Jefffrey commented on code in PR #20358: URL: https://github.com/apache/datafusion/pull/20358#discussion_r2825630743
########## datafusion/spark/src/function/map/map_func.rs: ########## @@ -0,0 +1,186 @@ +// 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; + +use crate::function::map::utils::{ + map_from_keys_values_offsets_nulls, map_type_from_key_value_types, +}; +use arrow::array::{Array, ArrayRef, NullArray, new_null_array}; +use arrow::buffer::OffsetBuffer; +use arrow::compute::concat; +use arrow::compute::kernels::cast; +use arrow::datatypes::DataType; +use datafusion_common::{Result, exec_err}; +use datafusion_expr::type_coercion::binary::comparison_coercion; +use datafusion_expr::{ColumnarValue, ScalarUDFImpl, Signature, Volatility}; +use datafusion_functions::utils::make_scalar_function; + +/// Spark-compatible `map` expression +/// <https://spark.apache.org/docs/latest/api/sql/index.html#map> +/// +/// Creates a map from alternating key-value pairs. +/// Supports 0 or more pairs. +/// Example: map(key1, value1, key2, value2, ...) -> {key1: value1, key2: value2, ...} +/// Example: map() -> {} +#[derive(Debug, PartialEq, Eq, Hash)] +pub struct Map { + signature: Signature, +} + +impl Default for Map { + fn default() -> Self { + Self::new() + } +} + +impl Map { + pub fn new() -> Self { + Self { + signature: Signature::variadic_any(Volatility::Immutable), + } + } +} + +impl ScalarUDFImpl for Map { + fn as_any(&self) -> &dyn Any { + self + } + + fn name(&self) -> &str { + "map" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> { + if !arg_types.len().is_multiple_of(2) { + return exec_err!( + "map requires an even number of arguments, got {}", + arg_types.len() + ); + } + + if arg_types.is_empty() { + return Ok(map_type_from_key_value_types( + &DataType::Null, + &DataType::Null, + )); + } + + let key_type = arg_types + .iter() + .step_by(2) + .cloned() + .reduce(|common, t| comparison_coercion(&common, &t).unwrap_or(common)) + .unwrap(); + let value_type = arg_types + .iter() + .skip(1) + .step_by(2) + .cloned() + .reduce(|common, t| comparison_coercion(&common, &t).unwrap_or(common)) + .unwrap(); + + Ok(map_type_from_key_value_types(&key_type, &value_type)) + } + + fn invoke_with_args( + &self, + args: datafusion_expr::ScalarFunctionArgs, + ) -> Result<ColumnarValue> { + make_scalar_function(map_inner, vec![])(&args.args) + } +} + +fn map_inner(args: &[ArrayRef]) -> Result<ArrayRef> { + if args.is_empty() { + let offsets = OffsetBuffer::new(vec![0].into()); + return map_from_keys_values_offsets_nulls( + &new_null_array(&DataType::Null, 0), + &new_null_array(&DataType::Null, 0), + offsets.as_ref(), + offsets.as_ref(), + None, + None, + ); + } + + let num_rows = args[0].len(); + let num_pairs = args.len() / 2; + + let all_null = args + .iter() + .all(|arg| matches!(arg.data_type(), DataType::Null)); + + if all_null { + return Ok(cast( + &NullArray::new(num_rows), + &map_type_from_key_value_types(&DataType::Null, &DataType::Null), + )?); + } + + // Collect key arrays and value arrays from alternating arguments + let key_arrays: Vec<&dyn Array> = + (0..num_pairs).map(|i| args[i * 2].as_ref()).collect(); + let value_arrays: Vec<&dyn Array> = + (0..num_pairs).map(|i| args[i * 2 + 1].as_ref()).collect(); + + // Concatenate all keys and all values into flat arrays + let flat_keys: ArrayRef = if key_arrays.is_empty() { + new_null_array(args[0].data_type(), 0) + } else { + concat(&key_arrays)? + }; + let flat_values: ArrayRef = if value_arrays.is_empty() { + new_null_array(args[1].data_type(), 0) + } else { + concat(&value_arrays)? + }; + + // flat_keys layout: [row0_key0, row1_key0, ..., rowN_key0, row0_key1, row1_key1, ..., rowN_key1, ...] + // But we need: [row0_key0, row0_key1, ..., row0_keyM, row1_key0, row1_key1, ..., row1_keyM, ...] + // Rearrange for each row, gather keys from each pair. + // Source index for (row, pair) is (pair * num_rows + row) + let total_entries = num_rows * num_pairs; Review Comment: Could we use [`interleave`](https://docs.rs/arrow/latest/arrow/compute/fn.interleave.html) here to avoid the concat -> take? ########## datafusion/spark/src/function/map/map_func.rs: ########## @@ -0,0 +1,186 @@ +// 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; + +use crate::function::map::utils::{ + map_from_keys_values_offsets_nulls, map_type_from_key_value_types, +}; +use arrow::array::{Array, ArrayRef, NullArray, new_null_array}; +use arrow::buffer::OffsetBuffer; +use arrow::compute::concat; +use arrow::compute::kernels::cast; +use arrow::datatypes::DataType; +use datafusion_common::{Result, exec_err}; +use datafusion_expr::type_coercion::binary::comparison_coercion; +use datafusion_expr::{ColumnarValue, ScalarUDFImpl, Signature, Volatility}; +use datafusion_functions::utils::make_scalar_function; + +/// Spark-compatible `map` expression +/// <https://spark.apache.org/docs/latest/api/sql/index.html#map> +/// +/// Creates a map from alternating key-value pairs. +/// Supports 0 or more pairs. +/// Example: map(key1, value1, key2, value2, ...) -> {key1: value1, key2: value2, ...} +/// Example: map() -> {} +#[derive(Debug, PartialEq, Eq, Hash)] +pub struct Map { + signature: Signature, +} + +impl Default for Map { + fn default() -> Self { + Self::new() + } +} + +impl Map { + pub fn new() -> Self { + Self { + signature: Signature::variadic_any(Volatility::Immutable), + } + } +} + +impl ScalarUDFImpl for Map { + fn as_any(&self) -> &dyn Any { + self + } + + fn name(&self) -> &str { + "map" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> { + if !arg_types.len().is_multiple_of(2) { + return exec_err!( + "map requires an even number of arguments, got {}", + arg_types.len() + ); + } + + if arg_types.is_empty() { + return Ok(map_type_from_key_value_types( + &DataType::Null, + &DataType::Null, + )); + } + + let key_type = arg_types + .iter() + .step_by(2) + .cloned() + .reduce(|common, t| comparison_coercion(&common, &t).unwrap_or(common)) + .unwrap(); + let value_type = arg_types + .iter() + .skip(1) + .step_by(2) + .cloned() + .reduce(|common, t| comparison_coercion(&common, &t).unwrap_or(common)) + .unwrap(); + + Ok(map_type_from_key_value_types(&key_type, &value_type)) + } + + fn invoke_with_args( + &self, + args: datafusion_expr::ScalarFunctionArgs, + ) -> Result<ColumnarValue> { + make_scalar_function(map_inner, vec![])(&args.args) + } +} + +fn map_inner(args: &[ArrayRef]) -> Result<ArrayRef> { + if args.is_empty() { + let offsets = OffsetBuffer::new(vec![0].into()); + return map_from_keys_values_offsets_nulls( Review Comment: If we can pass in `return_type` from `ScalarFunctionArgs` (from `invoke_with_args`) we can pass that into [`new_empty_array()`](https://docs.rs/arrow/latest/arrow/array/fn.new_empty_array.html) I believe? - Or is it [`new_null_array()`](https://docs.rs/arrow/latest/arrow/array/fn.new_null_array.html) happening here? ########## datafusion/spark/src/function/map/map_func.rs: ########## @@ -0,0 +1,186 @@ +// 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; + +use crate::function::map::utils::{ + map_from_keys_values_offsets_nulls, map_type_from_key_value_types, +}; +use arrow::array::{Array, ArrayRef, NullArray, new_null_array}; +use arrow::buffer::OffsetBuffer; +use arrow::compute::concat; +use arrow::compute::kernels::cast; +use arrow::datatypes::DataType; +use datafusion_common::{Result, exec_err}; +use datafusion_expr::type_coercion::binary::comparison_coercion; +use datafusion_expr::{ColumnarValue, ScalarUDFImpl, Signature, Volatility}; +use datafusion_functions::utils::make_scalar_function; + +/// Spark-compatible `map` expression +/// <https://spark.apache.org/docs/latest/api/sql/index.html#map> +/// +/// Creates a map from alternating key-value pairs. +/// Supports 0 or more pairs. +/// Example: map(key1, value1, key2, value2, ...) -> {key1: value1, key2: value2, ...