Abstract: When Kafka is applied in a production environment, its performance is not only limited by the machine’s hardware environment and system platform.Its own configuration items are the key element to judge whether it can achieve the desired performance under the condition of limited hardware resources, but it is manually configured.The efficiency of item modification and tuning is extremely poor.If the actual resource environment is not optimized, Kafka cannot guarantee its performance in each production environment using default configuration parameters.Because Kafka’s configuration bound is extremely large, the perfor-mance of traditional adaptive algorithms in large-scale production systems is poor.Therefore, in order to improve Kafka’s adaptive ability, eliminate complexity in the system, and obtain better operating performance, an adaptive performance tuning method for Kafka is proposed, which fully considers the influence weights of Kafka’s characteristic parameters and performance.It uses the principle of sampling to improve the efficiency of data sets generation and optimize the range of data selection, improve the efficiency of modeling and reduce the complexity of optimization methods.Experiments show that the algorithm optimizes the throughput rate and latency of the open source version Kafka, improves Kafka’s throughputs under a given system resource, and reduces latency.

Key words: Elastic net, Kafka, Latin hypercube sampling, Message queue, Performance tuning