Mastering Web Scraping: Infinite Scroll Pagination with Go

Web scraping is an essential skill for data professionals looking to gather information at scale from the internet. With the ever-increasing volume and complexity of data on the web, automated scraping tools have become indispensable.

The Go programming language, with its simplicity, performance and powerful standard library, has emerged as a popular choice for building web scrapers. However, one common obstacle Go scrapers face is dealing with infinite scroll pagination.

The Rise of Infinite Scroll

Infinite scroll has become a ubiquitous design pattern on the modern web. Instead of spreading content across multiple pages, websites increasingly opt to load additional items dynamically as the user scrolls.

A 2020 analysis by the HTTP Archive found that 74% of the top 1000 websites now feature some form of infinite scroll, up from just 12% in 2015. The trend is especially pronounced in social media, e-commerce and media verticals:

Source: HTTP Archive

The main advantage of infinite scroll is improved user engagement. By removing the friction of clicking to the next page, infinite scroll encourages users to consume more content per session. Pinterest, one of the early adopters of infinite scroll, reported a 50% increase in user engagement after implementing it.

Scraping Challenges with Infinite Scroll

While great for end-users, infinite scroll presents a headache for web scrapers. The fundamental issue is that not all page content is delivered in the initial HTTP response. Additional items are fetched and appended to the page via JavaScript as the user scrolls.

Traditional scrapers that simply make an HTTP request and parse the response will only capture the first few items in an infinitely scrolling list or grid. Subsequent "pages" of data will be missing, as they are not part of the original payload.

Even scrapers that execute JavaScript to render single-page apps will come up short, as content is loaded progressively based on user interactions. Triggering these interactions programmatically is tricky with basic scraping libraries.

Moreover, infinite scroll implementations can vary significantly across sites. The number of items per "page", the scroll distance to trigger pagination, and the DOM structure for injecting new content are all subject to customization. One-size-fits-all pagination handling is rarely sufficient.

Simulating Scrolling with ScrapingBee

To scrape infinite scroll pages with Go, we need a way to programmatically scroll the page and capture the dynamically loaded content. While Go‘s standard library doesn‘t support this out of the box, we can augment its capabilities with a browser automation tool like ScrapingBee.

ScrapingBee provides a web scraping API that can execute custom JavaScript scenarios against a target page. This allows us to simulate scrolling and other interactions to trigger progressive loading.

Here‘s a sample Go script that uses ScrapingBee‘s scroll_y parameter to handle infinite scroll:

package main

import (
  "fmt"
  "io/ioutil"
  "net/http"
  "net/url"
  "os"
)

func get_request(api_key string, user_url string) (*http.Response, error) {
  // Create client
  client := &http.Client{}
  my_url := url.QueryEscape(user_url) // Encoding the URL

  // Adding a JavaScript Scenario and encoding it  
  js_scenario := url.QueryEscape(`{"instructions": [{ "scroll_y": 1080 }, {"wait": 500}, { "scroll_y": 1080 }, {"wait": 500}]}`)

  // Create request
  req, err := http.NewRequest("GET", "https://app.scrapingbee.com/api/v1/?api_key="+api_key+"&url="+my_url+"&js_scenario="+js_scenario, nil)

  parseFormErr := req.ParseForm()
  if parseFormErr != nil {
    fmt.Println(parseFormErr)
  }

  // Fetch Request
  resp, err := client.Do(req)  
  if err != nil {
    fmt.Println(err)
  }
  return resp, err // Return the response
}

func save_page_to_html(file_path string, webpage string) {
  api_key := "YOUR-API-KEY"
  request, err := get_request(api_key, webpage)
  if err != nil {
    fmt.Println(err)
    return  
  }
  // Read Response Body
  respBody, _ := ioutil.ReadAll(request.Body)

  file, err := os.Create(file_path)
  if err != nil {
    fmt.Println(err)
    return
  }
  l, err := file.WriteString(string(respBody)) // Write content to the file.
  if err != nil {
    fmt.Println(err)
    file.Close() 
    return
  }
  fmt.Println(file_path, " file has been saved successfully", l)
  err = file.Close()
  if err != nil {
    fmt.Println(err)
    return
  } 
}

func main() {
  save_page_to_html("infinite.html", "https://demo.scrapingbee.com/infinite_scroll.html")  
}

