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Merge pull request #23782 from ClickHouse/merging-array-product-function

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Merging array product function
This commit is contained in:
Maksim Kita 2021-05-01 00:53:57 +03:00 committed by GitHub
commit fa1e9de7f7
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3 changed files with 147 additions and 36 deletions
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139
src/Functions/array/arrayAggregation.cpp
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@ -15,6 +15,8 @@ namespace ErrorCodes
{
extern const int ILLEGAL_TYPE_OF_ARGUMENT;
extern const int ILLEGAL_COLUMN;
extern const int DECIMAL_OVERFLOW;
extern const int ARGUMENT_OUT_OF_BOUND;
}
enum class AggregateOperation
@ -22,7 +24,8 @@ enum class AggregateOperation
min,
max,
sum,
average
average,
product
};
/**
@ -54,6 +57,12 @@ struct ArrayAggregateResultImpl<ArrayElement, AggregateOperation::average>
using Result = Float64;
};
template <typename ArrayElement>
struct ArrayAggregateResultImpl<ArrayElement, AggregateOperation::product>
{
using Result = Float64;
};
template <typename ArrayElement>
struct ArrayAggregateResultImpl<ArrayElement, AggregateOperation::sum>
{
@ -86,7 +95,7 @@ struct ArrayAggregateImpl
using Types = std::decay_t<decltype(types)>;
using DataType = typename Types::LeftType;
if constexpr (aggregate_operation == AggregateOperation::average)
if constexpr (aggregate_operation == AggregateOperation::average || aggregate_operation == AggregateOperation::product)
{
result = std::make_shared<DataTypeFloat64>();
@ -124,17 +133,17 @@ struct ArrayAggregateImpl
template <typename Element>
static NO_SANITIZE_UNDEFINED bool executeType(const ColumnPtr & mapped, const ColumnArray::Offsets & offsets, ColumnPtr & res_ptr)
{
using Result = ArrayAggregateResult<Element, aggregate_operation>;
using ResultType = ArrayAggregateResult<Element, aggregate_operation>;
using ColVecType = std::conditional_t<IsDecimalNumber<Element>, ColumnDecimal<Element>, ColumnVector<Element>>;
using ColVecResult = std::conditional_t<IsDecimalNumber<Result>, ColumnDecimal<Result>, ColumnVector<Result>>;
using ColVecResultType = std::conditional_t<IsDecimalNumber<ResultType>, ColumnDecimal<ResultType>, ColumnVector<ResultType>>;
/// For average of array we return Float64 as result, but we want to keep precision
/// so we convert to Float64 as last step, but intermediate sum is represented as result of sum operation
static constexpr bool is_average_operation = aggregate_operation == AggregateOperation::average;
/// For average and product of array we return Float64 as result, but we want to keep precision
/// so we convert to Float64 as last step, but intermediate value is represented as result of sum operation
static constexpr bool is_average_or_product_operation = aggregate_operation == AggregateOperation::average ||
aggregate_operation == AggregateOperation::product;
using SummAggregationType = ArrayAggregateResult<Element, AggregateOperation::sum>;
using AggregationType = std::conditional_t<is_average_operation, SummAggregationType, Result>;
using AggregationType = std::conditional_t<is_average_or_product_operation, SummAggregationType, ResultType>;
const ColVecType * column = checkAndGetColumn<ColVecType>(&*mapped);
@ -147,18 +156,15 @@ struct ArrayAggregateImpl
return false;
const AggregationType x = column_const->template getValue<Element>(); // NOLINT
const typename ColVecType::Container & data
= checkAndGetColumn<ColVecType>(&column_const->getDataColumn())->getData();
const auto & data = checkAndGetColumn<ColVecType>(&column_const->getDataColumn())->getData();
typename ColVecResult::MutablePtr res_column;
typename ColVecResultType::MutablePtr res_column;
if constexpr (IsDecimalNumber<Element>)
{
res_column = ColVecResult::create(offsets.size(), data.getScale());
