Implement a new aggregation pipeline in the statistics module that groups export events by month and user ID, enabling generation of monthly activity reports for administrators. The change adds a `getMonthlyExportStats` function that queries the exports collection with a $match stage filtering on the last 12 months, followed by $group on year-month and userId, and a $sort stage for chronological ordering. This provides the backend support for the upcoming admin dashboard export statistics widget.
- Implemented a new statistics module to record export events
- Added database migration for export_logs table with user_id and timestamp
- Updated export handler to increment counter on successful completion
Implement a new statistics module that records export operations per month,
including total revenue, item count, and average order value. The module
aggregates data from the orders and shipments tables, grouping results by
calendar month for reporting purposes.
The export statistics module now supports quarterly data aggregation and regional filtering parameters. The `ExportStats` class has been extended with new methods `getQuarterlyBreakdown()` and `filterByRegion()`. The database schema migration adds a `region` column to the `export_stats` table. The API endpoint `/api/export-stats` now accepts optional `quarter` and `region` query parameters.
- Implemented a new statistics module to record and monitor user export activities
- Added database schema changes to store export timestamps and file sizes
- Updated export controller to log each export event with relevant metadata
- Created API endpoint for retrieving aggregated export statistics
Implement a new export statistics module that aggregates monthly export data including total revenue, shipment counts, and per-category breakdowns. The module introduces a scheduled task that runs on the 1st of each month to compute and store statistics from the orders and shipments tables, enabling historical trend analysis and reporting.
The change introduces a new aggregation pipeline in the statistics module that computes monthly export counts grouped by product category. It adds a dedicated `ExportStatistic` model with fields for period, category, and total exports, and wires the aggregation into the existing admin statistics endpoint. The implementation uses MongoDB's `$group` and `$project` stages to transform raw export logs into the required summary format, ensuring the dashboard can display per-category trends over time.
The change introduces a new aggregation pipeline in the statistics module that groups export records by month, computing total exports per user and average file size. This data is stored in a dedicated `monthly_export_stats` collection to support the upcoming analytics dashboard feature.
Implement a new aggregation pipeline in the statistics module that groups export events by month and user ID, computing total exports per user per month. The change introduces a `getMonthlyExportStats` function that queries the exports collection with a `$group` stage on `userId` and `month` fields, returning sorted results by month descending. This enables the dashboard to display per-user export volume trends over time.
Implement a new export statistics module that records and aggregates metrics on user data export operations. The change introduces a dedicated statistics service that tracks export frequency, data volume, and user engagement patterns. This enables administrators to monitor export activity and optimize system performance based on usage trends.
Implement a new statistics module that records export operations per month,
including total revenue, item count, and average order value. The module
aggregates data from the orders and shipments tables, grouping results by
calendar month for reporting purposes.
Implement a new logging utility with configurable verbosity levels and integrate a rant_history data structure to persist user interaction records across sessions. The logging module includes timestamped entries with severity tags, while rant_history stores chronological entries with metadata for future analysis and replay features.
