Influencing factors in the scalability of distributed stream processing jobs

More and more use cases require fast, accurate, and reliable processing of large volumes of data. To do this, a distributed stream processing framework is needed which can distribute the load over several machines. In this work, we study and benchmark the scalability of stream processing jobs in four popular frameworks: Flink, Kafka Streams, Spark Streaming, and Structured Streaming. Besides that, we determine the factors that influence the performance and efficiency of scaling processing jobs with distinct characteristics. We evaluate horizontal, as well as vertical scalability. Our results show how the scaling efficiency is impacted by many factors including the initial cluster layout and direction of scaling, the pipeline design, the framework design, resource allocation, and data characteristics. Finally, we give some recommendations on how practitioners should undertake to scale their clusters.More and more use cases require fast, accurate, and reliable processing of large volumes of data. To do this, a distributed stream processing framework is needed which can distribute the load over several machines. In this work, we study and benchmark the scalability of stream processing jobs in four popular frameworks: Flink, Kafka Streams, Spark Streaming, and Structured Streaming. Besides that, we determine the factors that influence the performance and efficiency of scaling processing jobs with distinct characteristics. We evaluate horizontal, as well as vertical scalability. Our results show how the scaling efficiency is impacted by many factors including the initial cluster layout and direction of scaling, the pipeline design, the framework design, resource allocation, and data characteristics. Finally, we give some recommendations on how practitioners should undertake to scale their clusters.A