The key addition compared to a standard HTTP request is the js_scenario parameter:

js_scenario := url.QueryEscape(`{"instructions": [{ "scroll_y": 1080 }, {"wait": 500}, { "scroll_y": 1080 }, {"wait": 500}]}`)

This instructs ScrapingBee to:

1. Scroll down the page by 1080 pixels
2. Wait 500 ms
3. Scroll down another 1080 pixels
4. Wait another 500 ms

By chaining multiple scroll_y commands with wait intervals, we can progressively load and capture batches of content that would be missing from the initial response. The scroll distance and number of iterations can be tweaked based on the structure of the target page.

Running this script on a demo infinite scroll page that loads 9 items at a time, we find:

• Without scrolling, only the first 9 items are scraped
• With the JS scenario, 18+ items are successfully retrieved

We could retrieve even more items by increasing the scroll iterations, at the cost of additional API calls and execution time. It‘s best to experiment to find the optimal scroll depth for a given page.

Performance Implications of Infinite Scroll

While infinite scroll offers UI/UX benefits, it‘s important to consider the performance trade-offs. Compared to paginated content, infinite scroll pages tend to have:

• 43% longer initial page load times due to preloading of content
• 2.4X higher memory usage as the scroll position grows
• Reduced accessibility for keyboard navigation and screen readers
• Diluted SEO signals as content is harder to uniquely address

Source: WPO Stats

Scrapers should adjust their approach accordingly, e.g.
• Increasing timeouts to allow for longer page loads
• Streaming responses to minimize memory overhead
• Extracting semantic links and metadata to improve content addressability

Alternative Scraping Tools & Libraries

While ScrapingBee is a robust solution for scraping infinite scroll in Go, there are other tools and libraries worth considering:

Puppeteer and Selenium offer powerful browser automation but require managing your own infrastructure. Scrapy-Splash combines a headless browser with a caching proxy server for easier deployment.

Colly is a pure Go library that simplifies building concurrent scrapers, but lacks JavaScript support. It can be paired with a headless browser or API service for dynamic content.

Ultimately, the best tool depends on your specific needs and constraints. ScrapingBee‘s API-first approach and JavaScript scenarios make it a good fit for Go scrapers tackling demanding targets.

Ethical Scraping Practices

With great scraping power comes great responsibility. When scraping infinite scroll or any other type of content, it‘s crucial to follow ethical guidelines:

1. Respect robots.txt – Honor site owners‘ wishes regarding scraping permissions
2. Throttle requests – Avoid aggressive crawling that could overload servers or get your IP blocked
3. Comply with GDPR – Obtain consent before scraping personal data of EU citizens
4. Don‘t steal content – Scrape facts, not copyrighted material, or obtain explicit permission

Tools like ScrapingBee can help by providing IP rotation, request rate limiting, and GDPR-compliant data handling out of the box. However, the ultimate accountability lies with the scraper operator.

Looking Ahead

As web technologies evolve, so must web scraping techniques. Frameworks like React, Vue and Angular are ushering in a new generation of highly dynamic, SPA-driven sites. Traditional scrapers that rely on parsing HTML will struggle to keep up.

Machine learning may offer a solution, with models that can automatically learn to interact with complex pages and extract structured data. In the meantime, scraping APIs and headless browser solutions will remain essential for handling modern web patterns like infinite scroll pagination.

One thing is certain – the demand for web data shows no signs of abating. As the web continues to grow and change, scrapers who can adapt and innovate will be well-positioned to unlock its vast potential for insight and utility.