Key Trends of Flink 2.0: Compute‑Storage Separation, Unified Batch‑Stream, and Streaming Warehouse
The article reviews the major directions of Flink 2.0—including compute‑storage separation, a new Materialized Table for unified batch‑stream processing, and deeper integration with Paimon for streaming warehouses—while offering a cautious perspective on their practical impact and migration challenges.
Flink Forward Asia 2024 highlighted the upcoming Flink 2.0, focusing on major architectural changes and challenges.
Compute‑Storage Separation
All data‑system components are moving toward decoupling compute from storage; Flink 2.0 aims to achieve a cloud‑native “compute‑storage separation” architecture, addressing four demands: unbinding compute and storage, uniform container resource usage, leveraging cheap massive cloud storage, and rapid stateful scaling.
For this purpose Flink introduces a new ForSt DB to handle storage concerns, which should simplify migration and scaling of large‑state jobs.
Unified Batch‑Stream Solution
Flink 2.0 adds the Materialized Table concept, allowing a single code base to run in streaming, batch full‑refresh, or incremental refresh modes by adjusting the freshness definition.
The author remains skeptical, noting that code‑level unification only solves a small compatibility issue and that real‑world cost‑saving batch‑stream scenarios are limited.
Streaming Warehouse
The community plans deeper integration between Flink and Paimon, but the author doubts that Paimon brings revolutionary changes to traditional data‑warehouse development, merely addressing some pain points.
Overall, the article outlines these trends while acknowledging open questions and the limited impact on most users.
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Big Data Technology & Architecture
Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
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