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apache / doris / refs/heads/hello-stephen-patch-8 / . / be / src / vec / functions / function_cast.h
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// 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.
// This file is copied from
// https://github.com/ClickHouse/ClickHouse/blob/master/src/Functions/FunctionsConversion.h
// and modified by Doris
#pragma once
#include <cctz/time_zone.h>
#include <fmt/format.h>
#include <gen_cpp/FrontendService_types.h>
#include <glog/logging.h>
#include <stddef.h>
#include <stdint.h>
#include <algorithm>
#include <atomic>
#include <boost/iterator/iterator_facade.hpp>
#include <cmath>
#include <cstdint>
#include <functional>
#include <iterator>
#include <limits>
#include <memory>
#include <ostream>
#include <string>
#include <type_traits>
#include <utility>
#include <vector>
#include "common/compiler_util.h" // IWYU pragma: keep
#include "common/status.h"
#include "runtime/runtime_state.h"
#include "runtime/type_limit.h"
#include "udf/udf.h"
#include "util/jsonb_document.h"
#include "util/jsonb_stream.h"
#include "util/jsonb_writer.h"
#include "vec/aggregate_functions/aggregate_function.h"
#include "vec/columns/column.h"
#include "vec/columns/column_array.h"
#include "vec/columns/column_map.h"
#include "vec/columns/column_nullable.h"
#include "vec/columns/column_object.h"
#include "vec/columns/column_string.h"
#include "vec/columns/column_struct.h"
#include "vec/columns/column_vector.h"
#include "vec/columns/columns_common.h"
#include "vec/columns/columns_number.h"
#include "vec/common/assert_cast.h"
#include "vec/common/string_buffer.hpp"
#include "vec/common/string_ref.h"
#include "vec/core/block.h"
#include "vec/core/call_on_type_index.h"
#include "vec/core/column_numbers.h"
#include "vec/core/column_with_type_and_name.h"
#include "vec/core/columns_with_type_and_name.h"
#include "vec/core/field.h"
#include "vec/core/types.h"
#include "vec/data_types/data_type.h"
#include "vec/data_types/data_type_array.h"
#include "vec/data_types/data_type_bitmap.h"
#include "vec/data_types/data_type_date.h"
#include "vec/data_types/data_type_date_time.h"
#include "vec/data_types/data_type_decimal.h"
#include "vec/data_types/data_type_hll.h"
#include "vec/data_types/data_type_ipv4.h"
#include "vec/data_types/data_type_ipv6.h"
#include "vec/data_types/data_type_jsonb.h"
#include "vec/data_types/data_type_map.h"
#include "vec/data_types/data_type_nullable.h"
#include "vec/data_types/data_type_number.h"
#include "vec/data_types/data_type_object.h"
#include "vec/data_types/data_type_string.h"
#include "vec/data_types/data_type_struct.h"
#include "vec/data_types/data_type_time.h"
#include "vec/data_types/data_type_time_v2.h"
#include "vec/data_types/serde/data_type_serde.h"
#include "vec/functions/function.h"
#include "vec/functions/function_convert_tz.h"
#include "vec/functions/function_helpers.h"
#include "vec/io/reader_buffer.h"
#include "vec/runtime/vdatetime_value.h"
#include "vec/utils/util.hpp"
class DateLUTImpl;
namespace doris {
namespace vectorized {
template <typename T>
class ColumnDecimal;
} // namespace vectorized
} // namespace doris
namespace doris::vectorized {
/** Type conversion functions.
* toType - conversion in "natural way";
*/
inline UInt32 extract_to_decimal_scale(const ColumnWithTypeAndName& named_column) {
const auto* arg_type = named_column.type.get();
bool ok = check_and_get_data_type<DataTypeUInt64>(arg_type) ||
check_and_get_data_type<DataTypeUInt32>(arg_type) ||
check_and_get_data_type<DataTypeUInt16>(arg_type) ||
check_and_get_data_type<DataTypeUInt8>(arg_type);
if (!ok) {
throw doris::Exception(ErrorCode::INVALID_ARGUMENT, "Illegal type of toDecimal() scale {}",
named_column.type->get_name());
}
Field field;
named_column.column->get(0, field);
return field.get<UInt32>();
}
struct PrecisionScaleArg {
UInt32 precision;
UInt32 scale;
};
/** Cast from string or number to Time.
