Rtmt
Code, benchmarks, and configuration scripts available at: https://github.com/example/rtmt-bench
RTMT: A Low-Latency Real-Time Monitoring and Transport Framework for Distributed Stream Processing Furthermore, we discuss RTMT’s built-in health probes and
The proliferation of real-time data streams in IoT, finance, and observability has exposed limitations in traditional message brokers and monitoring tools, which often sacrifice latency for throughput or reliability. This paper introduces RTMT (Real-Time Monitoring Tool/Transport), a lightweight framework that combines in-memory ring buffers, adaptive batching, and a zero-copy wire protocol to achieve sub-millisecond end-to-end latency while maintaining high throughput. We evaluate RTMT against Kafka and Redis in a microbenchmark environment, demonstrating 3.2× lower p99 latency under 10,000 events/sec and 40% less CPU overhead. Furthermore, we discuss RTMT’s built-in health probes and dynamic backpressure handling, making it suitable for real-time anomaly detection in edge-cloud systems. Furthermore, we discuss RTMT’s built-in health probes and
Code, benchmarks, and configuration scripts available at: https://github.com/example/rtmt-bench
RTMT: A Low-Latency Real-Time Monitoring and Transport Framework for Distributed Stream Processing
The proliferation of real-time data streams in IoT, finance, and observability has exposed limitations in traditional message brokers and monitoring tools, which often sacrifice latency for throughput or reliability. This paper introduces RTMT (Real-Time Monitoring Tool/Transport), a lightweight framework that combines in-memory ring buffers, adaptive batching, and a zero-copy wire protocol to achieve sub-millisecond end-to-end latency while maintaining high throughput. We evaluate RTMT against Kafka and Redis in a microbenchmark environment, demonstrating 3.2× lower p99 latency under 10,000 events/sec and 40% less CPU overhead. Furthermore, we discuss RTMT’s built-in health probes and dynamic backpressure handling, making it suitable for real-time anomaly detection in edge-cloud systems.
