How to Build a Web Crawler for Data Extraction - 3 Ways

“As a newbie, I built a web crawler and successfully extracted 20k data from Amazon website.”

Do you also want to know how to make a web crawler and create a database that eventually turns into your asset at no cost? It’s not a difficult thing after you’ve learned the right tool and method. This article will share 3 different ways to help you build a web crawler step-by-step even if you don’t know anything about coding.

What Is A Web Crawler

A web crawler (also known as a web spider or web bot) is a type of automated software designed to browse the web and collect data from websites (read the definition on Wikipedia). The crawler systematically navigates websites by following links and indexing content. Web crawlers are used by search engines like Google to index webpages, but they can also be used for a variety of purposes, such as data scraping, price monitoring, market research, or gathering specific information from the internet.

You can view a web crawler as a particular program designed to crawl websites in orientation and glean data. However, you are unable to get the URL address of all web pages within a website containing many web pages in advance. Thus, what concerns us is how to fetch all the HTML web pages from a website.

Normally, we could define an entry page: One web page contains URLs of other web pages, and then we could retrieve these URLs from the current page and add all of these affiliated URLs into the crawling queue. Next, we crawl another page and repeat the same process as the first one, recursively. Essentially, we could assume the crawling scheme is a depth-search or breadth-traversal. And as long as we could access the Internet and analyze the web page, we could crawl a website. Fortunately, most programming languages offer HTTP client libraries to crawl web pages, and we can even use regular expressions for HTML analysis.

Some Tips for Web Crawling

Crawl Depth – How many clicks from the entry page do you want the crawler to traverse? In most cases, a depth of 5 is enough for crawling from most websites.
Distributed Crawling – The crawler will attempt to crawl the pages at the same time.
Pause – The length of time the crawler pauses before crawling to the next page.
Crawl Speed – The faster you set the crawler, the harder it will be on the server (At least 5-10 seconds between page clicks).
URL template – The template will determine which pages the crawler wants data from.
Save log – A saved log will store which URLs were visited and which were converted into data. It is used for debugging and prevents crawling a visited site repeatedly.

Here is a video that explains the web crawler and the difference between web crawlers and web scrapers.

You may be curious about is web crawler legal or not, well, it depends. But generally speaking, it’s totally legal in most countries to crawl public data on a website.

Why Do You Need A Web Crawler

Imagine a world without Google Search. How long do you think it will take to get a recipe for chicken nuggets from the Internet? There are 2.5 quintillion bytes of data being created online each day. Without search engines like Google, it will be like looking for a needle in a haystack.

From Hackernoon by Ethan Jarrell

A search engine is a unique kind of web crawler that indexes websites and finds web pages for us. Besides search engines, you can also build a customized web crawler to help you achieve:

1. Content aggregation: It works to compile information on niche subjects from various resources into one single platform. As such, it is necessary to crawl popular websites to fuel your platform in time.

2. Sentiment analysis: It is also called opinion mining. As the name indicates, it is the process to analyze public attitudes toward one product or service. It requires a monotonic set of data to evaluate accurately. A web crawler can extract tweets, reviews, and comments for analysis.

3. Lead generation: Every business needs sales leads. That’s how they survive and prosper. Let’s say you plan to make a marketing campaign targeting a specific industry. You can scrape email, phone number, and public profiles from an exhibitor or attendee list of Trade Fairs, like attendees of the 2018 Legal Recruiting Summit.

Method 1: Preset Data Crawler Templates Online

If you only want to crawl a specific site for data quickly, you can try to use a preset data scraping template. These templates are designed for popular scraped sites and can be used from the web page, which means that you don’t need to download any software to your device. They’re very friendly for beginners as you just need to enter several parameters and start crawling data.

Try the online Contact Details Scraper template below:

https://www.octoparse.com/template/contact-details-scraper

Method 2: FREE No-coding Web Crawler Tool

For customizing more data scraping needs on the crawler, Octoparse is the recommended choice for non-coders. It has an AI-based auto-detecting function to recognize the target data fields automatically. What’s more, its advanced functions like cloud scraping, IP proxies, pagination, etc. can also be used easily during your data scraping.

Octoparse: Easy Web Scraping for Anyone

Free Download

Turn website data into structured Excel, CSV, Google Sheets, and your database directly.

Scrape data easily with auto-detecting functions, no coding skills are required.

