Message brokers see widespread adoption in modern IT landscapes  . These systems are comparatively easy to use and configure and thus, present a flexible solution for various data storage scenarios. Their ability to scale horizontally enables users to adapt to growing data volumes and changing environments. However, one of the main challenges concerning message brokers is the danger of them becoming a bottleneck within an IT architecture. To prevent this,  the amount of data a given message broker can handle with a specific configuration  needs to be known . In this paper, we propose a monitoring architecture for message brokers and similar  systems. We present a comprehensive performance analysis of Apache Kafka, a popular message broker implementation, using our approach. As part of the benchmark, we study selected data producer settings and their impact on the achievable data ingestion rate .
Message brokers see widespread adoption in modern IT landscapes , with Apache Kafka being one of the most employed platforms . These systems feature well-defined APIs for use and configuration and present flexible solutions for various data storage scenarios. Their ability to scale horizontally enables users to adapt to growing data volumes and changing environments. However, one of the main challenges concerning message brokers is the danger of them becoming a bottleneck within an IT architecture. To prevent this, knowledge about the amount of data a message broker using a specific configuration can handle needs to be available . In this paper, we propose a monitoring architecture for message brokers and similar Java Virtual Machine-based systems. We present a comprehensive performance analysis of the popular Apache Kafka platform using our approach. As part of the benchmark, we study selected data ingestion scenarios with respect to their maximum data ingestion rates. The results show that we can achieve an ingestion rate of about 420,000 messages/second on the used commodity hardware and with the developed data sender tool .
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, with Apache Kafka being one of the most employed platforms
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are comparatively easy to use and configure and thus, present a flexible solution
feature well-defined APIs for use and configuration and present flexible solutions
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knowledge about
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given message broker can handle with
message broker using
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can handle
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known
available
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Java Virtual Machine-based
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Apache Kafka, a popular message broker implementation,
the popular Apache Kafka platform
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producer settings and their impact on the achievable data ingestion rate
ingestion scenarios with respect to their maximum data ingestion rates. The results show that we can achieve an ingestion rate of about 420,000 messages/second on the used commodity hardware and with the developed data sender tool