Spark 4.0: Add schema conversion support for default values#14407
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Overall I think this looks pretty good for the first part of respecting default values in Spark. Just some minor comments
Thank you @geruh !
huaxingao
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Oct 28, 2025
huaxingao
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Oct 28, 2025
huaxingao
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huaxingao
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@geruh Thanks for the PR! It looks good to me overall. Just left a few minor comments. |
amogh-jahagirdar
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Thanks @geruh, I'll leave it up for a bit in case anyone else has any comments.
huaxingao
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Thanks @geruh and @huaxingao for reviewing! |
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This PR adds support for default values in Spark. During the conversion of an Iceberg schema to Spark's StructType, default values are now passed through to Spark's column metadata using the
CURRENT_DEFAULTandEXISTS_DEFAULTkeys that Spark recognizes.The changes extend
TypeToSparkType()function to extract default values from Iceberg fields and convert them to Spark SQL string representations, enabling Spark to understand and utilize the defaults that were defined in Iceberg.Tests for initial defaults weren't added here since that functionality already works without these changes. So I'll follow up with some to be added to this new test suite.
Note: The current tests focus on default Write capabilities as partial column inserts for DSV2 tables aren't available until Spark 4.1.0 per apache/spark#50044.