Awesome GitHub RepositoriesDistributed Crawling Systems

Dieses Projekt ist eine umfassende Bildungsressource und ein Studienleitfaden, der sich auf die Architektur verteilter Systeme und das Design von Backend-Infrastrukturen konzentriert. Es bietet einen strukturierten Lehrplan zur Beherrschung der Prinzipien von Skalierbarkeit, Zuverlässigkeit und Leistung, die für den Entwurf komplexer Softwaresysteme erforderlich sind. Das Repository zeichnet sich durch einen methodischen Ansatz zur Vorbereitung auf technische Vorstellungsgespräche aus, der Entwurfsmuster, architektonische Kompromisse und Tools für räumliche Wiederholungen integriert, um Nutzern das Behalten komplexer Konzepte zu erleichtern. Es betont die einschränkungsgesteuerte Analyse und lehrt Nutzer, wie sie konkurrierende Anforderungen wie Latenz, Konsistenz und Verfügbarkeit beim Entwurf von Architekturen bewerten können. Der Inhalt deckt ein breites Spektrum an Systemdesign-Fähigkeiten ab, einschließlich Strategien für die Datenbankskalierung, Verkehrsmanagement und Infrastrukturoptimierung. Es werden Techniken für horizontale Skalierung, mehrschichtiges Caching, asynchrone Kommunikation und Service-Discovery detailliert beschrieben, während gleichzeitig Frameworks für die Durchführung von Ressourcenschätzungen und Kapazitätsplanungen bereitgestellt werden. Die Dokumentation ist als Studienleitfaden organisiert und bietet einen systematischen Pfad durch die Grundlagen des Backend-Engineerings und des großskaligen Systemdesigns.

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