Infer data type from schema for Values and add struct coercion to coalesce

datafusion/common/src/dfschema.rs

@@ -315,7 +315,6 @@ impl DFSchema {
                 None => self_unqualified_names.contains(field.name().as_str()),
             };
             if !duplicated_field {
-                // self.inner.fields.push(field.clone());
                 schema_builder.push(Arc::clone(field));
                 qualifiers.push(qualifier.cloned());
             }

datafusion/expr-common/Cargo.toml

@@ -40,4 +40,5 @@ path = "src/lib.rs"
 [dependencies]
 arrow = { workspace = true }
 datafusion-common = { workspace = true }
+itertools = { workspace = true }
 paste = "^1.0"

datafusion/expr-common/src/type_coercion/binary.rs

@@ -28,7 +28,10 @@ use arrow::datatypes::{
     DataType, Field, FieldRef, Fields, TimeUnit, DECIMAL128_MAX_PRECISION,
     DECIMAL128_MAX_SCALE, DECIMAL256_MAX_PRECISION, DECIMAL256_MAX_SCALE,
 };
-use datafusion_common::{exec_datafusion_err, plan_datafusion_err, plan_err, Result};
+use datafusion_common::{
+    exec_datafusion_err, exec_err, internal_err, plan_datafusion_err, plan_err, Result,
+};
+use itertools::Itertools;
 
 /// The type signature of an instantiation of binary operator expression such as
 /// `lhs + rhs`
@@ -372,6 +375,8 @@ impl From<&DataType> for TypeCategory {
 /// decimal precision and scale when coercing decimal types.
 ///
 /// This function doesn't preserve correct field name and nullability for the struct type, we only care about data type.
+///
+/// Returns Option because we might want to continue on the code even if the data types are not coercible to the common type
 pub fn type_union_resolution(data_types: &[DataType]) -> Option<DataType> {
     if data_types.is_empty() {
         return None;
@@ -529,6 +534,89 @@ fn type_union_resolution_coercion(
     }
 }
 
+/// Handle type union resolution including struct type and others.
+pub fn try_type_union_resolution(data_types: &[DataType]) -> Result<Vec<DataType>> {
+    let err = match try_type_union_resolution_with_struct(data_types) {
+        Ok(struct_types) => return Ok(struct_types),
+        Err(e) => Some(e),
+    };
+
+    if let Some(new_type) = type_union_resolution(data_types) {
+        Ok(vec![new_type; data_types.len()])
+    } else {
+        exec_err!("Fail to find the coerced type, errors: {:?}", err)
+    }
+}
+
+// Handle struct where we only change the data type but preserve the field name and nullability.
+// Since field name is the key of the struct, so it shouldn't be updated to the common column name like "c0" or "c1"
+pub fn try_type_union_resolution_with_struct(
+    data_types: &[DataType],
+) -> Result<Vec<DataType>> {
+    let mut keys_string: Option<String> = None;
+    for data_type in data_types {
+        if let DataType::Struct(fields) = data_type {
+            let keys = fields.iter().map(|f| f.name().to_owned()).join(",");
+            if let Some(ref k) = keys_string {
+                if *k != keys {
+                    return exec_err!("Expect same keys for struct type but got mismatched pair {} and {}", *k, keys);
+                }
+            } else {
+                keys_string = Some(keys);
+            }
+        } else {
+            return exec_err!("Expect to get struct but got {}", data_type);
+        }
+    }
+
+    let mut struct_types: Vec<DataType> = if let DataType::Struct(fields) = &data_types[0]
+    {
+        fields.iter().map(|f| f.data_type().to_owned()).collect()
+    } else {
+        return internal_err!("Struct type is checked is the previous function, so this should be unreachable");
+    };
+
+    for data_type in data_types.iter().skip(1) {
+        if let DataType::Struct(fields) = data_type {
+            let incoming_struct_types: Vec<DataType> =
+                fields.iter().map(|f| f.data_type().to_owned()).collect();
+            // The order of field is verified above
+            for (lhs_type, rhs_type) in
+                struct_types.iter_mut().zip(incoming_struct_types.iter())
+            {
+                if let Some(coerced_type) =
+                    type_union_resolution_coercion(lhs_type, rhs_type)
+                {
+                    *lhs_type = coerced_type;
+                } else {
+                    return exec_err!(
+                        "Fail to find the coerced type for {} and {}",
+                        lhs_type,
+                        rhs_type
+                    );
+                }
+            }
+        } else {
+            return exec_err!("Expect to get struct but got {}", data_type);
+        }
+    }
+
+    let mut final_struct_types = vec![];
+    for s in data_types {
+        let mut new_fields = vec![];
+        if let DataType::Struct(fields) = s {
+            for (i, f) in fields.iter().enumerate() {
+                let field = Arc::unwrap_or_clone(Arc::clone(f))
+                    .with_data_type(struct_types[i].to_owned());
+                new_fields.push(Arc::new(field));
+            }
+        }
+        final_struct_types.push(DataType::Struct(new_fields.into()))
+    }
+
+    Ok(final_struct_types)
+}
+
 /// Coerce `lhs_type` and `rhs_type` to a common type for the purposes of a
 /// comparison operation
 ///

