URLBird is a web application designed to process and categorize URLs efficiently. It leverages modern technologies such as Elasticsearch, Redis, and machine learning models to provide robust URL classification and data processing capabilities. The application is built using Python and Flask, with a modular architecture that ensures scalability and maintainability.
The system is divided into several components:
The core of URLBird is a Flask-based web application that provides RESTful APIs for URL processing, classification, and management. It serves as the entry point for user interactions and integrates with other backend services.
URLBird includes a background job processor that handles tasks such as URL indexing and data enrichment. These jobs are managed using Redis queues, ensuring asynchronous and efficient processing.
Elasticsearch is used as the primary data store for indexing and searching URLs. It provides powerful querying capabilities and supports the application's need for fast and scalable search operations.
Redis is employed for caching and queue management. It plays a critical role in reducing latency and enabling real-time data processing.
The application integrates machine learning models, including Sentence-BERT, for URL classification. These models are pre-trained and optimized for high accuracy in categorizing URLs based on their content.
URLBird is containerized using Docker, making it easy to deploy and scale. Kubernetes is used for orchestration, ensuring high availability and fault tolerance.
URLBird is deployed using Kubernetes in a production environment. The deployment configuration includes: