Kuaishou Data Lake Construction with Apache Hudi: Architecture, Challenges, and Solutions

Introduction Kuaishou built a data lake to replace traditional offline warehouses, aiming for unified, shareable, simple, high‑performance, and reliable data storage.

Lake Architecture The previous Lambda architecture suffered from latency, heterogeneous processing logic, and data silos. Kuaishou adopted a centralized lake using Apache Hudi, which offers massive storage, schema evolution, extensible data types, strong management, high‑performance analytics, and supports both batch and streaming workloads.

Why Hudi Hudi provides strong update capabilities, stream‑batch read/write, pluggable payloads, MOR table type, and compatibility with engines such as Spark and Trino, making it suitable for fast lake construction.

Key Advantages Data CRUD optimization, unified stream‑batch processing, massive data management, reduced resource consumption, and improved query performance.

Challenges & Solutions

1. Ingestion bottleneck – optimized write mode to stream‑write, dynamic load‑balancing, and automatic partition publishing.

2. Snapshot query using data time – added time‑version metadata to enable precise time‑travel queries.

3. Flink‑on‑Hudi update bottleneck – separated operations, enabled parallel execution, and used placeholder‑based compaction.

4. Insufficient merge concurrency – implemented logical bucketing and schema‑aware merge planning.

5. Production reliability – simplified configuration, added pre‑commit consistency checks, and enriched metrics for stability.

Practical Cases Four use‑cases demonstrate the lake’s impact: real‑time DWD generation, activity snapshot queries, retention data processing, and wide‑table feature production, each showing significant latency reductions (50%‑60%) and resource savings.

Future Plans Continue to improve metadata services, support real‑time tables, achieve seamless migration from existing pipelines, and provide unified query capabilities.

Q&A Detailed discussion on lock mechanisms, OCC, and compaction resource allocation.

Images in the original article illustrate architecture diagrams, performance charts, and screenshots of the Hudi‑Flink integration.