}
res_column = ColVecResultType::create(offsets.size(), data.getScale());
else
res_column = ColVecResult::create(offsets.size());
res_column = ColVecResultType::create(offsets.size());
typename ColVecResult::Container & res = res_column->getData();
auto & res = res_column->getData();
size_t pos = 0;
for (size_t i = 0; i < offsets.size(); ++i)
@ -178,13 +184,45 @@ struct ArrayAggregateImpl
{
if constexpr (IsDecimalNumber<Element>)
{
res[i] = DecimalUtils::convertTo<Result>(x, data.getScale());
res[i] = DecimalUtils::convertTo<ResultType>(x, data.getScale());
}
else
{
res[i] = x;
}
}
else if constexpr (aggregate_operation == AggregateOperation::product)
{
size_t array_size = offsets[i] - pos;
AggregationType product = x;
if constexpr (IsDecimalNumber<Element>)
{
using T = decltype(x.value);
T x_val = x.value;
for (size_t array_index = 1; array_index < array_size; ++array_index)
{
T product_val = product.value;
if (common::mulOverflow(x_val, product_val, product.value))
throw Exception(ErrorCodes::DECIMAL_OVERFLOW, "Decimal math overflow");
}
auto result_scale = data.getScale() * array_size;
if (unlikely(result_scale > DecimalUtils::max_precision<AggregationType>))
throw Exception(ErrorCodes::ARGUMENT_OUT_OF_BOUND, "Scale {} is out of bounds", result_scale);
res[i] = DecimalUtils::convertTo<ResultType>(product, data.getScale() * array_size);
}
else
{
for (size_t array_index = 1; array_index < array_size; ++array_index)
product = product * x;
res[i] = product;
}
}
pos = offsets[i];
}
@ -193,30 +231,30 @@ struct ArrayAggregateImpl
return true;
}
const typename ColVecType::Container & data = column->getData();
const auto & data = column->getData();
typename ColVecResult::MutablePtr res_column;
typename ColVecResultType::MutablePtr res_column;
if constexpr (IsDecimalNumber<Element>)
res_column = ColVecResult::create(offsets.size(), data.getScale());
res_column = ColVecResultType::create(offsets.size(), data.getScale());
else
res_column = ColVecResult::create(offsets.size());
res_column = ColVecResultType::create(offsets.size());
typename ColVecResult::Container & res = res_column->getData();
typename ColVecResultType::Container & res = res_column->getData();
size_t pos = 0;
for (size_t i = 0; i < offsets.size(); ++i)
{
AggregationType s = 0;
AggregationType aggregate_value = 0;
/// Array is empty
if (offsets[i] == pos)
{
res[i] = s;
res[i] = aggregate_value;
continue;
}
size_t count = 1;
s = data[pos]; // NOLINT
aggregate_value = data[pos]; // NOLINT
++pos;
for (; pos < offsets[i]; ++pos)
@ -226,20 +264,36 @@ struct ArrayAggregateImpl
if constexpr (aggregate_operation == AggregateOperation::sum ||
aggregate_operation == AggregateOperation::average)
{
s += element;
aggregate_value += element;
}
else if constexpr (aggregate_operation == AggregateOperation::min)
{
if (element < s)
if (element < aggregate_value)
{
s = element;
aggregate_value = element;
}
}
else if constexpr (aggregate_operation == AggregateOperation::max)
{
if (element > s)
if (element > aggregate_value)
{
s = element;
aggregate_value = element;
}
}
else if constexpr (aggregate_operation == AggregateOperation::product)
{
if constexpr (IsDecimalNumber<Element>)
{
using AggregateValueDecimalUnderlyingValue = decltype(aggregate_value.value);
AggregateValueDecimalUnderlyingValue current_aggregate_value = aggregate_value.value;
AggregateValueDecimalUnderlyingValue element_value = static_cast<AggregateValueDecimalUnderlyingValue>(element.value);
if (common::mulOverflow(current_aggregate_value, element_value, aggregate_value.value))
throw Exception(ErrorCodes::DECIMAL_OVERFLOW, "Decimal math overflow");
}
else
{
aggregate_value *= element;
}
}
@ -250,17 +304,26 @@ struct ArrayAggregateImpl
{
if constexpr (IsDecimalNumber<Element>)
{
s = s / count;
res[i] = DecimalUtils::convertTo<Result>(s, data.getScale());
aggregate_value = aggregate_value / count;
