feat(stats): 新增公开数据页 /stats，入口收敛到页脚

首页底部那张「站点统计」卡片下线，完整数据移到公开只读页 /stats：今日实时数、概览六格、近 30 天折线与 12 个月柱状、三个占比环、 7×24 时段热力图、提问字数与回答耗时分布。 - 聚合在 internal/db/analytics.go（SUM(CASE WHEN)，方言差异收敛在 analyticsDialectFor） - 结果按自然日缓存，跨日重算一次后全站共用；重算失败降级返回上一份快照 - 图表零前端依赖：服务端算坐标的 SVG + CSS grid，禁用 JS 页面仍完整 - 环比取「上月 vs 上上月」：本月未过完，否则每月 1 号会得出 -95% - 入口只在页脚「社区规则」与「找站长」之间；route.Home 不再计算统计 - 补 questions(created_at) 索引；启动后异步预热当天快照 - 测试：analytics 连真库验证软删除不计入、分桶下标、DOW 对齐
2026-10-24 00:57:08 +08:00
parent c680671b7e
commit a5441c99fd
25 changed files with 2280 additions and 80 deletions
@@ -0,0 +1,315 @@
+// Copyright 2022 E99p1ant. All rights reserved.
+// Use of this source code is governed by a MIT-style
+// license that can be found in the LICENSE file.
+
+package db
+
+import (
+	"context"
+	"fmt"
+	"time"
+
+	"github.com/pkg/errors"
+	"gorm.io/gorm"
+)
+
+// SiteAnalyticsStore 提供「全站公开数据页」所需的聚合查询。
+//
+// 与 internal/stats 的分工：本层只做数据库聚合（纯查询，不缓存、不生成文案），
+// 缓存、补齐空日期与展示模型都在 stats 层。这样聚合 SQL 可以脱离 HTTP 单测。
+type SiteAnalyticsStore interface {
+	SiteAnalytics(ctx context.Context, now time.Time) (*SiteAnalytics, error)
+}
+
+// Analytics 全站聚合查询的包级入口，在 Init 中随其他 store 一起初始化。
+var Analytics SiteAnalyticsStore
+
+// SiteAnalytics 是一次全站聚合的原始结果，所有计数都是「累及至今」或「窗口内」的口径，
+// 具体窗口由调用方传入的 now 决定（见 stats 包的 Overview）。
+type SiteAnalytics struct {
+	RegisteredUsers int64
+	TotalQuestions  int64
+	Answered        int64
+	Private         int64
+	Read            int64
+	Anonymous       int64
+	ActiveBoxes     int64 // 至少收到过一条提问的箱主数
+	AnsweringBoxes  int64 // 至少回答过一条提问的箱主数
+	TotalMessages   int64
+	LastQuestionAt  *time.Time
+	LastAnswerAt    *time.Time
+
+	Daily   []CountPoint // 近 30 天（按日）
+	Monthly []CountPoint // 近 12 个月（按月）
+
+	// Heatmap 一周 7 天 × 24 小时的提问量，下标 0 为周日（与 EXTRACT(DOW) 对齐）。
+	Heatmap     [7][24]int64
+	HeatmapDays int // Heatmap 的统计窗口天数，用于在页面上说明口径
+
+	LengthBins [6]int64 // 提问字数分布
+	ReplyBins  [6]int64 // 回答耗时分布（仅统计已回答的提问）
+
+	TodayQuestions int64
+	TodayAnswers   int64
+}
+
+// CountPoint 一个分组键上的计数，Key 已由数据库格式化成可直接展示的字符串。
+type CountPoint struct {
+	Key string
+	N   int64
+}
+
+func NewSiteAnalyticsStore(db *gorm.DB) SiteAnalyticsStore {
+	return &siteAnalytics{db}
+}
+
+type siteAnalytics struct {
+	*gorm.DB
+}
+
+const (
+	// 趋势窗口：近 30 天按日、近 12 个月按月。
+	analyticsDailyDays = 30
+	analyticsMonths    = 12
+	// 热力图窗口：一年太贵（全表聚合），半年足以代表「全站习惯」。
+	analyticsHeatmapDays = 180
+
+	// 字数分桶上限（左闭右开），最后一桶为 500 以上。
+	lengthBinExpr = `CASE
+		WHEN CHAR_LENGTH(content) < 20 THEN 0
+		WHEN CHAR_LENGTH(content) < 50 THEN 1
