Apache Spark Streaming, Kafka and HarmonicIO: A Performance Benchmark and Architecture Comparison for Enterprise and Scientific Computing

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12093)


Many scientific computing applications generate streams where message sizes exceed one megabyte, in contrast with smaller message sizes in enterprise contexts (order kilobytes, often XML or JSON). Furthermore, the processing cost of messages in scientific computing applications are usually an order of magnitude higher than in typical enterprise applications. Frameworks such as Apache Spark offer high throughput processing of streams with such ‘enterprise’ characteristics, as well as scalability, with high resilience and many other desirable features. Motivated by the development of near real-time image processing pipelines for roboticized microscopy, we evaluate the suitability of Apache Spark for streams more typical of scientific computing applications, those with large message sizes (up to 10 MB), and heavy per-message CPU load, under typical stream integrations. For comparison, we benchmark a P2P stream processing framework, HarmonicIO, developed in-house. Our study reveals a complex interplay of performance trade-offs, revealing the boundaries of good performance for each framework and integration over a wide domain of application loads. Based on these results, we suggest which are likely to offer good performance for a given load. Broadly, the advantages of Spark’s rich features makes its performance sensitive to message size in particular, whereas the simplicity of HarmonicIO offers more robust performance, and better CPU utilization.


Stream processing Apache Spark HarmonicIO High-throughput microscopy HPC Benchmark XaaS HASTE 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information Technology, Division of Scientific ComputingUppsala UniversityUppsalaSweden

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