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https://github.com/ClickHouse/ClickHouse.git
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369 lines
15 KiB
C++
369 lines
15 KiB
C++
#pragma once
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#include <type_traits>
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#include <Core/AccurateComparison.h>
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#include <DataTypes/DataTypesDecimal.h>
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#include <Columns/ColumnsNumber.h>
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#include <Functions/IFunction.h>
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#include <Functions/FunctionHelpers.h>
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#include <Functions/castTypeToEither.h>
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#include <IO/WriteHelpers.h>
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#include <Poco/Logger.h>
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#include <Loggers/Loggers.h>
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namespace DB
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{
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namespace ErrorCodes
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{
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extern const int DECIMAL_OVERFLOW;
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extern const int ILLEGAL_COLUMN;
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extern const int ILLEGAL_TYPE_OF_ARGUMENT;
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extern const int NUMBER_OF_ARGUMENTS_DOESNT_MATCH;
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}
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struct DecimalOpHelpers
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{
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/* These functions perform main arithmetic logic.
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* As soon as intermediate results may not fit Decimal256 (e.g. 1e36, scale 10),
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* we may not operate with Decimals. Later on this big number may be shrunk (e.g. result scale is 0 in the case above).
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* That's why we need to store intermediate results in a flexible extendable storage (here we use std::vector)
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* Here we operate on numbers using simple digit arithmetic.
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* This is the reason these functions are slower than traditional ones.
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*
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* Here and below we use UInt8 for storing digits (0-9 range with maximum carry of 9 will definitely fit this)
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*/
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static std::vector<UInt8> multiply(const std::vector<UInt8> & num1, const std::vector<UInt8> & num2)
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{
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UInt16 const len1 = num1.size();
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UInt16 const len2 = num2.size();
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if (len1 == 0 || len2 == 0)
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return {0};
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std::vector<UInt8> result(len1 + len2, 0);
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UInt16 i_n1 = 0;
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UInt16 i_n2;
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for (Int32 i = len1 - 1; i >= 0; --i)
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{
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UInt16 carry = 0;
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i_n2 = 0;
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for (Int32 j = len2 - 1; j >= 0; --j)
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{
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if (unlikely(i_n1 + i_n2 >= len1 + len2))
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throw DB::Exception(ErrorCodes::DECIMAL_OVERFLOW, "Numeric overflow: result bigger that Decimal256");
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UInt16 sum = num1[i] * num2[j] + result[i_n1 + i_n2] + carry;
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carry = sum / 10;
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result[i_n1 + i_n2] = sum % 10;
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++i_n2;
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}
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if (carry > 0)
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{
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if (unlikely(i_n1 + i_n2 >= len1 + len2))
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throw DB::Exception(ErrorCodes::DECIMAL_OVERFLOW, "Numeric overflow: result bigger that Decimal256");
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result[i_n1 + i_n2] += carry;
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}
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++i_n1;
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}
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// Maximum Int32 value exceeds 2 billion, we can safely use it for array length storing
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Int32 i = static_cast<Int32>(result.size() - 1);
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while (i >= 0 && result[i] == 0)
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{
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result.pop_back();
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--i;
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}
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if (i == -1)
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return {0};
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std::reverse(result.begin(), result.end());
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return result;
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}
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static std::vector<UInt8> divide(const std::vector<UInt8> & number, const Int256 & divisor)
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{
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std::vector<UInt8> result;
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const auto max_index = number.size() - 1;
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UInt16 idx = 0;
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Int256 temp = 0;
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while (temp < divisor && max_index > idx)
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{
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temp = temp * 10 + number[idx];
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++idx;
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}
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if (unlikely(temp == 0))
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return {0};
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while (max_index >= idx)
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{
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result.push_back(temp / divisor);
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temp = (temp % divisor) * 10 + number[idx];
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++idx;
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}
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result.push_back(temp / divisor);
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return result;
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}
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static std::vector<UInt8> toDigits(Int256 x)
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{
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std::vector<UInt8> result;
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if (x >= 10)
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result = toDigits(x / 10);
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result.push_back(x % 10);
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return result;
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}
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static UInt256 fromDigits(const std::vector<UInt8> & digits)
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{
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Int256 result = 0;
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Int256 scale = 0;
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for (auto i = digits.rbegin(); i != digits.rend(); ++i)
