Creating a Search Engine: From Scratch to Success

Table of Contents

1. Introduction
2. Building a Search Engine
   • 2.1 Server Side Implementation
   • 2.2 Extracting Keywords
   • 2.3 Calculating Match Score
3. Handling Plurals in WORD Matching
4. Scraping Websites for Data
   • 4.1 Web Scraping Basics
   • 4.2 Web Scraping Challenges
5. Overcoming Web Scraping Challenges
   • 5.1 Using a VPN for IP Rotation
   • 5.2 Dealing with Heap Allocation Errors
   • 5.3 Queueing Requests
   • 5.4 Dealing with Infinite Recursive Calls
6. Debugging Strategies
   • 6.1 Learning from Stack Overflow
   • 6.2 Bug in Node.js
   • 6.3 Handling Cloudflare Protection
7. Conclusion
8. Pros and Cons
9. Highlights
10. FAQ

Building a Search Engine

Building a search engine may seem like a complex task, but with the right approach, it can be done efficiently. In this article, we will explore the journey of creating a search engine from scratch, overcoming challenges, and implementing effective solutions.

1. Introduction

The power of search engines like Google is truly remarkable. They can index millions of websites and provide users with Relevant search results in a matter of seconds. Inspired by this, I embarked on a mission to create my own search engine, one that would link queries to the perfect websites without any external help or dependencies.

2. Building a Search Engine

2.1 Server Side Implementation

To begin with, I started by building the server-side infrastructure. However, my decision to use JavaScript raised doubts early on. The process of installing dependencies felt unnecessarily tedious and time-consuming. I couldn't help but wonder why it couldn't be as straightforward as using Package managers like pip.

2.2 Extracting Keywords

The first crucial step in creating a search engine is extracting keywords from web pages. I devised a method to extract every word from the page text and count their occurrences. Words with more than one occurrence were deemed significant and remembered for future queries. Additionally, keywords that appeared in the page title or description were given a higher priority in the matching process.

2.3 Calculating Match Score

When a user makes a query, the search engine calculates a match score based on the keywords. The higher the priority and occurrence of a keyword on both the search query and the page, the higher the match score. While the initial implementation seemed flawed, it laid the foundation for further improvements.

3. Handling Plurals in Word Matching

A significant challenge I encountered was dealing with plurals when matching words. For example, the word "kitten" should ideally match with a site named "kittens." Initially, I tried manually appending endings to nouns using a dictionary API, but this approach proved to be inefficient. After a relentless search, I found a downloadable dictionary that contained plurals for every word. This solution, although time-consuming, worked effectively.

4. Scraping Websites for Data

Web scraping plays a crucial role in the search engine's functionality. To Gather data, I needed to download thousands of websites from the internet and parse their content.

4.1 Web Scraping Basics

The most common method of web scraping involves fetching a website, extracting all links from the page, and recursively scraping those websites. However, implementing this process was not without its challenges. Mistakes in importing functions and handling promises resulted in frustrating debugging Sessions.

4.2 Web Scraping Challenges

One of the major challenges I faced was the risk of getting banned from websites due to frequent scraping requests. To overcome this, I employed a VPN service that automatically changed my IP address every five minutes. This helped me avoid detection and continue scraping without interruptions.

5. Overcoming Web Scraping Challenges

5.1 Using a VPN for IP Rotation

Rotating IP addresses through a VPN was a Game-changer in web scraping. By changing my IP every few minutes, I could effectively scrape websites without drawing unwanted attention.

5.2 Dealing with Heap Allocation Errors

As the scraping process intensified, I encountered heap allocation errors in JavaScript. To resolve this, I had to understand the limitations of JavaScript promises and create a more efficient queue system for making requests.

5.3 Queueing Requests

The queue system allowed me to run scraping functions sequentially, eliminating the errors caused by running multiple requests simultaneously. This approach significantly reduced crashes and improved the overall stability of the search engine.

5.4 Dealing with Infinite Recursive Calls

Initially, I believed the crashes were due to incorrect assumptions about recursive function calls. However, upon further investigation, I realized the root cause was a bug in Node.js itself. By consulting online resources and leveraging the expertise of the developer community, I was able to troubleshoot and overcome this challenge.

6. Debugging Strategies

Debugging played a crucial role throughout this project. I relied on various strategies to identify and fix issues as they arose.

6.1 Learning from Stack Overflow

One of the most valuable learning resources during this journey was Stack Overflow. Whenever I encountered a problem, I turned to the vast Knowledge Base of the community to find solutions and gain insights. Through this process, I developed a deeper understanding of programming concepts and best practices.

6.2 Bug in Node.js

At one point, I stumbled upon a peculiar bug in Node.js that caused unexpected behavior in my program. It involved the addition of a random STRING from URLs, which seemed illogical. I reported the bug, and it was later confirmed as a memory and read stream issue.

6.3 Handling Cloudflare Protection

Many websites employ cloud-based protection systems like Cloudflare, which can make scraping challenging. Unfortunately, there was no easy workaround for these instances, as the websites had strict bot protection measures in place.

7. Conclusion

Building a search engine from scratch was undoubtedly a challenging endeavor. Throughout the journey, I encountered numerous obstacles and learned valuable lessons in programming, web scraping, and debugging. While JavaScript had its own inconveniences and limitations, it was instrumental in developing a functional search engine.

Pros and Cons

Pros:

• Ability to create a search engine from scratch
• Understanding of server-side implementation and web scraping
• Experience in handling challenges like plurals and IP rotation
• Improved debugging skills through real-world problem-solving

Cons:

• Time-consuming process, especially when implementing complex solutions
• Dependency on resources like dictionaries and VPNs
• Inevitable encounters with bugs and limitations in programming tools

Highlights

• Building a search engine from scratch
• Practical implementation of server-side development
• Overcoming challenges in web scraping
• Strategies for debugging and problem-solving
• Lessons learned in programming and web development

FAQ

Q: How long did it take to build the search engine? A: The process took a considerable amount of time, including research, implementation, and debugging. Overall, it spanned several weeks of dedicated effort.

Q: Is JavaScript a suitable language for building a search engine? A: While JavaScript can be used for creating a search engine, it does have its limitations and challenges. However, with the right approach and problem-solving strategies, it is possible to overcome these obstacles.

Q: Are there any alternative methods to handle word matching with plurals? A: Yes, there are alternative methods to handle plurals. One approach is to use language processing libraries or APIs that provide stemming capabilities. Stemming reduces words to their base form, allowing for more effective matching.

Q: How did you handle websites protected by Cloudflare? A: Unfortunately, websites protected by Cloudflare posed a challenge in web scraping. Due to strict bot protection measures, there was no straightforward workaround. These instances often required manual intervention or alternative approaches.

Q: What were some of the key lessons learned from this project? A: Some key lessons learned include the importance of thorough debugging, the value of community resources like Stack Overflow, the need for efficient queueing systems in web scraping, and the significance of understanding and working around limitations in programming languages.

Q: Can this search engine compete with established search engines like Google? A: Building a search engine that can compete with established giants like Google is a monumental task. While this project offers insights into certain aspects of search engine development, it is unlikely to match the scale, sophistication, and accuracy of top search engines in the industry.

Q: How can someone get started with building their own search engine? A: Getting started with building a search engine requires a good understanding of web development, data parsing, and search algorithms. It is advisable to start small, experiment with simple crawling and indexing techniques, and gradually expand the functionality based on personal goals and requirements.

Resources:

• Node.js: https://nodejs.org/
• Stack Overflow: https://stackoverflow.com/
• Cloudflare: https://www.cloudflare.com/