Improve DenseTensorSchema.key_range #193
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This pull request has been linked to Shortcut Story #21626: Support for slicing on key dimension. |
| @property | ||
| def key_range(self) -> IntRange: | ||
| def key_range(self) -> ConstrainedPartitionsIntRange: | ||
| self._key_range: ConstrainedPartitionsIntRange |
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Minor and old...self._key_range is not defined in __init__.
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This is a cached property, it is defined (and cached) the first time the property is accessed instead of __init__. In Python 3.8+ this is provided by functools.cached_property, since we support 3.7 we do it manually instead.
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DenseTensorSchema.key_rangecurrently returns anIntRangeof the key dimension values for dense arrays. For improved efficiency and memory predictability however, it is better if every partition of this key range (generated bypartition_by_countand/orpartition_by_weight) starts at a tile boundary so that any integral TileDB tile is read (at most) once.This PR updates
DenseTensorSchema.key_rangeto return aConstrainedPartitionsIntRangewhosestart_offsetsare a range with step equal to the tile size of the key dimension, ensuring that every partition (with the possible exception of the first) starts at a tile boundary.While this change is in general beneficial, it is even more important for extending properly #191 to dense arrays (since it is more likely that the default
IntRangepartitioning doesn't align with tile boundaries for an arbitrary user-selected key dimension slice).