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[SPARK-51290][SQL] Enable filling default values in DSv2 writes #50044

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What changes were proposed in this pull request?

This PR enables filling default values in DSv2 writes.

Why are the changes needed?

These changes are needed for proper support of default values for DSv2 connectors.

Does this PR introduce any user-facing change?

Users will be able to omit columns with default values. There is no impact to existing jobs.

How was this patch tested?

This patch comes with tests.

Was this patch authored or co-authored using generative AI tooling?

No.

@github-actions github-actions bot added the SQL label Feb 21, 2025
@@ -3534,7 +3534,8 @@ class Analyzer(override val catalogManager: CatalogManager) extends RuleExecutor
TableOutputResolver.suitableForByNameCheck(v2Write.isByName,
expected = v2Write.table.output, queryOutput = v2Write.query.output)
val projection = TableOutputResolver.resolveOutputColumns(
v2Write.table.name, v2Write.table.output, v2Write.query, v2Write.isByName, conf)
v2Write.table.name, v2Write.table.output, v2Write.query, v2Write.isByName, conf,
supportColDefaultValue = true)
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I don't think there is value in validating if the catalog defines SUPPORT_COLUMN_DEFAULT_VALUE in capabilities during writes. If a connector includes default value metadata in its schema, it should be enough to fill default values. The flag exists for ALTER and CREATE/REPLACE statements.

@@ -718,6 +724,11 @@ private class BufferedRowsReader(
schema: StructType,
row: InternalRow): Any = {
val index = schema.fieldIndex(field.name)

if (index >= row.numFields) {
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This is needed for support for adding columns with default values to the end.

@@ -423,8 +423,8 @@ abstract class V2WriteAnalysisSuiteBase extends AnalysisTest {
assertNotResolved(parsedPlan)
assertAnalysisErrorCondition(
parsedPlan,
expectedErrorCondition = "INCOMPATIBLE_DATA_FOR_TABLE.CANNOT_FIND_DATA",
expectedMessageParameters = Map("tableName" -> "`table-name`", "colName" -> "`x`")
expectedErrorCondition = "INCOMPATIBLE_DATA_FOR_TABLE.EXTRA_COLUMNS",
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This is because of spark.sql.defaultColumn.useNullsForMissingDefaultValues and is aligned with V1 writes.

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