This article walks through three concrete methods for moving massive ClickHouse datasets—up to 100 billion rows—to Doris, detailing catalog integration, file export with stream load, and Spark‑based pipelines, while sharing real‑world performance results and pitfalls.
Facing a ClickHouse cluster shutdown, the author explores three migration methods—using Doris’s ClickHouse catalog, exporting to files with Broker/Stream Load, and Spark—to transfer ~10 billion rows to Doris, evaluating each for simplicity, bugs, and performance, and sharing detailed steps, code snippets, and benchmark results.
When a ClickHouse cluster needed to be decommissioned, the author evaluated three migration approaches—using Doris' ClickHouse catalog, exporting to files with Broker/Stream Load, and leveraging Spark—to move roughly 100 billion rows to Doris, comparing their complexity, reliability, and performance.
This article presents a complete Java‑based architecture for high‑throughput Doris stream loading, covering project structure, Maven dependencies, configuration properties, field‑mapping annotations, automatic mapper utilities, a robust parallel loader with retry and compression, plus performance tuning recommendations.
This guide walks through the complete architecture, Maven dependencies, configuration classes, annotation‑driven field mapping, utility mappers, a parallel Stream Load core, response handling, and performance tuning for integrating Apache Doris with a Spring Boot backend.
