rluvaton commented on code in PR #17220: URL: https://github.com/apache/datafusion/pull/17220#discussion_r2290436407
########## datafusion/functions-nested/src/array_filter.rs: ########## @@ -0,0 +1,364 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! [`ScalarUDFImpl`] definitions for array_filter function. + +use arrow::array::{Array, ArrayRef, GenericListArray, OffsetSizeTrait, RecordBatch}; +use arrow::buffer::OffsetBuffer; +use arrow::compute::filter; +use arrow::datatypes::DataType::{LargeList, List}; +use arrow::datatypes::{DataType, Field, Schema}; +use datafusion_common::cast::{as_boolean_array, as_large_list_array, as_list_array}; +use datafusion_common::utils::take_function_args; +use datafusion_common::{exec_err, plan_err, DFSchema, Result}; +use datafusion_expr::expr::{schema_name_from_exprs_ref, ScalarFunction}; +use datafusion_expr::{ + ColumnarValue, Documentation, ExprSchemable, ScalarUDFImpl, Signature, Volatility, +}; +use datafusion_expr::{Expr, LambdaPlanner, PhysicalLambda, ScalarUDF}; +use datafusion_macros::user_doc; + +use std::any::Any; +use std::hash::{Hash, Hasher}; +use std::sync::Arc; + +use crate::utils::make_scalar_function; + +make_udf_expr_and_func!(ArrayFilter, + array_filter, + array lambda, // arg names + "filters array elements using a lambda function, returning a new array with elements where the lambda returns true.", // doc + array_filter_udf // internal function name +); + +/// Implementation of the `array_filter` scalar user-defined function. +/// +/// This function filters array elements using a lambda function, returning a new array +/// containing only the elements for which the lambda function returns true. +/// +/// The struct maintains both logical and physical representations of the lambda: +/// - `lambda`: The logical lambda expression from the SQL query +/// - `physical_lambda`: The planned physical lambda that can be executed +/// - `signature`: Function signature indicating it operates on arrays +#[user_doc( + doc_section(label = "Array Functions"), + description = "Filters array elements using a lambda function.", + syntax_example = "array_filter(array, lambda)", + sql_example = r#"```sql +> select array_filter([1, 2, 3, 4, 5], x -> x > 3); ++--------------------------------------------------+ +| array_filter(List([1,2,3,4,5]), x -> x > 3) | ++--------------------------------------------------+ +| [4, 5] | ++--------------------------------------------------+ +```"#, + argument( + name = "array", + description = "Array expression. Can be a constant, column, or function, and any combination of array operators." + ), + argument( + name = "lambda", + description = "Lambda function with one argument that returns a boolean. The lambda is applied to each element of the array." + ) +)] +pub struct ArrayFilter { + signature: Signature, + lambda: Option<Box<Expr>>, + physical_lambda: Option<Box<dyn PhysicalLambda>>, +} + +impl PartialEq for ArrayFilter { + fn eq(&self, other: &Self) -> bool { + self.signature == other.signature && self.lambda == other.lambda + } +} + +impl Hash for ArrayFilter { + fn hash<H: Hasher>(&self, state: &mut H) { + self.signature.hash(state); + self.lambda.hash(state); + } +} + +impl Eq for ArrayFilter {} + +impl std::fmt::Debug for ArrayFilter { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + f.debug_struct("ArrayFilter") + .field("signature", &self.signature) + .field("lambda", &self.lambda) + .field( + "physical_lambda", + if self.physical_lambda.is_some() { + &"<PhysicalLambda>" + } else { + &"<None>" + }, + ) + .finish() + } +} + +impl Default for ArrayFilter { + fn default() -> Self { + Self::new() + } +} + +impl ArrayFilter { + /// Creates a new instance of ArrayFilter with default settings. + /// + /// Initializes the function with an array signature and no lambda expressions. + /// The lambda will be set later during query planning. + pub fn new() -> Self { + Self { + signature: Signature::array(Volatility::Immutable), + lambda: None, + physical_lambda: None, + } + } + + /// Creates a new ArrayFilter instance with a physical lambda attached. + /// + /// This is used during query execution when