Use SparkCache CUDA restore terminology - #26
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Expose SparkCache CUDA restore and CUDA placement names in connector configuration, environment variables, deployment profiles, CLI options, logs, and documentation. Preserve legacy native-restore keys and Python symbols as compatibility aliases; conflicting canonical and legacy values reject startup and legacy-only input warns once per process. Cache identity, digest salts, chunk geometry, and stored bytes are unchanged. Validation: 746 SparkCache tests passed with 7 skipped; 108 deployment tests passed with 1 skipped; Ruff passed.
This was referenced Aug 30, 2026
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Result
Status: implemented.
SparkCache exposes the capability as SparkCache CUDA restore and the data-movement component as SparkCache CUDA placement. Connector settings, environment variables, GLM deployment profiles, CLI options, runtime messages, and repository prose use those terms.
Canonical Python modules and class/function aliases provide CUDA-named imports. The implementation modules and symbols containing
nativeremain available as compatibility interfaces and refer to the same objects.Compatibility
Legacy connector keys, environment variables, profile arguments, and CLI flags remain accepted. Legacy-only input emits one concise warning per process. Supplying canonical and legacy values that disagree rejects startup.
Cache identity, digest salts, chunk geometry, on-disk schemas, the placement C ABI, and
libspark_cache_placement.soare unchanged. Cache namespace impact: none.Historical validation artifact paths remain stable so existing evidence links continue to resolve.
Validation
python -m pytest sparkcache -q: 746 passed, 7 skippedpython -m pytest deploy -q: 108 passed, 1 skippedpython -m ruff check .: passedThis draft is stacked on #25 because it names the reconstructed-page CUDA placement interface introduced by that change.