"As the system evolved and new features were added, we noticed a growing inefficiency: the number of HTTP calls to UCD had increased as new use cases were introduced to the product pipeline and the API side. It caused the HTTP requesting interaction between the Recommendation API and the UCD to resemble the Chatty Services Anti-Pattern. The number of unnecessary requests to the personalization source decreased the overall success of the feature quality and hurt the health metrics of the Recommendation API."
"The existing implementation approach in the Recommendation API violated the common Don't Repeat Yourself (DRY) Software Development Principle. As new recommendation use cases were introduced to the API, we often employed very similar implementation blocks to perform very similar needs. It resulted in introducing nearly implementation blocks to the API and having unnecessary repetition in the codebase. This post walks through how we identified the architectural bottlenecks, the refactoring strategies we applied, and the performance gains we achieved as a result."
Personalization requires real-time user context such as browsed products, purchases, and behavioral patterns. The Recommendation API depended on the Unified Customer Database (UCD) to fetch User Profile and User Events information. As new features and use cases were added, HTTP calls to UCD increased, creating a chatty-services anti-pattern that reduced feature success and harmed health metrics. The implementation also violated the DRY principle by reusing very similar code blocks across new recommendation cases, producing unnecessary repetition. Architectural bottlenecks were identified and refactoring strategies were applied to reduce redundant requests and duplicated code, yielding performance gains.
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