kube-batch VS sidekick

tldr; Knative Batch Job provider should support the respective coscheduling and kube-batch support. We had developed an in-house one for KubeFlow, from scratch. We had added Apache Arrow support into knative-serving with the respective CloudEvents interop layer, natively (i.e. secure shmem via IPC namespace, instead of message passing on the same host). We use it as a direct replacement for Apache Arrow Ballista, and had planned researching further DataFusion compat layer. Almost any modern ETL is pretty dubious without Apache Arrow.

Another aspect of batch jobs is that we’ll often want to run distributed computations where we split our data into chunks and run a function on each chunk. One popular option is to run Spark, which is built for exactly this use case, on top of Kubernetes. And there are other options for additional software to make running distributed computations on Kubernetes easier.

> That said, strain on the kube-scheduler is spiky. A new job may consist of many hundreds of pods all being created at once, then return to a relatively low rate of churn.

Last I checked, the default scheduler places Pods one at a time. It might be advantageous to use a gang/batch scheduler like kube-batch[0], Poseidon[1] or DCM[2].

[0] https://github.com/kubernetes-sigs/kube-batch

[1] https://github.com/kubernetes-sigs/poseidon

[2] https://github.com/vmware/declarative-cluster-management

Sidekick would be the recommended route (https://github.com/minio/sidekick). Other load balancers will work but can struggle with high I/O workloads and Sidekick is also incredibly simple to set up so that is a plus as well. Outside of some sort of load balancer, some people elect for RRDNS but that doesn't take nodes out of the available pool so isn't used as often.