* In Doris, the underlying storage type of the Time class is Float64.
*/
struct TimeCast {
// Cast from string
// Some examples of conversions.
// '300' -> 00:03:00 '20:23' -> 20:23:00 '20:23:24' -> 20:23:24
template <typename T>
static bool try_parse_time(char* s, size_t len, T& x, const cctz::time_zone& local_time_zone) {
/// TODO: Maybe we can move Timecast to the io_helper.
if (try_as_time(s, len, x, local_time_zone)) {
return true;
} else {
if (VecDateTimeValue dv {}; dv.from_date_str(s, len, local_time_zone)) {
// can be parse as a datetime
x = dv.hour() * 3600 + dv.minute() * 60 + dv.second();
return true;
}
return false;
}
}
template <typename T>
static bool try_as_time(char* s, size_t len, T& x, const cctz::time_zone& local_time_zone) {
char* first_char = s;
char* end_char = s + len;
int hour = 0, minute = 0, second = 0;
auto parse_from_str_to_int = [](char* begin, size_t len, auto& num) {
StringParser::ParseResult parse_result = StringParser::PARSE_SUCCESS;
auto int_value = StringParser::string_to_unsigned_int<uint64_t>(
reinterpret_cast<char*>(begin), len, &parse_result);
if (UNLIKELY(parse_result != StringParser::PARSE_SUCCESS)) {
return false;
}
num = int_value;
return true;
};
if (char* first_colon {nullptr};
(first_colon = (char*)memchr(first_char, ':', len)) != nullptr) {
if (char* second_colon {nullptr};
(second_colon = (char*)memchr(first_colon + 1, ':', end_char - first_colon - 1)) !=
nullptr) {
// find two colon
// parse hour
if (!parse_from_str_to_int(first_char, first_colon - first_char, hour)) {
// hour failed
return false;
}
// parse minute
if (!parse_from_str_to_int(first_colon + 1, second_colon - first_colon - 1,
minute)) {
return false;
}
// parse second
if (!parse_from_str_to_int(second_colon + 1, end_char - second_colon - 1, second)) {
return false;
}
} else {
// find one colon
// parse hour
if (!parse_from_str_to_int(first_char, first_colon - first_char, hour)) {
return false;
}
// parse minute
if (!parse_from_str_to_int(first_colon + 1, end_char - first_colon - 1, minute)) {
return false;
}
}
} else {
// no colon ,so try to parse as a number
size_t from {};
if (!parse_from_str_to_int(first_char, len, from)) {
return false;
}
return try_parse_time(from, x, local_time_zone);
}
// minute second must be < 60
if (minute >= 60 || second >= 60) {
return false;
}
x = hour * 3600 + minute * 60 + second;
return true;
}
// Cast from number
template <typename T, typename S>
//requires {std::is_arithmetic_v<T> && std::is_arithmetic_v<S>}
static bool try_parse_time(T from, S& x, const cctz::time_zone& local_time_zone) {
int64 seconds = int64(from / 100);
int64 hour = 0, minute = 0, second = 0;
second = int64(from - 100 * seconds);
from /= 100;
seconds = int64(from / 100);
minute = int64(from - 100 * seconds);
hour = seconds;
if (minute >= 60 || second >= 60) {
return false;
}
x = hour * 3600 + minute * 60 + second;
return true;
}
template <typename S>
static bool try_parse_time(__int128 from, S& x, const cctz::time_zone& local_time_zone) {
from %= (int64)(1000000000000);
int64 seconds = from / 100;
int64 hour = 0, minute = 0, second = 0;
second = from - 100 * seconds;
from /= 100;
seconds = from / 100;
minute = from - 100 * seconds;
hour = seconds;
if (minute >= 60 || second >= 60) {
return false;
}
x = hour * 3600 + minute * 60 + second;
return true;
}
};
/** Conversion of number types to each other, enums to numbers, dates and datetimes to numbers and back: done by straight assignment.
