rluvaton commented on code in PR #17220: URL: https://github.com/apache/datafusion/pull/17220#discussion_r2290725118
########## datafusion/sqllogictest/test_files/array_filter.slt: ########## @@ -0,0 +1,92 @@ +# array_filter function tests + +statement ok +set datafusion.sql_parser.dialect = 'Databricks'; + + +# Basic array_filter functionality +query ? +SELECT array_filter([1, 2, 3, 4, 5], x -> x > 3) +---- +[4, 5] + +# array_filter with empty array +query ? +SELECT array_filter(arrow_cast([], 'List(Int64)'), x -> x > 0) +---- +[] + +# array_filter with null array +query ? +SELECT array_filter(arrow_cast(NULL, 'List(Int64)'), x -> x > 0) +---- +NULL + +# array_filter with all elements filtered out +query ? +SELECT array_filter([1, 2, 3], x -> x > 10) +---- +[] + +# array_filter with string arrays +query ? +SELECT array_filter(['apple', 'banana', 'cherry'], x -> length(x)::bigint > 5) +---- +[banana, cherry] + +# array_filter with boolean arrays +query ? +SELECT array_filter([true, false, true, false], x -> x = true) +---- +[true, true] + +# array_filter with nested arrays +query ? +SELECT array_filter([[1, 2], [3, 4], [5, 6]], x -> array_length(x)::bigint > 1) +---- +[[1, 2], [3, 4], [5, 6]] + +# array_filter with complex lambda expressions +query ? +SELECT array_filter([1, 2, 3, 4, 5], x -> x % 2 = 0 AND x > 2) +---- +[4] + +# array_filter with multiple conditions +query ? +SELECT array_filter([10, 20, 30, 40, 50], x -> x >= 20 AND x <= 40) +---- +[20, 30, 40] + +# array_filter with type coercion +query ? +SELECT array_filter([1.5, 2.5, 3.5], x -> x > 2.0) +---- +[2.5, 3.5] + +statement error Invalid arithmetic operation: Int64 \+ Utf8 +SELECT array_filter([1, 2, 3], x -> x + 'invalid') + +# array_filter with multiple array columns +statement ok +CREATE TABLE test_arrays (arr1 ARRAY<INTEGER>, arr2 ARRAY<INTEGER>) AS VALUES ([1, 2, 3], [4, 5, 6]); Review Comment: Can you please add null list here as well and null items ########## 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() + ) + } + + fn args_with_lambda<'a>(&'a self, args: &'a [Expr]) -> Result<Vec<&'a Expr>> { + match (self.lambda.as_ref(), args) { + (Some(lambda), [expr]) => Ok(vec![expr, lambda.as_ref()]), + (None, [array, lambda]) if matches!(lambda, Expr::Lambda(_)) => { + Ok(vec![array, lambda]) + } + _ => plan_err!("{} requires 1 argument and 1 lambda", self.name()), + } + } +} + +fn array_filter_inner(array: &ArrayRef, lambda: &dyn PhysicalLambda) -> Result<ArrayRef> { + match array.data_type() { + List(field) => { + let array = as_list_array(&array)?; + filter_generic_list_array(array, lambda, field) + } + LargeList(field) => { + let array = as_large_list_array(&array)?; + filter_generic_list_array(array, lambda, field) + } + _ => exec_err!("array_filter does not support type {:?}", array.data_type()), + } +} + +fn filter_generic_list_array<OffsetSize: OffsetSizeTrait>( + list_array: &GenericListArray<OffsetSize>, + lambda: &dyn PhysicalLambda, + field: &Arc<Field>, +) -> Result<ArrayRef> { + let mut offsets = vec![OffsetSize::zero()]; + + let values = list_array.values(); + let value_offsets = list_array.value_offsets(); + let nulls = list_array.nulls(); + + let batch = RecordBatch::try_new( + Schema::new(vec![field + .as_ref() + .clone() + .with_name(lambda.params()[0].clone())]) + .into(), + vec![Arc::clone(values)], + )?; + + let filter_array = lambda.evaluate(&batch)?; + let ColumnarValue::Array(filter_array) = filter_array else { + return exec_err!( + "array_filter requires a lambda that returns an array of booleans" + ); + }; Review Comment: You can add optimization for scalar if you want or I can do it in a different PR ########## 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() + ) + } + + fn args_with_lambda<'a>(&'a self, args: &'a [Expr]) -> Result<Vec<&'a Expr>> { + match (self.lambda.as_ref(), args) { + (Some(lambda), [expr]) => Ok(vec![expr, lambda.as_ref()]), + (None, [array, lambda]) if matches!(lambda, Expr::Lambda(_)) => { + Ok(vec![array, lambda]) + } + _ => plan_err!