Flink Application Scenarios and Scale at Kuaishou

Kuaishou uses Apache Flink as a core stream‑processing engine to handle massive real‑time data workloads, with a focus on high‑throughput, low‑latency use cases across the platform.

The Flink cluster at Kuaishou operates at a large scale, employing dozens of task managers and job managers to meet the demands of billions of events per day.

Scenario optimizations include the use of Interval Join for correlating streams, detailed analysis of its principles, and targeted improvements to reduce state size and processing latency.

Performance challenges with RocksDB, such as access latency and disk pressure, are addressed through configuration tuning and storage strategy selection to maintain stable state handling.

Stability enhancements cover source throttling mechanisms that limit ingestion rates, preventing uncontrolled state growth, as well as JobManager reliability improvements and strategies to mitigate frequent job failures.

Platform‑level initiatives involve building a unified Flink platform, refining problem‑diagnosis workflows, and providing tooling to streamline deployment, monitoring, and troubleshooting across the organization.

The article concludes with references to additional Flink series articles covering network transmission, exactly‑once delivery, dynamic job configuration, and integration with Elasticsearch.