Standing-Man commented on code in PR #16936: URL: https://github.com/apache/datafusion/pull/16936#discussion_r2238684701
########## datafusion/spark/src/function/array/spark_array.rs: ########## @@ -0,0 +1,265 @@ +// 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. + +use std::{any::Any, sync::Arc}; + +use arrow::array::{ + make_array, new_null_array, Array, ArrayData, ArrayRef, Capacities, GenericListArray, + MutableArrayData, NullArray, OffsetSizeTrait, +}; +use arrow::buffer::OffsetBuffer; +use arrow::datatypes::{DataType, Field, FieldRef}; +use datafusion_common::utils::SingleRowListArrayBuilder; +use datafusion_common::{plan_datafusion_err, plan_err, Result}; +use datafusion_expr::type_coercion::binary::comparison_coercion; +use datafusion_expr::{ + ColumnarValue, ReturnFieldArgs, ScalarFunctionArgs, ScalarUDFImpl, Signature, + TypeSignature, Volatility, +}; + +use crate::function::functions_nested_utils::make_scalar_function; + +#[derive(Debug)] +pub struct SparkArray { + signature: Signature, + aliases: Vec<String>, +} + +impl Default for SparkArray { + fn default() -> Self { + Self::new() + } +} + +impl SparkArray { + pub fn new() -> Self { + Self { + signature: Signature::one_of( + vec![TypeSignature::UserDefined, TypeSignature::Nullary], + Volatility::Immutable, + ), + aliases: vec![String::from("spark_make_array")], + } + } +} + +impl ScalarUDFImpl for SparkArray { + fn as_any(&self) -> &dyn Any { + self + } + + fn name(&self) -> &str { + "array" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> { + match arg_types.len() { + 0 => Ok(empty_array_type()), + _ => { + let mut expr_type = DataType::Null; + for arg_type in arg_types { + if !arg_type.equals_datatype(&DataType::Null) { + expr_type = arg_type.clone(); + break; + } + } + + if expr_type.is_null() { + expr_type = DataType::Int32; + } + + Ok(DataType::List(Arc::new(Field::new_list_field( + expr_type, true, + )))) + } + } + } + + fn return_field_from_args(&self, args: ReturnFieldArgs) -> Result<FieldRef> { + let data_types = args + .arg_fields + .iter() + .map(|f| f.data_type()) + .cloned() + .collect::<Vec<_>>(); + let return_type = self.return_type(&data_types)?; + Ok(Arc::new(Field::new(self.name(), return_type, false))) + } + + fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> { + let ScalarFunctionArgs { args, .. } = args; + make_scalar_function(make_array_inner)(args.as_slice()) + } + + fn aliases(&self) -> &[String] { + &self.aliases + } + + fn coerce_types(&self, arg_types: &[DataType]) -> Result<Vec<DataType>> { + let first_type = arg_types.first().ok_or_else(|| { + plan_datafusion_err!("Spark array function requires at least one argument") + })?; + let new_type = + arg_types + .iter() + .skip(1) + .try_fold(first_type.clone(), |acc, x| { + // The coerced types found by `comparison_coercion` are not guaranteed to be + // coercible for the arguments. `comparison_coercion` returns more loose + // types that can be coerced to both `acc` and `x` for comparison purpose. + // See `maybe_data_types` for the actual coercion. + let coerced_type = comparison_coercion(&acc, x); + if let Some(coerced_type) = coerced_type { + Ok(coerced_type) + } else { + plan_err!("Coercion from {acc:?} to {x:?} failed.") + } + })?; + Ok(vec![new_type; arg_types.len()]) + } +} + +// Empty array is a special case that is useful for many other array functions +pub(super) fn empty_array_type() -> DataType { + DataType::List(Arc::new(Field::new_list_field(DataType::Int32, true))) +} + +/// `make_array_inner` is the implementation of the `make_array` function. +/// Constructs an array using the input `data` as `ArrayRef`. +/// Returns a reference-counted `Array` instance result. +pub fn make_array_inner(arrays: &[ArrayRef]) -> Result<ArrayRef> { + let mut data_type = DataType::Null; + for arg in arrays { + let arg_data_type = arg.data_type(); + if !arg_data_type.equals_datatype(&DataType::Null) { + data_type = arg_data_type.clone(); + break; + } + } + + match data_type { + // Either an empty array or all nulls: + DataType::Null => { + let length = arrays.iter().map(|a| a.len()).sum(); + // By default Int32 + let array = new_null_array(&DataType::Int32, length); + Ok(Arc::new( + SingleRowListArrayBuilder::new(array) + .with_nullable(true) + .build_list_array(), + )) + } + DataType::LargeList(..) => array_array::<i64>(arrays, data_type), + _ => array_array::<i32>(arrays, data_type), + } +} + +/// Convert one or more [`ArrayRef`] of the same type into a +/// `ListArray` or 'LargeListArray' depending on the offset size. +/// +/// # Example (non nested) +/// +/// Calling `array(col1, col2)` where col1 and col2 are non nested +/// would return a single new `ListArray`, where each row was a list +/// of 2 elements: +/// +/// ```text +/// ┌─────────┐ ┌─────────┐ ┌──────────────┐ +/// │ ┌─────┐ │ │ ┌─────┐ │ │ ┌──────────┐ │ +/// │ │ A │ │ │ │ X │ │ │ │ [A, X] │ │ +/// │ ├─────┤ │ │ ├─────┤ │ │ ├──────────┤ │ +/// │ │NULL │ │ │ │ Y │ │──────────▶│ │[NULL, Y] │ │ +/// │ ├─────┤ │ │ ├─────┤ │ │ ├──────────┤ │ +/// │ │ C │ │ │ │ Z │ │ │ │ [C, Z] │ │ +/// │ └─────┘ │ │ └─────┘ │ │ └──────────┘ │ +/// └─────────┘ └─────────┘ └──────────────┘ +/// col1 col2 output +/// ``` +/// +/// # Example (nested) +/// +/// Calling `array(col1, col2)` where col1 and col2 are lists +/// would return a single new `ListArray`, where each row was a list +/// of the corresponding elements of col1 and col2. +/// +/// ``` text +/// ┌──────────────┐ ┌──────────────┐ ┌─────────────────────────────┐ +/// │ ┌──────────┐ │ │ ┌──────────┐ │ │ ┌────────────────────────┐ │ +/// │ │ [A, X] │ │ │ │ [] │ │ │ │ [[A, X], []] │ │ +/// │ ├──────────┤ │ │ ├──────────┤ │ │ ├────────────────────────┤ │ +/// │ │[NULL, Y] │ │ │ │[Q, R, S] │ │───────▶│ │ [[NULL, Y], [Q, R, S]] │ │ +/// │ ├──────────┤ │ │ ├──────────┤ │ │ ├────────────────────────│ │ +/// │ │ [C, Z] │ │ │ │ NULL │ │ │ │ [[C, Z], NULL] │ │ +/// │ └──────────┘ │ │ └──────────┘ │ │ └────────────────────────┘ │ +/// └──────────────┘ └──────────────┘ └─────────────────────────────┘ +/// col1 col2 output +/// ``` +fn array_array<O: OffsetSizeTrait>( + args: &[ArrayRef], + data_type: DataType, +) -> Result<ArrayRef> { + // do not accept 0 arguments. + if args.is_empty() { + return plan_err!("Array requires at least one argument"); + } + + let mut data = vec![]; + let mut total_len = 0; + for arg in args { + let arg_data = if arg.as_any().is::<NullArray>() { + ArrayData::new_empty(&data_type) + } else { + arg.to_data() + }; + total_len += arg_data.len(); + data.push(arg_data); + } + + let mut offsets: Vec<O> = Vec::with_capacity(total_len); + offsets.push(O::usize_as(0)); + + let capacity = Capacities::Array(total_len); + let data_ref = data.iter().collect::<Vec<_>>(); + let mut mutable = MutableArrayData::with_capacities(data_ref, true, capacity); + + let num_rows = args[0].len(); + for row_idx in 0..num_rows { + for (arr_idx, arg) in args.iter().enumerate() { + if !arg.as_any().is::<NullArray>() + && !arg.is_null(row_idx) + && arg.is_valid(row_idx) + { + mutable.extend(arr_idx, row_idx, row_idx + 1); + } else { + mutable.extend_nulls(1); + } + } + offsets.push(O::usize_as(mutable.len())); + } + let data = mutable.freeze(); + + Ok(Arc::new(GenericListArray::<O>::try_new( + Arc::new(Field::new_list_field(data_type, true)), Review Comment: I noticed that the `Field` struct has `with_name` method which can be used to reset the `field_name`. another approach: ``` Ok(Arc::new(GenericListArray::<O>::try_new( Arc::new(with_list_field_name("item", Field::new_list_field(data_type, true))), OffsetBuffer::new(offsets.into()), make_array(data), None, )?)) } fn with_list_field_name(name: impl Into<String>, field: Field) -> Field { field.with_name(name) } ``` -- This is an automated message from the Apache Git Service. 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