("{} requires 1 argument and 1 lambda", self.name()), + } + } +} + +fn array_filter_inner(array: &ArrayRef, lambda: &dyn PhysicalLambda) -> Result<ArrayRef> { + match array.data_type() { + List(field) => { + let array = as_list_array(&array)?; + filter_generic_list_array(array, lambda, field) + } + LargeList(field) => { + let array = as_large_list_array(&array)?; + filter_generic_list_array(array, lambda, field) + } + _ => exec_err!("array_filter does not support type {:?}", array.data_type()), + } +} + +fn filter_generic_list_array<OffsetSize: OffsetSizeTrait>( + list_array: &GenericListArray<OffsetSize>, + lambda: &dyn PhysicalLambda, + field: &Arc<Field>, +) -> Result<ArrayRef> { + let mut offsets = vec![OffsetSize::zero()]; + + let values = list_array.values(); + let value_offsets = list_array.value_offsets(); + let nulls = list_array.nulls(); + + let batch = RecordBatch::try_new( + Schema::new(vec![field + .as_ref() + .clone() + .with_name(lambda.params()[0].clone())]) + .into(), + vec![Arc::clone(values)], + )?; Review Comment: I can do it in a separate PR. this will lead to unnecessary computation as it will include values that are not part of list "visible" values in case of either of the following. 1. the list is sliced, making the evaluate work on more data that is needed this is how to create that: ```rust let data = vec![ Some(vec![Some(0), Some(1), Some(2)]), Some(vec![Some(3), Some(4), Some(5)]), Some(vec![Some(6), Some(7)]), Some(vec![Some(8)]), ]; let list_array = ListArray::from_iter_primitive::<Int32Type, _, _>(data); let list_sliced_values = list_array.slice(1, 2); ``` 2. in case of nulls in the list that are not behind an empty list this is how to create that ````rust let data = vec![ Some(vec![Some(0), Some(1), Some(2)]), Some(vec![Some(3), Some(4), Some(5)]), Some(vec![Some(6), Some(7)]), ]; let list_array = ListArray::from_iter_primitive::<Int32Type, _, _>(data); let (field, offsets, values, nulls) = list_array.into_parts(); let list_array_with_null_pointing_to_non_empty_list = ListArray::try_new( field, offsets, values, Some(NullBuffer::from(&[true, false, true])) )?; ``` ########## 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() + ) + } + + fn args_with_lambda<'a>(&'a self, args: &'a [Expr]) -> Result<Vec<&'a Expr>> { + match (self.lambda.as_ref(), args) { + (Some(lambda), [expr]) => Ok(vec![expr, lambda.as_ref()]), + (None, [array, lambda]) if matches!(lambda, Expr::Lambda(_)) => { + Ok(vec![array, lambda]) + } + _ => plan_err!("{} requires 1 argument and 1 lambda", self.name()), + } + } +} + +fn array_filter_inner(array: &ArrayRef, lambda: &dyn PhysicalLambda) -> Result<ArrayRef> { + match array.data_type() { + List(field) => { + let array = as_list_array(&array)?; + filter_generic_list_array(array, lambda, field) + } + LargeList(field) => { + let array = as_large_list_array(&array)?; + filter_generic_list_array(array, lambda, field) + } + _ => exec_err!("array_filter does not support type {:?}", array.data_type()), + } +} + +fn filter_generic_list_array<OffsetSize: OffsetSizeTrait>( + list_array: &GenericListArray<OffsetSize>, + lambda: &dyn PhysicalLambda, + field: &Arc<Field>, +) -> Result<ArrayRef> { + let mut offsets = vec![OffsetSize::zero()]; + + let values = list_array.values(); + let value_offsets = list_array.value_offsets(); + let nulls = list_array.nulls(); + + let batch = RecordBatch::try_new( + Schema::new(vec![field + .as_ref() + .clone() + .with_name(lambda.params()[0].clone())]) + .into(), + vec![Arc::clone(values)], + )?; + + let filter_array = lambda.evaluate(&batch)?; + let ColumnarValue::Array(filter_array) = filter_array else { + return exec_err!( + "array_filter requires a lambda that returns an array of booleans" + ); + }; + let filter_array = as_boolean_array(&filter_array)?; + let filtered = filter(&values, filter_array)?; + + for row_index in 0..list_array.len() { + if list_array.is_null(row_index) { + // Handle null arrays by keeping the offset unchanged + offsets.push(offsets[row_index]); Review Comment: This have a bug in case of null value pointing to a non empty list and none of the underlying values were filtered -- This is an automated message from the Apache Git Service. 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