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Go1.19 Crawler Framework: Simplify automated crawling of site templates

2024-07-12

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introduction

Web scrapers are tools that automatically extract data from websites. They are widely used in data collection, search engine optimization, market research, and other fields. This article will detail how to use Go 1.19 to implement a simplified site template automated scraping tool to help developers efficiently collect data.

Table of contents

  1. Environment Preparation
  2. Basic concepts of web crawlers
  3. Go crawler framework selection
  4. The basic process of designing a crawler
  5. Implementing a simple web crawler
  6. Parsing HTML content
  7. Concurrency processing of crawlers
  8. data storage
  9. Error handling and retry mechanism
  10. Real-world example: scraping a news website
  11. Advanced features and optimizations
  12. in conclusion

1. Environmental Preparation

Before you begin, make sure you have Go 1.19 installed on your system. You can check the Go version with the following command:

go version

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If you don't have Go installed yet, you can install it from Go Official Website Download and install the latest version.

2. Basic concepts of web crawlers

The basic workflow of a web crawler is as follows:

  1. send request: Send an HTTP request to the target web page.
  2. Get Response: Receive the HTTP response returned by the server.
  3. Parsing content: Extract the required data from the response.
  4. Storing data: Save the extracted data to a local file or database.
  5. Handling Links: Extract links from web pages and continue crawling other pages.

3. Go crawler framework selection

In the Go language, there are several popular crawler frameworks, such as:

  • Colly: A fast and elegant crawler framework that provides rich features and good performance.
  • Goquery: A jQuery-like library for parsing and manipulating HTML documents.
  • HTTP client: The net/http package of the standard library can meet most simple HTTP request requirements.

This article will mainly use Colly and Goquery for web crawling and content parsing.

4. Basic process of designing crawlers

We will design a simplified site template automated crawler, the basic process is as follows:

  1. Initialize the crawler configuration.
  2. Send HTTP request to get web page content.
  3. Use Goquery to parse HTML content and extract required data.
  4. Save data to a local file or database.
  5. Handle errors and retry mechanisms.
  6. Use concurrent processing to improve crawling efficiency.

5. Implement a simple web crawler

First, create a new Go project:

mkdir go_scraper
cd go_scraper
go mod init go_scraper

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Then, install Colly and Goquery:

go get -u github.com/gocolly/colly
go get -u github.com/PuerkitoBio/goquery

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Next, write a simple crawler to crawl web content:

package main

import (
    "fmt"
    "github.com/gocolly/colly"
)

func main() {
    // 创建一个新的爬虫实例
    c := colly.NewCollector()

    // 设置请求时的回调函数
    c.OnRequest(func(r *colly.Request) {
        fmt.Println("Visiting", r.URL.String())
    })

    // 设置响应时的回调函数
    c.OnResponse(func(r *colly.Response) {
        fmt.Println("Visited", r.Request.URL)
        fmt.Println("Response:", string(r.Body))
    })

    // 设置错误处理的回调函数
    c.OnError(func(r *colly.Response, err error) {
        fmt.Println("Error:", err)
    })

    // 设置HTML解析时的回调函数
    c.OnHTML("title", func(e *colly.HTMLElement) {
        fmt.Println("Title:", e.Text)
    })

    // 开始爬取
    c.Visit("http://example.com")
}

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Running the above code will fetch the content of http://example.com and print the title of the webpage.

6. Parsing HTML content

In order to extract the required data from the web page, we need to use Goquery to parse the HTML content. The following example shows how to use Goquery to extract links and text from a web page:

package main

import (
    "fmt"
    "github.com/gocolly/colly"
    "github.com/PuerkitoBio/goquery"
)

func main() {
    c := colly.NewCollector()

    c.OnHTML("body", func(e *colly.HTMLElement) {
        e.DOM.Find("a").Each(func(index int, item *goquery.Selection) {
            link, _ := item.Attr("href")
            text := item.Text()
            fmt.Printf("Link #%d: %s (%s)n", index, text, link)
        })
    })

    c.Visit("http://example.com")
}

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7. Concurrency processing of crawlers

To improve the efficiency of the crawler, we can use the concurrency function of Colly:

package main

import (
    "fmt"
    "github.com/gocolly/colly"
    "github.com/PuerkitoBio/goquery"
    "log"
    "time"
)

func main() {
    c := colly.NewCollector(
        colly.Async(true), // 启用异步模式
    )

    c.Limit(&colly.LimitRule{
        DomainGlob:  "*",
        Parallelism: 2, // 设置并发数
        Delay:       2 * time.Second,
    })

    c.OnHTML("body", func(e *colly.HTMLElement) {
        e.DOM.Find("a").Each(func(index int, item *goquery.Selection) {
            link, _ := item.Attr("href")
            text := item.Text()
            fmt.Printf("Link #%d: %s (%s)n", index, text, link)
            c.Visit(e.Request.AbsoluteURL(link))
        })
    })

    c.OnRequest(func(r *colly.Request) {
        fmt.Println("Visiting", r.URL.String())
    })

    c.OnError(func(r *colly.Response, err error) {
        log.Println("Error:", err)
    })

    c.Visit("http://example.com")

    c.Wait() // 等待所有异步任务完成
}

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8. Data Storage

Save the captured data to a local file or database. Here we take a CSV file as an example:

package main

import (
    "encoding/csv"
    "fmt"
    "github.com/gocolly/colly"
    "github.com/PuerkitoBio/goquery"
    "log"
    "os"
    "time"
)

func main() {
    file, err := os.Create("data.csv")
    if err != nil {
        log.Fatalf("could not create file: %v", err)
    }
    defer file.Close()

    writer := csv.NewWriter(file)
    defer writer.Flush()