datafusion/expr/src/logical_plan/builder.rs

@@ -46,13 +46,15 @@ use crate::{
 
 use super::dml::InsertOp;
 use super::plan::ColumnUnnestList;
+use arrow::compute::can_cast_types;
 use arrow::datatypes::{DataType, Field, Fields, Schema, SchemaRef};
 use datafusion_common::display::ToStringifiedPlan;
 use datafusion_common::file_options::file_type::FileType;
 use datafusion_common::{
-    get_target_functional_dependencies, internal_err, not_impl_err, plan_datafusion_err,
-    plan_err, Column, DFSchema, DFSchemaRef, DataFusionError, FunctionalDependencies,
-    Result, ScalarValue, TableReference, ToDFSchema, UnnestOptions,
+    exec_err, get_target_functional_dependencies, internal_err, not_impl_err,
+    plan_datafusion_err, plan_err, Column, DFSchema, DFSchemaRef, DataFusionError,
+    FunctionalDependencies, Result, ScalarValue, TableReference, ToDFSchema,
+    UnnestOptions,
 };
 use datafusion_expr_common::type_coercion::binary::type_union_resolution;
 
@@ -172,12 +174,45 @@ impl LogicalPlanBuilder {
     /// `value`. See the [Postgres VALUES](https://www.postgresql.org/docs/current/queries-values.html)
     /// documentation for more details.
     ///
+    /// so it's usually better to override the default names with a table alias list.
+    ///
+    /// If the values include params/binders such as $1, $2, $3, etc, then the `param_data_types` should be provided.
+    pub fn values(values: Vec<Vec<Expr>>) -> Result<Self> {
+        if values.is_empty() {
+            return plan_err!("Values list cannot be empty");
+        }
+        let n_cols = values[0].len();
+        if n_cols == 0 {
+            return plan_err!("Values list cannot be zero length");
+        }
+        for (i, row) in values.iter().enumerate() {
+            if row.len() != n_cols {
+                return plan_err!(
+                    "Inconsistent data length across values list: got {} values in row {} but expected {}",
+                    row.len(),
+                    i,
+                    n_cols
+                );
+            }
+        }
+
+        // Infer from data itself
+        Self::infer_data(values)
+    }
+
+    /// Create a values list based relation, and the schema is inferred from data itself or table schema if provided, consuming
+    /// `value`. See the [Postgres VALUES](https://www.postgresql.org/docs/current/queries-values.html)
+    /// documentation for more details.
+    ///
     /// By default, it assigns the names column1, column2, etc. to the columns of a VALUES table.
     /// The column names are not specified by the SQL standard and different database systems do it differently,
     /// so it's usually better to override the default names with a table alias list.
     ///
     /// If the values include params/binders such as $1, $2, $3, etc, then the `param_data_types` should be provided.
-    pub fn values(mut values: Vec<Vec<Expr>>) -> Result<Self> {
+    pub fn values_with_schema(
+        values: Vec<Vec<Expr>>,
+        schema: &DFSchemaRef,
+    ) -> Result<Self> {
         if values.is_empty() {
             return plan_err!("Values list cannot be empty");
         }
@@ -196,16 +231,53 @@ impl LogicalPlanBuilder {
             }
         }
 
