🚀 Learn how to design a scalable Web Crawler system! 🎯
🌐 Understanding how search engines work behind the scenes
A must-know concept for cracking #TechInterviews 🔍.
Are You Wondering how Google, Bing, or other search engines crawl billions of web pages across the internet? 🤔
In this live session, we’ll break down the #SystemDesign, software architecture, and technologies behind a large-scale distributed Web Crawler system 🕷️.
A modern Web Crawler must efficiently discover, fetch, process, and index web pages at massive scale while handling challenges like rate limiting, duplicate detection, politeness policies, scheduling, and fault tolerance ⚡.
Here’s a generalized view of technologies and concepts used in this system with such a scale and complexity 🌍:
Load Balancer (LB): A load balancer ⚖️ distributes crawler traffic across multiple crawler nodes to ensure scalability and high availability 🌟.
Distributed Crawlers: Large-scale crawling systems use multiple distributed crawler workers 🕸️ that can fetch web pages in parallel while respecting domain-level rate limits 🚦.
URL Frontier & Scheduler: A URL frontier queue 📥 manages millions of URLs waiting to be crawled. Intelligent schedulers prioritize URLs based on freshness, popularity, and crawl policies ⏱️.
Message Queue with Kafka: Apache Kafka 📡 can be used to handle crawl events, distribute URLs, and communicate between microservices 🔄.
In-Memory Caching with Redis: Redis ⚡ may be used for deduplication, URL caching, crawl state management, and fast access to metadata 📈.
Database with Cassandra/Bigtable: Distributed databases 🗄️ like Cassandra or Bigtable are well-suited for storing massive amounts of crawl metadata and indexing information 📊.
Search Indexing with Elasticsearch: Elasticsearch 🔍 can help create searchable indexes from crawled web content, enabling fast querying and retrieval 🚀.
Content Parsing & Extraction: HTML parsers 📰 extract metadata, links, images, structured data, and relevant content from crawled pages.
Duplicate Detection: Techniques like hashing and Bloom Filters 🧠 help avoid crawling duplicate or near-duplicate content.
Container Orchestration with Kubernetes: Kubernetes 🐳 enables automated deployment, scaling, monitoring, and management of crawler services 🤖.
Programming Languages: Technologies such as Java ☕, Go 🐹, Python 🐍, and C++ 🔧 are commonly used for building high-performance crawling and indexing systems.
Monitoring & Observability: Prometheus 📉 and Grafana 📊 are commonly used to monitor crawler throughput, failures, latency, and system health.
Chapters
0:00 Introduction to Web Crawler System Design
2:30 Starting the Mock Interview
4:57 Defining Web Crawler and Functional Requirements
11:13 Detailed Functional and Non-functional Requirements
13:58 Understanding robots.txt
16:45 Non-functional Requirements & Scalability
20:53 Capacity Estimation & Data Volume Calculations
24:52 Database Selection (NoSQL vs Relational)
27:54 Core Architecture Components
30:57 URL Frontier & Scheduling Strategies
36:46 Deduplication and Hashing Techniques
38:59 Bloom Filters and Near-Duplicate Detection
43:08 Summarizing the Final System Architecture
47:06 Interview Recap and Key Takeaways
49:30 Feedback and Performance Assessment
🚀 Explore crucial System Design concepts and gain valuable insights to enhance your architecture skills 💡👨💻🌟.
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