    c := colly.NewCollector(
        colly.Async(true),
    )

    c.Limit(&colly.LimitRule{
        DomainGlob:  "*",
        Parallelism: 2,
        Delay:       2 * time.Second,
    })

    c.OnHTML("body", func(e *colly.HTMLElement) {
        e.DOM.Find("a").Each(func(index int, item *goquery.Selection) {
            link, _ := item.Attr("href")
            text := item.Text()
            fmt.Printf("Link #%d: %s (%s)n", index, text, link)
            writer.Write([]string{text, link})
            c.Visit(e.Request.AbsoluteURL(link))
        })
    })

    c.OnRequest(func(r *colly.Request) {
        fmt.Println("Visiting", r.URL.String())
    })

    c.OnError(func(r *colly.Response, err error) {
        log.Println("Error:", err)
    })

    c.Visit("http://example.com")

    c.Wait()
}

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9. Error handling and retry mechanism

In order to improve the stability of the crawler, we need to handle request errors and implement a retry mechanism:

package main

import (
    "fmt"
    "github.com/gocolly/colly"
    "github.com/PuerkitoBio/goquery"
    "log"
    "os"
    "time"
)

func main() {
    file, err := os.Create("data.csv")
    if err != nil {
        log.Fatalf("could not create file: %v", err)
    }
    defer file.Close()

    writer := csv.NewWriter(file)
    defer writer.Flush()

    c := colly.NewCollector(
        colly.Async(true),
        colly.MaxDepth(1),
    )

    c.Limit(&colly.LimitRule{
        DomainGlob:  "*",
        Parallelism: 2,
        Delay:       2 * time.Second,
    })

    c.OnHTML("body", func(e *colly.HTMLElement) {
        e.DOM.Find("a").Each(func(index int, item *goquery.Selection) {
            link, _ := item.Attr("href")
            text := item.Text()
            fmt.Printf("Link #%d: %s (%s)

n", index, text, link)
            writer.Write([]string{text, link})
            c.Visit(e.Request.AbsoluteURL(link))
        })
    })

    c.OnRequest(func(r *colly.Request) {
        fmt.Println("Visiting", r.URL.String())
    })

    c.OnError(func(r *colly.Response, err error) {
        log.Println("Error:", err)
        // 重试机制
        if r.StatusCode == 0 || r.StatusCode >= 500 {
            r.Request.Retry()
        }
    })

    c.Visit("http://example.com")

    c.Wait()
}

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10. Actual combat example: crawling news websites

The following example shows how to scrape the titles and links of a news website and save them to a CSV file:

package main

import (
    "encoding/csv"
    "fmt"
    "github.com/gocolly/colly"
    "log"
    "os"
    "time"
)

func main() {
    file, err := os.Create("news.csv")
    if err != nil {
        log.Fatalf("could not create file: %v", err)
    }
    defer file.Close()

    writer := csv.NewWriter(file)
    defer writer.Flush()

    writer.Write([]string{"Title", "Link"})

    c := colly.NewCollector(
        colly.Async(true),
    )

    c.Limit(&colly.LimitRule{
        DomainGlob:  "*",
        Parallelism: 5,
        Delay:       1 * time.Second,
    })

    c.OnHTML(".news-title", func(e *colly.HTMLElement) {
        title := e.Text
        link := e.ChildAttr("a", "href")
        writer.Write([]string{title, e.Request.AbsoluteURL(link)})
        fmt.Printf("Title: %snLink: %sn", title, e.Request.AbsoluteURL(link))
    })

    c.OnRequest(func(r *colly.Request) {
        fmt.Println("Visiting", r.URL.String())
    })

    c.OnError(func(r *colly.Response, err error) {
        log.Println("Error:", err)
        if r.StatusCode == 0 || r.StatusCode >= 500 {
            r.Request.Retry()
        }
    })

    c.Visit("http://example-news-site.com")

    c.Wait()
}

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11. Advanced features and optimization

Use a proxy

To avoid being blocked by the target website, you can use a proxy:

c.SetProxy("http://proxyserver:port")

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User Agent Masquerading

By setting the user agent, disguise as a different browser:

c.UserAgent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"

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Distributed Crawler

You can use Colly's extension library Colly-Redis to implement distributed crawlers:

import (
    "github.com/gocolly/redisstorage"
)

func main() {
    c := colly.NewCollector()
    redisStorage := &redisstorage.Storage{
        Address:  "localhost:6379",
        Password: "",
        DB:       0,
        Prefix:   "colly",
    }
    c.SetStorage(redisStorage)
}

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Dynamic web crawling

For dynamic web pages, you can use a headless browser such as chromedp:

import (
    "context"
    "github.com/chromedp/chromedp"
)

func main() {
    ctx, cancel := chromedp.NewContext(context.Background())
    defer cancel()

    var res string
    err := chromedp.Run(ctx,
        chromedp.Navigate("http://example.com"),
        chromedp.WaitVisible(`#some-element`),
        chromedp.InnerHTML(`#some-element`, &res),
    )

    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(res)
}

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12. Conclusion

Through the detailed introduction of this article, we learned how to use Go 1.19 to implement a simplified site template automated crawler. We started with the basic crawler design process, gradually delved into key links such as HTML parsing, concurrent processing, data storage, and error handling, and demonstrated how to crawl and process web page data through specific code examples.

Go language's powerful concurrent processing capabilities and rich third-party libraries make it an ideal choice for building efficient and stable web crawlers. Through continuous optimization and expansion, more complex and advanced crawler functions can be achieved, providing solutions for various data collection needs.

I hope this article can provide you with valuable references for implementing web crawlers in Go language and inspire you to explore and innovate more in this field.

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