MongoDB Aggregation Query Example: Statistical Analysis of User Data

MongoDB aggregation queries are multi-stage data processing tools that transform and analyze collection documents through pipeline operations. They are suitable for scenarios such as statistics on user counts, ages, and order amounts. Taking the `users` collection as an example, basic stages include `$match` (filtering), `$group` (grouping), `$project` (field selection), `$sort` (sorting), `$unwind` (array expansion), and accumulators (e.g., `$sum`, `$avg`). Key examples: 1. Statistics on user counts by gender: `$group` groups by `gender`, `$sum:1` counts entries, and `$sort` sorts results; 2. Average age by region: `$match` filters users with age data, and `$group` calculates the average age; 3. Total user consumption: `$unwind` expands the order array, and `$group` accumulates amounts; 4. Multi-dimensional region statistics: `$group` simultaneously uses `$sum`, `$avg`, and `$max` to count users, calculate average age, and track maximum age. Core operations: filtering, grouping statistics, field processing, sorting and pagination. It is recommended to start with simple groupings and practice complex scenarios by referencing official documentation.