* (Date is represented internally as number of days from some day; DateTime - as unix timestamp)
*/
template <typename FromDataType, typename ToDataType, typename Name>
struct ConvertImpl {
using FromFieldType = typename FromDataType::FieldType;
using ToFieldType = typename ToDataType::FieldType;
// `static_cast_set` is introduced to wrap `static_cast` and handle special cases.
// Doris uses `uint8` to represent boolean values internally.
// Directly `static_cast` to `uint8` can result in non-0/1 values,
// To address this, `static_cast_set` performs an additional check:
// For `uint8` types, it explicitly uses `static_cast<bool>` to ensure
// the result is either 0 or 1.
static void static_cast_set(ToFieldType& to, const FromFieldType& from) {
// uint8_t now use as boolean in doris
if constexpr (std::is_same_v<uint8_t, ToFieldType>) {
to = static_cast<bool>(from);
} else {
to = static_cast<ToFieldType>(from);
}
}
template <typename Additions = void*>
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
size_t result, size_t input_rows_count,
Additions additions = Additions()) {
const ColumnWithTypeAndName& named_from = block.get_by_position(arguments[0]);
using ColVecFrom =
std::conditional_t<IsDecimalNumber<FromFieldType>, ColumnDecimal<FromFieldType>,
ColumnVector<FromFieldType>>;
using ColVecTo = std::conditional_t<IsDecimalNumber<ToFieldType>,
ColumnDecimal<ToFieldType>, ColumnVector<ToFieldType>>;
if constexpr (IsDataTypeDecimal<FromDataType> || IsDataTypeDecimal<ToDataType>) {
if constexpr (!(IsDataTypeDecimalOrNumber<FromDataType> || IsTimeType<FromDataType> ||
IsTimeV2Type<FromDataType>) ||
!IsDataTypeDecimalOrNumber<ToDataType>)
return Status::RuntimeError("Illegal column {} of first argument of function {}",
named_from.column->get_name(), Name::name);
}
if (const ColVecFrom* col_from =
check_and_get_column<ColVecFrom>(named_from.column.get())) {
typename ColVecTo::MutablePtr col_to = nullptr;
UInt32 from_precision = NumberTraits::max_ascii_len<FromFieldType>();
UInt32 from_scale = 0;
if constexpr (IsDataTypeDecimal<FromDataType>) {
const auto& from_decimal_type = assert_cast<const FromDataType&>(*named_from.type);
from_precision = from_decimal_type.get_precision();
from_scale = from_decimal_type.get_scale();
}
UInt32 to_max_digits = 0;
UInt32 to_precision = 0;
UInt32 to_scale = 0;
ToFieldType max_result {0};
ToFieldType min_result {0};
if constexpr (IsDataTypeDecimal<ToDataType>) {
to_max_digits = NumberTraits::max_ascii_len<typename ToFieldType::NativeType>();
to_precision = ((PrecisionScaleArg)additions).precision;
ToDataType::check_type_precision(to_precision);
to_scale = ((PrecisionScaleArg)additions).scale;
ToDataType::check_type_scale(to_scale);
col_to = ColVecTo::create(0, to_scale);
max_result = ToDataType::get_max_digits_number(to_precision);
min_result = -max_result;
} else {
col_to = ColVecTo::create();
}
if constexpr (IsDataTypeNumber<ToDataType>) {
max_result = type_limit<ToFieldType>::max();
min_result = type_limit<ToFieldType>::min();
}
if constexpr (std::is_integral_v<ToFieldType>) {
to_max_digits = NumberTraits::max_ascii_len<ToFieldType>();
to_precision = to_max_digits;
}
const auto& vec_from = col_from->get_data();
auto& vec_to = col_to->get_data();
size_t size = vec_from.size();
vec_to.resize(size);
if constexpr (IsDataTypeDecimal<FromDataType> || IsDataTypeDecimal<ToDataType>) {
// the result is rounded when doing cast, so it may still overflow after rounding
// if destination integer digit count is the same as source integer digit count.
