Refactor decimal conversion in PyArrow tables to use direct casting #544
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jprakash-db
approved these changes
May 6, 2025
| precision, scale = description[i][4], description[i][5] | ||
| assert scale is not None | ||
| assert precision is not None | ||
| # Spark limits decimal to a maximum scale of 38, | ||
| # so 128 is guaranteed to be big enough | ||
| # create the target decimal type |
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nit: Feel this comment is unnecessary
| table = table.set_column(i, field, col_data) | ||
| return table | ||
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| # convert the column directly using PyArrow's cast operation |
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nit: Same don't think this comment is needed as code is descriptive enough
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What type of PR is this?
Description
This PR replaces the previous implementation of convert_decimals_in_arrow_table() with a more efficient approach that uses PyArrow's native casting operation instead of going through pandas conversion and array creation.
The new approach is more performant by avoiding pandas conversion overhead. The table below highlights substantial performance improvements when retrieving all rows from a table containing decimal columns, particularly when compression is disabled. Even greater gains were observed with compression enabled—showing approximately an 84% improvement (6 seconds compared to 39 seconds). Benchmarking was performed against e2-dogfood, with the client located in the us-west-2 region.
How is this tested?
Benchmarking was performed against e2-dogfood, with the client located in the us-west-2 region.
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