REDUCING REBALANCING IN STREAMING ARCHITECTURES BASED ON APACHE KAFKA

Abstract

This paper addresses the problem of ensuring the stability of streaming systems operating in dynamic and highly scalable environments. The architectural characteristics of systems based on Apache Kafka are analyzed, with particular attention given to consumer group coordination mechanisms and the partition rebalancing process. It is demonstrated that frequent changes in the composition of consumer groups, which are typical for cloud-native and microservice-based architectures, lead to repeated rebalancing events. These events cause temporary suspension of data processing, increased latency, and reduced throughput, ultimately affecting the overall system performance. The study examines the underlying causes of excessive rebalancing, including autoscaling, service restarts, and deployment updates, which trigger frequent join and leave operations within consumer groups. Existing approaches primarily focus on optimizing partition assignment strategies, such as range, round-robin, and cooperative rebalancing, aiming to reduce data movement during rebalancing. However, they do not sufficiently address the root cause of frequent rebalancing initiation. To overcome these limitations, an approach based on coordinated lifecycle management of consumers is proposed. The core idea of the approach is to reduce the number of events that trigger rebalancing by synchronizing consumer join and leave operations. This includes controlled startup of new consumers, delayed shutdown to allow completion of in-progress processing, and explicit coordination of group membership changes. The proposed method does not require modification of the underlying Kafka mechanisms and can be integrated into existing distributed systems. The analysis shows that the implementation of the proposed approach leads to improved stability of partition distribution, reduced frequency of rebalancing events, and minimized processing interruptions. As a result, the efficiency and reliability of real-time streaming systems are significantly enhanced. The findings highlight the importance of managing consumer lifecycle events as a key factor in achieving stable and efficient stream processing in dynamic environments.