Add lambda support and array_transform udf

[gstvg]

New version of this PR available at #21679

this is a breaking change due to adding variant to Expr enum and possibly more

This PR adds support for lambdas with column capture and the array_transform function used to test the lambda implementation.

Example usage:

SELECT array_transform([2, 3], v -> v !=2);
[false, true]
-- arbitrally nested lambdas are also supportedSELECT array_transform([[[2, 3]]], m -> array_transform(m, l -> array_transform(l, v -> v*2)));
[[[4, 6]]]

Note: column capture has been removed for now and will be added on a follow on PR, see #21172

Some comments on code snippets of this doc show what value each struct, variant or field would hold after planning the first example above. Some literals are simplified pseudo code

3 new Expr variants are added, HigherOrderFunction, owing a new trait HigherOrderUDF, which is like a ScalarFunction/ScalarUDFImpl with support for lambdas, Lambda, for the lambda body and it's parameters names, and LambdaVariable, which is like Column but for lambdas parameters.

Their logical representations:

enumExpr{// array_transform([2, 3], v -> v != 2)HigherOrderFunction(HigherOrderFunction),// v -> v != 2Lambda(Lambda),// v, of the lambda body: v != 2LambdaVariable(LambdaVariable),
...
}// array_transform([2, 3], v -> v != 2)structHigherOrderFunction{// global instance of array_transformpubfunc:Arc<dynHigherOrderUDF>,// [Expr::ScalarValue([2, 3]), Expr::Lambda(v -> v != 2)]pubargs:Vec<Expr>,}// v -> v != 2structLambda{// ["v"]pubparams:Vec<String>,// v != 2pubbody:Box<Expr>,}// v, of the lambda body: v != 2structLambdaVariable{// "v"pubname:String,// Field::new("", DataType::Int32, false) // Note: a follow on PR will make this field optional// to free expr_api from specifying it beforehand, // and add resolve_lambda_variables method to Expr,// similar to Expr::Placeholder, see #21172pubfield:FieldRef,pubspans:Spans,}

The example would be planned into a tree like this:

HigherOrderFunctionExpression
name: array_transform
children:
1. ListExpression [2,3]
2. LambdaExpression
parameters: ["v"]
body:
BinaryExpression (!=)
left:
LambdaVariableExpression("v", Field::new("", Int32, false))
right:
LiteralExpression("2")

The physical counterparts definition:

structHigherOrderFunctionExpr{// global instance of array_transformfun:Arc<dynHigherOrderUDF>,// "array_transform"name:String,// [LiteralExpr([2, 3], LambdaExpr("v -> v != 2"))]args:Vec<Arc<dynPhysicalExpr>>,// [1], the positions at args that contains lambdaslambda_positions:Vec<usize>,// Field::new("", DataType::new_list(DataType::Boolean, false), false)return_field:FieldRef,config_options:Arc<ConfigOptions>,}structLambdaExpr{// ["v"]params:Vec<String>,// v -> v != 2body:Arc<dynPhysicalExpr>,}structLambdaVariable{// Field::new("v", DataType::Int32, false)field:FieldRef,// 0, the first and only parameter, "v"index:usize,}

Note: For those who primarly wants to check if this lambda implementation supports their usecase and don't want to spend much time here, it's okay to skip most collapsed blocks, as those serve mostly to help code reviewers, with the exception of HigherOrderUDF and the array_transform implementation of HigherOrderUDF relevant methods, collapsed due to their size

The added HigherOrderUDF trait is almost a clone of ScalarUDFImpl, with the exception of:

1. return_field_from_args and invoke_with_args, where now args.args is a list of enums with two variants: Value or Lambda instead of a list of values
2. the addition of lambda_parameters, which return a Field for each parameter supported for every lambda argument based on the Field of the non lambda arguments
3. the removal of return_field and the deprecated ones is_nullable and display_name.
4. Not yet includes analogues to the methods preimage, placement, evaluate_bounds, propagate_constraints, output_ordering and preserves_lex_ordering

HigherOrderUDF

traitHigherOrderUDF{/// Return the field of all the parameters supported by all the supported lambdas of this function/// based on the field of the value arguments. If a lambda support multiple parameters, or if multiple/// lambdas are supported and some are optional, all should be returned,/// regardless of whether they are used on a particular invocation////// Tip: If you have a [`HigherOrderFunction`] invocation, you can call the helper/// [`HigherOrderFunction::lambda_parameters`] instead of this method directly////// [`HigherOrderFunction`]: crate::expr::HigherOrderFunction/// [`HigherOrderFunction::lambda_parameters`]: crate::expr::HigherOrderFunction::lambda_parameters////// Example for array_transform:////// `array_transform([2.0, 8.0], v -> v > 4.0)`////// ```ignore/// let lambda_parameters = array_transform.lambda_parameters(&[/// Arc::new(Field::new("", DataType::new_list(DataType::Float32, false))), // the Field of the literal `[2, 8]`/// ])?;////// assert_eq!(/// lambda_parameters,/// vec![/// // the lambda supported parameters, regardless of how many are actually used/// vec![/// // the value being transformed/// Field::new("", DataType::Float32, false),/// ]/// ]/// )/// ```////// The implementation can assume that some other part of the code has coerced/// the actual argument types to match [`Self::signature`].fnlambda_parameters(&self,value_fields:&[FieldRef]) -> Result<Vec<Vec<Field>>>;fnreturn_field_from_args(&self,args:LambdaReturnFieldArgs) -> Result<FieldRef>;fninvoke_with_args(&self,args:HigherOrderFunctionArgs) -> Result<ColumnarValue>;// ... omitted methods that are similar in ScalarUDFImpl}/// An argument to a lambda function#[derive(Clone,Debug,PartialEq,Eq)]pubenumValueOrLambda<V,L>{/// A value with associated dataValue(V),/// A lambda with associated dataLambda(L),}/// Information about arguments passed to the function////// This structure contains metadata about how the function was called/// such as the type of the arguments, any scalar arguments and if the/// arguments can (ever) be null////// See [`HigherOrderUDF::return_field_from_args`] for more information#[derive(Clone,Debug)]pubstructLambdaReturnFieldArgs<'a>{/// The data types of the arguments to the function////// If argument `i` to the function is a lambda, it will be the field of the result of the/// lambda if evaluated with the parameters returned from [`HigherOrderUDF::lambda_parameters`]////// For example, with `array_transform([1], v -> v == 5)`/// this field will be `[/// ValueOrLambda::Value(Field::new("", DataType::List(DataType::Int32), false)),/// ValueOrLambda::Lambda(Field::new("", DataType::Boolean, false))/// ]`pubarg_fields:&'a[ValueOrLambda<FieldRef,FieldRef>],/// Is argument `i` to the function a scalar (constant)?////// If the argument `i` is not a scalar, it will be None////// For example, if a function is called like `array_transform([1], v -> v == 5)`/// this field will be `[Some(ScalarValue::List(...), None]`pubscalar_arguments:&'a[Option<&'aScalarValue>],}/// Arguments passed to [`HigherOrderUDF::invoke_with_args`] when invoking a/// lambda function.#[derive(Debug,Clone)]pubstructHigherOrderFunctionArgs{/// The evaluated arguments and lambdas to the functionpubargs:Vec<ValueOrLambda<ColumnarValue,LambdaArgument>>,/// Field associated with each arg, if it exists/// For lambdas, it will be the field of the result of/// the lambda if evaluated with the parameters/// returned from [`HigherOrderUDF::lambda_parameters`]pubarg_fields:Vec<ValueOrLambda<FieldRef,FieldRef>>,/// The number of rows in record batch being evaluatedpubnumber_rows:usize,/// The return field of the lambda function returned/// (from `return_field_from_args`) when creating the/// physical expression from the logical expressionpubreturn_field:FieldRef,/// The config options at execution timepubconfig_options:Arc<ConfigOptions>,}/// A lambda argument to a HigherOrderFunction#[derive(Clone,Debug)]pubstructLambdaArgument{/// The parameters defined in this lambda////// For example, for `array_transform([2], v -> -v)`,/// this will be `vec![Field::new("v", DataType::Int32, true)]`params:Vec<FieldRef>,/// The body of the lambda////// For example, for `array_transform([2], v -> -v)`,/// this will be the physical expression of `-v`body:Arc<dynPhysicalExpr>,}implLambdaArgument{/// Evaluate this lambda/// `args` should evalute to the value of each parameter/// of the correspondent lambda returned in [HigherOrderUDF::lambda_parameters].pubfnevaluate(&self,args:&[&dynFn() -> Result<ArrayRef>],) -> Result<ColumnarValue>{let columns = args
.iter().take(self.params.len()).map(|arg| arg()).collect::<Result<_>>()?;let schema = Arc::new(Schema::new(self.params.clone()));let batch = RecordBatch::try_new(schema, columns)?;self.body.evaluate(&batch)}}

