Hot List Site recently launched a demo version of its historical tracking feature, which allows users to click the trend button next to hot topics to view all similar recent trending topics. Technically, the system only stores 10 days of historical data, processing approximately 15 million entries daily, with a maximum total storage of 150 million data points. Data is saved to disk in parquet format, queries use the duckdb engine, and similarity between hot topics is determined using Hamming distance calculation rather than AI methods, mainly to avoid the high computational costs associated with AI. The developers candidly admitted in their sharing that this Hamming distance-based approach may have misjudgments. Hot List Site (tgmeng.com) now invites users to test and provide feedback to further optimize this feature. This implementation demonstrates how to achieve efficient data similarity matching through traditional algorithms without using AI, providing reference value for readers interested in big data processing and similarity matching technologies.
