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Go vs. Python for Web Scraping: Which Is Best?

March 20, 2024 · 9 min read

Are you pondering the choice between Python's versatility and Go's speed for your web scraping projects? Choosing the right language can significantly impact your project outcome. 

But which one is best for web scraping: Golang or Python? Read on as we delve into the comparison to discover the best language 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 an interpreted programming language known for its simplicity and versatility. Its robust ecosystem of Python web scraping libraries like Scrapy and BeautifulSoup makes it a popular choice for extracting data from the web. However, Python's interpreted nature can delay execution time and impact performance. 

On the other hand, Golang is a compiled language, which means it's translated directly into machine code before execution. This results in faster execution times and better performance than interpreted languages like Python. However, while its standard library is robust, the Golang web scraping ecosystem may not be as extensive as Python's.

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.

Golang Python
Best for Performance and scalability Ease of use and rich library ecosystem
Ease of use Steeper learning curve Beginner-friendly and easy-to-write
Scraping libraries Limited Abundant
JavaScript rendering Supports JavaScript rendering with tools like Chromedp Can render JavaScript using libraries like Selenium, Playwright, and Splash
Data processing Efficient Moderate
Scalability High scalable due to its superior performance and optimized memory management Moderately scalable
Limitations Code verbosity and less extensive web scraping ecosystem Focuses on simplicity more than performance
Community support Moderate Extensive
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Go Has Superior Performance in Large-Scale Scraping

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

First, let's re-iterate Go's compiled nature, indicating that 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 will scrape 500 million URLs in 343 days, whereas it will take 649 days for Python to scrape the same number of URLs.

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, Go 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 optimize memory usage and execution paths accordingly. 

All this and more 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 abundant learning resources, making it easy to start. 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 data, Python provides many tools and functionalities 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 means that variable types are determined at compile time. So, the Go compiler knows the size and type beforehand and can allocate memory accordingly. Conversely, the interpreter allocates memory in Python's dynamic typing as the 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 means better performance and makes scaling easier than Python.

Code in Python is Simpler to Write

Python is one of the easiest languages to write because of its beginner-friendly and 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 Pokemon names from ScrapeMe, a test website. 

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

Example
import requests
from bs4 import BeautifulSoup
 
# Send a GET request to the target page
response = requests.get("https://scrapeme.live/shop/")
 
# print(response.text)
 
# Parse the HTML content
soup = BeautifulSoup(response.text, "html.parser")
 
# Find all h2s (elements containing the pokemon names)
pokemon_names = soup.find_all("h2")
 
# Extract the text from each element
for name in pokemon_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.

Example
package main
 
import (
    "fmt"
    "net/http"
    "github.com/PuerkitoBio/goquery"
)
 
func main() {
    // URL to make the HTTP request to
    url := "https://scrapeme.live/shop"
 
    // 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 Pokémon
    var pokemonNames []string
    doc.Find("h2").Each(func(i int, s *goquery.Selection) {
        pokemonNames = append(pokemonNames, s.Text())
    })
 
    // Print the extracted Pokémon names
    for _, name := range pokemonNames {
        fmt.Println(name)
    }
}

Leaving out the import statements, the Python script consists off five lines, whereas the Go snippet needed ten lines of code to perform the same actions (make HTTP request and parse 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 (Requests and BeautifulSoup) allow for concise code. On the other hand, Go 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 based on user actions. Therefore, you must execute the JavaScript code to gain access to such content. 

Python offers different libraries enabling you to render web pages like a browser. Some of these include Selenium, Playwright, Splash. To learn more, check out our Selenium web scraping guide in Python

Similarly, Go libraries like Chromedp provide a high-level API for controlling Chrome through the DevTools protocol. Check out this Golang headless browser web scraping tutorial to learn more.

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 recording over 2.7 million GitHub users. 

This large community translates into extensive support and resources. The community has garnered a vast knowledge base, including tutorials, documentation, blogs, etc. Also, platforms like StackOverflow, Reddit, and Ycombinator 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, getting blocked is still challenging. Luckily, ZenRows enables you to scrape undetected using any programming language.

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