feat(dataset-mount): mount datasets onto a computing unit#6896
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Perform the FUSE mount for dataset repositories outside the (unprivileged) computing-unit pod. A per-node privileged `texera-mounter` DaemonSet runs GeeseFS on the pod's behalf and the read-only mount is exposed back into the pod via mount propagation, scoped to that computing unit. A JWT-authenticated S3 proxy in file-service fronts the LakeFS S3 gateway: it verifies the pod's JWT, checks the user's read access, and re-signs to LakeFS with credentials held only server-side, so no global credential ever enters the pod. - `texera-mounter` DaemonSet: mounter.py (+ tests), dockerfile, helm daemonset/rbac/values. - Unprivileged CU pod + mount-propagation wiring (KubernetesClient) and mounter config/env. - File-service JWT S3 proxy (S3ProxyServlet). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01H27sHgS29u2JcLYPPR6Hdx
Add per-computing-unit dataset mounting on top of the mount
infrastructure. A user can mount / list / unmount a dataset version on a
running computing unit; the platform asks that CU's node mounter to
perform the mount and reports the in-pod path back.
- ComputingUnitManagingResource: GET/POST/DELETE
/computing-unit/{cuid}/mounts, backed by a credential-free MounterClient
that talks to the node mounter over HTTP.
- FileResolver: resolve a dataset path to (repo, versionHash) and back,
so mounts can be shown to the user as /owner/dataset/version.
- "Mount datasets into computing unit" UI on the computing-unit selector.
At this point datasets can be mounted onto a CU but are not yet consumed
by any operator; that follows in the Python-UDF integration.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01H27sHgS29u2JcLYPPR6Hdx
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| config | throughput | MB/s | latency | max Δ latest / 7d | |
|---|---|---|---|---|---|
| 🔴 | bs=10 sw=10 sl=64 | 390 | 0.238 | 24,720/36,464/36,464 us | 🔴 +9.7% / 🔴 +119.7% |
| 🟢 | bs=100 sw=10 sl=64 | 815 | 0.497 | 121,565/149,291/149,291 us | 🟢 -6.2% / 🔴 +36.8% |
| ⚪ | bs=1000 sw=10 sl=64 | 919 | 0.561 | 1,083,463/1,139,503/1,139,503 us | ⚪ within ±5% / 🔴 +8.9% |
Baseline details
Latest main 57d4dba from same runner
| config | metric | PR | latest main | 7d avg | Δ latest | Δ 7d |
|---|---|---|---|---|---|---|
| bs=10 sw=10 sl=64 | throughput | 390 tuples/sec | 416 tuples/sec | 754.55 tuples/sec | -6.3% | -48.3% |
| bs=10 sw=10 sl=64 | MB/s | 0.238 MB/s | 0.254 MB/s | 0.461 MB/s | -6.3% | -48.3% |
| bs=10 sw=10 sl=64 | p50 | 24,720 us | 22,530 us | 12,816 us | +9.7% | +92.9% |
| bs=10 sw=10 sl=64 | p95 | 36,464 us | 34,386 us | 16,594 us | +6.0% | +119.7% |
| bs=10 sw=10 sl=64 | p99 | 36,464 us | 34,386 us | 19,806 us | +6.0% | +84.1% |
| bs=100 sw=10 sl=64 | throughput | 815 tuples/sec | 811 tuples/sec | 969.38 tuples/sec | +0.5% | -15.9% |
| bs=100 sw=10 sl=64 | MB/s | 0.497 MB/s | 0.495 MB/s | 0.592 MB/s | +0.4% | -16.0% |
| bs=100 sw=10 sl=64 | p50 | 121,565 us | 117,400 us | 103,584 us | +3.5% | +17.4% |
| bs=100 sw=10 sl=64 | p95 | 149,291 us | 159,118 us | 109,097 us | -6.2% | +36.8% |
| bs=100 sw=10 sl=64 | p99 | 149,291 us | 159,118 us | 117,304 us | -6.2% | +27.3% |
| bs=1000 sw=10 sl=64 | throughput | 919 tuples/sec | 937 tuples/sec | 1,004 tuples/sec | -1.9% | -8.4% |
| bs=1000 sw=10 sl=64 | MB/s | 0.561 MB/s | 0.572 MB/s | 0.613 MB/s | -1.9% | -8.4% |
| bs=1000 sw=10 sl=64 | p50 | 1,083,463 us | 1,066,430 us | 1,002,357 us | +1.6% | +8.1% |
| bs=1000 sw=10 sl=64 | p95 | 1,139,503 us | 1,124,801 us | 1,046,463 us | +1.3% | +8.9% |
| bs=1000 sw=10 sl=64 | p99 | 1,139,503 us | 1,124,801 us | 1,073,661 us | +1.3% | +6.1% |
Raw CSV
config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,512.93,200,128000,390,0.238,24719.60,36464.37,36464.37
1,100,10,64,20,2454.36,2000,1280000,815,0.497,121564.67,149290.87,149290.87
2,1000,10,64,20,21753.03,20000,12800000,919,0.561,1083462.81,1139502.72,1139502.72
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What changes were proposed in this PR?
Add per-computing-unit dataset mounting on top of the mount infrastructure (#6866) — the platform step of the dataset-mounting feature (#6606).
A user can mount / list / unmount a dataset version on a running computing unit. The platform asks that CU's node mounter to perform the mount and reports the in-pod path back; nothing consumes the mount yet — that is the follow-up Python-UDF PR.
GET/POST/DELETE /computing-unit/{cuid}/mounts(ComputingUnitManagingResource), backed by a credential-freeMounterClientthat talks to the CU's node mounter over HTTP. Authorization stays in file-service via the pod's JWT — no credential enters the platform path.FileResolver— resolve a dataset path to(repo, versionHash)and back, so a mount is shown to the user as/owner/dataset/version.Any related issues, documentation, discussions?
Closes #6863 · part of #6606 · builds on #6866.
How was this PR tested?
sbt ComputingUnitManagingService/Test/compilegreen (main + test).MounterClientSpecexercises mount / list / unmount against a stub node-mounter HTTP server, asserting the request shape and non-2xx error handling.Was this PR authored or co-authored using generative AI tooling?
Generated-by: Claude Opus 4.8