array_transform lambda_parameters implementation

implHigherOrderUDFforArrayTransform{fnlambda_parameters(&self,value_fields:&[FieldRef]) -> Result<Vec<Vec<Field>>>{let list = if value_fields.len() == 1{&value_fields[0]}else{returnplan_err!("{} function requires 1 value arguments, got {}",self.name(),
value_fields.len());};let field = match list.data_type(){DataType::List(field) => field,DataType::LargeList(field) => field,DataType::FixedSizeList(field, _) => field,
_ => returnplan_err!("expected list, got {list}"),};// we don't need to check whether the lambda contains more than two parameters,// e.g. array_transform([], (v, i, j) -> v+i+j), as datafusion will do that for uslet value = Field::new("", field.data_type().clone(), field.is_nullable()).with_metadata(field.metadata().clone());Ok(vec![vec![value]])}}

array_transform return_field_from_args implementation

fnvalue_lambda_pair<'a,V:Debug,L:Debug>(name:&str,args:&'a[ValueOrLambda<V,L>],) -> Result<(&'aV,&'aL)>{let[value, lambda] = take_function_args(name, args)?;let(ValueOrLambda::Value(value),ValueOrLambda::Lambda(lambda)) = (value, lambda)else{returnplan_err!("{name} expects a value followed by a lambda, got {value:?} and {lambda:?}");};Ok((value, lambda))}implHigherOrderUDFforArrayTransform{fnreturn_field_from_args(&self,args:HigherOrderReturnFieldArgs,) -> Result<Arc<Field>>{let(list, lambda) = value_lambda_pair(self.name(), args.arg_fields)?;// lambda is the resulting field of executing the lambda body// with the parameters returned in lambda_parameterslet field = Arc::new(Field::new(Field::LIST_FIELD_DEFAULT_NAME,
lambda.data_type().clone(),
lambda.is_nullable(),));let return_type = match list.data_type(){DataType::List(_) => DataType::List(field),DataType::LargeList(_) => DataType::LargeList(field),DataType::FixedSizeList(_, size) => DataType::FixedSizeList(field,*size),
other => plan_err!("expected list, got {other}")?,};Ok(Arc::new(Field::new("", return_type, list.is_nullable())))}}

array_transform invoke_with_args implementation

implHigherOrderUDFforArrayTransform{fninvoke_with_args(&self,args:HigherOrderFunctionArgs) -> Result<ColumnarValue>{let(list, lambda) = value_lambda_pair(self.name(),&args.args)?;let list_array = list.to_array(args.number_rows)?;// Fast path for fully null input array and also the only way to safely work with// a fully null fixed size list array as it can't be handled by remove_list_null_values belowif list_array.null_count() == list_array.len(){returnOk(ColumnarValue::Array(new_null_array(
args.return_type(),
list_array.len(),)));}// as per list_values docs, if list_array is sliced, list_values will be sliced too,// so before constructing the transformed array below, we must adjust the list offsets with// adjust_offsets_for_slicelet list_values = list_values(&list_array)?;// by passing closures, lambda.evaluate can evaluate only those actually neededlet values_param = || Ok(Arc::clone(&list_values));// call the transforming lambdalet transformed_values = lambda
.evaluate(&[&values_param])?
.into_array(list_values.len())?;let field = match args.return_field.data_type(){DataType::List(field)
| DataType::LargeList(field)
| DataType::FixedSizeList(field, _) => Arc::clone(field),
_ => {returnexec_err!("{} expected ScalarFunctionArgs.return_field to be a list, got {}",self.name(),
args.return_field
);}};let transformed_list = match list_array.data_type(){DataType::List(_) => {let list = list_array.as_list();// since we called list_values above which would return sliced values for// a sliced list, we must adjust the offsets here as otherwise they would be invalidlet adjusted_offsets = adjust_offsets_for_slice(list);Arc::new(ListArray::new(
field,
adjusted_offsets,
transformed_values,
list.nulls().cloned(),))asArrayRef}DataType::LargeList(_) => {let large_list = list_array.as_list();// since we called list_values above which would return sliced values for// a sliced list, we must adjust the offsets here as otherwise they would be invalidlet adjusted_offsets = adjust_offsets_for_slice(large_list);Arc::new(LargeListArray::new(
field,
adjusted_offsets,
transformed_values,
large_list.nulls().cloned(),))}DataType::FixedSizeList(_, value_length) => {Arc::new(FixedSizeListArray::new(
field,*value_length,
transformed_values,
list_array.as_fixed_size_list().nulls().cloned(),))}
other => exec_err!("expected list, got {other}")?,};Ok(ColumnarValue::Array(transformed_list))}}

How relevant HigherOrderUDF methods would be called and what they would return during planning and evaluation of the example

// this is called at sql planninglet lambda_parameters = lambda_udf.lambda_parameters(&[Field::new("",DataType::new_list(DataType::Int32,false),false),// the Field of the [2, 3] literal])?;assert_eq!(
lambda_parameters,
vec![// the parameters that *can* be declared on the lambda, and not only // those actually declared: the implementation doesn't need to care // about it
vec![Field::new("",DataType::Int32,false),// the list inner value]]);// this is called every time ExprSchemable is called on a HigherOrderFunctionlet return_field = array_transform.return_field_from_args(&LambdaReturnFieldArgs{arg_fields:&[ValueOrLambda::Value(Field::new("",DataType::new_list(DataType::Int32,false),false)),ValueOrLambda::Lambda(Field::new("",DataType::Boolean,false)),// the return_field of the expression "v != 2" when "v" is of the type returned in lambda_parameters],
scalar_arguments // irrelevant})?;assert_eq!(return_field,Field::new("",DataType::new_list(DataType::Boolean,false),false));let value = array_transform.evaluate(&HigherOrderFunctionArgs{args:vec![ValueOrLambda::Value(List([2,3])),ValueOrLambda::Lambda(LambdaArgument of `v -> v != 2`),],
arg_fields,// same as abovenumber_rows:1,
return_field,// same as above
config_options,// irrelevant})?;assert_eq!(value,BooleanArray::from([false,true]))

A pair HigherOrderUDF/HigherOrderUDFImpl like ScalarFunction was not used because those exist only to maintain backwards compatibility with the older API#8045

Why LambdaVariable and not Column:

Existing tree traversals that operate on columns would break if some column nodes referenced to a lambda parameter and not a real column. In the example query, projection pushdown would try to push the lambda parameter "v", which won't exist in table "t".

