Awesome GitHub RepositoriesDistributed Crawling Systems

Acest proiect este o resursă educațională cuprinzătoare și un ghid de studiu axat pe arhitectura sistemelor distribuite și designul infrastructurii backend. Oferă un curriculum structurat pentru stăpânirea principiilor de scalabilitate, fiabilitate și performanță necesare pentru a proiecta sisteme software complexe. Repository-ul se distinge prin oferirea unei abordări metodice pentru pregătirea interviurilor tehnice, încorporând tipare de design, compromisuri arhitecturale și instrumente de repetiție spațiată pentru a ajuta utilizatorii să rețină concepte complexe. Pune accent pe analiza bazată pe constrângeri, învățând utilizatorii cum să evalueze cerințele concurente precum latența, consistența și disponibilitatea atunci când schițează design-uri arhitecturale. Conținutul acoperă un spectru larg de capabilități de design de sistem, inclusiv strategii pentru scalarea bazelor de date, gestionarea traficului și optimizarea infrastructurii. Detaliază tehnici pentru scalarea orizontală, caching-ul pe mai multe niveluri, comunicarea asincronă și descoperirea serviciilor, oferind în același timp framework-uri pentru efectuarea estimărilor de resurse și planificarea capacității. Documentația este organizată ca un ghid de studiu, oferind o cale sistematică prin fundamentele ingineriei backend și designul sistemelor la scară largă.

Crawl4AI is an AI-powered web crawling and data extraction engine designed to transform complex web content into structured formats. It functions as a headless browser orchestrator, enabling the navigation of dynamic websites, the execution of custom scripts, and the capture of visual assets like screenshots and PDFs. By integrating language models directly into the extraction workflow, the system converts raw HTML into clean, structured data or Markdown files optimized for downstream ingestion. The platform distinguishes itself through a distributed, self-hosted infrastructure that manages l

Scrapy is a comprehensive framework designed for automated web data extraction and large-scale crawling. It operates on an asynchronous, event-driven engine that manages non-blocking network requests and data processing tasks, allowing for the efficient retrieval of structured information from web documents using path-based selectors. The system distinguishes itself through a highly modular architecture that supports complex data collection workflows. Users can implement custom middleware and signal handlers to intercept and modify request flows, while a priority-based scheduler manages concu

Crawlee is a web scraping framework designed for building scalable, reliable, and distributed data extraction pipelines. It provides a unified interface for managing headless browser automation and lightweight HTTP requests, allowing developers to handle complex web navigation, dynamic content rendering, and large-scale data collection within a single, modular architecture. The project distinguishes itself through its resource-aware concurrency controller, which dynamically scales task execution based on real-time CPU and memory usage to prevent host machine exhaustion. It also features a rob

This project is a comprehensive educational guide and framework for building web scrapers using Python. It provides a course-based approach to data extraction, combining a Python crawler framework with tutorials on web reverse engineering and network traffic analysis. The project distinguishes itself by covering advanced extraction challenges, including the decryption of obfuscated JavaScript and the bypass of anti-scraping measures. It specifically addresses mobile application scraping through the simulation of user interactions and the interception of network traffic. The capability surfac

PySpider is a Python web crawling framework designed for automated data extraction. It provides a pipeline for periodically fetching web content, processing HTML, and persisting scraped information into database backends. The system features a web-based management interface for editing scraping scripts, monitoring task progress, and reviewing collected data. It includes a headless browser JavaScript renderer to capture rendered HTML from dynamic web pages and a distributed architecture that uses message queues to scale crawling workloads across multiple nodes. The framework also covers task

This project is a comprehensive knowledge base and study resource designed for mastering technical interviews. It provides structured guides, roadmaps, and curricula focused on data structures, algorithms, system design, and frontend engineering to help candidates prepare for software engineering screenings. The repository distinguishes itself by offering a holistic approach to professional advancement. Beyond technical drills, it includes a career development handbook covering resume optimization, salary benchmarking, and strategic negotiation coaching. It also provides detailed methodologie