aicam opened a new pull request, #6866:
URL: https://github.com/apache/texera/pull/6866

   ### What changes were proposed in this PR?
   
   Perform the FUSE mount for dataset repositories **outside** the 
(unprivileged) computing-unit pod — the infrastructure foundation of the 
dataset-mounting feature (#6606).
   
   - **`texera-mounter` DaemonSet** — a per-node privileged agent 
(`bin/mounter/mounter.py` + tests, dockerfile, helm daemonset/rbac/values) that 
runs GeeseFS on a pod's behalf. The read-only mount is exposed back into the CU 
pod via Kubernetes **mount propagation**, scoped to that computing unit, so the 
pod that runs untrusted user code stays **unprivileged**.
   - **Unprivileged CU pod wiring** (`KubernetesClient`) — the propagation 
volume + node-IP / mounter env.
   - **File-service JWT S3 proxy** (`S3ProxyServlet`) — fronts the LakeFS S3 
gateway: verifies the pod's JWT, checks the user's read access, and re-signs to 
LakeFS with credentials held only server-side. No global credential ever enters 
the pod.
   
   Foundation only — nothing triggers a mount yet; the platform integration 
(engine client + per-CU mount API + UI + UDF bindings) comes in the follow-up 
PR.
   
   ### Any related issues, documentation, discussions?
   
   Closes #6862 · part of #6606.
   
   ### How was this PR tested?
   
   `sbt FileService/compile ComputingUnitManagingService/compile` green. The 
mounter has its own pytest suite (`bin/mounter/tests`). The whole feature (this 
PR + the integration follow-up) was validated end-to-end on minikube: a Python 
UDF read a ~2 GB sharded PyTorch model from a propagated mount via 
`torch.load`, bit-exact; the mounter's reaper/unmount and the proxy's JWT 
authorization (positive + negative) were exercised.
   
   ### Was this PR authored or co-authored using generative AI tooling?
   
   Generated-by: Claude Opus 4.8
   


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