res[i] = DecimalUtils::convertTo<ResultType>(aggregate_value, data.getScale());
}
else
{
res[i] = static_cast<Result>(s) / count;
res[i] = static_cast<ResultType>(aggregate_value) / count;
}
}
else if constexpr (aggregate_operation == AggregateOperation::product && IsDecimalNumber<Element>)
{
auto result_scale = data.getScale() * count;
if (unlikely(result_scale > DecimalUtils::max_precision<AggregationType>))
throw Exception(ErrorCodes::ARGUMENT_OUT_OF_BOUND, "Scale {} is out of bounds", result_scale);
res[i] = DecimalUtils::convertTo<ResultType>(aggregate_value, result_scale);
}
else
{
res[i] = s;
res[i] = aggregate_value;
}
}
@ -291,7 +354,7 @@ struct ArrayAggregateImpl
executeType<Decimal128>(mapped, offsets, res))
return res;
else
throw Exception("Unexpected column for arraySum: " + mapped->getName(), ErrorCodes::ILLEGAL_COLUMN);
throw Exception(ErrorCodes::ILLEGAL_COLUMN, "Unexpected column for arraySum: {}" + mapped->getName());
}
};
@ -307,12 +370,16 @@ using FunctionArraySum = FunctionArrayMapped<ArrayAggregateImpl<AggregateOperati
struct NameArrayAverage { static constexpr auto name = "arrayAvg"; };
using FunctionArrayAverage = FunctionArrayMapped<ArrayAggregateImpl<AggregateOperation::average>, NameArrayAverage>;
struct NameArrayProduct { static constexpr auto name = "arrayProduct"; };
using FunctionArrayProduct = FunctionArrayMapped<ArrayAggregateImpl<AggregateOperation::product>, NameArrayProduct>;
void registerFunctionArrayAggregation(FunctionFactory & factory)
{
factory.registerFunction<FunctionArrayMin>();
factory.registerFunction<FunctionArrayMax>();
factory.registerFunction<FunctionArraySum>();
factory.registerFunction<FunctionArrayAverage>();
factory.registerFunction<FunctionArrayProduct>();
}
}

18
tests/queries/0_stateless/01768_array_product.reference Normal file
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@ -0,0 +1,18 @@
Array product with constant column
720 Float64
24 Float64
3.5 Float64
6 Float64
Array product with non constant column
24
0
6
24
0
6
Types of aggregation result array product
Float64 Float64 Float64 Float64
Float64 Float64 Float64 Float64
Float64 Float64 Float64
Float64 Float64
Float64 Float64 Float64

26
tests/queries/0_stateless/01768_array_product.sql Normal file
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@ -0,0 +1,26 @@
SELECT 'Array product with constant column';
SELECT arrayProduct([1,2,3,4,5,6]) as a, toTypeName(a);
SELECT arrayProduct(array(1.0,2.0,3.0,4.0)) as a, toTypeName(a);
SELECT arrayProduct(array(1,3.5)) as a, toTypeName(a);
SELECT arrayProduct([toDecimal64(1,8), toDecimal64(2,8), toDecimal64(3,8)]) as a, toTypeName(a);
SELECT 'Array product with non constant column';
DROP TABLE IF EXISTS test_aggregation;
CREATE TABLE test_aggregation (x Array(Int)) ENGINE=TinyLog;
INSERT INTO test_aggregation VALUES ([1,2,3,4]), ([]), ([1,2,3]);
SELECT arrayProduct(x) FROM test_aggregation;
DROP TABLE test_aggregation;
CREATE TABLE test_aggregation (x Array(Decimal64(8))) ENGINE=TinyLog;
INSERT INTO test_aggregation VALUES ([1,2,3,4]), ([]), ([1,2,3]);
SELECT arrayProduct(x) FROM test_aggregation;
DROP TABLE test_aggregation;
SELECT 'Types of aggregation result array product';
SELECT toTypeName(arrayProduct([toInt8(0)])), toTypeName(arrayProduct([toInt16(0)])), toTypeName(arrayProduct([toInt32(0)])), toTypeName(arrayProduct([toInt64(0)]));
SELECT toTypeName(arrayProduct([toUInt8(0)])), toTypeName(arrayProduct([toUInt16(0)])), toTypeName(arrayProduct([toUInt32(0)])), toTypeName(arrayProduct([toUInt64(0)]));
SELECT toTypeName(arrayProduct([toInt128(0)])), toTypeName(arrayProduct([toInt256(0)])), toTypeName(arrayProduct([toUInt256(0)]));
SELECT toTypeName(arrayProduct([toFloat32(0)])), toTypeName(arrayProduct([toFloat64(0)]));
SELECT toTypeName(arrayProduct([toDecimal32(0, 8)])), toTypeName(arrayProduct([toDecimal64(0, 8)])), toTypeName(arrayProduct([toDecimal128(0, 8)]));