open and not require any new loading until a logout event occurs, or Force Sync to update is triggered.
We are always performing benchmarks on performance for loading and other actions in the POS. If you do encounter any issues around other functionalities for large stores, please report to us. The more data we have from large stores the better we can optimize the POS performance.