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{
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result += DecimalUtils::scaleMultiplier<Decimal256>(scale) * (*i);
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++scale;
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}
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return result;
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}
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};
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template <typename ResultType, typename Transform>
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struct Processor
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{
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const Transform transform;
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explicit Processor(Transform transform_)
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: transform(std::move(transform_))
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{}
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template <typename FirstArgVectorType, typename SecondArgType>
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void NO_INLINE
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vectorConstant(const FirstArgVectorType & vec_first, const SecondArgType second_value,
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PaddedPODArray<typename ResultType::FieldType> & vec_to, UInt16 scale_a, UInt16 scale_b, UInt16 result_scale) const
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{
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size_t size = vec_first.size();
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vec_to.resize(size);
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for (size_t i = 0; i < size; ++i)
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vec_to[i] = transform.execute(vec_first[i], second_value, scale_a, scale_b, result_scale);
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}
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template <typename FirstArgVectorType, typename SecondArgVectorType>
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void NO_INLINE
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vectorVector(const FirstArgVectorType & vec_first, const SecondArgVectorType & vec_second,
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PaddedPODArray<typename ResultType::FieldType> & vec_to, UInt16 scale_a, UInt16 scale_b, UInt16 result_scale) const
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{
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size_t size = vec_first.size();
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vec_to.resize(size);
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for (size_t i = 0; i < size; ++i)
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vec_to[i] = transform.execute(vec_first[i], vec_second[i], scale_a, scale_b, result_scale);
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}
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template <typename FirstArgType, typename SecondArgVectorType>
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void NO_INLINE
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constantVector(const FirstArgType & first_value, const SecondArgVectorType & vec_second,
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PaddedPODArray<typename ResultType::FieldType> & vec_to, UInt16 scale_a, UInt16 scale_b, UInt16 result_scale) const
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{
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size_t size = vec_second.size();
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vec_to.resize(size);
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for (size_t i = 0; i < size; ++i)
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vec_to[i] = transform.execute(first_value, vec_second[i], scale_a, scale_b, result_scale);
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}
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};
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template <typename FirstArgType, typename SecondArgType, typename ResultType, typename Transform>
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struct DecimalArithmeticsImpl
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{
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static ColumnPtr execute(Transform transform, const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type)
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{
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using FirstArgValueType = typename FirstArgType::FieldType;
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using FirstArgColumnType = typename FirstArgType::ColumnType;
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using SecondArgValueType = typename SecondArgType::FieldType;
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using SecondArgColumnType = typename SecondArgType::ColumnType;
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using ResultColumnType = typename ResultType::ColumnType;
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UInt16 scale_a = getDecimalScale(*arguments[0].type);
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UInt16 scale_b = getDecimalScale(*arguments[1].type);
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UInt16 result_scale = getDecimalScale(*result_type->getPtr());
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auto op = Processor<ResultType, Transform>{std::move(transform)};
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auto result_col = result_type->createColumn();
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auto col_to = assert_cast<ResultColumnType *>(result_col.get());
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const auto * first_col = checkAndGetColumn<FirstArgColumnType>(arguments[0].column.get());
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const auto * second_col = checkAndGetColumn<SecondArgColumnType>(arguments[1].column.get());
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const auto * first_col_const = typeid_cast<const ColumnConst *>(arguments[0].column.get());
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const auto * second_col_const = typeid_cast<const ColumnConst *>(arguments[1].column.get());
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if (first_col)
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{
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if (second_col_const)
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op.vectorConstant(first_col->getData(), second_col_const->template getValue<SecondArgValueType>(), col_to->getData(), scale_a, scale_b, result_scale);
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else
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op.vectorVector(first_col->getData(), second_col->getData(), col_to->getData(), scale_a, scale_b, result_scale);
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}
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else if (first_col_const)
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{
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op.constantVector(first_col_const->template getValue<FirstArgValueType>(), second_col->getData(), col_to->getData(), scale_a, scale_b, result_scale);
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}
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else
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{
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throw Exception(ErrorCodes::ILLEGAL_COLUMN, "Illegal column {} of first argument of function {}",
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arguments[0].column->getName(), Transform::name);
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}
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return result_col;
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}
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};
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template <typename Transform>
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class FunctionsDecimalArithmetics : public IFunction
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{
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public:
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static constexpr auto name = Transform::name;
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static FunctionPtr create(ContextPtr) { return std::make_shared<FunctionsDecimalArithmetics>(); }
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String getName() const override
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{
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return name;
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}
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bool isVariadic() const override { return true; }