Preset scraping templates for hot websites to get data in clicks.

Never get blocked with IP proxies and advanced API.

Cloud service to schedule data scraping at any time you want.

3 steps to build a web crawler without coding

Step 1: Download Octoparse and copy a web page link

Download and install Octoparse on your device, and paste the target web page URL to the main panel. It will begin detecting automatically to build a crawler workflow, or you can select “Advanced Mode” to try more customized options.

Step 2: Customize the web crawler data fields

You can simply preview the data that is detected, and click the “Create workflow” button to set the crawler. You can customize the data field as you need by clicking the target data position with the tips. Octoparse supports setting up pagination by clicking the “Next Page” button so that the crawler can navigate through it.

Step 3: Run the web crawler to extract data and export as Excel files

Once you finish setting up the extraction fields, click the “Run” button to execute the crawler. You can download the data to local devices by Excel or CSV.

If you still have questions, move to the user guide to learn more details.

Method 3: Build A Web Crawler with Coding Script

Writing scripts with computer languages is predominantly used by programmers. It can be as powerful as you create it to be. Below is a step-by-step guide to help you build a basic web crawler using Python. We’ll use Requests for sending HTTP requests and BeautifulSoup for parsing HTML content.

Steps to create a web crawler using Python

Step 1: Install and import the required libraries

Before you start coding, you’ll need to install the following libraries:

Requests: To send HTTP requests.
BeautifulSoup: To parse HTML content.
lxml (optional): For faster parsing with BeautifulSoup.

You can install them using

pip install requests beautifulsoup4 lxml

In your Python script, import the necessary libraries:

import requests
from bs4 import BeautifulSoup

Step 2: Send an HTTP Request

Next, you’ll need to send an HTTP request to the website you want to crawl and get the content of the page. For example, to crawl the homepage of a website, use the requests.get() method:

url = 'https://example.com'  # Replace with the website you want to crawl
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    print("Successfully fetched the webpage!")
else:
    print(f"Failed to retrieve the webpage. Status code: {response.status_code}")

Step 3: Parse the HTML Content

Once you have the content of the page, you need to parse it. Use BeautifulSoup to parse the HTML and extract useful data:

# Parse the content using BeautifulSoup
soup = BeautifulSoup(response.text, 'html.parser')

# Print the parsed content
print(soup.prettify())  # To view the page structure

Step 4: Extract Information

Now that the page is parsed, you can extract specific data, like the titles of articles, URLs, or images. For example, if you want to extract all the hyperlinks on the page, you can find all the <a> tags:

# Extract all links (anchor tags)
links = soup.find_all('a')

# Print all the links
for link in links:
    print(link.get('href'))

Step 5: Follow Links to Crawl Other Pages

A basic web crawler will not only scrape the current page but will also follow the links found on the page and continue crawling. To achieve this, you can loop through the links and recursively call your function to crawl them:

# Function to crawl a webpage
def crawl(url, depth=2):
    if depth == 0:
        return  # Limit crawling depth to avoid infinite loops

    print(f"Crawling URL: {url}")
    response = requests.get(url)

    if response.status_code != 200:
        print(f"Failed to fetch {url}")
        return

    soup = BeautifulSoup(response.text, 'html.parser')

    # Extract links from the current page
    links = soup.find_all('a', href=True)

    # Print links on the page
    for link in links:
        print(link.get('href'))

    # Recursively crawl the linked pages
    for link in links:
        next_url = link.get('href')
        # Ensure the link is an absolute URL
        if not next_url.startswith('http'):
            next_url = url + next_url  # Handle relative links

        # Recurse to crawl further pages
        crawl(next_url, depth - 1)

# Start crawling from the homepage
crawl('https://example.com', depth=2)

Step 6: Run and Test the Crawler

Once you’ve completed the code, run your Python script, and the crawler will visit the starting URL, extract data, and follow the links (if specified) to gather more data. You can adjust the crawling depth or add additional data extraction logic depending on your needs.

Final Thoughts

Writing scripts can be painful as it has high initial and maintenance costs. No single web page is identical, and we need to write a script for every single site. It is not sustainable if you need to crawl many different websites. Besides, websites tend to change layouts and structures after some time. As a result, we have to debug and adjust the crawler accordingly. A free web crawler like Octoparse or data crawler templates is more practical for beginners with less effort.