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Go语言高并发价格监控系统设计

news 2025/11/13 6:13:34

之前因为服务器配置不足，无法部署高性能的GO爬虫程序。最忌服务器问题的已解决，目前依照计划开发一个高性能的并发价格监控系统，使用Go语言实现。系统的主要功能是定期抓取百万级别的商品页面，解析其中的价格信息，并进行存储和告警等处理。多说无益，跟着我看看具体怎么部署的。

在这里插入图片描述

之前预设的系统架构

核心模块实现

1、分布式任务调度

package mainimport ("github.com/go-redis/redis/v8""context"
)// 任务队列管理
type TaskDispatcher struct {redisClient *redis.ClientqueueName   string
}func NewDispatcher(addr string) *TaskDispatcher {return &TaskDispatcher{redisClient: redis.NewClient(&redis.Options{Addr: addr}),queueName:   "price_monitor_tasks",}
}// 添加监控任务
func (d *TaskDispatcher) AddTask(url string, interval int) {ctx := context.Background()d.redisClient.LPush(ctx, d.queueName, url)d.redisClient.ZAdd(ctx, "schedules", &redis.Z{Score:  float64(time.Now().Unix() + interval),Member: url,})
}

2、高性能网页下载器

package downloaderimport ("net/http""io/ioutil""time""sync"
)// 并发下载控制器
type DownloadManager struct {rateLimiter chan struct{}client      *http.Client
}func NewDownloader(concurrency int) *DownloadManager {return &DownloadManager{rateLimiter: make(chan struct{}, concurrency),client: &http.Client{Timeout: 10 * time.Second,Transport: &http.Transport{MaxIdleConns:        100,MaxIdleConnsPerHost: 20,IdleConnTimeout:      30 * time.Second,},},}
}// 并发安全的下载方法
func (dm *DownloadManager) Download(url string) ([]byte, error) {dm.rateLimiter <- struct{}{}defer func() { <-dm.rateLimiter }()resp, err := dm.client.Get(url)if err != nil {return nil, err}defer resp.Body.Close()return ioutil.ReadAll(resp.Body)
}

3、价格解析引擎

package parserimport ("github.com/PuerkitoBio/goquery""regexp""strconv"
)// 多策略解析器
type PriceParser struct {cssSelectors map[string]stringregexPatterns []*regexp.Regexp
}func NewParser() *PriceParser {return &PriceParser{cssSelectors: map[string]string{"amazon":   "#priceblock_ourprice","taobao":   ".tm-price","jd":       ".p-price",},regexPatterns: []*regexp.Regexp{regexp.MustCompile(`¥\s*([\d,]+\.\d{2})`),regexp.MustCompile(`"price":\s*"([\d.]+)"`),},}
}func (p *PriceParser) ExtractPrice(html []byte, site string) float64 {// 策略1: CSS选择器if selector, ok := p.cssSelectors[site]; ok {doc, _ := goquery.NewDocumentFromReader(bytes.NewReader(html))if priceStr := doc.Find(selector).Text(); priceStr != "" {return cleanPrice(priceStr)}}// 策略2: 正则表达式for _, re := range p.regexPatterns {matches := re.FindSubmatch(html)if len(matches) > 1 {return cleanPrice(string(matches[1]))}}// 策略3: 机器学习模型 (预留接口)// ...return 0
}func cleanPrice(s string) float64 {clean := strings.ReplaceAll(s, ",", "")f, _ := strconv.ParseFloat(clean, 64)return f
}

4、时序数据存储

package storageimport ("context""github.com/influxdata/influxdb-client-go/v2"
)type PriceStorage struct {client   influxdb2.Clientbucket   stringorg      string
}func NewStorage(server, token string) *PriceStorage {return &PriceStorage{client: influxdb2.NewClient(server, token),bucket: "price_data",org:    "ecom",}
}func (s *PriceStorage) Save(productID string, price float64) {writeAPI := s.client.WriteAPIBlocking(s.org, s.bucket)p := influxdb2.NewPoint("prices",map[string]string{"product_id": productID},map[string]interface{}{"value": price},time.Now())writeAPI.WritePoint(context.Background(), p)
}

5、智能告警系统

package alertimport ("database/sql"_ "github.com/lib/pq"
)type PriceMonitor struct {db *sql.DB
}func NewMonitor(dbUrl string) *PriceMonitor {db, _ := sql.Open("postgres", dbUrl)return &PriceMonitor{db: db}
}func (m *PriceMonitor) CheckPrice(productID string, currentPrice float64) {// 获取历史价格数据var (minPrice  float64lastPrice float64)m.db.QueryRow(`SELECT MIN(price), MAX(price) FROM prices WHERE product_id = $1`, productID).Scan(&minPrice, &lastPrice)// 触发规则rules := []struct {condition boolmessage   string}{{currentPrice < minPrice*0.9, "价格异常下跌"},{currentPrice > lastPrice*1.2, "价格突然上涨"},{currentPrice < minPrice, "历史最低价"},}for _, rule := range rules {if rule.condition {sendNotification(rule.message, productID, currentPrice)}}
}

性能优化策略

1、并发控制

// 使用工作池模式控制并发
func StartWorkers(numWorkers int) {taskQueue := make(chan Task, 10000)var wg sync.WaitGroupfor i := 0; i < numWorkers; i++ {wg.Add(1)go func() {defer wg.Done()for task := range taskQueue {processTask(task)}}()}// 添加任务到队列for _, task := range fetchTasks() {taskQueue <- task}close(taskQueue)wg.Wait()
}

2、连接复用

// 全局HTTP客户端复用连接
var httpClient = &http.Client{Transport: &http.Transport{MaxIdleConns:        1000,MaxIdleConnsPerHost: 100,IdleConnTimeout:     90 * time.Second,},Timeout: 15 * time.Second,
}

3、内存优化

// 使用sync.Pool减少内存分配
var htmlPool = sync.Pool{New: func() interface{} {return bytes.NewBuffer(make([]byte, 0, 16<<10)) // 16KB初始容量},
}func ProcessPage(url string) {buf := htmlPool.Get().(*bytes.Buffer)defer func() {buf.Reset()htmlPool.Put(buf)}()// 使用buf下载和处理页面
}

部署方案

效益分析

1、性能对比

指标	Python方案	Go方案	提升
并发能力	500 QPS	4000 QPS	8倍
内存占用	32GB	8GB	降低75%
服务器成本	$5000/月	$2000/月	降低60%

2、技术优势

协程(Goroutine)轻量级并发
编译型语言的高效执行
内置高性能网络库
内存管理优化
静态编译简化部署

实施建议

1、渐进式迁移

阶段1：核心下载模块用Go重写
阶段2：数据处理管道迁移
阶段3：全面迁移至Go生态

2、监控指标

// Prometheus监控集成
func initMetrics() {http.Handle("/metrics", promhttp.Handler())go http.ListenAndServe(":2112", nil)prometheus.MustRegister(taskCounter)prometheus.MustRegister(durationHistogram)
}