Creating a production ready Web Crawler

Introduction

In the vast expanse of the internet, web crawlers serve as our digital explorers, tirelessly mapping the landscape of interconnected websites. Whether you’re a data enthusiast, a search engine optimizer, or simply curious about the inner workings of the web, understanding how to build a robust web crawler is an invaluable skill.

In this article, we’ll dive deep into the creation of an advanced web crawler, leveraging the power of asynchronous programming and the flexibility of graph databases. By the end, you’ll have a comprehensive understanding of how to build a crawler that’s not just efficient, but also respectful of web etiquette.

What Is a Web Crawler?

A web crawler, also known as Spider is a program designed to systematically browse the web, extracting data from websites. Developers often use them for data mining, SEO analysis, and content aggregation.

Use Cases for Web Crawlers

Web crawlers have a wide range of applications across various industries. Let’s explore some popular use cases:

1. Search Engine Optimization (SEO) Web crawlers are essential tools for SEO professionals to:

• Analyze website structure and identify broken links
• Check page load times and mobile-friendliness
• Track keyword usage and density across web pages
• Monitor backlinks and discover new link-building opportunities

2. Market Research and Competitive Analysis Businesses use web crawlers to:

• Track competitors’ pricing strategies
• Monitor product launches and feature updates
• Gather customer reviews and sentiment analysis
• Identify market trends and emerging opportunities

3. Academic Research Researchers leverage web crawlers to:

• Collect large datasets for analysis
• Track changes in online content over time
• Study social networks and online communities
• Analyze language patterns and usage across the web

4. E-commerce and Price Monitoring Online retailers and consumers benefit from crawlers that:

• Compare prices across multiple e-commerce platforms
• Track product availability and stock levels
• Monitor shipping costs and delivery times
• Identify discounts and special offers

5. Job Market Analysis Recruiters and job seekers use specialized crawlers to:

• Aggregate job postings from multiple job boards
• Analyze salary trends and job market demands
• Track company growth through hiring patterns
• Identify emerging skills and qualifications in various industries

The Anatomy of Our Web Crawler

Before we delve into the nitty-gritty details, let’s take a bird’s eye view of our crawler’s architecture:

Our web crawler consists of five main components:

1. Scraper: The heart of our system, responsible for fetching and parsing web pages.
2. LinkManager: Our data wrangler, managing link information using Neo4j.
3. ConfigManager: The control center, handling all configuration settings.
4. RobotsHandler: Our ethical compass, ensuring we respect robots.txt rules.
5. ParserManager: The polyglot of our system, managing various content parsers.

Each of these components plays a crucial role in creating a crawler that’s not just powerful, but also flexible and considerate.

Asynchronous Magic: The Key to Efficient Crawling

One of the standout features of our crawler is its use of asynchronous programming. But why is this so important?

Imagine you’re at a library, tasked with reading a hundred books. Would you read one book cover to cover before moving to the next? Or would you start reading multiple books simultaneously, switching between them while waiting for new pages to arrive?

Asynchronous programming allows our crawler to do the latter. It can initiate multiple web requests concurrently, dramatically improving performance.

Here’s a snippet of our asynchronous crawling logic:

async def crawl(self, start_url, max_pages=None):
    visited = set()
    to_visit = asyncio.Queue()
    await to_visit.put(start_url)

    while not to_visit.empty() and len(visited) < max_pages:
        url = await to_visit.get()
        if url not in visited and await self.robots_handler.is_allowed(url):
            # Fetch and process the page
            # Add new URLs to to_visit queue

This approach allows our crawler to efficiently manage hundreds or even thousands of concurrent requests, making it significantly faster than traditional synchronous crawlers.

Neo4j: A Web of Connections

Traditional databases struggle with representing the inherently interconnected nature of the web. That’s where Neo4j, a graph database, comes to our rescue.

With Neo4j, we can easily represent web pages as nodes and the links between them as edges. This makes operations like finding all pages linking to a specific URL or identifying the shortest path between two pages incredibly efficient.

Here’s how we add or update a link in our Neo4j database:

def add_or_update_link(self, url, parent_url=None, content_hash=None, last_modified=None):
    with self.driver.session() as session:
        session.execute_write(self._create_or_update_link, url, parent_url, content_hash, last_modified)

@staticmethod
def _create_or_update_link(tx, url, parent_url, content_hash, last_modified):
    query = (
        "MERGE (l:Link {url: $url}) "
        "SET l.lastChecked = datetime(), "
        "l.contentHash = $content_hash, "
        "l.lastModified = $last_modified "
    )
    tx.run(query, url=url, content_hash=content_hash, last_modified=last_modified)

    if parent_url:
        query = (
            "MATCH (l:Link {url: $url}) "
            "MERGE (p:Link {url: $parent_url}) "
            "MERGE (p)-[:LINKS_TO]->(l)"
        )
        tx.run(query, url=url, parent_url=parent_url)

This approach allows us to efficiently store and query the web’s structure, opening up possibilities for advanced analysis and traversal.

Being a Good Net Citizen: Respecting Robots.txt

Web crawling isn’t just about efficiency; it’s also about etiquette. That’s where our RobotsHandler comes in.

class RobotsHandler:
    def __init__(self):
        self.parser = RobotFileParser()
        self.cache = {}

    async def is_allowed(self, url):
        domain = urlparse(url).netloc
        if domain not in self.cache:
            await self.fetch_robots_txt(f"https://{domain}/robots.txt")
        return self.parser.can_fetch("*", url)

This handler ensures that our crawler respects the rules set out in each website’s robots.txt file, preventing us from accessing pages that site owners have requested to be left alone.

Flexibility is Key: The Parser Manager

Different websites may require different parsing strategies. Our ParserManager allows for easy integration of custom parsers:

class ParserManager:
    def __init__(self):
        self.parsers = {'default': DefaultParser()}

    def add_parser(self, name, parser):
        self.parsers[name] = parser

    def parse_content(self, content, parser_name='default'):
        parser = self.parsers.get(parser_name, self.parsers['default'])
        return parser.parse(content)

This flexibility allows our crawler to adapt to various types of websites and content structures.

Conclusion: The Web is Your Oyster

Building a web crawler is more than just writing code to fetch web pages. It’s about creating a system that’s efficient, flexible, and respectful of the web ecosystem. With the power of asynchronous programming, the flexibility of Neo4j, and a modular architecture, our crawler is well-equipped to explore the vast expanses of the internet.

Whether you’re looking to build your own search engine, conduct web research, or simply learn more about how the web works, I hope this deep dive into our web crawler project has been illuminating.

Remember, with great power comes great responsibility. Always crawl responsibly and respect website owners’ wishes.

Happy crawling!

This article is based on an open-source web crawler project. You can find the full source code and contribute to the project on GitHub.