Comments (5)
Oguzozcn
commented on May 30, 2024
Hi, so all I have to do is set up a testing environment, install the necessary versions of PySpark and configure CI on my system to run tests against multiple versions, and then run the existing test suite to ensure it passes, right?
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MrPowers
commented on May 30, 2024
@Oguzozcn - Yea, the scope is to run the CI test suite against multiple versions of Spark. This will ensure that the project works for multiple supported Spark versions. Let me know if you'd like me to assign this issue to you.
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Oguzozcn
commented on May 30, 2024
Sure, I will try my best.
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MrPowers
commented on May 30, 2024
Thanks @Oguzozcn, assigned the issue to you. Feel free to ping me if you need any help.
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MrPowers
commented on May 30, 2024
@Oguzozcn - unassigning you cause it looks like this didn't ever get worked on.
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Related Issues (20)
- Flatten dataframe HOT 2
- Highlight mismatching values
- Fix the badges in the project README HOT 1
- The it_does_not_throw_with_different_schema test exposes a bug HOT 17
- pkg_resources is deprecated, prettytable produces warning HOT 1
- Reconfigure CI to run tests on pull requests
- Refactor code to conform to PEP8 HOT 5
- Font colors in error messages are bad in some terminals HOT 1
- ignore_schema in assert_df_equality removed in 9.3? HOT 6
- assert_df_equality throws SchemasNotEqualError when the dataframes are identical (except for the metadata) HOT 3
- Replace poetry dev-dependencies with dependency groups HOT 2
- Add unit tests to highlight limitations of this library HOT 1
- Make proper StructField comparer and DataType comparer abstractions
- Give user control to customize output formatting HOT 2
- SchemaNotEqualError not showing the difference in metadata
- Issues on Python 3.10 due to six 1.15.0 HOT 4
- Unit tests are only run against a single version of Python on the `main` branch.
- underline_cells failing if dataframes are different lengths HOT 1
- assert None while ignore_metadata=True
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