sundynix-pets/pets-be/internal/service/admin_analytics.go

package service

import (
	"time"

	"github.com/sundynix/pets-be/internal/model"
)

// 后台看板的分析数据。真实用户口径（排除 bot），活跃口径=当天产生过
// 健康记录/发帖/评论中的任意一种。数据量在预生产规模下很小，直接拉进内存算，
// 省掉一堆按天分组的 SQL。

// DayPoint 折线/柱状的一个点。Date 视粒度是 MM-DD 或 YYYY-MM
type DayPoint struct {
	Date  string `json:"date"`
	Count int    `json:"count"`
}

// RetPoint 留存曲线一个点：注册后第 Day 天仍活跃的比例
type RetPoint struct {
	Day  int     `json:"day"`  // 注册后天数
	Rate float64 `json:"rate"` // 0~1
	Base int     `json:"base"` // 该口径下够“年龄”的用户数（分母）
}

type Analytics struct {
	Summary struct {
		TotalUsers   int `json:"total_users"` // 真实用户总数
		NewToday     int `json:"new_today"`
		NewMonth     int `json:"new_month"`
		DAU          int `json:"dau"` // 今日活跃
		WAU          int `json:"wau"` // 近 7 日活跃
		MAU          int `json:"mau"` // 近 30 日活跃
		TotalPets    int `json:"total_pets"`
		TotalPosts   int `json:"total_posts"`
		TotalRecords int `json:"total_records"`
	} `json:"summary"`
	NewTrend    []DayPoint `json:"new_trend"`    // 每日新增，近 days 天
	ActiveTrend []DayPoint `json:"active_trend"` // 每日活跃(DAU)，近 days 天
	MauTrend    []DayPoint `json:"mau_trend"`    // 月活，近 6 个月
	Retention   []RetPoint `json:"retention"`    // 留存曲线
}

// dayKey 把时间压成 yyyymmdd 整数，单调，便于比较和当 map key
func dayKey(t time.Time) int {
	return t.Year()*10000 + int(t.Month())*100 + t.Day()
}

// AdminAnalytics days=趋势天数（默认 30，封顶 180）
func (s *Service) AdminAnalytics(days int) (*Analytics, error) {
	if days <= 0 || days > 180 {
		days = 30
	}
	loc := time.Local
	now := time.Now().In(loc)
	today := time.Date(now.Year(), now.Month(), now.Day(), 0, 0, 0, 0, loc)

	out := &Analytics{}

	// —— 真实用户（排除 bot）——
	type uRow struct {
		ID        string
		CreatedAt time.Time
	}
	var users []uRow
	s.db.Model(&model.User{}).
		Where("is_bot IS NULL OR is_bot = 0").
		Select("id, created_at").Scan(&users)

	signup := make(map[string]int, len(users)) // userID -> 注册日 dayKey
	signupTime := make(map[string]time.Time, len(users))
	ids := make([]string, 0, len(users))
	todayKey := dayKey(today)
	monthStart := dayKey(time.Date(today.Year(), today.Month(), 1, 0, 0, 0, 0, loc))
	for _, u := range users {
		t := u.CreatedAt.In(loc)
		signup[u.ID] = dayKey(t)
		signupTime[u.ID] = t
		ids = append(ids, u.ID)
		k := dayKey(t)
		if k == todayKey {
			out.Summary.NewToday++
		}
		if k >= monthStart {
			out.Summary.NewMonth++
		}
	}
	out.Summary.TotalUsers = len(users)

	// —— 活跃事件：近 180 天，真实用户的 记录/帖子/评论 ——
	windowStart := today.AddDate(0, 0, -180)
	type ev struct {
		UserID    string
		CreatedAt time.Time
	}
	// userID -> 活跃日集合；以及每日活跃用户集合（供 DAU/MAU 用）
	userActive := make(map[string]map[int]bool)
	dayUsers := make(map[int]map[string]bool)
	mark := func(table string) {
		if len(ids) == 0 {
			return
		}
		var rows []ev
		s.db.Table(table).Select("user_id, created_at").
			Where("created_at >= ?", windowStart).
			Where("user_id IN ?", ids).Scan(&rows)
		for _, r := range rows {
			k := dayKey(r.CreatedAt.In(loc))
			if userActive[r.UserID] == nil {
				userActive[r.UserID] = map[int]bool{}
			}
			userActive[r.UserID][k] = true
			if dayUsers[k] == nil {
				dayUsers[k] = map[string]bool{}
			}
			dayUsers[k][r.UserID] = true
		}
	}
	mark("sundynix_health_records")
	mark("sundynix_posts")
	mark("sundynix_comments")

	// distinctInRange 统计 [fromDay, toDay] 内的去重活跃用户
	distinctInRange := func(from, to time.Time) int {
		seen := map[string]bool{}
		for d := from; !d.After(to); d = d.AddDate(0, 0, 1) {
			for u := range dayUsers[dayKey(d)] {
				seen[u] = true
			}
		}
		return len(seen)
	}
	out.Summary.DAU = len(dayUsers[todayKey])
	out.Summary.WAU = distinctInRange(today.AddDate(0, 0, -6), today)
	out.Summary.MAU = distinctInRange(today.AddDate(0, 0, -29), today)

	// —— 趋势：新增 + DAU，近 days 天 ——
	for i := days - 1; i >= 0; i-- {
		d := today.AddDate(0, 0, -i)
		k := dayKey(d)
		label := d.Format("01-02")
		newN := 0
		for _, sk := range signup {
			if sk == k {
				newN++
			}
		}
		out.NewTrend = append(out.NewTrend, DayPoint{Date: label, Count: newN})
		out.ActiveTrend = append(out.ActiveTrend, DayPoint{Date: label, Count: len(dayUsers[k])})
	}

	// —— 月活：近 6 个月 ——
	for i := 5; i >= 0; i-- {
		mStart := time.Date(today.Year(), today.Month(), 1, 0, 0, 0, 0, loc).AddDate(0, -i, 0)
		mEnd := mStart.AddDate(0, 1, -1)
		out.MauTrend = append(out.MauTrend, DayPoint{
			Date:  mStart.Format("2006-01"),
			Count: distinctInRange(mStart, mEnd),
		})
	}

	// —— 留存曲线：注册后第 K 天仍活跃（滚动口径：K 天当天或之后还有活跃）——
	// 分母只算“年龄”够 K 天、且在观测窗内的用户
	offsets := []int{1, 3, 7, 14, 30}
	minSignup := dayKey(windowStart)
	for _, K := range offsets {
		base, retained := 0, 0
		for id, sk := range signup {
			if sk < minSignup {
				continue // 太老，活跃窗看不全
			}
			plusK := dayKey(signupTime[id].AddDate(0, 0, K))
			if plusK > todayKey {
				continue // 还没到第 K 天，不够“年龄”
			}
			base++
			for ak := range userActive[id] {
				if ak >= plusK {
					retained++
					break
				}
			}
		}
		rate := 0.0
		if base > 0 {
			rate = float64(retained) / float64(base)
		}
		out.Retention = append(out.Retention, RetPoint{Day: K, Rate: rate, Base: base})
	}

	var pets, posts, records int64
	s.db.Model(&model.Pet{}).Count(&pets)
	s.db.Model(&model.Post{}).Count(&posts)
	s.db.Model(&model.HealthRecord{}).Count(&records)
	out.Summary.TotalPets = int(pets)
	out.Summary.TotalPosts = int(posts)
	out.Summary.TotalRecords = int(records)
	return out, nil
}