SNOW-1887901: fix partitioning logic #2944
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sfc-gh-yuwang
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sfc-gh-jdu and
sfc-gh-jrose
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| if column_type != int | ||
| else int(processed_lower_bound + i * stride) | ||
| l_bound = ( | ||
| f"{column} >= {self._to_external_value(current_value, column_type)}" |
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in spark
partitionColumn must be a numeric, date, or timestamp column
do we handle all 3 types here?
also can you check the pyspark behavior if passing column unsupported type
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I found I actually wrote the code to support all types, so I just added a test here
tests/integ/test_data_source_api.py
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| @@ -8,6 +8,8 @@ | |||
| from unittest.mock import MagicMock | |||
| import pytest | |||
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| from snowflake.snowpark.types import IntegerType | |||
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let's add tests for a couple of more types? like float, decimal, datetime, date
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also unsupported type test
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Which Jira issue is this PR addressing? Make sure that there is an accompanying issue to your PR.
Fixes SNOW-1887901
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fix partition logic, now it would generate same results as spark do