-        let empty_schema = DFSchema::empty();
+        // Check the type of value against the schema
+        Self::infer_values_from_schema(values, schema)
+    }
+
+    fn infer_values_from_schema(
+        values: Vec<Vec<Expr>>,
+        schema: &DFSchema,
+    ) -> Result<Self> {
+        let n_cols = values[0].len();
+        let mut field_types: Vec<DataType> = Vec::with_capacity(n_cols);
+        for j in 0..n_cols {
+            let field_type = schema.field(j).data_type();
+            for row in values.iter() {
+                let value = &row[j];
+                let data_type = value.get_type(schema)?;
+
+                if !data_type.equals_datatype(field_type) {
+                    if can_cast_types(&data_type, field_type) {
+                    } else {
+                        return exec_err!(
+                            "type mistmatch and can't cast to got {} and {}",
+                            data_type,
+                            field_type
+                        );
+                    }
+                }
+            }
+            field_types.push(field_type.to_owned());
+        }
+
+        Self::infer_inner(values, &field_types, schema)
+    }
+
+    fn infer_data(values: Vec<Vec<Expr>>) -> Result<Self> {
+        let n_cols = values[0].len();
+        let schema = DFSchema::empty();
+
         let mut field_types: Vec<DataType> = Vec::with_capacity(n_cols);
         for j in 0..n_cols {
             let mut common_type: Option<DataType> = None;
             for (i, row) in values.iter().enumerate() {
                 let value = &row[j];
-                let data_type = value.get_type(&empty_schema)?;
+                let data_type = value.get_type(&schema)?;
                 if data_type == DataType::Null {
                     continue;
                 }
+
                 if let Some(prev_type) = common_type {
                     // get common type of each column values.
                     let data_types = vec![prev_type.clone(), data_type.clone()];
@@ -221,14 +293,22 @@ impl LogicalPlanBuilder {
             // since the code loop skips NULL
             field_types.push(common_type.unwrap_or(DataType::Null));
         }
+
+        Self::infer_inner(values, &field_types, &schema)
+    }
+
+    fn infer_inner(
+        mut values: Vec<Vec<Expr>>,
+        field_types: &[DataType],
+        schema: &DFSchema,
+    ) -> Result<Self> {
         // wrap cast if data type is not same as common type.
         for row in &mut values {
             for (j, field_type) in field_types.iter().enumerate() {
                 if let Expr::Literal(ScalarValue::Null) = row[j] {
                     row[j] = Expr::Literal(ScalarValue::try_from(field_type)?);
                 } else {
-                    row[j] =
-                        std::mem::take(&mut row[j]).cast_to(field_type, &empty_schema)?;
+                    row[j] = std::mem::take(&mut row[j]).cast_to(field_type, schema)?;
                 }
             }
         }
@@ -243,6 +323,7 @@ impl LogicalPlanBuilder {
             .collect::<Vec<_>>();
         let dfschema = DFSchema::from_unqualified_fields(fields.into(), HashMap::new())?;
         let schema = DFSchemaRef::new(dfschema);
+
         Ok(Self::new(LogicalPlan::Values(Values { schema, values })))
     }
 

