Latency measurement of fine-grained operations in benchmarking distributed stream processing frameworks

This paper describes a benchmark for stream processing frameworks allowing accurate latency benchmarking of fine-grained individual stages of a processing pipeline. By determining the latency of distinct common operations in the processing flow instead of the end-to-end latency, we can form guidelines for efficient processing pipeline design. Additionally, we address the issue of defining time in distributed systems by capturing time on one machine and defining the baseline latency. We validate our benchmark for Apache Flink using a processing pipeline comprising common stream processing operations. Our results show that joins are the most time consuming operation in our processing pipeline. The latency incurred by adding a join operation is 4.5 times higher than for a parsing operation, and the latency gradually becomes more dispersed after adding additional stages.This paper describes a benchmark for stream processing frameworks allowing accurate latency benchmarking of fine-grained individual stages of a processing pipeline. By determining the latency of distinct common operations in the processing flow instead of the end-to-end latency, we can form guidelines for efficient processing pipeline design. Additionally, we address the issue of defining time in distributed systems by capturing time on one machine and defining the baseline latency. We validate our benchmark for Apache Flink using a processing pipeline comprising common stream processing operations. Our results show that joins are the most time consuming operation in our processing pipeline. The latency incurred by adding a join operation is 4.5 times higher than for a parsing operation, and the latency gradually becomes more dispersed after adding additional stages.P