Analyzing C++ Stream Parallelism in Shared-Memory when Porting to Flink and Storm

Stream processing plays a crucial role in various information-oriented digital systems. Two popular frameworks for real-time data processing, Flink and Storm, provide solutions for effective parallel stream processing in Java. An option to leverage Java's mature ecosystem for distributed stream processing involves porting legacy C++ applications to Java. However, this raises considerations on the adequacy of the equivalent Java mechanisms and potential degradation in throughput. Therefore, our objective is to evaluate programmability and performance when converting stream processing applications from C++ to Java while also exploring the parallelization capabilities offered by Flink and Storm. Furthermore, we aim to assess the throughput of Flink and Storm on shared-memory manycore machines, a hardware architecture commonly found in cloud environments. To achieve this, we conduct experiments involving four different stream processing applications. We highlight challenges encountered when porting C++ to Java and working with Flink and Storm. Furthermore, we discuss throughput, latency, CPU, and memory usage results.