gnemoug/distribute_crawler
Distribute Crawler
Distribute crawler is a distributed web scraping framework that integrates with Scrapy to coordinate multiple crawler instances across clusters. It utilizes a centralized task queue to manage and scale concurrent data collection operations, enabling horizontal scaling of scraping tasks across multiple worker nodes.
The framework distinguishes itself through its focus on large-scale data management and traffic control. It persists scraped items and binary assets into document-oriented database clusters, utilizing deduplication logic to optimize bandwidth and storage. To maintain consistent data collection from protected sources, it employs automated anti-blocking strategies, including request identity rotation, dynamic velocity throttling, and concurrency adjustments based on target site responsiveness.
The system includes comprehensive infrastructure for operational oversight, featuring real-time performance monitoring and metric aggregation. It supports a modular request pipeline that allows for the injection of custom headers and behavior management during the crawling process.
Features
- Distributed Web Crawling - Provides a distributed web scraping framework that scales data collection across multiple nodes using centralized task queues.
- Horizontal Crawler Scaling - Coordinates multiple crawler nodes by distributing work items through a centralized Redis queue for horizontal scaling.
- Redis-Backed Queues - Uses Redis as a centralized message broker to manage and scale concurrent crawling tasks across worker nodes.
- Scrapy-Framework-Based Crawlers - Integrates the Scrapy framework with Redis to build high-performance, distributed crawling architectures.
- MongoDB Persistence - Persists scraped items and binary assets into sharded MongoDB clusters for high-volume data management.
- NoSQL Data Modeling - Utilizes NoSQL document-oriented storage to handle large volumes of scraped data with flexible schemas.
- Bloom Filters - Uses probabilistic membership testing to track visited pages and prevent redundant processing.
- Scraped Data Persistence - Maps extracted web content directly into flexible document-oriented database schemas for scalable storage.
- Network Throttling - Dynamically adjusts request transmission rates and download delays based on real-time network latency and target server responsiveness.
- Anti-Blocking Configurations - Configures anti-blocking strategies like user-agent rotation and cached page access to maintain consistent data collection.
- Proxy and User-Agent Rotation Middleware - Implements middleware that automatically rotates user-agent strings and manages cookies to bypass anti-scraping protections during data collection.
- Deduplicated Downloaders - Fetches remote files while verifying content against a database to avoid redundant downloads and save bandwidth.
- Asynchronous Event Loops - Implements a non-blocking event loop to manage concurrent network I/O and data processing tasks efficiently.
- Time-Series Monitoring Systems - Aggregates and exports operational performance metrics to external monitoring systems for real-time health visualization.
- Real-time Performance Monitoring - Integrates with monitoring systems to track real-time crawling metrics and operational health.
- Concurrent Target Managers - Dynamically adjusts request concurrency based on target site responsiveness to maintain stable throughput.
- Request Middleware Pipelines - Provides a modular pipeline for intercepting and modifying outgoing network requests with custom headers and concurrency logic.
- Anti-Detection Automations - Employs automated strategies like identity rotation and velocity throttling to bypass site-specific scraping protections.
- Crawler Health Monitoring - Tracks operational status and performance metrics to visualize the health of scraping processes.
Star history
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