Scalehouse: Real-time Website Traffic Analysis with ClickHouse

This project aims to build a real-time analysis of website traffic using ClickHouse. The system will ingest website visit data in real-time, store it efficiently, and provide interactive visualization of key metrics to derive insights into user behavior.

Components

Data Source

• Set up a web server log collection mechanism (Nginx access.log module) to capture website visit data.

Data Processing Pipeline

• Utilize a stream processing framework like Apache Kafka or Apache Flink to receive and process log data continuously.
• Extract relevant fields from logs (timestamp, request method, URL, HTTP status code, user agent...).
• Clean and transform data, including filtering out bots and optionally enriching with additional context.

ClickHouse Integration

• Define a ClickHouse table optimized for efficient storage of website traffic data (DateTime for timestamps, UInt32 for counts, String for URLs).
• Implement a partitioning strategy (e.g., by date or hour) to enhance query performance.
• Configure ClickHouse for data compression (e.g., LZ4) and appropriate merge tree settings (SummingMergeTree) for managing large volumes of real-time data.

Data Visualization

• Visualization platform (Grafana or ClickHouse web interface) to interact with ClickHouse via SQL queries.
• Create dashboards displaying key metrics such as total visits, unique visitors, popular pages, average time on site, traffic sources, and error rates.
• Use time-series visualizations (e.g., line charts, heatmaps) to analyze traffic patterns over time.

Benefits

• Real-time Insights: Immediate understanding of website traffic patterns for quick decision-making.
• Scalability: ClickHouse efficiently handles large datasets, supporting real-time analysis of high-volume traffic.

Additional Considerations

• Security: Implement user authentication and access control in ClickHouse to safeguard sensitive data.
• Privacy: Adhere to privacy regulations by anonymizing user data or obtaining necessary consent.
• Monitoring: Set up alerts for significant traffic changes or processing errors.

This project provides a powerful platform to gain actionable insights from real-time website traffic data using ClickHouse. By integrating efficient data processing and visualization components, it enables data-driven decisions for improving website performance and user experience.

How to run the application locally?

# install all dependencies
npm install

• Run all required services using docker compose

docker compose up -d

To run kafka producer and consumer, open seperate terminals

npm run start:producer
# and
npm run start:consumer