Intelligent Autoscaling for Kafka Consumers

The Kubernetes native HPA algorithms (K8S HPA mechanism) result in modest savings and much larger lags (latency). By using Machine Learning technologies for predictions and analysis, achieves much better performance (reduced latency) with much fewer resources (reduced number of Kafka consumers) and makes implementing autoscaling of Kafka consumers simple and straightforward.
Effective workload predictions uses message production rate of a Kafka topic and target KPI metrics such as the desired latency as the key metrics for autoscaling Kafka consumers. Predictions of message production rate give a more accurate indication of real workloads for Kafka consumers.
Cost-effective application deployments integrates the workload metrics, workload predictions, and application KPI in deciding the right number of replicas and achieves more cost-effective application deployments.
Achieving desired performance

Without guessing or experimenting on what metric threshold to set in Kubernetes native HPA, achieves better use of resources for desired performance automatically.

Please check out this video on why’s intelligent autoscaling is better than Kubernetes native HPA.

Please follow the steps below to start your journey with for FREE
Step 1

Confirm your Kafka version: Strimzi/kafka:0.17.0-kafka-2.4.0

Step 2
Download by submitting free software request
Step 3
Open software, click on ‘Add Application’ on the Configuration/ Applications page, and select ‘Kafka Consumer’
Step 4
Fill in Consumer Group information
Step 5
Enjoy your trial on and its recommendations