More properly handle nullability of types/literals in Substrait #10640
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Blizzara
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This isn't perfect; some things are still assumed to just always be nullable (e.g. Literal list elements). But it's still giving a closer match than just assuming everything is nullable.
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jonahgao
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| @@ -1686,7 +1691,7 @@ fn to_substrait_bounds(window_frame: &WindowFrame) -> Result<(Bound, Bound)> { | |||
| )) | |||
| } | |||
|
|
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| fn to_substrait_literal(value: &ScalarValue) -> Result<Literal> { | |||
| fn to_substrait_literal(value: &ScalarValue, nullable: bool) -> Result<Literal> { | |||
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According to ExprSchemable::nullable(), the nullability of a literal depends on its ScalarValue, so I think the nullable argument for this function could be omitted.
fn to_substrait_literal(value: &ScalarValue, nullable: bool) -> Result<Literal> {
if value.is_null() {
return Ok(Literal {
nullable: true,
type_variation_reference: DEFAULT_TYPE_REF,
literal_type: Some(LiteralType::Null(to_substrait_type(
&value.data_type(),
true,
)?)),
});
}At the same time, we can also remove try_to_substrait_null.
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I think you're right, thanks! That does indeed simplify things. Fixed in cbd2264
jonahgao
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May 24, 2024
Co-authored-by: Jonah Gao <jonahgao@msn.com>
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jayzhan211
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…he#10640) * More properly handle nullability of types/literals in Substrait This isn't perfect; some things are still assumed to just always be nullable (e.g. Literal list elements). But it's still giving a closer match than just assuming everything is nullable. * Avoid cloning and creating DataFusionError Co-authored-by: Jonah Gao <jonahgao@msn.com> * simplify Literal/ScalarValue null handling --------- Co-authored-by: Jonah Gao <jonahgao@msn.com>
This was referenced May 27, 2024
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DataFusion (= Arrow) is quite strict about nullability, specifically, when using e.g. LogicalPlan::Values, the given schema must match the given literals exactly - including nullability. This is non-trivial to do when converting schema and literals separately. The existing implementation for from_substrait_literal already creates lists that are always nullable (see ScalarValue::new_list => array_into_list_array). This reverts part of apache#10640 to align from_substrait_type with that behavior. This is the error I was hitting: ``` ArrowError(InvalidArgumentError("column types must match schema types, expected List(Field { name: \"item\", data_type: Int32, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }) but found List(Field { name: \"item\", data_type: Int32, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }) at column index 0"), None) ```
alamb
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…0874) * Ignore nullability of list elements when consuming Substrait DataFusion (= Arrow) is quite strict about nullability, specifically, when using e.g. LogicalPlan::Values, the given schema must match the given literals exactly - including nullability. This is non-trivial to do when converting schema and literals separately. The existing implementation for from_substrait_literal already creates lists that are always nullable (see ScalarValue::new_list => array_into_list_array). This reverts part of #10640 to align from_substrait_type with that behavior. This is the error I was hitting: ``` ArrowError(InvalidArgumentError("column types must match schema types, expected List(Field { name: \"item\", data_type: Int32, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }) but found List(Field { name: \"item\", data_type: Int32, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }) at column index 0"), None) ``` * use `Field::new_list_field` in `array_into_(large_)list_array` just for consistency, to reduce the places where "item" is written out * add a test for non-nullable lists
findepi
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…he#10640) * More properly handle nullability of types/literals in Substrait This isn't perfect; some things are still assumed to just always be nullable (e.g. Literal list elements). But it's still giving a closer match than just assuming everything is nullable. * Avoid cloning and creating DataFusionError Co-authored-by: Jonah Gao <jonahgao@msn.com> * simplify Literal/ScalarValue null handling --------- Co-authored-by: Jonah Gao <jonahgao@msn.com>
findepi
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Jul 16, 2024
…ache#10874) * Ignore nullability of list elements when consuming Substrait DataFusion (= Arrow) is quite strict about nullability, specifically, when using e.g. LogicalPlan::Values, the given schema must match the given literals exactly - including nullability. This is non-trivial to do when converting schema and literals separately. The existing implementation for from_substrait_literal already creates lists that are always nullable (see ScalarValue::new_list => array_into_list_array). This reverts part of apache#10640 to align from_substrait_type with that behavior. This is the error I was hitting: ``` ArrowError(InvalidArgumentError("column types must match schema types, expected List(Field { name: \"item\", data_type: Int32, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }) but found List(Field { name: \"item\", data_type: Int32, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }) at column index 0"), None) ``` * use `Field::new_list_field` in `array_into_(large_)list_array` just for consistency, to reduce the places where "item" is written out * add a test for non-nullable lists
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Which issue does this PR close?
Extracted from #10531
Builds on top of #10622 so I'll rebase this once it's merged.
Rationale for this change
More closely propagate null-ability through Substrait conversions
What changes are included in this PR?
Handles the nullability of struct fields during Substrait conversions. This isn't perfect; some things are still assumed to just always be nullable (e.g. Literal list elements).
Are these changes tested?
Tested through round-trip unit tests
Are there any user-facing changes?
Nulls are handled more accurately