Mike Heffner and Ray Jenkins from Streamfold explore the efficiency improvements achieved with Rotel, an open-source Rust project designed for high-performance OpenTelemetry data collection, in comparison to the OpenTelemetry Collector. By constructing a benchmarking pipeline to stream trace data into ClickHouse, they demonstrate how Rotel scales throughput from 1.1 million trace spans per second to 3.7 million on the same hardware, achieving nearly four times the efficiency of the OpenTelemetry Collector. This is accomplished through various optimization techniques, such as JSON binary serialization, improved task management, and leveraging LZ4 compression, as well as addressing allocator lock contention that significantly reduced CPU usage. The authors stress the importance of efficiency at scale, especially in large systems, and highlight future areas of exploration including enhancements to Rotel's handling of logs and metrics, as well as further optimization of memory allocation impacts. The post emphasizes the potential for significant resource savings at large scales, positioning Rotel as a highly efficient solution for streaming OpenTelemetry data into ClickHouse.