Hi Spark community, I'm reaching out to initiate a conversation about the possibility of developing a Java-based Kubernetes operator for Apache Spark. Following the operator pattern ( https://kubernetes.io/docs/concepts/extend-kubernetes/operator/), Spark users may manage applications and related components seamlessly using native tools like kubectl. The primary goal is to simplify the Spark user experience on Kubernetes, minimizing the learning curve and operational complexities and therefore enable users to focus on the Spark application development.
Although there are several open-source Spark on Kubernetes operators available, none of them are officially integrated into the Apache Spark project. As a result, these operators may lack active support and development for new features. Within this proposal, our aim is to introduce a Java-based Spark operator as an integral component of the Apache Spark project. This solution has been employed internally at Apple for multiple years, operating millions of executors in real production environments. The use of Java in this solution is intended to accommodate a wider user and contributor audience, especially those who are familiar with Scala. Ideally, this operator should have its dedicated repository, similar to Spark Connect Golang or Spark Docker, allowing it to maintain a loose connection with the Spark release cycle. This model is also followed by the Apache Flink Kubernetes operator. We believe that this project holds the potential to evolve into a thriving community project over the long run. A comparison can be drawn with the Flink Kubernetes Operator: Apple has open-sourced internal Flink Kubernetes operator, making it a part of the Apache Flink project ( https://github.com/apache/flink-kubernetes-operator). This move has gained wide industry adoption and contributions from the community. In a mere year, the Flink operator has garnered more than 600 stars and has attracted contributions from over 80 contributors. This showcases the level of community interest and collaborative momentum that can be achieved in similar scenarios. More details can be found at SPIP doc : Spark Kubernetes Operator https://docs.google.com/document/d/1f5mm9VpSKeWC72Y9IiKN2jbBn32rHxjWKUfLRaGEcLE Thanks, -- *Zhou JIANG*
