-
How to extract random IP addresses from an online dataset using Go?
Extracting random IP addresses from an online dataset can be useful for network testing or analysis. Go’s lightweight concurrency model and the Colly library make it an excellent choice for efficiently scraping structured datasets. Start by inspecting the website to locate the IP addresses, often found in tables or lists. If the dataset is dynamically loaded, you may need to analyze network traffic for API calls or use a tool like chromedp to render the page. Ensuring that you handle pagination or infinite scrolling is key to collecting a comprehensive dataset.
Here’s an example using Go and Colly to scrape random IP addresses from a static dataset:package main import ( "fmt" "log" "github.com/gocolly/colly" ) func main() { c := colly.NewCollector() c.OnHTML(".ip-list-item", func(e *colly.HTMLElement) { ip := e.Text fmt.Println("IP Address:", ip) }) err := c.Visit("https://example.com/random-ip-dataset") if err != nil { log.Fatalf("Failed to scrape: %v", err) } }For dynamically loaded datasets, using chromedp to render the page and scrape content is more reliable. Managing request headers and delays helps avoid triggering anti-scraping mechanisms. How do you handle validation of the extracted IP addresses?
-
2 Replies
-
When dealing with dynamic datasets, I prefer using chromedp to fully render JavaScript-loaded elements. It’s efficient and ensures I capture all IP addresses.
-
Storing the scraped data in a database allows me to track updates and changes in movie listings over time, making it easier to maintain a current dataset.
Log in to reply.