E-commerce Product Ranking System Migration: Technical Implementation and Storage Optimization
The article describes how an e‑commerce product ranking system was migrated to the new “Liao Yue” platform, decoupling it from the search module, introducing fresh metrics and Elasticsearch‑based sorting, then optimizing storage by separating B‑end and C‑end data—cutting costs 60%—with a gray‑scale rollout, dual‑read validation, rollback safeguards, and completing the two‑week, zero‑failure migration that delivered a closed‑loop, faster iteration system.
This article details the technical migration and optimization of an e-commerce product ranking system. The system faced challenges including rapid data growth, high concurrency demands, and strong coupling between search and product modules. The solution involved migrating ranking production to a new platform (referred to as "Liao Yue") to leverage its existing selection and sorting capabilities, thereby eliminating dependency on the search module.
The migration process included creating new metrics and sorting rules for both control and experimental groups, synchronizing these to Elasticsearch for selection and ranking, and implementing a two-phase approach: first completing the link migration, then performing storage optimization. The storage optimization involved separating B-end and C-end data storage, using the new platform's storage structure for B-end queries while maintaining a MySQL table for C-end queries to reduce storage costs by 60%.
The implementation also featured a comprehensive gray-scale design with incremental data maintenance, dual-read checks, and rollback strategies to ensure smooth and stable traffic switching. The entire process was completed over two weeks with zero failures, successfully achieving a closed-loop system and significantly improving business iteration efficiency.
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