e412dd12e8
Currently, there are some useless includes in the codebase. We can use a tool named include-what-you-use to optimize these includes. By using a strict include-what-you-use policy, we can get lots of benefits from it.
161 lines
7.3 KiB
C++
161 lines
7.3 KiB
C++
// Licensed to the Apache Software Foundation (ASF) under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing,
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// software distributed under the License is distributed on an
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// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations
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// under the License.
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#include <stddef.h>
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#include <stdint.h>
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#include <algorithm>
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#include <boost/iterator/iterator_facade.hpp>
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#include <memory>
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#include <utility>
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#include "common/status.h"
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#include "vec/aggregate_functions/aggregate_function.h"
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#include "vec/columns/column.h"
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#include "vec/columns/column_vector.h"
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#include "vec/columns/columns_number.h"
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#include "vec/core/block.h"
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#include "vec/core/column_numbers.h"
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#include "vec/core/column_with_type_and_name.h"
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#include "vec/core/types.h"
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#include "vec/data_types/data_type.h"
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#include "vec/data_types/data_type_nullable.h"
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#include "vec/data_types/data_type_number.h"
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#include "vec/functions/function.h"
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#include "vec/functions/simple_function_factory.h"
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namespace doris {
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class FunctionContext;
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} // namespace doris
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namespace doris::vectorized {
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class FunctionWidthBucket : public IFunction {
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public:
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static constexpr auto name = "width_bucket";
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static FunctionPtr create() { return std::make_shared<FunctionWidthBucket>(); }
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/// Get function name.
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String get_name() const override { return name; }
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bool is_variadic() const override { return false; }
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size_t get_number_of_arguments() const override { return 4; }
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DataTypePtr get_return_type_impl(const DataTypes& arguments) const override {
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return std::make_shared<DataTypeInt64>();
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}
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Status execute_impl(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
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size_t result, size_t input_rows_count) override {
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ColumnPtr expr_ptr =
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block.get_by_position(arguments[0]).column->convert_to_full_column_if_const();
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ColumnPtr min_value_ptr =
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block.get_by_position(arguments[1]).column->convert_to_full_column_if_const();
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ColumnPtr max_value_ptr =
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block.get_by_position(arguments[2]).column->convert_to_full_column_if_const();
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ColumnPtr num_buckets_ptr =
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block.get_by_position(arguments[3]).column->convert_to_full_column_if_const();
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int64_t num_buckets = num_buckets_ptr->get_int(0);
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auto nested_column_ptr = ColumnInt64::create(input_rows_count, 0);
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DataTypePtr expr_type = block.get_by_position(arguments[0]).type;
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_execute_by_type(*expr_ptr, *min_value_ptr, *max_value_ptr, num_buckets, *nested_column_ptr,
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expr_type);
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block.replace_by_position(result, std::move(nested_column_ptr));
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return Status::OK();
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}
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private:
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template <typename ColumnType>
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void _execute(const IColumn& expr_column, const IColumn& min_value_column,
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const IColumn& max_value_column, const int64_t num_buckets,
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IColumn& nested_column) {
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const ColumnType& expr_column_concrete = reinterpret_cast<const ColumnType&>(expr_column);
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const ColumnType& min_value_column_concrete =
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reinterpret_cast<const ColumnType&>(min_value_column);
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const ColumnType& max_value_column_concrete =
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reinterpret_cast<const ColumnType&>(max_value_column);
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ColumnInt64& nested_column_concrete = reinterpret_cast<ColumnInt64&>(nested_column);
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size_t input_rows_count = expr_column.size();
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for (size_t i = 0; i < input_rows_count; ++i) {
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auto min_value = min_value_column_concrete.get_data()[i];
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auto max_value = max_value_column_concrete.get_data()[i];
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if (expr_column_concrete.get_data()[i] < min_value) {
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continue;
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} else if (expr_column_concrete.get_data()[i] >= max_value) {
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nested_column_concrete.get_data()[i] = num_buckets + 1;
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} else {
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if ((max_value - min_value) / num_buckets == 0) {
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continue;
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}
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nested_column_concrete.get_data()[i] =
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(int64_t)(1 + (expr_column_concrete.get_data()[i] - min_value) /
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((max_value - min_value) / num_buckets));
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}
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}
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}
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void _execute_by_type(const IColumn& expr_column, const IColumn& min_value_column,
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const IColumn& max_value_column, const int64_t num_buckets,
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IColumn& nested_column_column, DataTypePtr& expr_type) {
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WhichDataType which(remove_nullable(expr_type));
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if (which.is_int8()) {
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_execute<ColumnInt8>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_int16()) {
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_execute<ColumnInt16>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_int32()) {
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_execute<ColumnInt32>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_int64()) {
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_execute<ColumnInt64>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_float32()) {
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_execute<ColumnFloat32>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_float64()) {
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_execute<ColumnFloat64>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_decimal128()) {
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_execute<ColumnDecimal128>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_date()) {
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_execute<ColumnDate>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_date_v2()) {
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_execute<ColumnDateV2>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_date_time()) {
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_execute<ColumnDateTime>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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} else if (which.is_date_time_v2()) {
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_execute<ColumnDateTimeV2>(expr_column, min_value_column, max_value_column, num_buckets,
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nested_column_column);
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}
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}
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};
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void register_function_width_bucket(SimpleFunctionFactory& factory) {
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factory.register_function<FunctionWidthBucket>();
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}
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} // namespace doris::vectorized
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