Exploring Flink and Apache Hudi for Streaming Data Lakes: Design, Practices, and Roadmap
This article presents a comprehensive overview of using Flink with Apache Hudi to build streaming data lake solutions, covering Hudi's background, core features, Flink‑Hudi integration design, practical use cases, recent roadmap updates, and a Q&A session.
Introduction – The speaker, a technical expert from Alibaba Cloud, introduces his work on Flink and Hudi integration and outlines four main topics: Hudi background, Flink‑Hudi design, application scenarios, and the Hudi roadmap.
Apache Hudi Background – Hudi is positioned as a next‑generation data‑warehouse solution that extends traditional Hive, offering openness to multiple data sources and query engines, rich transaction support, ACID‑based incremental processing, and intelligent file‑layout management.
Flink + Hudi Design
Write pipeline: a serverless micro‑service architecture that handles concurrent writes, bucket assignment, and small‑file mitigation, ensuring ACID semantics and exactly‑once guarantees.
Small‑file strategy: bucket selection based on remaining space, hash‑based bucket assignment for parallelism, and periodic compaction to control file proliferation.
Full‑ and incremental read: timeline‑based snapshot management, metadata indexing, and time‑travel queries for efficient batch and streaming reads.
Application Scenarios
Near‑real‑time DB ingestion into the lake using CDC tools (Debezium, Maxwell) to achieve minute‑level freshness.
Near‑real‑time OLAP with open query engines (Presto, Trino, StarRocks, Redshift) providing a unified storage layer.
Near‑real‑time ETL that unifies Lambda and Kappa architectures, offering both storage and queue capabilities.
Alibaba Cloud VVP real‑time lake ingestion leveraging built‑in Flink‑Hudi connectors and schema‑evolution features.
Hudi Roadmap – Upcoming features in Hudi 0.12/1.0 include CDC Feed, a pluggable Meta Service, secondary indexes for column‑level point queries, and column‑wise update capabilities for feature‑engineering workloads.
Q&A – Discusses trade‑offs between Hudi and StarRocks for updates, and recommends MOR tables for high‑volume streaming data.
DataFunTalk
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