comphead commented on code in PR #26266: URL: https://github.com/apache/datafusion/pull/26266#discussion_r4261071508
########## datafusion/spark/src/function/generator/stack.rs: ########## @@ -0,0 +1,462 @@ +// 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::sync::Arc; + +use arrow::array::{ + Array, ArrayRef, AsArray, BooleanBufferBuilder, ListArray, PrimitiveArray, + StructArray, downcast_primitive, new_empty_array, new_null_array, +}; +use arrow::buffer::{NullBuffer, OffsetBuffer}; +use arrow::compute::{cast, concat, interleave}; +use arrow::datatypes::{ArrowPrimitiveType, DataType, Field, FieldRef, Fields}; +use datafusion_common::types::NativeType; +use datafusion_common::{ + Result, ScalarValue, exec_datafusion_err, internal_err, plan_err, +}; +use datafusion_expr::{ + ColumnarValue, ReturnFieldArgs, ScalarFunctionArgs, ScalarUDFImpl, Signature, + Volatility, +}; + +/// Spark-compatible `stack` expression +/// +/// <https://spark.apache.org/docs/latest/api/sql/index.html#stack> +/// +/// `stack(n, expr1, ..., exprk)` separates `expr1`, ..., `exprk` into `n` rows of +/// `ceil(k / n)` columns named `col0`, `col1`, ... The values fill the rows in +/// order, and the last rows are padded with NULLs. +/// +/// Note: In Spark, `stack` is a Generator that produces rows. In DataFusion, a +/// ScalarUDF can only return one value per row, so the `n` rows are returned as a +/// `List<Struct<col0, col1, ...>>`, which is the data type of Spark's Generator. +/// The caller (e.g. Comet) is expected to unnest the list. +/// +/// As in Spark, `n` must be an integer literal in `[1, 2147483647]`, and the +/// values of a column must have the same type. Only untyped NULLs are coerced, to +/// the type of the first non-NULL value in their column. +#[derive(Debug, PartialEq, Eq, Hash)] +pub struct SparkStack { + signature: Signature, +} + +impl Default for SparkStack { + fn default() -> Self { + Self::new() + } +} + +impl SparkStack { + pub fn new() -> Self { + Self { + signature: Signature::user_defined(Volatility::Immutable), + } + } +} + +impl ScalarUDFImpl for SparkStack { + fn name(&self) -> &str { + "stack" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> { + internal_err!("return_field_from_args should be used instead") + } + + /// Validates the arguments in the order Spark's `Stack.checkInputDataTypes` does. + fn return_field_from_args(&self, args: ReturnFieldArgs) -> Result<FieldRef> { + let [n, values @ ..] = args.arg_fields else { + return plan_err!( + "The `stack` requires > 1 parameters but the actual number is 0." + ); + }; + if values.is_empty() { + return plan_err!( + "The `stack` requires > 1 parameters but the actual number is 1." + ); + } + if !n.data_type().is_integer() { + return plan_err!( + "The first parameter requires the \"INT\" type, however \"{}\" has the type \"{}\".", + n.name(), + n.data_type() + ); + } + let Some(n_value) = args.scalar_arguments.first().copied().flatten() else { + return plan_err!( + "the input `n` should be a foldable \"INT\" expression; however, got \"{}\". Only an integer literal is supported.", + n.name() + ); + }; + let num_fields = values.len().div_ceil(parse_n(n_value)?); + let fields = column_types(values, num_fields)? + .into_iter() + .enumerate() + .map(|(i, data_type)| Field::new(format!("col{i}"), data_type, true)) + .collect::<Fields>(); + Ok(Arc::new(Field::new( + self.name(), + DataType::new_list(DataType::Struct(fields), false), + false, + ))) + } + + fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> { + let ScalarFunctionArgs { + args, + number_rows, + return_field, + .. + } = args; + let DataType::List(item) = return_field.data_type() else { + return internal_err!("stack: unexpected return type {return_field}"); + }; + let [ColumnarValue::Scalar(n), values @ ..] = args.as_slice() else { + return internal_err!