bool narrow_integral = context->check_overflow_for_decimal() &&
(to_precision - to_scale) <= (from_precision - from_scale);
bool multiply_may_overflow = context->check_overflow_for_decimal();
if (to_scale > from_scale) {
multiply_may_overflow &=
(from_precision + to_scale - from_scale) >= to_max_digits;
}
std::visit(
[&](auto multiply_may_overflow, auto narrow_integral) {
if constexpr (IsDataTypeDecimal<FromDataType> &&
IsDataTypeDecimal<ToDataType>) {
convert_decimal_cols<FromDataType, ToDataType,
multiply_may_overflow, narrow_integral>(
vec_from.data(), vec_to.data(), from_precision,
vec_from.get_scale(), to_precision, vec_to.get_scale(),
vec_from.size());
} else if constexpr (IsDataTypeDecimal<FromDataType>) {
convert_from_decimal<FromDataType, ToDataType, narrow_integral>(
vec_to.data(), vec_from.data(), from_precision,
vec_from.get_scale(), min_result, max_result, size);
} else {
convert_to_decimal<FromDataType, ToDataType, multiply_may_overflow,
narrow_integral>(
vec_to.data(), vec_from.data(), from_scale, to_precision,
to_scale, min_result, max_result, size);
}
},
make_bool_variant(multiply_may_overflow),
make_bool_variant(narrow_integral));
block.replace_by_position(result, std::move(col_to));
return Status::OK();
} else if constexpr (IsTimeType<FromDataType>) {
for (size_t i = 0; i < size; ++i) {
if constexpr (IsTimeType<ToDataType>) {
vec_to[i] = static_cast<ToFieldType>(vec_from[i]);
if constexpr (IsDateTimeType<ToDataType>) {
DataTypeDateTime::cast_to_date_time(vec_to[i]);
} else {
DataTypeDate::cast_to_date(vec_to[i]);
}
} else if constexpr (IsDateV2Type<ToDataType>) {
DataTypeDateV2::cast_from_date(vec_from[i], vec_to[i]);
} else if constexpr (IsDateTimeV2Type<ToDataType>) {
DataTypeDateTimeV2::cast_from_date(vec_from[i], vec_to[i]);
} else {
static_cast_set(
vec_to[i],
reinterpret_cast<const VecDateTimeValue&>(vec_from[i]).to_int64());
}
}
} else if constexpr (IsTimeV2Type<FromDataType>) {
for (size_t i = 0; i < size; ++i) {
if constexpr (IsTimeV2Type<ToDataType>) {
if constexpr (IsDateTimeV2Type<ToDataType> && IsDateV2Type<FromDataType>) {
DataTypeDateV2::cast_to_date_time_v2(vec_from[i], vec_to[i]);
} else if constexpr (IsDateTimeV2Type<FromDataType> &&
IsDateV2Type<ToDataType>) {
DataTypeDateTimeV2::cast_to_date_v2(vec_from[i], vec_to[i]);
} else {
UInt32 scale = additions;
vec_to[i] = ToFieldType(vec_from[i] / std::pow(10, 6 - scale));
}
} else if constexpr (IsTimeType<ToDataType>) {
if constexpr (IsDateTimeType<ToDataType> && IsDateV2Type<FromDataType>) {
DataTypeDateV2::cast_to_date_time(vec_from[i], vec_to[i]);
} else if constexpr (IsDateType<ToDataType> && IsDateV2Type<FromDataType>) {
DataTypeDateV2::cast_to_date(vec_from[i], vec_to[i]);
} else if constexpr (IsDateTimeType<ToDataType> &&
IsDateTimeV2Type<FromDataType>) {
DataTypeDateTimeV2::cast_to_date_time(vec_from[i], vec_to[i]);
} else if constexpr (IsDateType<ToDataType> &&
IsDateTimeV2Type<FromDataType>) {
DataTypeDateTimeV2::cast_to_date(vec_from[i], vec_to[i]);
} else {
return Status::InvalidArgument("Wrong cast expression!");
}
} else {
if constexpr (IsDateTimeV2Type<FromDataType>) {
static_cast_set(
vec_to[i],
reinterpret_cast<const DateV2Value<DateTimeV2ValueType>&>(
vec_from[i])
.to_int64());
} else {
static_cast_set(vec_to[i],
reinterpret_cast<const DateV2Value<DateV2ValueType>&>(
