viirya commented on code in PR #518: URL: https://github.com/apache/datafusion-comet/pull/518#discussion_r1628506549
########## core/src/execution/datafusion/expressions/unbound.rs: ########## @@ -0,0 +1,110 @@ +// 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 crate::execution::datafusion::expressions::utils::down_cast_any_ref; +use arrow_array::RecordBatch; +use arrow_schema::{DataType, Schema}; +use datafusion::physical_plan::ColumnarValue; +use datafusion_common::{internal_err, Result}; +use datafusion_physical_expr::PhysicalExpr; +use std::{ + any::Any, + hash::{Hash, Hasher}, + sync::Arc, +}; + +/// This is similar to `UnKnownColumn` in DataFusion, but it has data type. +/// This is only used when the column is not bound to a schema, for example, the +/// inputs to aggregation functions in final aggregation. In the case, we cannot +/// bind the aggregation functions to the input schema which is grouping columns +/// and aggregate buffer attributes in Spark (DataFusion has different design). +/// But when creating certain aggregation functions, we need to know its input +/// data types. As `UnKnownColumn` doesn't have data type, we implement this +/// `UnboundColumn` to carry the data type. +#[derive(Debug, Hash, PartialEq, Eq, Clone)] +pub struct UnboundColumn { + name: String, + datatype: DataType, +} + +impl UnboundColumn { + /// Create a new unbound column expression + pub fn new(name: &str, datatype: DataType) -> Self { + Self { + name: name.to_owned(), + datatype, + } + } + + /// Get the column name + pub fn name(&self) -> &str { + &self.name + } +} + +impl std::fmt::Display for UnboundColumn { + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + write!(f, "{}, datatype: {}", self.name, self.datatype) + } +} + +impl PhysicalExpr for UnboundColumn { + /// Return a reference to Any that can be used for downcasting + fn as_any(&self) -> &dyn std::any::Any { + self + } + + /// Get the data type of this expression, given the schema of the input + fn data_type(&self, _input_schema: &Schema) -> Result<DataType> { + Ok(self.datatype.clone()) + } + + /// Decide whehter this expression is nullable, given the schema of the input Review Comment: I copied it from DataFusion. 😂 ########## core/src/execution/datafusion/expressions/unbound.rs: ########## @@ -0,0 +1,110 @@ +// 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 crate::execution::datafusion::expressions::utils::down_cast_any_ref; +use arrow_array::RecordBatch; +use arrow_schema::{DataType, Schema}; +use datafusion::physical_plan::ColumnarValue; +use datafusion_common::{internal_err, Result}; +use datafusion_physical_expr::PhysicalExpr; +use std::{ + any::Any, + hash::{Hash, Hasher}, + sync::Arc, +}; + +/// This is similar to `UnKnownColumn` in DataFusion, but it has data type. +/// This is only used when the column is not bound to a schema, for example, the +/// inputs to aggregation functions in final aggregation. In the case, we cannot +/// bind the aggregation functions to the input schema which is grouping columns +/// and aggregate buffer attributes in Spark (DataFusion has different design). +/// But when creating certain aggregation functions, we need to know its input +/// data types. As `UnKnownColumn` doesn't have data type, we implement this +/// `UnboundColumn` to carry the data type. +#[derive(Debug, Hash, PartialEq, Eq, Clone)] +pub struct UnboundColumn { + name: String, + datatype: DataType, +} + +impl UnboundColumn { + /// Create a new unbound column expression + pub fn new(name: &str, datatype: DataType) -> Self { + Self { + name: name.to_owned(), + datatype, + } + } + + /// Get the column name + pub fn name(&self) -> &str { + &self.name + } +} + +impl std::fmt::Display for UnboundColumn { + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + write!(f, "{}, datatype: {}", self.name, self.datatype) + } +} + +impl PhysicalExpr for UnboundColumn { + /// Return a reference to Any that can be used for downcasting + fn as_any(&self) -> &dyn std::any::Any { + self + } + + /// Get the data type of this expression, given the schema of the input + fn data_type(&self, _input_schema: &Schema) -> Result<DataType> { + Ok(self.datatype.clone()) + } + + /// Decide whehter this expression is nullable, given the schema of the input Review Comment: ```suggestion /// Decide whether this expression is nullable, given the schema of the input ``` -- This is an automated message from the Apache Git Service. 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