Karmada is an increasingly popular open source tool for deploying and managing cloud-native applications across Kubernetes clusters. It can also be used to boost workload resiliency with its existing failover support. But what happens if we need to conserve state?

Within the context of data processing (e.g., Apache Flink or Apache Spark), the state is often critical to making sure workloads are able to gracefully resume in the event of a disruption. In collaboration with the Karmada community, the Bloomberg Streaming Analytics team has worked to bridge this gap in Karmada’s existing failover features.

During this talk, we’ll use a real-life Flink on Karmada use case to discuss:
- The complexities related to intelligently scheduling stateful workloads, improving resiliency, and ensuring state consistency during failover on multi-cluster K8s
- The open source enhancements to Karmada to manage these challenges
- How to leverage Karmada to support other stateful use cases!