("stack: `n` must be a scalar"); + }; + let n = parse_n(n)?; + if values.iter().all(|v| matches!(v, ColumnarValue::Scalar(_))) { + let list = stack(values, item, n, 1)?; + return Ok(ColumnarValue::Scalar(ScalarValue::List(Arc::new(list)))); + } + Ok(ColumnarValue::Array(Arc::new(stack( + values, + item, + n, + number_rows, + )?))) + } + + fn coerce_types(&self, arg_types: &[DataType]) -> Result<Vec<DataType>> { + // Spark does no implicit casts. NULLs are coerced per column at execution. + Ok(arg_types.to_vec()) + } +} + +/// Returns the number of rows `n` to generate per input row. +fn parse_n(n: &ScalarValue) -> Result<usize> { + match n.cast_to(&DataType::Int32) { + Ok(ScalarValue::Int32(Some(v))) if v > 0 => Ok(v as usize), + _ => plan_err!("The `n` must be between (0, 2147483647] (current value = {n})."), + } +} + +/// Returns the type of each output column, which is the type of its first +/// non-NULL value as in Spark's `StackCoercion`. Errors on the first value, in +/// argument order, whose type differs from its column's. +fn column_types(values: &[FieldRef], num_fields: usize) -> Result<Vec<DataType>> { + let mut types = vec![DataType::Null; num_fields]; + for (i, value) in values.iter().enumerate() { + let column_type = &mut types[i % num_fields]; + if column_type.is_null() { + *column_type = value.data_type().clone(); + } + } + for (i, value) in values.iter().enumerate() { + let col = i % num_fields; + let (expected, actual) = (&types[col], value.data_type()); + // Compare logical types, so `Utf8` and `Utf8View` are both Spark's STRING + if !actual.is_null() + && actual != expected + && NativeType::from(actual) != NativeType::from(expected) + { + return plan_err!( + "The data type of the column ({col}) do not have the same type: \"{expected}\" ({}) <> \"{actual}\" ({}).", + col + 1, + i + 1 + ); + } + } + Ok(types) +} + +/// Generates `n` rows of `item`'s struct type for each of `num_rows` input rows. +/// Field `j` of generated row `i` is `values[i * num_fields + j]`, or NULL past the +/// last value. +fn stack( + values: &[ColumnarValue], + item: &FieldRef, + n: usize, + num_rows: usize, +) -> Result<ListArray> { + let DataType::Struct(fields) = item.data_type() else { + return internal_err!("stack: unexpected element type {item}"); + }; + let offsets = OffsetBuffer::try_from_repeated_length(n, num_rows).map_err(|_| { + exec_datafusion_err!( + "stack: {num_rows} rows of {n} generated rows exceed the maximum list size" + ) + })?; + let columns = if n == 1 { + // Every value is a column as is, without copying arrays + values + .iter() + .zip(fields.iter()) + .map(|(value, field)| cast_to(value.to_array(num_rows)?, field.data_type())) + .collect::<Result<_>>()? + } else { + stack_columns(values, fields, n, num_rows)? + }; + let rows = StructArray::try_new(fields.clone(), columns, None)?; + Ok(ListArray::try_new( + Arc::clone(item), + offsets, + Arc::new(rows), + None, + )?) +} + +/// Builds each output column from its `n` values. An array value is read at the +/// input row. A scalar or NULL is a single value array read at index 0, so it is +/// never broadcast. +fn stack_columns( + values: &[ColumnarValue], + fields: &Fields, + n: usize, + num_rows: usize, +) -> Result<Vec<ArrayRef>> { + macro_rules! primitive_helper { + ($t:ty, $sources:ident, $per_row:ident, $num_rows:ident, $data_type:ident) => { + Ok(stack_primitive::<$t>( + &$sources, &$per_row, $num_rows, $data_type, + )) + }; + } + let num_fields = fields.len(); + let mut indices = vec![]; + fields + .iter() + .enumerate() + .map(|(col, field)| { + let data_type = field.data_type(); + if data_type.is_null() { + return Ok(new_null_array(data_type, num_rows * n)); + } + let null = new_null_array(data_type, 1); + let mut sources = Vec::with_capacity(n); + let mut per_row = Vec::with_capacity(n); + for row in 0..n { + let (source, is_array) = match values.get(row * num_fields + col) { + Some(ColumnarValue::Array(array)) if !array.data_type().is_null() => { + (cast_to(Arc::clone(array), data_type)?, true) + } + Some(ColumnarValue::Scalar(scalar)) if !scalar.is_null() => { + (cast_to(scalar.to_array()?, data_type)?, false) + } + _ => (Arc::clone(&null), false), + }; + sources.push(source); + per_row.push(is_array); + } + if !per_row.contains(&true) { + // The same `n` values for every input row + let sources = sources.iter().map(AsRef::as_ref).collect::<Vec<_>>(); + return repeat(concat(&sources)?, num_rows); + } + downcast_primitive! { + data_type => (primitive_helper, sources, per_row, num_rows, data_type), + _ => { + indices.clear(); + indices.try_reserve(num_rows * n).map_err(|e| { + exec_datafusion_err!("stack: failed to allocate indices: {e}") + })?; + for input_row in 0..num_rows { + indices.extend(per_row.iter().enumerate().map(|(i, &per_row)| { + (i, if per_row { input_row } else { 0 }) + })); + } + let sources = sources.iter().map(AsRef::as_ref).collect::<Vec<_>>(); + Ok(interleave(&sources, &indices)?) + } + } + }) + .collect() +} + +/// Writes source `i` to every `n`-th slot starting at `i`. This avoids the +/// index lookups of `interleave`, and only visits the NULL slots of the +/// validity. It is about 2-3x faster for primitive types. +fn stack_primitive<T: ArrowPrimitiveType>( + sources: &[ArrayRef], + per_row: &[bool], + num_rows: usize, + data_type: &DataType, +) -> ArrayRef { + // `values[i..]` below needs at least one row + if num_rows == 0 { + return new_empty_array(data_type); + } + let n = sources.len(); + let len = num_rows * n; + let mut values = vec![T::Native::default(); len]; + let mut validity: Option<BooleanBufferBuilder> = None; Review Comment: `NullBufferBuilder::new_with_len(len)` does this lazily. `set_bit` materializes an all-valid bitmap on first use and `build()` returns `Option<NullBuffer>`, which replaces the `Option<BooleanBufferBuilder>`, the `get_or_insert_with` block and the final `map`. ########## datafusion/spark/src/function/generator/stack.rs: ########## @@ -0,0 +1,462 @@ +// 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::sync::Arc; + +use arrow::array::{ + Array, ArrayRef, AsArray, BooleanBufferBuilder, ListArray, PrimitiveArray, + StructArray, downcast_primitive, new_empty_array, new_null_array, +}; +use arrow::buffer::{NullBuffer, OffsetBuffer}; +use arrow::compute::{cast, concat, interleave}; +use arrow::datatypes::{ArrowPrimitiveType, DataType, Field, FieldRef, Fields}; +use datafusion_common::types::NativeType; +use datafusion_common::{ + Result, ScalarValue, exec_datafusion_err, internal_err, plan_err, +}; +use datafusion_expr::{ + ColumnarValue, ReturnFieldArgs, ScalarFunctionArgs, ScalarUDFImpl, Signature, + Volatility, +}; + +/// Spark-compatible `stack` expression +/// +/// <https://spark.apache.org/docs/latest/api/sql/index.html#stack> +/// +/// `stack(n, expr1, ..., exprk)` separates `expr1`, ..., `exprk` into `n` rows of +/// `ceil(k / n)` columns named `col0`, `col1`, ... The values fill the rows in +/// order, and the last rows are padded with NULLs. +/// +/// Note: In Spark, `stack` is a Generator that produces rows. In DataFusion, a +/// ScalarUDF can only return one value per row, so the `n` rows are returned as a +/// `List<Struct<col0, col1, ...>>`, which is the data type of Spark's Generator. +/// The caller (e.g. Comet) is expected to unnest the list. +/// +/// As in Spark, `n` must be an integer literal in `[1, 2147483647]`, and the Review Comment: "As in Spark" is wrong for the literal rule. Spark accepts any foldable `INT` (`hasFoldableNumRows`), as the PR description and `stack.slt` say. The `expr_fn` doc in `generator/mod.rs` also does not mention that `n` must be a literal or that the result needs `unnest`. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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