Example of code of another traversal that would break:

fnminimize_join_filter(expr:Arc<dynPhysicalExpr>, ...) -> JoinFilter{letmut used_columns = HashSet::new();
expr.apply(|expr| {ifletSome(col) = expr.as_any().downcast_ref::<Column>(){// if this is a lambda column, this function will break
used_columns.insert(col.index());}Ok(TreeNodeRecursion::Continue)});
...}

Furthermore, the implemention of ExprSchemable and PhysicalExpr::return_field for Column expects that the schema it receives as a argument contains an entry for its name, which is not the case for lambda parameters.

By including a FieldRef on LambdaVariable that should be resolved during construction time in the sql planner, ExprSchemable and PhysicalExpr::return_field simply return it's own Field:

LambdaVariable ExprSchemable and PhysicalExpr::return_field implementation

implExprSchemableforExpr{fnto_field(&self,schema:&dynExprSchema,) -> Result<(Option<TableReference>,Arc<Field>)>{let(relation, schema_name) = self.qualified_name();let field = matchself{Expr::LambdaVariable(l) => Ok(Arc::clone(&l.field)),
...}?;Ok((
relation,Arc::new(field.as_ref().clone().with_name(schema_name)),))}
...
}implPhysicalExprforLambdaVariable{fnreturn_field(&self,_input_schema:&Schema) -> Result<FieldRef>{Ok(Arc::clone(&self.field))}
...
}

Possible alternatives discarded due to complexity, requiring downstream changes and implementation size:

1. Add a new set of TreeNode methods that provides the set of lambdas parameters names seen during the traversal, so column nodes can be tested if they refer to a regular column or to a lambda parameter. Any downstream user that wants to support lambdas would need use those methods instead of the existing ones. This also would add 1k+ lines to the PR.

implExpr{pubfntransform_with_lambdas_params<F:FnMut(Self,&HashSet<String>) -> Result<Transformed<Self>>,>(self,mutf:F,) -> Result<Transformed<Self>>{}}

How minimize_join_filter would looks like:

fnminimize_join_filter(expr:Arc<dynPhysicalExpr>, ...) -> JoinFilter{letmut used_columns = HashSet::new();
expr.apply_with_lambdas_params(|expr, lambdas_params| {ifletSome(col) = expr.as_any().downcast_ref::<Column>(){// dont include lambdas parametersif !lambdas_params.contains(col.name()){
used_columns.insert(col.index());}}Ok(TreeNodeRecursion::Continue)})
...
}

2. Add a flag to the Column node indicating if it refers to a lambda parameter. Still requires checking for it on existing tree traversals that works on Columns (30+) and also downstream.

//logicalstructColumn{pubrelation:Option<TableReference>,pubname:String,pubspans:Spans,pubis_lambda_parameter:bool,}//physicalstructColumn{name:String,index:usize,is_lambda_parameter:bool,}

How minimize_join_filter would look like:

fnminimize_join_filter(expr:Arc<dynPhysicalExpr>, ...) -> JoinFilter{letmut used_columns = HashSet::new();
expr.apply(|expr| {ifletSome(col) = expr.as_any().downcast_ref::<Column>(){// dont include lambdas parametersif !col.is_lambda_parameter{
used_columns.insert(col.index());}}Ok(TreeNodeRecursion::Continue)})
...
}

1. Add a new set of TreeNode methods that provides a schema that includes the lambdas parameters for the scope of the node being visited/transformed:

implExpr{pubfntransform_with_schema<F:FnMut(Self,&DFSchema) -> Result<Transformed<Self>>,>(self,schema:&DFSchema,f:F,) -> Result<Transformed<Self>>{ ...}
... other methods
}

For any given HigherOrderFunction found during the traversal, a new schema is created for each lambda argument that contains it's parameter, returned from HigherOrderUDF::lambda_parameters
How it would look like:

pubfn infer_placeholder_types(self,schema:&DFSchema) -> Result<(Expr,bool)>{letmut has_placeholder = false;// Provide the schema as the first argument. // Transforming closure receive an adjusted_schema as argumentself.transform_with_schema(schema, |mut expr, adjusted_schema| {match&mut expr {// Default to assuming the arguments are the same typeExpr::BinaryExpr(BinaryExpr{ left,op: _, right }) => {// use adjusted_schema and not schema. Those expressions may contain // columns referring to a lambda parameter, which Field would only be// available in adjusted_schema and not in schemarewrite_placeholder(left.as_mut(), right.as_ref(), adjusted_schema)?;rewrite_placeholder(right.as_mut(), left.as_ref(), adjusted_schema)?;}
....

2. Make available trought LogicalPlan and ExecutionPlan nodes a schema that includes all lambdas parameters from all expressions owned by the node, and use this schema for tree traversals. For nodes which won't own any expression, the regular schema can be returned

implLogicalPlan{fnlambda_extended_schema(&self) -> &DFSchema;}traitExecutionPlan{fnlambda_extended_schema(&self) -> &DFSchema;}//usageimplLogicalPlan{pubfn replace_params_with_values(self,param_values:&ParamValues,) -> Result<LogicalPlan>{self.transform_up_with_subqueries(|plan| {// use plan.lambda_extended_schema() containing lambdas parameters// instead of plan.schema() which wontlet lambda_extended_schema = Arc::clone(plan.lambda_extended_schema());let name_preserver = NamePreserver::new(&plan);
plan.map_expressions(|e| {// if this expression is child of lambda and contain columns referring it's parameters// the lambda_extended_schema already contain themlet(e, has_placeholder) = e.infer_placeholder_types(&lambda_extended_schema)?;
....

[gstvg]

Outdated

# Traversing Expr trees with a schema that include lambdas parameters

The parameters of a lambda aren't present in the schema of the plan they belong to. During tree traversals that use a schema to check expressions datatype, nullability and metadata, there must be a way to access a schema which includes those parameters.

Expr tree traversal with wrong schema

┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, (b, i) -> array_transform(b, b -> b + c + i)) ╷ ╷ ╷ ╷ a = Int32 ╷ ╷ b = List(List(Int32)) ╷ ╷ c = Int32 ╷ ╷ ╷ ╷ ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ (b, i) -> array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ a = Int32 ╷ ╷ b = List(List(Int32)) ╷ ╷ c = Int32 ╷ ╷ ╷ ╷ !! missing "i", incorrect "b" type !! ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ a = Int32 ╷ ╷ b = List(List(Int32)) ╷ ╷ c = Int32 ╷ ╷ ╷ ╷ !! missing "i", incorrect "b" type !! ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ b -> b + c + i ╷ ╷ ╷ ╷ a = Int32 ╷ ╷ b = List(List(Int32)) ╷ ╷ c = Int32 ╷ ╷ ╷ ╷ !! missing "i", incorrect "b" type !! ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ 

Option 1. Per-lambda schema with a new set of TreeNode methods: *_with_schema

Once again, this PR adds another set of TreeNode-like methods on logical and physical expressions, that while traversing expression trees, when they find a ScalarUDF that contains a lambda on its arguments, uses ScalarUDF::lambdas_parameters to create a schema adjusted for each of its arguments, and pass it as an argument to the visiting/transforming function.

implExpr{pubfntransform_with_schema<F:FnMut(Self,&DFSchema) -> Result<Transformed<Self>>,>(self,schema:&DFSchema,f:F,) -> Result<Transformed<Self>>{}}

Example usage:

pubfn infer_placeholder_types(self,schema:&DFSchema) -> Result<(Expr,bool)>{letmut has_placeholder = false;// Provide the schema as the first argument. // Transforming closure receive an adjusted_schema as argumentself.transform_with_schema(schema, |mut expr, adjusted_schema| {match&mut expr {// Default to assuming the arguments are the same typeExpr::BinaryExpr(BinaryExpr{ left,op: _, right }) => {// use adjusted_schema and not schema. Those expressions may contain // columns referring to a lambda parameter, which Field would only be// available in adjusted_schema and not in schemarewrite_placeholder(left.as_mut(), right.as_ref(), adjusted_schema)?;rewrite_placeholder(right.as_mut(), left.as_ref(), adjusted_schema)?;}
....