} +/// Example: map() -> {} +#[derive(Debug, PartialEq, Eq, Hash)] +pub struct Map { + signature: Signature, +} + +impl Default for Map { + fn default() -> Self { + Self::new() + } +} + +impl Map { + pub fn new() -> Self { + Self { + signature: Signature::variadic_any(Volatility::Immutable), + } + } +} + +impl ScalarUDFImpl for Map { + fn as_any(&self) -> &dyn Any { + self + } + + fn name(&self) -> &str { + "map" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> { + if !arg_types.len().is_multiple_of(2) { + return exec_err!( + "map requires an even number of arguments, got {}", + arg_types.len() + ); + } + + if arg_types.is_empty() { + return Ok(map_type_from_key_value_types( + &DataType::Null, + &DataType::Null, + )); + } + + let key_type = arg_types + .iter() + .step_by(2) + .cloned() + .reduce(|common, t| comparison_coercion(&common, &t).unwrap_or(common)) + .unwrap(); + let value_type = arg_types + .iter() + .skip(1) + .step_by(2) + .cloned() + .reduce(|common, t| comparison_coercion(&common, &t).unwrap_or(common)) + .unwrap(); + + Ok(map_type_from_key_value_types(&key_type, &value_type)) + } + + fn invoke_with_args( + &self, + args: datafusion_expr::ScalarFunctionArgs, + ) -> Result<ColumnarValue> { + make_scalar_function(map_inner, vec![])(&args.args) + } +} + +fn map_inner(args: &[ArrayRef]) -> Result<ArrayRef> { + if args.is_empty() { + let offsets = OffsetBuffer::new(vec![0].into()); + return map_from_keys_values_offsets_nulls( + &new_null_array(&DataType::Null, 0), + &new_null_array(&DataType::Null, 0), + offsets.as_ref(), + offsets.as_ref(), + None, + None, + ); + } + + let num_rows = args[0].len(); + let num_pairs = args.len() / 2; + + let all_null = args + .iter() + .all(|arg| matches!(arg.data_type(), DataType::Null)); + + if all_null { + return Ok(cast( + &NullArray::new(num_rows), + &map_type_from_key_value_types(&DataType::Null, &DataType::Null), + )?); + } + + // Collect key arrays and value arrays from alternating arguments + let key_arrays: Vec<&dyn Array> = + (0..num_pairs).map(|i| args[i * 2].as_ref()).collect(); + let value_arrays: Vec<&dyn Array> = + (0..num_pairs).map(|i| args[i * 2 + 1].as_ref()).collect(); + + // Concatenate all keys and all values into flat arrays + let flat_keys: ArrayRef = if key_arrays.is_empty() { Review Comment: Can `key_arrays` be empty by this point? Didn't we check for `args` being non-empty above? Same for values below ########## datafusion/spark/src/function/map/map_func.rs: ########## @@ -0,0 +1,186 @@ +// 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; + +use crate::function::map::utils::{ + map_from_keys_values_offsets_nulls, map_type_from_key_value_types, +}; +use arrow::array::{Array, ArrayRef, NullArray, new_null_array}; +use arrow::buffer::OffsetBuffer; +use arrow::compute::concat; +use arrow::compute::kernels::cast; +use arrow::datatypes::DataType; +use datafusion_common::{Result, exec_err}; +use datafusion_expr::type_coercion::binary::comparison_coercion; +use datafusion_expr::{ColumnarValue, ScalarUDFImpl, Signature, Volatility}; +use datafusion_functions::utils::make_scalar_function; + +/// Spark-compatible `map` expression +/// <https://spark.apache.org/docs/latest/api/sql/index.html#map> +/// +/// Creates a map from alternating key-value pairs. +/// Supports 0 or more pairs. +/// Example: map(key1, value1, key2, value2, ...) -> {key1: value1, key2: value2, ...} +/// Example: map() -> {} +#[derive(Debug, PartialEq, Eq, Hash)] +pub struct Map { + signature: Signature, +} + +impl Default for Map { + fn default() -> Self { + Self::new() + } +} + +impl Map { + pub fn new() -> Self { + Self { + signature: Signature::variadic_any(Volatility::Immutable), + } + } +} + +impl ScalarUDFImpl for Map { + fn as_any(&self) -> &dyn Any { + self + } + + fn name(&self) -> &str { + "map" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> { Review Comment: I feel we should define this as user defined signature so we can encode all these rules in the `coerce_types` function (needing to be multiple of 2, calculating return type, etc.) -- This is an automated message from the Apache Git Service. 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