Improving Data Processing Efficiency at Kuaishou with Apache Hudi
This article explains how Kuashou tackled latency and efficiency problems in large‑scale data pipelines by adopting Apache Hudi, detailing the pain points, reasons for choosing Hudi, its architecture, model design, handling of bursty updates, back‑fill scenarios, and operational safeguards.
Kuaishou's data content team faced three major pain points: delayed data readiness due to late scheduling, long‑running large‑scale data synchronization jobs, and costly full‑volume back‑fills for partial updates. These issues caused SLA pressure and inefficient resource usage.
To address these challenges, the team evaluated several solutions and selected Apache Hudi because of its rich feature set, tight integration with Kuaishou's architecture, high automation level, strong Flink compatibility, and active open‑source community.
Hudi enables real‑time data ingestion from sources such as Binlog and Kafka, processed by Flink or Spark Streaming, while supporting CRUD operations on offline tables. Its architecture includes data shuffling to avoid skew, partition‑based parallelism, and index‑driven file lookup to improve write efficiency.
The team designed Hudi models focusing on primary key and partition strategy, merge and index policies, and concurrency controls. They optimized small‑file handling, used partition‑level Bloom indexes, and applied discard strategies to filter out duplicate or out‑of‑order records.
Four key challenges were solved: (1) adapting Hudi model design to business requirements, (2) handling bursty update traffic during promotional events by controlling concurrency and merge strategies, (3) enabling efficient partial back‑fills through versioned row updates, and (4) establishing operational guarantees with multi‑stage monitoring, data probes for accuracy, and strict model review processes.
Through this comprehensive Hudi‑based solution, Kuaishou achieved faster data availability, reduced resource consumption, and a reusable template that improves overall development efficiency for both real‑time and batch data processing workloads.
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