An Introduction to the Hudi and Flink Integration
Blog post from Onehouse
In September 2020, the data lakehouse concept began gaining traction, offering a blend of raw data storage and structured querying that departed from traditional data handling methods, but many engineers faced challenges in adapting due to limited documentation and integration questions. While exploring data lakehouse technologies, the Flink community identified a need for a system supporting changelog ingestion and efficient row-level updates, leading to attempts at integrating Flink with Apache Iceberg, which proved minimally beneficial due to Iceberg's batch processing roots. Consequently, an integration with Apache Hudi was pursued, offering ACID semantics and supporting efficient upsert and delete capabilities, resonating well with the Flink community for use cases like incremental ingestion of database changelogs and near-real-time materialization views. As of May 2023, the Hudi-Flink integration is utilized by over 100 companies and more than 3600 enterprises, with ongoing contributions from global communities to enhance low-latency and near-real-time data applications. The integration redesigned the index layer to overcome challenges associated with Bloom filter indexes in streaming workloads, leveraging Flink's stateful operators for efficient data processing. The integration allows for streaming ingestion with changelogs, incremental ETL pipelines, and the creation of incremental materialized views, showcasing the complementary strengths of both technologies.
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