To be clear: I am generally supportive of the idea (+1) but have some follow-up questions:
Have we taken the time to learn from the other operators? Do we have a compatible CRD/API or not (and if so why?) The API seems to assume that everything is packaged in the container in advance, but I imagine that might not be the case for many folks who have Java or Python packages published to cloud storage and they want to use? What's our plan for the testing on the potential version explosion (not tying ourselves to operator version -> spark version makes a lot of sense, but how do we reasonably assure ourselves that the cross product of Operator Version, Kube Version, and Spark Version all function)? Do we have CI resources for this? Is there a current (non-open source operator) that folks from Apple are using and planning to open source, or is this a fresh "from the ground up" operator proposal? One of the key reasons for this is listed as "An out-of-the-box automation solution that scales effectively" but I don't see any discussion of the target scale or plans to achieve it? On Thu, Nov 9, 2023 at 9:02 PM Zhou Jiang <zhou.c.ji...@gmail.com> wrote: > 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* > > -- Twitter: https://twitter.com/holdenkarau Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9 <https://amzn.to/2MaRAG9> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