the logical lambda has been + /// planned into an executable physical lambda. + /// + /// # Arguments + /// * `physical_lambda` - The planned physical lambda function + fn with_physical_lambda(&self, physical_lambda: Box<dyn PhysicalLambda>) -> Self { + Self { + signature: self.signature.clone(), + lambda: self.lambda.clone(), + physical_lambda: Some(physical_lambda), + } + } + + /// Creates a new ArrayFilter instance with a logical lambda expression. + /// + /// This is used during query planning when the lambda expression has been + /// parsed but not yet converted to a physical representation. + /// + /// # Arguments + /// * `lambda` - The logical lambda expression from the SQL query + fn with_lambda(&self, lambda: &Expr) -> Self { + Self { + signature: self.signature.clone(), + lambda: Some(Box::new(lambda.clone())), + physical_lambda: None, + } + } +} + +impl ScalarUDFImpl for ArrayFilter { + fn as_any(&self) -> &dyn Any { + self + } + + fn name(&self) -> &str { + "array_filter" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> { + let [arg_type] = take_function_args(self.name(), arg_types)?; + match arg_type { + List(_) | LargeList(_) => Ok(arg_type.clone()), + _ => plan_err!("{} does not support type {}", self.name(), arg_type), + } + } + + fn invoke_with_args( + &self, + args: datafusion_expr::ScalarFunctionArgs, + ) -> Result<ColumnarValue> { + let Some(lambda) = self.physical_lambda.as_ref() else { + if self.lambda.is_none() { + return exec_err!("{} requires lambda", self.name()); + } else { + return exec_err!("lambda in {} is not planned", self.name()); + } + }; + make_scalar_function(|ar| -> Result<ArrayRef> { + let [array] = take_function_args(self.name(), ar)?; + array_filter_inner(array, lambda.as_ref()) + })(&args.args) + } + + fn documentation(&self) -> Option<&Documentation> { + self.doc() + } + + fn plan( + &self, + planner: &dyn LambdaPlanner, + args: &[Expr], + input_dfschema: &DFSchema, + ) -> Result<Option<Arc<dyn ScalarUDFImpl>>> { + let arg_types = args + .iter() + .map(|arg| arg.data_type_and_nullable(input_dfschema)) + .collect::<Result<Vec<_>>>()?; + let arg_types = arg_types.iter().map(|(dt, _)| dt).collect::<Vec<_>>(); + match (self.lambda.as_ref(), arg_types.as_slice()) { + (Some(lambda), &[List(field) | LargeList(field)]) => match lambda.as_ref() { + Expr::Lambda(lambda) => { + let schema = Schema::new(vec![field + .as_ref() + .clone() + .with_name(lambda.params[0].clone())]); + let lambda_dfschema = DFSchema::try_from(schema)?; + let lambda_physical_lambda = + planner.plan_lambda(lambda, &lambda_dfschema)?; + Ok(Some(Arc::new( + self.with_physical_lambda(lambda_physical_lambda), + ))) + } + _ => plan_err!("{} requires a lambda expression", self.name()), + }, + _ => plan_err!( + "{} requires List or LargeList as the first argument", + self.name() + ), + } + } + + fn display_name(&self, args: &[Expr]) -> Result<String> { + let names: Vec<String> = self + .args_with_lambda(args)? + .iter() + .map(ToString::to_string) + .collect(); + Ok(std::format!("{}({})", self.name(), names.join(", "))) + } + + fn schema_name(&self, args: &[Expr]) -> Result<String> { + let args = self.args_with_lambda(args)?; + Ok(std::format!( + "{}({})", + self.name(), + schema_name_from_exprs_ref(&args)? + )) + } + + fn try_call(&self, args: &[Expr]) -> Result<Option<Expr>> { + match (self.lambda.as_ref(), args) { + (Some(_), [_]) => Ok(None), + (None, [array, lambda @ Expr::Lambda(func)]) => { + if func.params.len() != 1 { + return exec_err!( + "{} requires a lambda with 1 argument", + self.name() + ); + } + let func = Arc::new(ScalarUDF::new_from_impl(self.with_lambda(lambda))); + let expr = Expr::ScalarFunction(ScalarFunction::new_udf( + func, + vec![array.clone()], + )); + Ok(Some(expr)) + } + _ => plan_err!("{} requires 1 argument and 1 lambda", self.name()), + } + } + + fn coerce_types(&self, _arg_types: &[DataType]) -> Result<Vec<DataType>> { + datafusion_common::not_impl_err!( + "Function {} does not implement coerce_types", + self.name() + ) + } Review Comment: This is the default implementation, can you please remove it? 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