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size_t getNumberOfArguments() const override { return 0; }
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bool isSuitableForShortCircuitArgumentsExecution(const DataTypesWithConstInfo & /*arguments*/) const override { return false; }
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DataTypePtr getReturnTypeImpl(const ColumnsWithTypeAndName & arguments) const override
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{
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if (arguments.size() != 2 && arguments.size() != 3)
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throw Exception(ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH,
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"Number of arguments for function {} does not match: 2 or 3 expected", getName());
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if (!isDecimal(arguments[0].type) || !isDecimal(arguments[1].type))
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throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT, "Arguments for {} function must be Decimal", getName());
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UInt8 scale = std::max(getDecimalScale(*arguments[0].type->getPtr()), getDecimalScale(*arguments[1].type->getPtr()));
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if (arguments.size() == 3)
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{
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WhichDataType which_scale(arguments[2].type.get());
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if (!which_scale.isUInt8())
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throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT, "Illegal type {} of third argument of function {}. "
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"Should be constant UInt8 from range[0, 76]", arguments[2].type->getName(), getName());
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const ColumnConst * scale_column = checkAndGetColumnConst<ColumnUInt8>(arguments[2].column.get());
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if (!scale_column)
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throw Exception(ErrorCodes::ILLEGAL_COLUMN, "Illegal column of third argument of function {}. "
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"Should be constant UInt8", getName());
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scale = scale_column->getValue<UInt8>();
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}
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/**
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At compile time, result is unknown. We only know the Scale (number of fractional digits) at runtime.
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Also nothing is known about size of whole part.
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As in simple division/multiplication for decimals, we scale the result up, but is is explicit here and no downscale is performed.
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It guarantees that result will have given scale and it can also be MANUALLY converted to other decimal types later.
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**/
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if (scale > DecimalUtils::max_precision<Decimal256>)
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throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT, "Illegal value of third argument of function {}: "
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"must be integer in range [0, 76]", this->getName());
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return std::make_shared<DataTypeDecimal256>(DecimalUtils::max_precision<Decimal256>, scale);
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}
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bool useDefaultImplementationForConstants() const override { return true; }
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ColumnNumbers getArgumentsThatAreAlwaysConstant() const override { return {2}; }
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ColumnPtr executeImpl(const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type, size_t /*input_rows_count*/) const override
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{
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return resolveOverload(arguments, result_type);
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}
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private:
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// long resolver to call proper templated func
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ColumnPtr resolveOverload(const ColumnsWithTypeAndName & arguments, const DataTypePtr & result_type) const
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{
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WhichDataType which_dividend(arguments[0].type.get());
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WhichDataType which_divisor(arguments[1].type.get());
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if (which_dividend.isDecimal32())
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{
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using DividendType = DataTypeDecimal32;
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if (which_divisor.isDecimal32())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal32, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal64())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal64, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal128())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal128, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal256())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal256, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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}
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else if (which_dividend.isDecimal64())
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{
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using DividendType = DataTypeDecimal64;
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if (which_divisor.isDecimal32())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal32, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal64())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal64, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal128())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal128, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal256())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal256, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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}
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else if (which_dividend.isDecimal128())
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{
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using DividendType = DataTypeDecimal128;
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if (which_divisor.isDecimal32())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal32, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal64())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal64, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal128())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal128, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal256())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal256, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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}
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else if (which_dividend.isDecimal256())
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{
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using DividendType = DataTypeDecimal256;
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if (which_divisor.isDecimal32())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal32, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal64())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal64, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal128())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal128, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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else if (which_divisor.isDecimal256())
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return DecimalArithmeticsImpl<DividendType, DataTypeDecimal256, DataTypeDecimal256, Transform>::execute(Transform{}, arguments, result_type);
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}
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// the compiler is happy now
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return nullptr;
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}
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};
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}
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