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This article explains how Kuaishou tackled the growing complexity of its recommendation system by developing the Dragonfly strategy engine framework, detailing the challenges, architectural abstractions, DSL-based workflow composition, data handling, ecosystem tools, and future development plans.
Kuaishou’s Dragonfly framework replaces tightly‑coupled C++ recommendation code with a Python‑centric DSL that compiles to high‑performance JSON, offering DAG‑based operators, schema‑free dataframes, and a full ecosystem of debugging, tracing, and governance tools, dramatically cutting code volume while enabling fast, scalable scenario development.
This article describes how Kuaishou built the Dragonfly strategy engine framework—covering problem analysis, architecture design, DSL-based workflow orchestration, process and data abstractions, ecosystem tools, and future plans—to solve the scalability, coupling, and maintenance issues of its rapidly expanding recommendation services.
Strategy Engine 2.0 redesigns the original system by introducing fully configurable tags, modular management and matching services, business‑line data and traffic isolation, and performance optimizations such as parallel execution and circuit‑breakers, enabling real‑time, fine‑grained audience segmentation that scales across multiple business units.