In order to add the lambda parameters to schema, we need to take into account DFSchema properties:

"Unqualified fields must be unique not only amongst themselves, but also must have a distinct name from any qualified field names"

Since lambdas parameters are always unqualified, they may conflict with columns of the outer schema, even though those being qualified. To fix this conflict, we can either:

1: Replace the existing column with the lambda parameter, in the same index of the vec of fields of the schema, in order to not change the index of columns to the right of it. That's the current approach in this PR

Expr tree traversal with adjusted schema, replacing conflicts

+------------------------------------------------------------------+ 
| array_transform(b, (b, i) -> array_transform(b, b -> b + c + i)) | | | | a = Int32 | | b = List(List(Int32)) | | c = Int32 | +------------------------------------------------------------------+ 
| | v +------------------------------------------------------------------+ 
| (b, i) -> array_transform(b, b -> b + c + i) | | | | a = Int32 | | b = List(Int32) ! replaced ! | | c = Int32 | | i = Int32 | +------------------------------------------------------------------+ 
| | v +------------------------------------------------------------------+ 
| array_transform(b, b -> b + c + i) | | | | a = Int32 | | b = List(Int32) ! replaced ! | | c = Int32 | | i = Int32 | +------------------------------------------------------------------+ 
| | v +------------------------------------------------------------------+ 
| b -> b + c + i | | | | a = Int32 | | b = Int32 ! replaced ! | | c = Int32 | | i = Int32 | +------------------------------------------------------------------+ 

2: Rename the shadowed column to an unique, non-conflicting name and add the lambda parameter to the end of the vec of fields of the schema. This option allows checking if a physical column refers to a lambda parameter by checking if its index is greater or equal than the number of fields of the outer schema. When this information is available, it eliminates the need to use the with_lambdas_params variations of TreeNode methods. It's trivial to change the PR to use this.

Expr tree traversal with adjusted schema, renaming conflicts

┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, (b, i) -> array_transform(b, b -> b + c + i)) ╷ ╷ ╷ ╷ ╷ ╷ a = Int32 ╷ ╷ b = List(List(Int32)) ╷ ╷ c = Int32 ╷ ╷ ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ (b, i) -> array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ ╷ ╷ a = Int32 ╷ ╷ b_shadowed1 = List(List(Int32)) ╷ ╷ c = Int32 ╷ ╷ b = List(Int32) ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ ╷ ╷ a = Int32 ╷ ╷ b_shadowed1 = List(List(Int32)) ╷ ╷ c = Int32 ╷ ╷ b = List(Int32) ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ b -> b + c + i ╷ ╷ ╷ ╷ a = Int32 ╷ ╷ b_shadowed1 = List(List(Int32)) ╷ ╷ c = Int32 ╷ ╷ b_shadowed2 = List(Int32) ╷ ╷ b = Int32 ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ 

Lambdas usually are evaluated with a different number of rows than that of the outer scope, as in the example, where array_transform is executed with one row, and its lambda with two rows, one for each element of the array. The UDF implementation is responsible for adjusting the captured columns with the number of rows of its parameters with whatever logic makes sense to it. For array_transform, its to copy the value of the captured column for each element of the arrays:

 copied once a [1]------------------> a 1 copied 2 times b [2, 3] --------------> b 2 \ not copied c [] ------------> b 3 

This adjustment is costly, so it's necessary to provide a way to the implementation to avoid adjusting uncaptured columns.

It's intuitive to just remove the uncaptured columns, but note in the diagram and in the query below that it can change the index of captured columns. The "c" column has index 2 in the outer scope but ends up with index 1 in the others scopes

Expr tree traversal with a schema with uncaptured columns removed

┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, (b, i) -> array_transform(b, b -> b + c + i)) ╷ ╷ ╷ ╷ ╷ ╷ a@0= Int32 ╷ ╷ b@1= List(List(Int32)) ╷ ╷ c@2= Int32 ╷ ╷ ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ (b, i) -> array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ ╷ ╷ b@0= List(Int32) ╷ ╷ c@1= Int32 ╷ ╷ i@2= Int32 ╷ ╷ ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ ╷ ╷ b@0= List(Int32) ╷ ╷ c@1= Int32 ╷ ╷ i@2= Int32 ╷ ╷ ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ b -> b + c + i ╷ ╷ ╷ ╷ ╷ ╷ b@0= Int32 ╷ ╷ c@1= Int32 ╷ ╷ i@2= Int32 ╷ ╷ ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ 

select a@0, b@1, c@2, array_transform(b@0, (b@0, i@2) -> array_transform(b@0, b@0-> b@0+ c@1+ i@2)) from t;

Option 1a: Nullify uncaptured columns

To keep the indices stable, this PR won't remove uncaptured columns, as such, they are still present in the adjusted schema with their original type during tree traversals with the new _with_schema methods. However, to avoid the costly adjustment, when they are passed to the UDF in invoke_with_args, they are substituted with columns with the Null datatype.

Expr execution/evaluation RecordBatch schema with uncaptured columns substituted with Null columns

┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, (b, i) -> array_transform(b, b -> b + c + i)) ╷ ╷ ╷ ╷ ╷ ╷ a = Int32 ╷ ╷ b = List(List(Int32)) ╷ ╷ c = Int32 ╷ ╷ ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ (b, i) -> array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ ╷ ╷ a =Null ! nullified ! ╷ ╷ b = List(Int32) ╷ ╷ c = Int32 ╷ ╷ i = Int32 ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ ╷ ╷ a =Null ! nullified ! ╷ ╷ b = List(Int32) ╷ ╷ c = Int32 ╷ ╷ i = Int32 ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ b -> b + c + i ╷ ╷ ╷ ╷ ╷ ╷ a =Null ! nullified ! ╷ ╷ b = Int32 ╷ ╷ c = Int32 ╷ ╷ i = Int32 ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ 

Option 1b TreeNode *_with_indices_mapping

To avoid keeping uncaptured columns in the schema and substituting them with null in the batch, is possible to add another set of TreeNode-like methods on physical expressions that calls the visiting/transforming function with a second parameter of type HashMap<usize, usize> mapping the indices of the current scope with the ones from the outermost scope. This requires that before calling the visiting/transforming function for a physical lambda expression, all its subtree be visited to collect all the captured columns to build the indices mapping. Inner lambdas require the process again and can't reuse the work of the outer lambda. This may be costly for lambdas with complex expressions and/or highly nested.

implPhysicalExprExtforArc<dynPhysicalExpr>{pubfntransform_with_indices_mapping<F:FnMut(Self,&HashMap<usize,usize>) -> Result<Transformed<Self>>,>(self,mutf:F,) -> Result<Transformed<Self>>{}}

Expr tree traversal with indices_mapping: "c" has index 2 in the root scope but 1 in the others

┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, (b, i) -> array_transform(b, b -> b + c + i)) ╷ ╷ ╷ ╷ ╷ ╷ indices_mapping = {} ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ (b, i) -> array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ ╷ ╷ indices_mapping = { 1=>2 } ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ ╷ ╷ indices_mapping = { 1=>2 } ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ b -> b + c + i ╷ ╷ ╷ ╷ ╷ ╷ indices_mapping = { 1=>2 } ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ 

The code on minimize_join_filter would change to:

fnminimize_join_filter(expr:Arc<dynPhysicalExpr>, ...) -> JoinFilter{letmut used_columns = HashSet::new();
expr.apply_with_indices_mapping(|expr, indices_mapping| {ifletSome(col) = expr.as_any().downcast_ref::<Column>(){// this column may be child of a lambda, where this indice would refer to the lambda// scoped schema, which won't include uncaptured columns from the plan input,// and therefore may differ from the indices of the schema of the input plan// In such cases, indices_mapping contain the mapping to the index of the input plan// if a mapping is not found, it should be a column referring to a lambda parameterlet scoped_index = col.index();ifletSome(plan_index) = indices_mapping.get(scoped_index){
used_columns.insert(plan_index);}}Ok(TreeNodeRecursion::Continue)})
...
}

Option 2. Create a schema with all parameters from all lambdas for tree traversals

Use a secondary schema containing all parameters from all lambdas. For that, expressions must be transformed, normalizing all lambda parameters and its references, with a unique qualifier per lambda, so they can coexist without conflicts in this schema. A qualifier field would be added to the lambda expr

pubstructLambda{pubqualifier:Option<String>,pubparams:Vec<String>,pubbody:Box<Expr>,}

Schema of the example:

t.a: Int32
t.b: List(List(Int32))
lambda1.b: List(Int32)
lambda1.i: UInt32
lambda2.b: Int32

From my understanding of this video, this is similar to what DuckDB does on its binder, although with differences in the evaluation part. I didn't find any other resource for other open source engines with lambda support, like Clickhouse and Spark.

This works well when dealing with plans nodes, where, during plan creation time or schema recomputation, we can normalize its lambdas, create the extended schema and save it as plan field, exposing it with a method like "lambda_extended_schema", although with an added cost to plan creation/schema recomputation. The lambda normalization actually requires two passes, a first to collect any existing lambda qualifier to avoid reusing them in the last, normalizing pass.

How code would look like:

//from
expr.transform_with_schema(plan.schema(), |node, adjusted_schema| ...)//toletschema = plan.lambda_extended_schema();
expr.transform(|node| ...)

Another example:

implLogicalPlan{pubfn replace_params_with_values(self,param_values:&ParamValues,) -> Result<LogicalPlan>{self.transform_up_with_subqueries(|plan| {// use plan.lambda_extended_schema() containing lambdas parameters// instead of plan.schema() which wontlet lambda_extended_schema = Arc::clone(plan.lambda_extended_schema());let name_preserver = NamePreserver::new(&plan);
plan.map_expressions(|e| {// if this expression is child of lambda and contain columns referring it's parameters// the lambda_extended_schema already contain themlet(e, has_placeholder) = e.infer_placeholder_types(&lambda_extended_schema)?;
....

However, when working with functions/methods that deal directly with expressions, detached from a plan, the expression lambdas may be unnormalized, and the extended schema is unavailable. There's a few public methods/functions like that, like infer_placeholder_types for example:

implExpr{pubfninfer_placeholder_types(self,schema:&DFSchema) -> Result<(Expr,bool)>{letmut has_placeholder = false;self.transform(|mut expr| ...)
...
}}

It could either:

1: Require to be only called with normalized expressions, and that the schema argument be the extended schema, or return an error otherwise, which is restrictive and put strain on users

2: Allow to be called with unnormalized expressions, visit the whole expr tree collecting the existing lambdas qualifiers to avoid to avoid duplicate qualifiers in the next step, perform a first transformation to guarantee that the expression lambdas are normalized, create the extended schema, for only then perform the second transformation to infer the placeholder types using the extended schema. While it can document that the returned expression is normalized, it's still a regular Expr which doesn't encode that property in its type. Also, without changing the method signature, it wouldn't even be possible to return the extended schema to allow it to be used again in other places without recomputation. This is costly and won't allow reuse of its costly work

Normalized example:

selectt.a, t.b, array_transform(t.b, (lambda1.b, lambda1.i) -> array_transform(lambda1.b, lambda2.b->lambda2.b+t.a+lambda1.i)) from t;

Just like with the first option, this also sets uncaptured columns to Null, as well as unavailable/out-of-scope lambdas parameters.

Expr tree batch evaluation with a single extended schema

┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, (b, i) -> array_transform(b, b -> b + c + i)) ╷ ╷ ╷ ╷ t.a= Int32 ╷ ╷ t.b= List(List(Int32)) ╷ ╷ t.c= Int32 ╷ ╷ lambda1.b=Null ╷ ╷ lambda1.i=Null ╷ ╷ lambda2.b=Null ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ (b, i) -> array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ t.a=Null ╷ ╷ t.b=Null ╷ ╷ t.c= Int32 ╷ ╷ lambda1.b= List(Int32) ╷ ╷ lambda1.i= Int32 ╷ ╷ lambda2.b=Null ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ array_transform(b, b -> b + c + i) ╷ ╷ ╷ ╷ t.a=Null ╷ ╷ t.b=Null ╷ ╷ t.c= Int32 ╷ ╷ lambda1.b= List(Int32) ╷ ╷ lambda1.i= Int32 ╷ ╷ lambda2.b=Null ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ │ ▼ ┌╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┐ ╷ b -> b + c + i ╷ ╷ ╷ ╷ t.a=Null ╷ ╷ t.b=Null ╷ ╷ t.c= Int32 ╷ ╷ lambda1.b=Null ╷ ╷ lambda1.i= Int32 ╷ ╷ lambda2.b= Int32 ╷ └╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶╶┘ 

With this option, indices are always stable across the whole tree

This option allows checking if a physical column refers to a lambda parameter by checking if its index is greater or equal than the number of fields of the outer schema. When this information is available, it eliminates the need to use the with_lambdas_params variations of TreeNode methods.

Comparison between options:

*	per-lambda schema with uncaptured columns set to null	per-lambda schema with indices_mapping	single extended schema
New set of TreeNode methods	Yes, 1, _with_schema for both logical and physical expressions	Yes, 2, _with_schema for both logical and physical expressions, and _with_indices_mapping for physical expressions	No
Tree traversal added cost	Only when encountering a lambda	Only when encountering a lambda	Zero
Plan creation/ recompute schema added cost	Zero	Zero	Always, regardless of existence of any lambda
Code change, internal	New set of TreeNode methods and using them instead of the current ones when applicable	2 new set of TreeNode methods and using them instead of the current ones when applicable	Untried, unpredictable
Code change, downstream	if lambda support is desired, use the new TreeNode methods instead of the current ones when applicable otherwise none	if lambda support is desired, use the new TreeNode methods instead of the current ones when applicable otherwise none	Variable, medium when closely associated with a Plan, just call plan. lambda_extended_schema() Unpredictable when plan is unavailable or doesn't exist
Change uncaptured columns DataType to Null	Yes	No	Yes
Presence of unneeded Null columns in the schema during planning and optimizing and in the RecordBatch during execution as a padding/filler to keep indices stable	Yes	No	Yes
Stable column indices across the whole expr tree	Yes	No	Yes
Make _with_lambdas_params unnecessary for physical expressions if Expr::Column is used	No	Yes	No

Splitting this into smaller PRs

If this PR is decided to move forward, it will likely be with smaller PRs. In that case, I already planned a division shown below. It's necessary to analyze the graph, as it doesn't help with the discussion of this text, and it's included here just to show a reasonable granularity of smaller PRs I could find in case it helps decide whether to move this forward or not.