+		WHEN CHAR_LENGTH(content) < 100 THEN 2
+		WHEN CHAR_LENGTH(content) < 200 THEN 3
+		WHEN CHAR_LENGTH(content) < 500 THEN 4
+		ELSE 5 END`
+)
+
+// analyticsDialect 把两套数据库里含义相同、写法不同的表达式收敛到一处。
+//
+// 项目实际部署用 PostgreSQL（见 Init 的索引分支），MySQL 分支只是保留可用性：
+// 配置切到 MySQL 时不应整页 500，而应安静地少几个数。
+type analyticsDialect struct {
+	day      string
+	month    string
+	dow      string
+	hour     string
+	replyMin string
+}
+
+func analyticsDialectFor(db *gorm.DB) analyticsDialect {
+	if db.Dialector.Name() == "mysql" {
+		return analyticsDialect{
+			day:      "DATE_FORMAT(created_at, '%Y-%m-%d')",
+			month:    "DATE_FORMAT(created_at, '%Y-%m')",
+			dow:      "(DAYOFWEEK(created_at) - 1)",
+			hour:     "HOUR(created_at)",
+			replyMin: "TIMESTAMPDIFF(MINUTE, created_at, updated_at)",
+		}
+	}
+	// PostgreSQL：EXTRACT 返回 numeric，显式转 int 以免扫描进 int64 时类型不匹配。
+	return analyticsDialect{
+		day:      "to_char(created_at, 'YYYY-MM-DD')",
+		month:    "to_char(created_at, 'YYYY-MM')",
+		dow:      "CAST(EXTRACT(DOW FROM created_at) AS INTEGER)",
+		hour:     "CAST(EXTRACT(HOUR FROM created_at) AS INTEGER)",
+		replyMin: "CAST(EXTRACT(EPOCH FROM (updated_at - created_at)) / 60 AS INTEGER)",
+	}
+}
+
+func (db *siteAnalytics) SiteAnalytics(ctx context.Context, now time.Time) (*SiteAnalytics, error) {
+	loc := now.Location()
+	startOfDay := time.Date(now.Year(), now.Month(), now.Day(), 0, 0, 0, 0, loc)
+	dailySince := startOfDay.AddDate(0, 0, -(analyticsDailyDays - 1))
+	// 先归到当月 1 号再回退，避免月底 AddDate 溢出（3-31 减 11 个月会滚到 3-03）。
+	monthSince := time.Date(now.Year(), now.Month(), 1, 0, 0, 0, 0, loc).AddDate(0, -(analyticsMonths - 1), 0)
+	heatmapSince := startOfDay.AddDate(0, 0, -analyticsHeatmapDays)
+
+	out := &SiteAnalytics{HeatmapDays: analyticsHeatmapDays}
+
+	if err := db.aggregateTotals(ctx, out); err != nil {
+		return nil, err
+	}
+	if err := db.aggregateTrends(ctx, out, dailySince, monthSince); err != nil {
+		return nil, err
+	}
+	if err := db.aggregateHeatmap(ctx, out, heatmapSince); err != nil {
+		return nil, err
+	}
+	if err := db.aggregateContent(ctx, out); err != nil {
+		return nil, err
+	}
+
+	if err := db.WithContext(ctx).Model(&User{}).Count(&out.RegisteredUsers).Error; err != nil {
+		return nil, errors.Wrap(err, "count registered users")
+	}
+	if err := db.WithContext(ctx).Model(&Message{}).Count(&out.TotalMessages).Error; err != nil {
+		return nil, errors.Wrap(err, "count messages")
+	}
+
+	// 今日数字单独查：它们不进「每天一次」的缓存窗口，页面顶部要显示实时值。
+	if err := db.WithContext(ctx).Model(&Question{}).
+		Where("created_at >= ?", startOfDay).Count(&out.TodayQuestions).Error; err != nil {
+		return nil, errors.Wrap(err, "count today's questions")
+	}
+	if err := db.WithContext(ctx).Model(&Question{}).
+		Where("answer <> '' AND updated_at >= ?", startOfDay).Count(&out.TodayAnswers).Error; err != nil {
+		return nil, errors.Wrap(err, "count today's answers")
+	}
+
+	return out, nil
+}
+
+// aggregateTotals 用一条 SQL 取回所有「不分时间窗口」的计数。
+//
+// 用 SUM(CASE WHEN ...) 而不是 COUNT(*) FILTER (WHERE ...)：前者 MySQL 也支持，
+// 后者只在 PostgreSQL 上可用。
+func (db *siteAnalytics) aggregateTotals(ctx context.Context, out *SiteAnalytics) error {
+	var row struct {
+		Total          int64
+		Answered       int64
+		Private        int64
+		Read           int64
+		Anonymous      int64
+		ActiveBoxes    int64      `gorm:"column:active_boxes"`
+		AnsweringBoxes int64      `gorm:"column:answering_boxes"`
+		LastQ          *time.Time `gorm:"column:last_q"`
+		LastA          *time.Time `gorm:"column:last_a"`
+	}
+	err := db.WithContext(ctx).Model(&Question{}).
+		Select(`COUNT(*) AS total,
+			COALESCE(SUM(CASE WHEN answer <> '' THEN 1 ELSE 0 END), 0) AS answered,
+			COALESCE(SUM(CASE WHEN is_private THEN 1 ELSE 0 END), 0) AS private,
+			COALESCE(SUM(CASE WHEN is_read OR answer <> '' THEN 1 ELSE 0 END), 0) AS read,
+			COALESCE(SUM(CASE WHEN asker_user_id = 0 THEN 1 ELSE 0 END), 0) AS anonymous,
+			COUNT(DISTINCT user_id) AS active_boxes,
+			COUNT(DISTINCT CASE WHEN answer <> '' THEN user_id END) AS answering_boxes,
+			MAX(created_at) AS last_q,
+			MAX(CASE WHEN answer <> '' THEN updated_at END) AS last_a`).
+		Scan(&row).Error
+	if err != nil {
+		return errors.Wrap(err, "aggregate question totals")
+	}
+
+	out.TotalQuestions = row.Total
+	out.Answered = row.Answered
+	out.Private = row.Private
+	out.Read = row.Read
+	out.Anonymous = row.Anonymous
+	out.ActiveBoxes = row.ActiveBoxes
+	out.AnsweringBoxes = row.AnsweringBoxes
+	out.LastQuestionAt = row.LastQ
+	out.LastAnswerAt = row.LastA
+	return nil
+}
+
+// aggregateTrends 按日与按月各取一次计数。未被 GROUP BY 覆盖的空日期由 stats 层补齐。
+func (db *siteAnalytics) aggregateTrends(ctx context.Context, out *SiteAnalytics, dailySince, monthSince time.Time) error {
+	d := analyticsDialectFor(db.DB)
+
+	var daily []struct {
+		Key string
+		N   int64
+	}
+	if err := db.WithContext(ctx).Model(&Question{}).
+		Select(d.day+" AS key, COUNT(*) AS n").
+		Where("created_at >= ?", dailySince).
+		Group(d.day).Order(d.day).
