Thoughts and Practices on ByteDance Streaming Data Warehouse and Real‑Time Service Analysis
The article presents ByteDance's challenges with massive real‑time data processing and describes how they integrated a streaming data warehouse with Flink Table Store, cloud‑native architecture, and real‑time service analysis to achieve low‑latency, high‑throughput analytics and end‑to‑end consistency.
ByteDance operates products such as Douyin and Toutiao that generate petabytes of data daily, requiring both batch and streaming processing for real‑time recommendation and analytics, which creates significant storage, compute, and system‑redundancy challenges.
The team addressed these issues by adopting a streaming data warehouse combined with real‑time service analysis, leveraging Flink Table Store to eliminate data and system redundancy, ensure data consistency, and improve serving performance.
Key technical components include:
Flink Table Store offering Snapshot + Log storage, full SQL support, and columnar file system integration.
A unified streaming‑batch architecture that supports both streaming reads (Log Changes) and batch reads (Snapshots) as well as hybrid reads.
Merge Tree structures for fast updates, point queries, and data skipping.
Optimizations to Flink OLAP that increase QPS and reduce latency, with contributions back to the open‑source community.
To achieve end‑to‑end exactly‑once semantics, the solution relies on Flink's built‑in Exactly‑Once guarantees and automatic resource scheduling for both streaming and batch pipelines.
The platform is built on a cloud‑native stack: serverless Flink for streaming, serverless Spark/Ray for batch, and Volcano Engine’s CloudFS/Iceberg for unified storage, all managed through containerized, serverless operators that enable elastic scaling and multi‑cloud deployment.
A Q&A section covers topics such as ETL Exactly‑Once guarantees, performance comparisons with StarRocks, and the advantages of Flink Table Store in stream‑batch integration.
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