Introducing Streamhouse: the open data architecture for AI | Learn More
In the world of real-time data processing using Kafka, the challenge of optimizing consumer services for scalability and efficiency while meeting SLAs is quite significant. Join us on a journey where we delve into how we enhance Kafka consumer scalability with the help of Kubernetes and KEDA. Our experimentation begins with HPA, utilizing CPU and memory metrics to increase CPU utilization from a modest 20% to 60%. However, realizing that relying solely on averages has room for improvement becomes a pivotal insight.
Now, enter KEDA – a true game-changer in this landscape. Discover the impact of KEDA's custom metrics, specifically tailored for Kafka-based metrics, causing a revolution in scaling. The outcome? An astonishing 75% improvement in efficiency, all the while maintaining our commitment to data freshness SLAs.
Yet, our narrative unfolds further to unveil our secret weapon – the Custom Controller Resource Definition (CRD). This innovative tool is designed for dynamic vertical scaling within Kafka topics. While KEDA adeptly handles the day-to-day traffic, our CRD steps in to optimize resources, ensuring adaptability to organic changes in traffic over time. Join us in exploring the advantages of our comprehensive approach and gain insights into the lessons learned along the path to achieving peak efficiency for Kafka consumers.