<p>Low latency and high throughput are crucial for distributed stream computing systems. Existing operator reconfiguration strategies often have poor performance under resource-limited and latency-constraint scenarios. The challenge lies in the elasticity of operator parallelism and reconfiguration of operators that balances performance constraints and performance improvement. To address these issues, we propose Er-Stream, an elastic reconfiguration strategy for various application scenarios. This paper discusses the Er-Stream from the following aspects: (1) We model task topology as a queuing network to evaluate system latency, and construct a communication cost model to formalize the reconfiguration problem; (2) we proposed an elastic strategy for operator parallelism to rationally utilize the available resources and reduce the processing latency of topology; (3) we proposed a reconfiguration strategy for operators to reduce the communication cost, and set thresholds added to control its trigger frequency; (4) we design and implement Er-Stream and integrated it into Apache Storm. We evaluate key metrics such as latency, throughput, resource usage, and CPU utilization in a real-world distributed stream computing environment. Results demonstrate significant improvements achieved by Er-Stream. In comparison with Storm’s existing strategies, it reduces average system latency by up to 30%, increases average system throughput by 1.89 times, lowers average resource usage by 26.6%, and increases CPU utilization by 19.8%.</p>