datafusion/functions-nested/src/make_array.rs

@@ -28,15 +28,15 @@ use arrow_array::{
 use arrow_buffer::OffsetBuffer;
 use arrow_schema::DataType::{LargeList, List, Null};
 use arrow_schema::{DataType, Field};
-use datafusion_common::{exec_err, internal_err};
 use datafusion_common::{plan_err, utils::array_into_list_array_nullable, Result};
-use datafusion_expr::binary::type_union_resolution;
+use datafusion_expr::binary::{
+    try_type_union_resolution_with_struct, type_union_resolution,
+};
 use datafusion_expr::scalar_doc_sections::DOC_SECTION_ARRAY;
 use datafusion_expr::TypeSignature;
 use datafusion_expr::{
     ColumnarValue, Documentation, ScalarUDFImpl, Signature, Volatility,
 };
-use itertools::Itertools;
 
 use crate::utils::make_scalar_function;
 
@@ -111,33 +111,16 @@ impl ScalarUDFImpl for MakeArray {
     }
 
     fn coerce_types(&self, arg_types: &[DataType]) -> Result<Vec<DataType>> {
-        if let Some(new_type) = type_union_resolution(arg_types) {
-            // TODO: Move the logic to type_union_resolution if this applies to other functions as well
-            // Handle struct where we only change the data type but preserve the field name and nullability.
-            // Since field name is the key of the struct, so it shouldn't be updated to the common column name like "c0" or "c1"
-            let is_struct_and_has_same_key = are_all_struct_and_have_same_key(arg_types)?;
-            if is_struct_and_has_same_key {
-                let data_types: Vec<_> = if let DataType::Struct(fields) = &arg_types[0] {
-                    fields.iter().map(|f| f.data_type().to_owned()).collect()
-                } else {
-                    return internal_err!("Struct type is checked is the previous function, so this should be unreachable");
-                };
-
-                let mut final_struct_types = vec![];
-                for s in arg_types {
-                    let mut new_fields = vec![];
-                    if let DataType::Struct(fields) = s {
-                        for (i, f) in fields.iter().enumerate() {
-                            let field = Arc::unwrap_or_clone(Arc::clone(f))
-                                .with_data_type(data_types[i].to_owned());
-                            new_fields.push(Arc::new(field));
-                        }
-                    }
-                    final_struct_types.push(DataType::Struct(new_fields.into()))
-                }
-                return Ok(final_struct_types);
+        let mut errors = vec![];
+        match try_type_union_resolution_with_struct(arg_types) {
+            Ok(r) => return Ok(r),
+            Err(e) => {
+                errors.push(e);
             }
+        }
 
+        if let Some(new_type) = type_union_resolution(arg_types) {
+            // TODO: Move FixedSizeList to List in type_union_resolution
             if let DataType::FixedSizeList(field, _) = new_type {
                 Ok(vec![DataType::List(field); arg_types.len()])
             } else if new_type.is_null() {
@@ -147,9 +130,10 @@ impl ScalarUDFImpl for MakeArray {
             }
         } else {
             plan_err!(
-                "Fail to find the valid type between {:?} for {}",
+                "Fail to find the valid type between {:?} for {}, errors are {:?}",
                 arg_types,
-                self.name()
+                self.name(),
+                errors
             )
         }
     }
@@ -188,26 +172,6 @@ fn get_make_array_doc() -> &'static Documentation {
     })
 }
 
-fn are_all_struct_and_have_same_key(data_types: &[DataType]) -> Result<bool> {
-    let mut keys_string: Option<String> = None;
-    for data_type in data_types {
-        if let DataType::Struct(fields) = data_type {
-            let keys = fields.iter().map(|f| f.name().to_owned()).join(",");
-            if let Some(ref k) = keys_string {
-                if *k != keys {
-                    return exec_err!("Expect same keys for struct type but got mismatched pair {} and {}", *k, keys);
-                }
-            } else {
-                keys_string = Some(keys);
-            }
-        } else {
-            return Ok(false);
-        }
-    }
-
-    Ok(true)
-}
-
 // Empty array is a special case that is useful for many other array functions
 pub(super) fn empty_array_type() -> DataType {
     DataType::List(Arc::new(Field::new("item", DataType::Int64, true)))