Golang视频分享网站www.vidoomy.com的技术实现探讨

这是一个典型的Go语言后端开发职位，主要涉及高并发广告服务器开发。以下是基于职位描述的技术实现要点和示例代码：

1. 高并发HTTP服务器处理3亿+请求/天

package main

import (
    "fmt"
    "log"
    "net/http"
    "time"
    
    "github.com/gin-gonic/gin"
    "github.com/redis/go-redis/v9"
    "gorm.io/gorm"
)

// 广告请求结构体
type AdRequest struct {
    UserID    string `json:"user_id"`
    Placement string `json:"placement"`
    IP        string `json:"ip"`
    Timestamp int64  `json:"timestamp"`
}

// 广告响应结构体
type AdResponse struct {
    AdID      string `json:"ad_id"`
    VASTURL   string `json:"vast_url"`
    Price     float64 `json:"price"`
    Creative  string `json:"creative"`
}

var (
    redisClient *redis.Client
    db          *gorm.DB
)

func main() {
    // 初始化Redis连接
    redisClient = redis.NewClient(&redis.Options{
        Addr:     "localhost:6379",
        Password: "",
        DB:       0,
    })
    
    // 设置Gin路由
    router := gin.Default()
    
    // 广告请求端点
    router.GET("/ad", handleAdRequest)
    
    // 启动服务器
    log.Println("广告服务器启动，端口:8080")
    router.Run(":8080")
}

// 处理广告请求
func handleAdRequest(c *gin.Context) {
    start := time.Now()
    
    // 解析请求参数
    adReq := AdRequest{
        UserID:    c.Query("uid"),
        Placement: c.Query("placement"),
        IP:        c.ClientIP(),
        Timestamp: time.Now().Unix(),
    }
    
    // 1. 频率控制（使用Redis）
    key := fmt.Sprintf("freq:%s:%s", adReq.UserID, adReq.Placement)
    count, _ := redisClient.Incr(c, key).Result()
    redisClient.Expire(c, key, time.Hour)
    
    if count > 100 { // 限制每小时展示次数
        c.JSON(http.StatusOK, gin.H{"error": "frequency limit"})
        return
    }
    
    // 2. 实时竞价逻辑
    adResp, err := runRTBAuction(adReq)
    if err != nil {
        c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
        return
    }
    
    // 3. 记录展示
    go recordImpression(adReq, adResp)
    
    // 4. 返回VAST响应
    c.Header("Content-Type", "application/xml")
    c.String(http.StatusOK, generateVAST(adResp))
    
    // 记录处理时间
    elapsed := time.Since(start)
    log.Printf("请求处理时间: %v", elapsed)
}

// 实时竞价逻辑
func runRTBAuction(req AdRequest) (*AdResponse, error) {
    // 这里实现RTB竞价逻辑
    // 1. 从数据库获取可用广告
    // 2. 进行竞价排序
    // 3. 返回获胜广告
    
    return &AdResponse{
        AdID:     "ad_12345",
        VASTURL:  "https://cdn.vidoomy.com/vast/ad12345.xml",
        Price:    2.5,
        Creative: "https://cdn.vidoomy.com/creatives/banner.jpg",
    }, nil
}

// 生成VAST XML
func generateVAST(ad *AdResponse) string {
    return fmt.Sprintf(`<?xml version="1.0" encoding="UTF-8"?>
<VAST version="3.0">
    <Ad id="%s">
        <InLine>
            <AdSystem>Vidoomy</AdSystem>
            <AdTitle>Video Ad</AdTitle>
            <Impression><![CDATA[%s/track/impression]]></Impression>
            <Creatives>
                <Creative>
                    <Linear>
                        <Duration>00:00:30</Duration>
                        <MediaFiles>
                            <MediaFile delivery="progressive" type="video/mp4" width="640" height="360">
                                <![CDATA[%s]]>
                            </MediaFile>
                        </MediaFiles>
                    </Linear>
                </Creative>
            </Creatives>
        </InLine>
    </Ad>
</VAST>`, ad.AdID, ad.VASTURL, ad.Creative)
}