Each rectangular node is a PR. Asymmetrical nodes are a collection of smaller PRs which share the same dependencies and aren't a dependency of any other PR. Green ones can be opened immediately. Gray ones contain unmet dependencies. Merged PRs will be colored with blue.

Left-to-Right full-screen link

Details

graph LR
classDef blue fill:blue
classDef green fill:green
subgraph "SQL" [SQL 84LOC]
SQLP[SQL Parse 65LOC]
SQLU[SQL Unparse 19LOC]
end
LL[Logical Expr::Lambda 100LOC]:::green
LL --> CSE[CSE 50LOC]
P[Plan logical lambda into physical expr 25LOC]
UDF1["Extend ScalarUDF[Impl] 300LOC"]
UDF2[ScalarUDF lambda schema helpers 100LOC]
AM[Non functional array_transform 270LOC]
PSFL[ScalarFunction PhysicalExpr lambda aware 30LOC]
PL --> PSFL
UDF1 --> PSFL
UDF1 --> AM
%%CILP[Column::is_lambda_parameter 6LOC]
subgraph "Expr::*_with_schema"
LTN[Expr::*_with_schema API def 50LOC]:::green
LTNB[make Expr::*_with_schema lambda aware 70LOC]
LTN --> LTNES[Expr Simplifier 100LOC]
LTNA>"
use Expr::*_with_schema in existing code
Type Coercion 110LOC
normalize_col[with_schemas_and_ambiguity_check] 31LOC
SessionState::create_physical_expr 4LOC
Expr::infer_placeholder_type 1LOC
ApplyFunctionRewrites 10LOC
optmize_projections::rewrite_expr 2LOC
SqlToRel::try_process_group_by_unnest 10LOC
sql resolve_columns 5LOC
Example type_coercion_demo 10LOC
"]
end
PL[Physical Lambda Expr 108LOC]:::green
subgraph "PhysicalExpr::*_with_schema"
PTN[PhysicalExpr::*_with_schema API def 25LOC]:::green
PTNA>"
use PhysicalExpr::*_with_schema in ProjectionMapping 32LOC
PhysicalExprSimplifier 20LOC
unwrap_cast_in_comparison 10LOC
AsyncMapper::find_references 1LOC
Example CustomCastsPhysicalExprAdapter 10LOC
"]
PTNB[make PhysicalExpr::*_with_schema lambda aware 160LOC]
end
subgraph capture
CS[Capture support 30LOC]
LCH["List capture helpers 60LOC(4x15)"]
MCH["Merge capture with arguments helper 66LOC(2x33)"]
AMCT[array_transform capture support 20LOC]
end
PSFL --> CS
PTN --> CS
LL --> SQL
LL --> P
UDF2 --> LTNES
UDF2 --> LTNB
UDF2 --> PTNB
LTN --> LTNA
LTN --> LTNB
LL --> LTNB
LTN --> UDF2
LL --> UDF1 --> UDF2
UDF2 --> P
PL --> P
PL --> PTNB
PTN --> PTNB
PTN --> PTNA
AM --> AMCT
CS --> AMCT
LCH --> AMCT
MCH --> AMCT
LL --> LTNLP2
subgraph "Expr::*_with_lambdas_params"
LTNLP --> LTNLP2[make Expr::*_with_lambdas_params lambda aware]
LTNLP[Expr::*_with_lambdas_params API def 50LOC]:::green -->
AAAAA>"
expr_applicable_for_cols 5LOC
Expr::add_column_refs[_counts]/any_column_refs 15LOC
expr_to_columns 10LOC
columnize_expr 15LOC
find_columns_referenced_by_expr 10LOC
find_column_indexes_referenced_by_expr 5LOC
normalize_col 10LOC
normalize_col_with_schemas_and_ambiguity_check 15LOC
replace_col 10LOC
transform_up_with_lambdas_params 10LOC
filter_exprs_evaluation_result_on_empty_batch 10LOC
replace_cols_by_name 10LOC
optimizer::push_down_filter::contain 15LOC
ScalarSubqueryToJoin 40LOC
TableAliasRewriter 20LOC
Example ShreddedJsonRewriter 30LOC
"]
end
PL --> PTNLP2
subgraph "PhysicalExpr::*_with_lambdas_params"
PTNLP --> PTNLP2[make PhysicalExpr::*_with_lambdas_params lambda aware]
PTNLP[PhysicalExpr::*_with_lambdas_params API def 50LOC]:::green -->
BBBBB>"
CustomPhysicalExprAdapter 3LOC
ParquetPushdownChecker 13LOC
add_offset_to_expr 3LOC
update_expr 10LOC
project_ordering 20LOC
with_new_schema 10LOC
collect_columns 10LOC
reassign_expr_columns 10LOC
DefaultPhysicalExprAdapter 40LOC
projection pushdown 50LOC
sort pushdown 40LOC
projection 50LOC
stream_join_utils 40LOC
pruning predicate rewrite_column_expr 5LOC
Example DefaultValuePhysicalExprAdapter 20LOC
"]
end

