With the advent of the big data era, massive volumes of user data have empowered numerous data-driven industry applications, such as smart grids, intelligent transportation, and product recommendations. In scenarios where real-time data is crucial, the business value embedded within data rapidly diminishes over time. Consequently, data analysis systems require high throughput and low latency. Stream processing systems in big data, exemplified by Apache Flink, have been widely applied. Flink enhances system throughput by parallelizing computing tasks across cluster nodes. However, current research indicates that Flink has weak single-point performance and poor cluster scalability. To improve the throughput of stream processing systems, researchers have focused on optimizations in designing control planes, implementing system operators, and improving vertical scalability. However, there is still a lack of attention to the data flow in streaming analysis applications. These applications are driven by event streams and employ stateful processing functions, including low voltage detection in smart grids and advertising recommendation. This study analyzes the data flow characteristics of typical streaming analysis applications, identifies three bottlenecks in optimizing scalability, and proposes corresponding optimization strategies: the key-level watermark strategy, the dynamic load distribution strategy, and the the key-value based exchange strategy. Based on these optimization strategies, this study implements Trilink based on Flink and applies it to various applications such as low voltage detection, bridge arch crowns monitoring, and the Yahoo Streaming Benchmark. Experimental results show that the modified system, Trilink, achieves more than a 5-fold increase in throughput in a single-machine environment and over a 1.6-fold improvement in horizontal scalability acceleration in an 8-node setup, compared to Flink.