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

Kuaishou built a data lake to achieve the core goals of standardized, shareable, simple, high‑performance, and reliable data infrastructure, moving away from a traditional Lambda architecture that suffered from latency, heterogeneous processing logic, and data silos.

The chosen solution is Apache Hudi, which offers massive storage, schema evolution, extensible data types, strong data management, and high‑performance analytics, supporting both streaming and batch workloads.

Key challenges addressed include:

Ingestion bottlenecks caused by uneven bucket distribution and back‑pressure; solved by stream‑style writes, dynamic load balancing, and automatic partition publishing.

Inability to query snapshots by data time; solved by adding time‑version metadata to Hudi's Time Travel feature.

Flink‑on‑Hudi update bottlenecks due to resource mismatches; solved by separating operations, enabling parallel execution, and using appropriate indexing strategies.

Insufficient multi‑task merge capability; solved by logical bucketing and schema‑aware merge planning.

Production‑grade assurance difficulties; solved by configuration simplification, consistency checks, and enhanced metrics for stability.

Practical case studies demonstrate significant improvements: real‑time DWD generation with >50% latency reduction, snapshot query latency cut from hours to minutes with 15% resource savings, and streamlined retention table updates achieving 50% faster processing.

Future plans aim to enrich metadata services, support real‑time tables, achieve seamless migration from existing pipelines, and deliver a unified stream‑batch data production and query platform.