Loading

Top-to-Bottom full-screen link

Details

graph TB
classDef blue fill:blue
classDef green fill:green
subgraph "SQL" [SQL 84LOC]
SQLP[SQL Parse 65LOC]
SQLU[SQL Unparse 19LOC]
end
LL[Logical Expr::Lambda 100LOC]:::green
LL --> CSE[CSE 50LOC]
P[Plan logical lambda into physical expr 25LOC]
UDF1["Extend ScalarUDF[Impl] 300LOC"]
UDF2[ScalarUDF lambda schema helpers 100LOC]
AM[Non functional array_transform 270LOC]
PSFL[ScalarFunction PhysicalExpr lambda aware 30LOC]
PL --> PSFL
UDF1 --> PSFL
UDF1 --> AM
%%CILP[Column::is_lambda_parameter 6LOC]
subgraph "Expr::*_with_schema"
LTN[Expr::*_with_schema API def 50LOC]:::green
LTNB[make Expr::*_with_schema lambda aware 70LOC]
LTN --> LTNES[Expr Simplifier 100LOC]
LTNA>"
use Expr::*_with_schema in existing code
Type Coercion 110LOC
normalize_col[with_schemas_and_ambiguity_check] 31LOC
SessionState::create_physical_expr 4LOC
Expr::infer_placeholder_type 1LOC
ApplyFunctionRewrites 10LOC
optmize_projections::rewrite_expr 2LOC
SqlToRel::try_process_group_by_unnest 10LOC
sql resolve_columns 5LOC
Example type_coercion_demo 10LOC
"]
end
PL[Physical Lambda Expr 108LOC]:::green
subgraph "PhysicalExpr::*_with_schema"
PTN[PhysicalExpr::*_with_schema API def 25LOC]:::green
PTNA>"
use PhysicalExpr::*_with_schema in ProjectionMapping 32LOC
PhysicalExprSimplifier 20LOC
unwrap_cast_in_comparison 10LOC
AsyncMapper::find_references 1LOC
Example CustomCastsPhysicalExprAdapter 10LOC
"]
PTNB[make PhysicalExpr::*_with_schema lambda aware 160LOC]
end
subgraph capture
CS[Capture support 30LOC]
LCH["List capture helpers 60LOC(4x15)"]
MCH["Merge capture with arguments helper 66LOC(2x33)"]
AMCT[array_transform capture support 20LOC]
end
PSFL --> CS
PTN --> CS
LL --> SQL
LL --> P
UDF2 --> LTNES
UDF2 --> LTNB
UDF2 --> PTNB
LTN --> LTNA
LTN --> LTNB
LL --> LTNB
LTN --> UDF2
LL --> UDF1 --> UDF2
UDF2 --> P
PL --> P
PL --> PTNB
PTN --> PTNB
PTN --> PTNA
AM --> AMCT
CS --> AMCT
LCH --> AMCT
MCH --> AMCT
LL --> LTNLP2
subgraph "Expr::*_with_lambdas_params"
LTNLP --> LTNLP2[make Expr::*_with_lambdas_params lambda aware]
LTNLP[Expr::*_with_lambdas_params API def 50LOC]:::green -->
AAAAA>"
expr_applicable_for_cols 5LOC
Expr::add_column_refs[_counts]/any_column_refs 15LOC
expr_to_columns 10LOC
columnize_expr 15LOC
find_columns_referenced_by_expr 10LOC
find_column_indexes_referenced_by_expr 5LOC
normalize_col 10LOC
normalize_col_with_schemas_and_ambiguity_check 15LOC
replace_col 10LOC
transform_up_with_lambdas_params 10LOC
filter_exprs_evaluation_result_on_empty_batch 10LOC
replace_cols_by_name 10LOC
optimizer::push_down_filter::contain 15LOC
ScalarSubqueryToJoin 40LOC
TableAliasRewriter 20LOC
Example ShreddedJsonRewriter 30LOC
"]
end
PL --> PTNLP2
subgraph "PhysicalExpr::*_with_lambdas_params"
PTNLP --> PTNLP2[make PhysicalExpr::*_with_lambdas_params lambda aware]
PTNLP[PhysicalExpr::*_with_lambdas_params API def 50LOC]:::green -->
BBBBB>"
CustomPhysicalExprAdapter 3LOC
ParquetPushdownChecker 13LOC
add_offset_to_expr 3LOC
update_expr 10LOC
project_ordering 20LOC
with_new_schema 10LOC
collect_columns 10LOC
reassign_expr_columns 10LOC
DefaultPhysicalExprAdapter 40LOC
projection pushdown 50LOC
sort pushdown 40LOC
projection 50LOC
stream_join_utils 40LOC
pruning predicate rewrite_column_expr 5LOC
Example DefaultValuePhysicalExprAdapter 20LOC
"]
end

Loading

Right-to-Left full-screen link

Details

graph RL
classDef blue fill:blue
classDef green fill:green
subgraph "SQL" [SQL 84LOC]
SQLP[SQL Parse 65LOC]
SQLU[SQL Unparse 19LOC]
end
LL[Logical Expr::Lambda 100LOC]:::green
LL --> CSE[CSE 50LOC]
P[Plan logical lambda into physical expr 25LOC]
UDF1["Extend ScalarUDF[Impl] 300LOC"]
UDF2[ScalarUDF lambda schema helpers 100LOC]
AM[Non functional array_transform 270LOC]
PSFL[ScalarFunction PhysicalExpr lambda aware 30LOC]
PL --> PSFL
UDF1 --> PSFL
UDF1 --> AM
%%CILP[Column::is_lambda_parameter 6LOC]
subgraph "Expr::*_with_schema"
LTN[Expr::*_with_schema API def 50LOC]:::green
LTNB[make Expr::*_with_schema lambda aware 70LOC]
LTN --> LTNES[Expr Simplifier 100LOC]
LTNA>"
use Expr::*_with_schema in existing code
Type Coercion 110LOC
normalize_col[with_schemas_and_ambiguity_check] 31LOC
SessionState::create_physical_expr 4LOC
Expr::infer_placeholder_type 1LOC
ApplyFunctionRewrites 10LOC
optmize_projections::rewrite_expr 2LOC
SqlToRel::try_process_group_by_unnest 10LOC
sql resolve_columns 5LOC
Example type_coercion_demo 10LOC
"]
end
PL[Physical Lambda Expr 108LOC]:::green
subgraph "PhysicalExpr::*_with_schema"
PTN[PhysicalExpr::*_with_schema API def 25LOC]:::green
PTNA>"
use PhysicalExpr::*_with_schema in ProjectionMapping 32LOC
PhysicalExprSimplifier 20LOC
unwrap_cast_in_comparison 10LOC
AsyncMapper::find_references 1LOC
Example CustomCastsPhysicalExprAdapter 10LOC
"]
PTNB[make PhysicalExpr::*_with_schema lambda aware 160LOC]
end
subgraph capture
CS[Capture support 30LOC]
LCH["List capture helpers 60LOC(4x15)"]
MCH["Merge capture with arguments helper 66LOC(2x33)"]
AMCT[array_transform capture support 20LOC]
end
PSFL --> CS
PTN --> CS
LL --> SQL
LL --> P
UDF2 --> LTNES
UDF2 --> LTNB
UDF2 --> PTNB
LTN --> LTNA
LTN --> LTNB
LL --> LTNB
LTN --> UDF2
LL --> UDF1 --> UDF2
UDF2 --> P
PL --> P
PL --> PTNB
PTN --> PTNB
PTN --> PTNA
AM --> AMCT
CS --> AMCT
LCH --> AMCT
MCH --> AMCT
LL --> LTNLP2
subgraph "Expr::*_with_lambdas_params"
LTNLP --> LTNLP2[make Expr::*_with_lambdas_params lambda aware]
LTNLP[Expr::*_with_lambdas_params API def 50LOC]:::green -->
AAAAA>"
expr_applicable_for_cols 5LOC
Expr::add_column_refs[_counts]/any_column_refs 15LOC
expr_to_columns 10LOC
columnize_expr 15LOC
find_columns_referenced_by_expr 10LOC
find_column_indexes_referenced_by_expr 5LOC
normalize_col 10LOC
normalize_col_with_schemas_and_ambiguity_check 15LOC
replace_col 10LOC
transform_up_with_lambdas_params 10LOC
filter_exprs_evaluation_result_on_empty_batch 10LOC
replace_cols_by_name 10LOC
optimizer::push_down_filter::contain 15LOC
ScalarSubqueryToJoin 40LOC
TableAliasRewriter 20LOC
Example ShreddedJsonRewriter 30LOC
"]
end
PL --> PTNLP2
subgraph "PhysicalExpr::*_with_lambdas_params"
PTNLP --> PTNLP2[make PhysicalExpr::*_with_lambdas_params lambda aware]
PTNLP[PhysicalExpr::*_with_lambdas_params API def 50LOC]:::green -->
BBBBB>"
CustomPhysicalExprAdapter 3LOC
ParquetPushdownChecker 13LOC
add_offset_to_expr 3LOC
update_expr 10LOC
project_ordering 20LOC
with_new_schema 10LOC
collect_columns 10LOC
reassign_expr_columns 10LOC
DefaultPhysicalExprAdapter 40LOC
projection pushdown 50LOC
sort pushdown 40LOC
projection 50LOC
stream_join_utils 40LOC
pruning predicate rewrite_column_expr 5LOC
Example DefaultValuePhysicalExprAdapter 20LOC
"]
end

