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Pulsar is an open-source distributed pub/sub messaging system originally catered towards queuing use cases. Clients produce or consume events directly to/from a cluster of brokers, which read/write events durably to the underlying file system and also automatically replicate the events synchronously or asynchronously within the cluster for fault tolerance and high availability. At its core, Kafka is designed as a replicated, distributed, persistent commit log that is used to power event-driven microservices or large-scale stream processing applications.
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WHAT HARDWARE AFFECTS LINPACK BENCHMARK SOFTWARE
Kafka is an open source distributed event streaming platform, and one of the five most active projects of the Apache Software Foundation. Overviewįirst, let’s discuss each of the systems briefly to understand their high-level design and architecture, looking at the trade-offs each system makes. For a deeper look at features, architecture, ecosystem, and more, read this complete guide comparing Kafka, Pulsar, and RabbitMQ. We always encourage readers to compare using their own workloads/setups, to understand how these translate to production deployments. As with most benchmarks, we compare performance on a setup for a specific workload. All of these are open source, so curious readers can reproduce the results for themselves or dig deeper into the collected Prometheus metrics. It will finish with an explanation of the results using the various system and application metrics. This blog post is structured to first walk you through the benchmarking framework we used, followed by a description of the testbed and the workloads. Furthermore, the impact of mirroring is significant at higher throughput and better latencies can be achieved by using just classic queues without mirroring.
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*RabbitMQ latencies degrade significantly at throughputs higher than the 30 MB/s. At lower throughputs, RabbitMQ delivers messages at very low latencies. We found that Kafka delivers the best throughput while providing the lowest end-to-end latencies up to the p99.9th percentile. The latency test measures how close each system is to delivering real-time messaging including tail latencies of up to p99.9th percentile, a key requirement for real-time and mission-critical applications as well as microservices architectures. In particular, the throughput test measures how efficient each system is in utilizing the hardware, specifically the disks and the CPU. We focused on (1) system throughput and (2) system latency, as these are the primary performance metrics for event streaming systems in production. Kafka has been known to be fast, but how fast is it today, and how does it stack up against other systems? We decided to test Kafka’s performance on the latest cloud hardware to find out.įor comparisons, we chose a traditional message broker, RabbitMQ, and one of the Apache BookKeeper™ based message brokers, Apache Pulsar. There are many ways to compare systems in this space, but one thing everyone cares about is performance. Apache Kafka ® is one of the most popular event streaming systems.