vec_from[i])
.to_int64());
}
}
}
} else {
if constexpr (IsDataTypeNumber<FromDataType> &&
std::is_same_v<ToDataType, DataTypeTimeV2>) {
// 300 -> 00:03:00 360 will be parse failed , so value maybe null
ColumnUInt8::MutablePtr col_null_map_to;
ColumnUInt8::Container* vec_null_map_to = nullptr;
col_null_map_to = ColumnUInt8::create(size, 0);
vec_null_map_to = &col_null_map_to->get_data();
for (size_t i = 0; i < size; ++i) {
(*vec_null_map_to)[i] = !TimeCast::try_parse_time(
vec_from[i], vec_to[i], context->state()->timezone_obj());
vec_to[i] *= (1000 * 1000);
}
block.get_by_position(result).column =
ColumnNullable::create(std::move(col_to), std::move(col_null_map_to));
return Status::OK();
} else if constexpr ((std::is_same_v<FromDataType, DataTypeIPv4>)&&(
std::is_same_v<ToDataType, DataTypeIPv6>)) {
for (size_t i = 0; i < size; ++i) {
map_ipv4_to_ipv6(vec_from[i], reinterpret_cast<UInt8*>(&vec_to[i]));
}
} else {
for (size_t i = 0; i < size; ++i) {
static_cast_set(vec_to[i], vec_from[i]);
}
}
}
block.replace_by_position(result, std::move(col_to));
} else {
return Status::RuntimeError("Illegal column {} of first argument of function {}",
named_from.column->get_name(), Name::name);
}
return Status::OK();
}
private:
static void map_ipv4_to_ipv6(IPv4 ipv4, UInt8* buf) {
unaligned_store<UInt64>(buf, 0x0000FFFF00000000ULL | static_cast<UInt64>(ipv4));
unaligned_store<UInt64>(buf + 8, 0);
}
};
/** If types are identical, just take reference to column.
*/
template <typename T, typename Name>
requires(!T::is_parametric)
struct ConvertImpl<T, T, Name> {
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
size_t result, size_t /*input_rows_count*/) {
block.get_by_position(result).column = block.get_by_position(arguments[0]).column;
return Status::OK();
}
};
// using other type cast to Date/DateTime, unless String
// Date/DateTime
template <typename FromDataType, typename ToDataType, typename Name>
struct ConvertImplToTimeType {
using FromFieldType = typename FromDataType::FieldType;
using ToFieldType = typename ToDataType::FieldType;
static Status execute(Block& block, const ColumnNumbers& arguments, size_t result,
size_t /*input_rows_count*/) {
const ColumnWithTypeAndName& named_from = block.get_by_position(arguments[0]);
using ColVecFrom =
std::conditional_t<IsDecimalNumber<FromFieldType>, ColumnDecimal<FromFieldType>,
ColumnVector<FromFieldType>>;
using DateValueType = std::conditional_t<
IsTimeV2Type<ToDataType>,
std::conditional_t<IsDateV2Type<ToDataType>, DateV2Value<DateV2ValueType>,
DateV2Value<DateTimeV2ValueType>>,
VecDateTimeValue>;
using ColVecTo = ColumnVector<ToFieldType>;
if (const ColVecFrom* col_from =
check_and_get_column<ColVecFrom>(named_from.column.get())) {
const auto& vec_from = col_from->get_data();
size_t size = vec_from.size();
// create nested column
auto col_to = ColVecTo::create(size);
auto& vec_to = col_to->get_data();
// create null column
ColumnUInt8::MutablePtr col_null_map_to;
col_null_map_to = ColumnUInt8::create(size, 0);
auto& vec_null_map_to = col_null_map_to->get_data();
UInt32 from_precision = 0;
UInt32 from_scale = 0;
UInt32 to_precision = NumberTraits::max_ascii_len<Int64>();
if constexpr (IsDecimalNumber<FromFieldType>) {
const auto& from_decimal_type = assert_cast<const FromDataType&>(*named_from.type);
from_precision = from_decimal_type.get_precision();
