Web Scraping Golang vs Python: Which Should You Choose?

Choosing between Go and Python for web scraping comes down to your project priorities. Python wins on simplicity and library support, while Go excels at performance and scalability.

Here's a break down of both languages for scraping so you can make the right choice for your project.

Web Scraping With Golang or Python: Which One You Should Use?

Typically, your choice depends on your overall project requirements. Python is a flexible, easy-to-use language with a strong set of web scraping libraries, such as Scrapy and BeautifulSoup, making it a go-to choice for many developers. However, its interpreted nature can slow down execution. Go, being a compiled language, offers faster performance and efficiency, though its scraping library ecosystem isn’t as mature as Python’s.

So, choose Python if you want simplicity and a rich library ecosystem, and opt for Golang if you prioritize performance and efficiency.

Go vs. Python Comparison for Web Scraping

Below is a comparison table showing Golang vs Python web scraping capabilities.

Continue reading to learn more. 

Go Has Superior Performance in Large-Scale Scraping

We've mentioned that Golang offers better performance than Python. But why is that? 

First, let's reiterate that Golang is compiled, indicating it was designed with speed in mind. Thus, most benchmarks have Go significantly outperforming Python. For example, a web scraping comparison test documented on Medium found that Golang can scrape 50 million URLs in 305 days, whereas Python would take 649 days to do the same.

This result is unsurprising as there are many reasons for this performance gap. One, Go has built-in support for concurrency through goroutines. These are lightweight threads managed by the Go runtime, allowing you to execute multiple tasks concurrently without the overhead associated with traditional threading models. 

Also, Golang is statically typed, meaning its variable types are explicitly declared and checked at compile time rather than at runtime, as in Python's dynamic typing. This lets the compiler know the data types beforehand and optimizes memory usage and execution paths accordingly.

This makes Go the preferred choice for large-scale scraping tasks where performance is critical.

It’s Easier to Start Scraping With Python

While Go offers better performance, Python is generally one of the easiest languages to learn, particularly for beginners diving into web scraping. When you read Python code, it's easy to understand. Its syntax is simple, clean, and readable, making it easy for beginners to write code quickly.

But that's not all.

Python’s extensive community has led to an abundance learning resources, making it easy to get started. Python also boasts a rich ecosystem of web scraping libraries. Some provide high-level abstractions, pre-built functionalities, and intuitive APIs that simplify the scraping process. 

Python Offers a Rich Library Ecosystem for Scraping

The previous section reiterated Python's rich ecosystem. There's a Python library for practically every aspect of web scraping. From making HTTP requests to parsing HTML, Python provides many tools and features that simplify the scraping process. Some of the most popular ones are BeautifulSoup, Scrapy, and Requests. 

These libraries provide intuitive APIs that enable you to initiate scraping operations using a few lines of code. For example, you only need one line to make HTTP requests using Python's Requests library. While there are similar libraries in Go, they're not as prominent as their Python counterparts, and Go's ecosystem is not as extensive. 

Overall, libraries save time and effort as they allow you to leverage prebuilt functionalities rather than re-inventing the wheel or building things from scratch. However, they can also increase external dependencies and overall project size.

Go Optimizes Memory Management in Large Projects

Go is more memory efficient than Python. Its statically typed nature and compilation process mean that variable types are determined at compile time. So the Go compiler knows the size and type in advance and can allocate memory accordingly. Conversely, the interpreter allocates memory in Python's dynamically typed system, as data is stored at runtime.

This efficiency can be advantageous, particularly for large-scale web scraping tasks. The ability to handle memory-intensive operations effectively leads to better performance and makes scaling easier than in Python.

Code in Python is Simpler to Write

Python is one of the easiest languages to write in because of its beginner-friendly, easily readable syntax. You'll require more lines of code to perform an action in Go than in Python. 

But don't just take our word for it. Let's compare basic scraping scripts in Python and Go for fetching product names from ScrapingCourse, a test e-commerce website. 

The Python code below uses Requests to make a GET request and BeautifulSoup to parse the retrieved HTML. 

# pip3 install beautifulsoup4 requests
import requests
from bs4 import BeautifulSoup
 
# send a GET request to the target page
response = requests.get("https://www.scrapingcourse.com/ecommerce/")
 
# parse the HTML content
soup = BeautifulSoup(response.text, "html.parser")
 
# find all h2s (elements containing the product names)
product_names = soup.find_all("h2")
 
# extract the text from each element
for name in product_names:
    print(name.text)

Similarly, the Go code below uses the built-in Go library, `net/http`, to make a GET request and Goquery to parse the corresponding HTML.

package main
 
import (
    "fmt"
    "net/http"
    "github.com/PuerkitoBio/goquery"
)
 
func main() {
    // URL to make the HTTP request to
    url := "https://www.scrapingcourse.com/ecommerce/"
 
    // make the GET request
    resp, _ := http.Get(url)
    defer resp.Body.Close()
 
    // use goquery to parse the HTML
    doc, _ := goquery.NewDocumentFromReader(resp.Body)
 
    // extract names of products
    var productNames []string
    doc.Find("h2").Each(func(i int, s *goquery.Selection) {
        productNames = append(productNames, s.Text())
    })
 
    // print the extracted product names
    for _, name := range productNames {
        fmt.Println(name)
    }
}

Excluding the import statements, the Python script consists of five lines, whereas the Go snippet requires ten lines of code to perform the same actions (make an HTTP request and parse the response).

You could argue that things could be simpler in both cases. But the overall picture remains the same. Python's simplicity and high-level abstractions (such as Requests and BeautifulSoup) allow for concise code. On the other hand, Golang is just more verbose. 

They Can Both Render Javascript

Both Python and Go can render JavaScript. This functionality is crucial because many modern websites rely on JavaScript to display content or load data in response to user actions. Therefore, you must execute the JavaScript code to gain access to such content.

Python offers various libraries that enable you to render web pages as a browser would. Some of these include Selenium and Playwright.

Similarly, Go libraries such as Chromedp provide a high-level API for controlling Chrome via the DevTools Protocol. Check out this Golang headless browser web scraping tutorial to learn more. However, modern websites use anti-bot systems that can detect and block headless browser automation.

Python’s Community Provides Extensive Support and Resources

It's no secret that Python has one of the largest and most active developer communities among programming languages. It's widely adopted, even for web scraping, with popular libraries like Requests, which has over 54k GitHub stars.

This large community translates into extensive support and resources. The community has built a vast knowledge base, including tutorials, documentation and blogs. Also, platforms like Stack Overflow, Reddit, and Y Combinator are filled with learning resources and developers willing to provide support.

While Python benefits from a large and established community, Go, a relatively newer language, is still growing. Its community, support, and available resources may not be as extensive as Python's.

Conclusion

Both Python and Go offer distinct advantages depending on your use case. Python's rich library ecosystem, less verbose code, and extensive resources make it an excellent choice for beginner web scrapers and instances where performance isn't prioritized. 

On the other hand, Go's superior performance and optimized memory management offer compelling advantages, particularly for large-scale web scraping. 

However, regardless of your choice, being blocked remains challenging. Luckily, ZenRows lets you scrape without getting blocked, regardless of your programming language. Try ZenRows for free now or speak with sales!