Loading

Bottom-to-Top full-screen link

Details

graph BT
classDef blue fill:blue
classDef green fill:green
subgraph "SQL" [SQL 84LOC]
SQLP[SQL Parse 65LOC]
SQLU[SQL Unparse 19LOC]
end
LL[Logical Expr::Lambda 100LOC]:::green
LL --> CSE[CSE 50LOC]
P[Plan logical lambda into physical expr 25LOC]
UDF1["Extend ScalarUDF[Impl] 300LOC"]
UDF2[ScalarUDF lambda schema helpers 100LOC]
AM[Non functional array_transform 270LOC]
PSFL[ScalarFunction PhysicalExpr lambda aware 30LOC]
PL --> PSFL
UDF1 --> PSFL
UDF1 --> AM
%%CILP[Column::is_lambda_parameter 6LOC]
subgraph "Expr::*_with_schema"
LTN[Expr::*_with_schema API def 50LOC]:::green
LTNB[make Expr::*_with_schema lambda aware 70LOC]
LTN --> LTNES[Expr Simplifier 100LOC]
LTNA>"
use Expr::*_with_schema in existing code
Type Coercion 110LOC
normalize_col[with_schemas_and_ambiguity_check] 31LOC
SessionState::create_physical_expr 4LOC
Expr::infer_placeholder_type 1LOC
ApplyFunctionRewrites 10LOC
optmize_projections::rewrite_expr 2LOC
SqlToRel::try_process_group_by_unnest 10LOC
sql resolve_columns 5LOC
Example type_coercion_demo 10LOC
"]
end
PL[Physical Lambda Expr 108LOC]:::green
subgraph "PhysicalExpr::*_with_schema"
PTN[PhysicalExpr::*_with_schema API def 25LOC]:::green
PTNA>"
use PhysicalExpr::*_with_schema in ProjectionMapping 32LOC
PhysicalExprSimplifier 20LOC
unwrap_cast_in_comparison 10LOC
AsyncMapper::find_references 1LOC
Example CustomCastsPhysicalExprAdapter 10LOC
"]
PTNB[make PhysicalExpr::*_with_schema lambda aware 160LOC]
end
subgraph capture
CS[Capture support 30LOC]
LCH["List capture helpers 60LOC(4x15)"]
MCH["Merge capture with arguments helper 66LOC(2x33)"]
AMCT[array_transform capture support 20LOC]
end
PSFL --> CS
PTN --> CS
LL --> SQL
LL --> P
UDF2 --> LTNES
UDF2 --> LTNB
UDF2 --> PTNB
LTN --> LTNA
LTN --> LTNB
LL --> LTNB
LTN --> UDF2
LL --> UDF1 --> UDF2
UDF2 --> P
PL --> P
PL --> PTNB
PTN --> PTNB
PTN --> PTNA
AM --> AMCT
CS --> AMCT
LCH --> AMCT
MCH --> AMCT
LL --> LTNLP2
subgraph "Expr::*_with_lambdas_params"
LTNLP --> LTNLP2[make Expr::*_with_lambdas_params lambda aware]
LTNLP[Expr::*_with_lambdas_params API def 50LOC]:::green -->
AAAAA>"
expr_applicable_for_cols 5LOC
Expr::add_column_refs[_counts]/any_column_refs 15LOC
expr_to_columns 10LOC
columnize_expr 15LOC
find_columns_referenced_by_expr 10LOC
find_column_indexes_referenced_by_expr 5LOC
normalize_col 10LOC
normalize_col_with_schemas_and_ambiguity_check 15LOC
replace_col 10LOC
transform_up_with_lambdas_params 10LOC
filter_exprs_evaluation_result_on_empty_batch 10LOC
replace_cols_by_name 10LOC
optimizer::push_down_filter::contain 15LOC
ScalarSubqueryToJoin 40LOC
TableAliasRewriter 20LOC
Example ShreddedJsonRewriter 30LOC
"]
end
PL --> PTNLP2
subgraph "PhysicalExpr::*_with_lambdas_params"
PTNLP --> PTNLP2[make PhysicalExpr::*_with_lambdas_params lambda aware]
PTNLP[PhysicalExpr::*_with_lambdas_params API def 50LOC]:::green -->
BBBBB>"
CustomPhysicalExprAdapter 3LOC
ParquetPushdownChecker 13LOC
add_offset_to_expr 3LOC
update_expr 10LOC
project_ordering 20LOC
with_new_schema 10LOC
collect_columns 10LOC
reassign_expr_columns 10LOC
DefaultPhysicalExprAdapter 40LOC
projection pushdown 50LOC
sort pushdown 40LOC
projection 50LOC
stream_join_utils 40LOC
pruning predicate rewrite_column_expr 5LOC
Example DefaultValuePhysicalExprAdapter 20LOC
"]
end

Loading

[fbx31]

Thanks a lot @gstvg, huge amount of work apparently (even if I am not enough skilled to judge). I am waiting for such functions since a very long time.. IMHO, this is a MUST HAVE in datafusion. All serious analytics engine provide this kind of functions, DuckDB implementation is very mature, Polars seems to have it as well even if I didn"t tried it yet and I really hope that datafusion will jump into this enhancement very quickly as it is again a big miss.

Use cases like list_filter(list, expr), list_map(list, expr) and list_reduce(list, base, expr) will be available soon.

Again, thanks a lot for your work and I cross the fingers for a quick progress.

[shehabgamin]

@gstvg This is super exciting! I'll review over the next couple of days.

cc @SparkApplicationMaster@andygrove would be great to get your thoughts too 😃

[gstvg]

Thanks @fbx31. This is high priority for me right now. I also hope we can finish this quickly. It's indeed a lot work, but now I believe it ended up that way because I went to fast in the wrong direction... More on the comment below. Thanks again!

[gstvg]

Thanks @shehabgamin. I analyzed the Spark implementation one last time, and realized I could have done similarly since the beginning... I've already tested it locally and pretend to push soon, the diff reduced to ~2K LOC, mostly in new, self contained code with small changes in existing code and without requiring changes in downstream code. I hope you haven't put time reviewing it yet because I believe the spark approach is much better and also easier/faster to review

Basically, for planning, we lazily set the Field in the lambda column itself, instead of injecting it into a DFSchema

//logicalstructLambdaColumn{name:String,field:Option<FieldRef>,}

And for evaluation, again, we lazily set the value of a lambda column instead of injecting it into a RecordBatch

//physicalstructLambdaColumn{name:String,field:FieldRef,value:Option<ColumnarValue>,}

I think I initially ruled that out because other expressions doesn't contain it's own Field, and this have been discussed before in #12604 *, and didn't went forward, which the difference that the other expressions Field's naturally exist in the plan schema, which now I believe justifies this difference.
And most importantly, because I had no idea of how much work and downstream churn injecting the lambdas parameters into the Schema/RecordBatch would cause 🤦

I will push soon and update the PR description, thanks again!

*This is being discussed again in #18845

[gstvg]

@rluvaton@LiaCastaneda merge conflicts and the comments have been resolved (except for #18921 (review)).

Since the last review, I also pushed 2 minor commits, one to include the index in lambda variable formatting 5c2c72a and one to include higher order functions with scalar functions at the sql user guide 3c9fe39 (I missed that and they weren't being rendered anywhere)

[gstvg]

@rluvaton

[comphead]

@rluvaton@gstvg WDYT for renaming this beautiful PR as WIP and create brand new with this PR code change, so people can review it properly?

I think we passed the poc/design phase discussions, so would be beneficial for folks to go through clean PR, having this as a reference?

[gstvg]

@comphead@rluvaton I think it's great, new PR created at #21679

[rluvaton]

@gstvg I'm closing this branch since the new pr was merged

[gstvg]

Many thanks for everyone involved, both reviewers and also those who showed interest on the feature. Reviewing such a big PR is not easy and I'm very grateful for it, thanks again ❤️

[fbx31]

@gstvg thanks a lot for your work, cannot wait for future release and test it ! Thanks also to all reviewers. 🥰