User Profile Platform: Architecture, Core Functions, and Engineering Optimization Strategies

The speaker, Zhang Xinglong, shares his experience building a user profile platform at Kuaishou, highlighting the platform's role in turning massive user data into actionable insights for operations and business value.

Platform Value : User profiling enables precise segmentation (e.g., "Beijing male users") and drives efficient marketing, making a profiling platform a foundational infrastructure for any data‑driven company.

Common Functions : Typical modules include tag management (creation, CRUD, quality monitoring), tag service (API‑based tag queries), crowd selection (rule‑based and imported crowds), and profile analysis (distribution, trend, value analysis).

Typical Architecture & Open‑Source Solutions : A layered design is recommended – a data layer (HDFS, Spark/Flink, Yarn, DolphinScheduler for offline/real‑time tags), a storage layer (ClickHouse, Kudu, Doris, HBase, Redis, OSS), a service layer (SpringBoot/SpringCloud for micro‑services), and an application layer (visualization or SDK). Open‑source tools such as RoaringBitmap are used for efficient bitmap handling.

Engineering Optimization Ideas :

Wide‑table optimization: consolidate scattered tag tables into a single wide table to simplify queries, reduce permission complexity, and improve performance via divide‑and‑conquer parallel processing and a dedicated data‑loading layer.

Crowd selection optimization: cache tag data in ClickHouse, generate BitMap representations, and perform in‑memory set operations to accelerate crowd building and reduce Hive dependency.

Profile analysis optimization: sync wide tables and crowd results to ClickHouse or use BitMap intersections for fast metric calculations.

Crowd existence (判存) optimization: adopt incremental updates, versioned writes, and in‑memory BitMap storage to achieve sub‑second response while controlling memory usage.

Task‑mode processing: break long pipelines into independent tasks, schedule them with priority control, and improve reliability and scalability.

Industry Development Status : Emphasis on real‑time data, multi‑dimensional profiling, intelligent operation, and the integration of machine learning and large language models to enhance crowd generation and analysis.

The presentation concludes with a thank‑you and references to related technical talks.