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[air] Record workload submission and source staging telemetry - #6904

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Record AIR submission telemetry with requested compute, configuration-presence flags, outcome/latency, and job run ID. Include compressed source tarball bytes, GIT_ARCHIVE/PLAIN_TAR packaging mode, and separate packaging/upload durations. Cache hits report zero phase durations; failed attempts retain measurements already collected. Update the fake workspace to return file sizes for cache-hit coverage.

Why

Bring workload visibility in line with Python AIR and help explain slow source staging. The companion ingestion schema is https://github.com/databricks-eng/universe/pull/2720990 and should land first. Events use the existing upload-at-exit pipeline and telemetry opt-out; first-log timing is deferred.

Tests

Added coverage for new uploads, cache reuse, absent versus zero measurements, Git and working-tree packaging, and failed packaging/upload/submission. ./task fmt, ./task checks, full ./task lint, doctests, and all root/tools module unit tests passed (11,641 tests; 143 skipped).

Full ./task test reached acceptance tests but stopped at the new prerequisite requiring jq >= 1.7 (this machine has 1.6).

This PR was written with Codex.

@github-actions github-actions Bot added the AIR Databricks AI Runtime CLI label Oct 1, 2026
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Integration test report

Commit: df62798

Run: 36893736458

Env ✅​pass 🙈​skip Time
✅​ aws linux 276 15 5:16
✅​ aws windows 278 13 4:45
✅​ azure linux 275 15 5:37
✅​ azure windows 277 13 5:33
✅​ gcp linux 276 15 5:38
✅​ gcp windows 278 13 4:47
Top 6 slowest tests (at least 2 minutes):
duration env testname
5:32 azure windows TestAccept
4:43 gcp windows TestAccept
4:43 aws windows TestAccept
4:12 gcp linux TestAccept
3:58 azure linux TestAccept
3:50 aws linux TestAccept

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