Today I focused on building the backend logic necessary to fully support our calendar-based shift display system in the new PHP/Laravel environment. This involved creating sophisticated data transformation layers and optimized query structures to ensure the calendar performs well even with large datasets.
I started by implementing specialized controllers for the various calendar data needs. The ShiftCalendarController now handles all requests related to calendar views, providing endpoints for different time ranges (day, week, month) and filtering options. Each endpoint utilizes query builders with eager loading to minimize database hits, addressing a performance issue we had with the previous Google Sheets implementation where separate queries were needed for related data.
A significant challenge was handling recurring shifts efficiently. In our previous system, recurring shifts were essentially duplicated records in Google Sheets, leading to data bloat and consistency issues when updates were needed. I implemented a more elegant solution using a base shift record with a recurrence rule pattern, then dynamically generating occurrences based on these rules when calendar views are requested. This approach significantly reduces storage requirements while making it easier to update all instances of a recurring shift simultaneously.
I also built a sophisticated query optimizer that adjusts how shifts are retrieved based on the current calendar view. For month views where potentially hundreds of shifts might be displayed, the system now retrieves minimal data initially (just enough to render the calendar events), then loads full details on demand when users interact with specific shifts. This lazy-loading approach dramatically improves the initial page load experience compared to our previous implementation.
For the timezone handling, I implemented a server-side conversion system that ensures shifts always display correctly regardless of the user’s local timezone. This required careful manipulation of datetime objects, storing everything in UTC internally but converting to appropriate local times for display. The new implementation properly handles edge cases like shifts that span across daylight saving time transitions, which occasionally caused display issues in our previous system.
I also created a caching layer for calendar data that intelligently invalidates when relevant shifts are modified. This cache significantly improves performance for views that users frequently return to, like the current week’s schedule. The cache implementation uses Redis for storage with carefully structured keys that include relevant filter parameters, ensuring users always see fresh data after changes while benefiting from cached responses when appropriate.
By the end of the day, I had completed the backend infrastructure necessary to support all our calendar visualization needs. The new implementation not only maintains feature parity with our original Google Apps Script system but significantly improves performance, especially for larger organizations with hundreds of shifts per week. Tomorrow I’ll focus on connecting these backend services to the frontend calendar components to complete the integration.