Distributed Scraping Architecture: The DataLab's Infrastructure

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In an era where data is the cornerstone of research and innovation, the ability to collect vast amounts of information efficiently and reliably is paramount. Web scraping, the automated extraction of data from websites, has become an essential tool. However, building a system that is not only effective but also scalable, resilient, and manageable presents a significant technical challenge. This article details the distributed scraping architecture developed at the DataLab, a robust pipeline designed for large-scale data acquisition. We will explore the core components, the guiding principles behind their selection, and how they operate in concert to form an effective data collection engine.

Guiding Architectural Principles 🔰

The design of our infrastructure was guided by a set of core principles aimed at creating a flexible and powerful system that is easy to manage and grow.

Ease of Use: We prioritized open-source tools with intuitive interfaces and strong community support. This approach lowers the barrier to entry, allowing both seasoned developers and new researchers to quickly deploy and manage scraping tasks, thereby accelerating the pace of experimentation.
Scalability (Vertical & Horizontal): The architecture is built to grow with our needs. Vertical scalability is achieved by adding more resources (CPU, RAM) to existing machines. For larger demands, horizontal scalability allows us to seamlessly integrate new machines into the network, distributing the workload and expanding our capacity without a proportional increase in administrative overhead.
Interoperability: Using open-source standards ensures smooth communication between different services, machines, and software components. This creates a cohesive and flexible environment where each part of the stack can be optimized or replaced without disrupting the entire workflow.
Observability: Full visibility into the system's health and performance is non-negotiable. Our integrated monitoring stack allows for real-time tracking of metrics, enabling proactive problem-solving, minimizing downtime, and ensuring the reliability of our data collection operations.

Core Components and Workflow

Our data pipeline is a collection of carefully selected open-source tools, each serving a critical function. From task orchestration to data storage and anonymization, these components work together to automate the entire scraping lifecycle.

Orchestration and Execution

At the heart of our operation is Windmill, an open-source orchestration tool that schedules and manages all our scraping scripts. Windmill initiates jobs that execute one of our two primary scraping methods, depending on the target website's architecture

API Reverse Engineering: Whenever possible, we reverse-engineer a website's internal API. This is the most efficient and reliable method, as it allows us to request structured data (usually in JSON format) directly from the source, bypassing the need to parse HTML.
Browser Automation with Selenium: For websites that are heavily reliant on JavaScript or lack accessible APIs, we use Selenium. It automates browser interactions to mimic human behavior, allowing us to navigate pages, fill out forms, and extract dynamically loaded content. To handle large-scale tasks, we employ Selenium Grid, which distributes the scraping workload across multiple machines, enabling parallel execution and significantly boosting speed and efficiency.

Anonymization and Network Management

Intensive scraping operations can lead to IP address blocking. To ensure uninterrupted access, we use GlueTun, a lightweight VPN and proxy client. All network traffic generated by our scrapers is routed through GlueTun, which masks our server's true IP address by using a rotating pool of VPNs and proxies. This practice is essential for maintaining access and collecting data ethically and without interruption.

Data Storage and Backup

Scraped data is immediately stored in a MongoDB database. As a NoSQL database, MongoDB is perfectly suited for the semi-structured and often diverse nature of web data. Its flexible schema allows us to store complex JSON objects without rigid preprocessing, making it an ideal central repository for our raw data.

To ensure data integrity and resilience, we have a robust backup strategy. Automated scripts perform daily incremental backups of our MongoDB collections and upload them to an S3-compatible object storage solution. While we currently use AWS S3, we are planning a transition to MinIO, an open-source alternative that offers greater control, potential cost savings, and tighter integration with our on-premises infrastructure.

Management and Monitoring

The entire infrastructure runs on containerized services managed with Portainer, a universal container management tool. Portainer provides a user-friendly graphical interface to deploy, monitor, and manage our Docker containers across all machines in the DataLab.

For observability, we rely on a classic monitoring stack:

Prometheus: Collects and stores time-series metrics from all our services (containers, databases, servers).
Grafana: Visualizes the metrics collected by Prometheus through interactive dashboards, allowing us to monitor system health, resource usage, and the performance of our scraping jobs in real-time.
Exporters: These are specialized agents that gather metrics from specific services (like MongoDB or the host system) and expose them in a format that Prometheus can understand.

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