// 异步记录展示数据
func recordImpression(req AdRequest, resp *AdResponse) {
    // 使用goroutine异步处理，避免阻塞主请求
    go func() {
        // 存储到Redis队列
        impressionData := map[string]interface{}{
            "user_id":    req.UserID,
            "ad_id":      resp.AdID,
            "placement":  req.Placement,
            "price":      resp.Price,
            "timestamp":  req.Timestamp,
            "ip":         req.IP,
        }
        
        // 推送到Redis流供后续处理
        redisClient.XAdd(context.Background(), &redis.XAddArgs{
            Stream: "impressions",
            Values: impressionData,
        })
        
        // 同时更新计数器
        redisClient.Incr(context.Background(), "stats:impressions:total")
        redisClient.Incr(context.Background(), fmt.Sprintf("stats:impressions:%s", req.Placement))
    }()
}

2. 高性能数据处理管道

package main

import (
    "context"
    "encoding/json"
    "fmt"
    "sync"
    "time"
    
    "github.com/redis/go-redis/v9"
)

// 数据处理工作者
type DataProcessor struct {
    redisClient *redis.Client
    workerCount int
    wg          sync.WaitGroup
}

func NewDataProcessor(redisAddr string, workers int) *DataProcessor {
    return &DataProcessor{
        redisClient: redis.NewClient(&redis.Options{
            Addr: redisAddr,
        }),
        workerCount: workers,
    }
}

// 启动数据处理管道
func (dp *DataProcessor) StartProcessing(ctx context.Context) {
    for i := 0; i < dp.workerCount; i++ {
        dp.wg.Add(1)
        go dp.worker(ctx, i)
    }
}

// 工作者处理Redis流数据
func (dp *DataProcessor) worker(ctx context.Context, id int) {
    defer dp.wg.Done()
    
    lastID := "0"
    
    for {
        select {
        case <-ctx.Done():
            return
        default:
            // 从Redis流读取数据
            result, err := dp.redisClient.XRead(ctx, &redis.XReadArgs{
                Streams: []string{"impressions", lastID},
                Count:   100,
                Block:   time.Second,
            }).Result()
            
            if err != nil {
                time.Sleep(time.Second)
                continue
            }
            
            for _, stream := range result {
                for _, message := range stream.Messages {
                    lastID = message.ID
                    
                    // 处理消息
                    dp.processMessage(message.Values)
                    
                    // 确认处理
                    dp.redisClient.XAck(ctx, "impressions", "processing_group", message.ID)
                }
            }
        }
    }
}

// 处理单条消息
func (dp *DataProcessor) processMessage(values map[string]interface{}) {
    // 解析数据
    data, _ := json.Marshal(values)
    var impression ImpressionData
    json.Unmarshal(data, &impression)
    
    // 批量聚合统计
    dp.aggregateStats(impression)
    
    // 存储到MySQL
    dp.storeToMySQL(impression)
}

// 批量聚合统计
func (dp *DataProcessor) aggregateStats(imp ImpressionData) {
    // 使用Redis哈希进行实时统计
    today := time.Now().Format("2006-01-02")
    
    // 按广告商聚合
    key := fmt.Sprintf("stats:advertiser:%s:%s", imp.AdvertiserID, today)
    dp.redisClient.HIncrBy(context.Background(), key, "impressions", 1)
    dp.redisClient.HIncrByFloat(context.Background(), key, "revenue", imp.Price)
    
    // 按地域聚合
    geoKey := fmt.Sprintf("stats:geo:%s:%s", imp.Country, today)
    dp.redisClient.HIncrBy(context.Background(), geoKey, "impressions", 1)
}

// 存储到MySQL
func (dp *DataProcessor) storeToMySQL(imp ImpressionData) {
    // 使用批量插入优化性能
    // 这里可以连接MySQL数据库进行存储
}

type ImpressionData struct {
    UserID      string  `json:"user_id"`
    AdID        string  `json:"ad_id"`
    AdvertiserID string `json:"advertiser_id"`
    Placement   string  `json:"placement"`
    Price       float64 `json:"price"`
    Country     string  `json:"country"`
    Timestamp   int64   `json:"timestamp"`
}

3. 视频广告VAST/VPAID处理

package main

import (
    "encoding/xml"
    "net/http"
    "time"
    
    "github.com/gin-gonic/gin"
)