from_scale = from_decimal_type.get_scale();
}
bool narrow_integral = to_precision < (from_precision - from_scale);
std::visit(
[&](auto narrow_integral) {
for (size_t i = 0; i < size; ++i) {
auto& date_value = reinterpret_cast<DateValueType&>(vec_to[i]);
vec_null_map_to[i] = !date_value.from_date_int64(int64_t(vec_from[i]));
// DateType of VecDateTimeValue should cast to date
if constexpr (IsDateType<ToDataType>) {
date_value.cast_to_date();
} else if constexpr (IsDateTimeType<ToDataType>) {
date_value.to_datetime();
}
}
},
make_bool_variant(narrow_integral));
block.get_by_position(result).column =
ColumnNullable::create(std::move(col_to), std::move(col_null_map_to));
} else {
return Status::RuntimeError("Illegal column {} of first argument of function {}",
named_from.column->get_name(), Name::name);
}
return Status::OK();
}
};
// Generic conversion of any type to String.
struct ConvertImplGenericToString {
static Status execute(Block& block, const ColumnNumbers& arguments, size_t result) {
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
const IDataType& type = *col_with_type_and_name.type;
const IColumn& col_from = *col_with_type_and_name.column;
auto col_to = ColumnString::create();
type.to_string_batch(col_from, *col_to);
block.replace_by_position(result, std::move(col_to));
return Status::OK();
}
static Status execute2(FunctionContext* /*ctx*/, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t /*input_rows_count*/) {
return execute(block, arguments, result);
}
};
//this is for data in compound type
struct ConvertImplGenericFromString {
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) {
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
const IColumn& col_from = *col_with_type_and_name.column;
// result column must set type
DCHECK(block.get_by_position(result).type != nullptr);
auto data_type_to = block.get_by_position(result).type;
if (const ColumnString* col_from_string = check_and_get_column<ColumnString>(&col_from)) {
auto col_to = data_type_to->create_column();
auto serde = data_type_to->get_serde();
size_t size = col_from.size();
col_to->reserve(size);
ColumnUInt8::MutablePtr col_null_map_to = ColumnUInt8::create(size, 0);
ColumnUInt8::Container* vec_null_map_to = &col_null_map_to->get_data();
const bool is_complex = is_complex_type(data_type_to);
DataTypeSerDe::FormatOptions format_options;
format_options.converted_from_string = true;
format_options.escape_char = '\\';
for (size_t i = 0; i < size; ++i) {
const auto& val = col_from_string->get_data_at(i);
// Note: here we should handle the null element
if (val.size == 0) {
col_to->insert_default();
// empty string('') is an invalid format for complex type, set null_map to 1
if (is_complex) {
(*vec_null_map_to)[i] = 1;
}
continue;
}
Slice string_slice(val.data, val.size);
Status st = serde->deserialize_one_cell_from_json(*col_to, string_slice,
format_options);
// if parsing failed, will return null
(*vec_null_map_to)[i] = !st.ok();
if (!st.ok()) {
col_to->insert_default();
}
}
block.get_by_position(result).column =
ColumnNullable::create(std::move(col_to), std::move(col_null_map_to));
} else {
return Status::RuntimeError(
"Illegal column {} of first argument of conversion function from string",
col_from.get_name());
}
return Status::OK();
}
};
// Generic conversion of number to jsonb.