+		Scan(&daily).Error; err != nil {
+		return errors.Wrap(err, "aggregate daily trend")
+	}
+	for _, row := range daily {
+		out.Daily = append(out.Daily, CountPoint{Key: row.Key, N: row.N})
+	}
+
+	var monthly []struct {
+		Key string
+		N   int64
+	}
+	if err := db.WithContext(ctx).Model(&Question{}).
+		Select(d.month+" AS key, COUNT(*) AS n").
+		Where("created_at >= ?", monthSince).
+		Group(d.month).Order(d.month).
+		Scan(&monthly).Error; err != nil {
+		return errors.Wrap(err, "aggregate monthly trend")
+	}
+	for _, row := range monthly {
+		out.Monthly = append(out.Monthly, CountPoint{Key: row.Key, N: row.N})
+	}
+	return nil
+}
+
+func (db *siteAnalytics) aggregateHeatmap(ctx context.Context, out *SiteAnalytics, since time.Time) error {
+	d := analyticsDialectFor(db.DB)
+
+	var rows []struct {
+		Dow int
+		Hr  int
+		N   int64
+	}
+	if err := db.WithContext(ctx).Model(&Question{}).
+		Select(d.dow+" AS dow, "+d.hour+" AS hr, COUNT(*) AS n").
+		Where("created_at >= ?", since).
+		Group(d.dow + ", " + d.hour).
+		Scan(&rows).Error; err != nil {
+		return errors.Wrap(err, "aggregate weekday/hour heatmap")
+	}
+
+	for _, row := range rows {
+		// 越界值直接丢弃：MySQL 的 DAYOFWEEK 或异常时区数据都可能给出 7 以上的下标，
+		// 落进固定数组会 panic，而这份数据只影响一张图，不值得让整页失败。
+		if row.Dow < 0 || row.Dow > 6 || row.Hr < 0 || row.Hr > 23 {
+			continue
+		}
+		out.Heatmap[row.Dow][row.Hr] = row.N
+	}
+	return nil
+}
+
+func (db *siteAnalytics) aggregateContent(ctx context.Context, out *SiteAnalytics) error {
+	d := analyticsDialectFor(db.DB)
+
+	var lengths []struct {
+		Bin int
+		N   int64
+	}
+	if err := db.WithContext(ctx).Model(&Question{}).
+		Select(lengthBinExpr + " AS bin, COUNT(*) AS n").
+		Group(lengthBinExpr).
+		Scan(&lengths).Error; err != nil {
+		return errors.Wrap(err, "aggregate content length distribution")
+	}
+	for _, row := range lengths {
+		if row.Bin >= 0 && row.Bin < len(out.LengthBins) {
+			out.LengthBins[row.Bin] = row.N
+		}
+	}
+
+	// 回答耗时：只用已回答的提问，按分钟分桶。
+	// 口径注意：updated_at 也会被置顶等写操作刷新，因此这里算出的耗时偏大；
+	// 展示时只呈现「24 小时内回答占比」这类粗粒度结论，不宣称精确中位数。
+	replyBinExpr := fmt.Sprintf(`CASE
+		WHEN %[1]s < 60 THEN 0
+		WHEN %[1]s < 360 THEN 1
+		WHEN %[1]s < 1440 THEN 2
+		WHEN %[1]s < 4320 THEN 3
+		WHEN %[1]s < 10080 THEN 4
+		ELSE 5 END`, d.replyMin)
+
+	var replies []struct {
+		Bin int
+		N   int64
+	}
+	if err := db.WithContext(ctx).Model(&Question{}).
+		Select(replyBinExpr + " AS bin, COUNT(*) AS n").
+		Where("answer <> ''").
+		Group(replyBinExpr).
+		Scan(&replies).Error; err != nil {
+		return errors.Wrap(err, "aggregate reply latency distribution")
+	}
+	for _, row := range replies {
+		if row.Bin >= 0 && row.Bin < len(out.ReplyBins) {
+			out.ReplyBins[row.Bin] = row.N
+		}
+	}
+	return nil
+}