// VAST数据结构
type VAST struct {
    XMLName xml.Name `xml:"VAST"`
    Version string   `xml:"version,attr"`
    Ads     []Ad     `xml:"Ad"`
}

type Ad struct {
    ID     string  `xml:"id,attr"`
    InLine *InLine `xml:"InLine"`
}

type InLine struct {
    AdSystem  string     `xml:"AdSystem"`
    AdTitle   string     `xml:"AdTitle"`
    Impressions []string `xml:"Impression"`
    Creatives Creatives  `xml:"Creatives"`
}

type Creatives struct {
    Creative []Creative `xml:"Creative"`
}

type Creative struct {
    ID     string `xml:"id,attr"`
    Linear Linear `xml:"Linear"`
}

type Linear struct {
    Duration string      `xml:"Duration"`
    TrackingEvents []TrackingEvent `xml:"TrackingEvents>Tracking"`
    MediaFiles   []MediaFile   `xml:"MediaFiles>MediaFile"`
}

// VPAID处理器
type VPAIDHandler struct {
    // VPAID相关处理逻辑
}

func (v *VPAIDHandler) HandleVPAIDRequest(c *gin.Context) {
    // 处理VPAID广告请求
    vpaidScript := `
        function VpaidAd() {
            this.handshakeVersion = '2.0';
            this.initAd = function(width, height, viewMode, desiredBitrate, creativeData, environmentVars) {
                // 初始化广告
                return true;
            };
            this.startAd = function() {
                // 开始播放广告
            };
            this.stopAd = function() {
                // 停止广告
            };
        }
        var vpaidAd = new VpaidAd();
    `
    
    c.Header("Content-Type", "application/javascript")
    c.String(http.StatusOK, vpaidScript)
}

// 广告事件追踪
func trackAdEvent(c *gin.Context) {
    eventType := c.Query("event")
    adID := c.Query("ad_id")
    
    // 记录广告事件
    switch eventType {
    case "start":
        log.Printf("广告开始播放: %s", adID)
    case "firstQuartile":
        log.Printf("广告播放25%%: %s", adID)
    case "midpoint":
        log.Printf("广告播放50%%: %s", adID)
    case "thirdQuartile":
        log.Printf("广告播放75%%: %s", adID)
    case "complete":
        log.Printf("广告播放完成: %s", adID)
    case "click":
        log.Printf("广告被点击: %s", adID)
    }
    
    // 更新Redis统计
    key := fmt.Sprintf("ad:events:%s:%s", adID, time.Now().Format("2006-01-02"))
    redisClient.HIncrBy(c, key, eventType, 1)
    
    c.Status(http.StatusOK)
}

4. 性能优化配置

package main

import (
    "runtime"
    "time"
    
    "github.com/gin-gonic/gin"
)

func optimizeServer() {
    // 设置GOMAXPROCS
    runtime.GOMAXPROCS(runtime.NumCPU())
    
    // Gin性能优化
    gin.SetMode(gin.ReleaseMode)
    
    // 连接池配置
    configureConnectionPools()
}

func configureConnectionPools() {
    // MySQL连接池配置示例
    // db.SetMaxOpenConns(100)
    // db.SetMaxIdleConns(20)
    // db.SetConnMaxLifetime(time.Hour)
    
    // Redis连接池配置
    // redisClient.Options().PoolSize = 100
    // redisClient.Options().MinIdleConns = 20
    // redisClient.Options().ConnMaxLifetime = time.Hour
}

// 监控中间件
func monitoringMiddleware() gin.HandlerFunc {
    return func(c *gin.Context) {
        start := time.Now()
        
        c.Next()
        
        // 记录请求指标
        duration := time.Since(start)
        status := c.Writer.Status()
        
        // 推送到监控系统
        metrics := map[string]interface{}{
            "path":     c.Request.URL.Path,
            "method":   c.Request.Method,
            "status":   status,
            "duration": duration.Seconds(),
            "time":     time.Now().Unix(),
        }
        
        // 可以推送到Prometheus或自定义监控
        recordMetrics(metrics)
    }
}

func recordMetrics(metrics map[string]interface{}) {
    // 实现指标记录逻辑
    // 可以使用Prometheus客户端库
}

这些示例展示了Vidoomy广告服务器可能需要的核心技术实现，包括高并发处理、实时竞价、VAST/VPAID支持、数据聚合和性能优化。实际实现需要根据具体业务需求进行调整。