Big Data 20 min read

Kuaishou Metric Middle Platform: Development Journey, Architecture, and Practices

This article summarizes the Kuaishou Data Platform’s metric middle‑platform sharing from the 2022 DataFun forum, detailing its three‑year evolution, key concepts, architectural design, implementation challenges, and practical lessons for building an enterprise‑grade metric platform that unifies data definition, production, and consumption across the company.

DataFunSummit
DataFunSummit
DataFunSummit
Kuaishou Metric Middle Platform: Development Journey, Architecture, and Practices

Kuaishou Data Platform presented at the 2022 DataFun "Kuaishou Metric Middle Platform" forum, sharing best practices and the evolution of its metric middle‑platform over more than three years.

Kuaishou is a large digital community with 363 million daily active users and 626 million monthly active users; its data platform aims to improve decision‑making efficiency by providing unified data services.

The metric domain is introduced with clear definitions of metric, dimension, and operator, emphasizing the need for a unified semantic layer to ensure consistent definitions, avoid duplicated calculations, and support analysis across the organization.

Two major challenges in big‑data environments are highlighted: data inconsistency caused by siloed warehouses and low analysis efficiency due to manual, case‑by‑case data‑service development.

To address these, Kuaishou built an enterprise‑level metric middle‑platform called OneMetric, which unifies metric definitions, automates metric production, and offers a unified service (OneService) that drives data both downstream (production) and upstream (analysis).

The architecture follows a four‑layer data model—source, table, model, dataset—and includes standards, system design, an analysis side (Octo query engine, semantic layer, Headless BI service) and a production side (automated and manual pipelines, task orchestration).

Key operational practices include aligning organization goals, enforcing strict metric governance, embedding standards into tools, leveraging low‑code development, and exposing open APIs for a headless‑BI ecosystem.

Results reported include over 30 000 metrics, more than 300 applications, 3 million daily queries, a ten‑fold increase in analysis efficiency, and high data‑quality stability.

The QA section compares OneMetric with other metric‑store solutions (e.g., Airbnb, Kyligence, Meituan), discusses metric‑warehouse coupling, and advises on unified implementation across the company.

Data EngineeringanalyticsBig DataKuaishoumetric platformMetric Governance
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