V3: Fix invalid downcasting for nanos #2397
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raulcd
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Aug 28, 2025
tests/io/test_pyarrow.py
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| @pytest.mark.parametrize("format_version", [1, 2, 3]) | ||
| def test_task_to_record_batches_nanos(format_version: TableVersion, tmpdir: str) -> None: | ||
| from datetime import datetime |
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this import seems unnecessary now.
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Good one, I expected the linter to clean that up 🚀
rambleraptor
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Aug 28, 2025
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One small question, but looks good to me otherwise.
| self._bound_row_filter = bind(table_metadata.schema(), row_filter, case_sensitive=case_sensitive) | ||
| self._case_sensitive = case_sensitive | ||
| self._limit = limit | ||
| self._downcast_ns_timestamp_to_us = Config().get_bool(DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE) |
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Do we need to check the format version for downcasting? (We have the table_metadata already, so we have access to it)
raulcd
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Approving as my previous comment has been fixed
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would be great to add an integration test showing spark writing V3 with nanos and reading with pyiceberg. We can do that as a follow up |
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Thanks for the PR @Fokko and thanks @rambleraptor and @raulcd for the review |
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Rationale for this change
It looks like we downcast Arrow nanosecond types always to microseconds.
cc @sungwy @kevinjqliu
Are these changes tested?
Are there any user-facing changes?