feat: handle nulls when creating indices with cuda #2910
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westonpace
commented
Sep 18, 2024
python/python/lance/sampler.py
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| if len(columns) == 1 and filter.lower() == f"{columns[0]} is not null": | ||
| table = pc.drop_null(table) | ||
| elif filter is not None: | ||
| raise Exception(f"Can't yet run filter <{filter}> in-memory") |
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This is maybe a bit frail at the moment.
The problem is that I can't apply the filter with lance because I am using a limit/offset and they won't be pushed down if a filter is present.
Potential solutions:
- Add a scan_range to lance that interprets limit/offset as a pre-filter limit/offset.
- Add filter support to take (and just pass a contiguous range to take)
- Use duckdb or something else to apply the filter here
eddyxu
approved these changes
Sep 18, 2024
jacketsj
reviewed
Sep 23, 2024
| logging.info("Done sampling: centroids shape: %s", init_centroids.shape) | ||
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| ds = TorchDataset( | ||
| dataset, | ||
| batch_size=20480, | ||
| columns=[column], | ||
| samples=sample_size, | ||
| filter=filt, |
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Using filters here seems to cause a significant performance hit now (on the order of several minutes vs several seconds for kmeans clustering).
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Hmm, maybe worth a fast path if we know there are no nulls then.
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