template <typename ColumnType>
struct ConvertImplNumberToJsonb {
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) {
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
auto column_string = ColumnString::create();
JsonbWriter writer;
const auto* col =
check_and_get_column<const ColumnType>(col_with_type_and_name.column.get());
const auto& data = col->get_data();
for (size_t i = 0; i < input_rows_count; i++) {
writer.reset();
if constexpr (std::is_same_v<ColumnUInt8, ColumnType>) {
writer.writeBool(data[i]);
} else if constexpr (std::is_same_v<ColumnInt8, ColumnType>) {
writer.writeInt8(data[i]);
} else if constexpr (std::is_same_v<ColumnInt16, ColumnType>) {
writer.writeInt16(data[i]);
} else if constexpr (std::is_same_v<ColumnInt32, ColumnType>) {
writer.writeInt32(data[i]);
} else if constexpr (std::is_same_v<ColumnInt64, ColumnType>) {
writer.writeInt64(data[i]);
} else if constexpr (std::is_same_v<ColumnInt128, ColumnType>) {
writer.writeInt128(data[i]);
} else if constexpr (std::is_same_v<ColumnFloat64, ColumnType>) {
writer.writeDouble(data[i]);
} else {
LOG(FATAL) << "unsupported type ";
__builtin_unreachable();
}
column_string->insert_data(writer.getOutput()->getBuffer(),
writer.getOutput()->getSize());
}
block.replace_by_position(result, std::move(column_string));
return Status::OK();
}
};
struct ConvertImplStringToJsonbAsJsonbString {
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) {
auto data_type_to = block.get_by_position(result).type;
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
const IColumn& col_from = *col_with_type_and_name.column;
auto dst = ColumnString::create();
ColumnString* dst_str = assert_cast<ColumnString*>(dst.get());
const auto* from_string = assert_cast<const ColumnString*>(&col_from);
JsonbWriter writer;
if (from_string->size() < input_rows_count) {
return Status::RuntimeError(
"Illegal column {} of first argument of conversion function",
col_from.get_name());
}
for (size_t i = 0; i < input_rows_count; i++) {
auto str_ref = from_string->get_data_at(i);
writer.reset();
// write raw string to jsonb
writer.writeStartString();
writer.writeString(str_ref.data, str_ref.size);
writer.writeEndString();
dst_str->insert_data(writer.getOutput()->getBuffer(), writer.getOutput()->getSize());
}
block.replace_by_position(result, std::move(dst));
return Status::OK();
}
};
struct ConvertImplGenericFromJsonb {
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) {
auto data_type_to = block.get_by_position(result).type;
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
const IColumn& col_from = *col_with_type_and_name.column;
if (const ColumnString* col_from_string = check_and_get_column<ColumnString>(&col_from)) {
auto col_to = data_type_to->create_column();
size_t size = col_from.size();
col_to->reserve(size);
ColumnUInt8::MutablePtr col_null_map_to = ColumnUInt8::create(size, 0);
ColumnUInt8::Container* vec_null_map_to = &col_null_map_to->get_data();
const bool is_complex = is_complex_type(data_type_to);
const bool is_dst_string = is_string_or_fixed_string(data_type_to);
for (size_t i = 0; i < size; ++i) {
const auto& val = col_from_string->get_data_at(i);
JsonbDocument* doc = nullptr;
auto st = JsonbDocument::checkAndCreateDocument(val.data, val.size, &doc);
if (!st.ok() || !doc || !doc->getValue()) [[unlikely]] {
(*vec_null_map_to)[i] = 1;
col_to->insert_default();
continue;
}
// value is NOT necessary to be deleted since JsonbValue will not allocate memory
JsonbValue* value = doc->getValue();
if (UNLIKELY(!value)) {
(*vec_null_map_to)[i] = 1;
col_to->insert_default();
continue;
}
// Note: here we should handle the null element
if (val.size == 0) {
col_to->insert_default();
// empty string('') is an invalid format for complex type, set null_map to 1
if (is_complex) {
(*vec_null_map_to)[i] = 1;
}
continue;
}
// add string to string column
if (context->jsonb_string_as_string() && is_dst_string && value->isString()) {
const auto* blob = static_cast<const JsonbBlobVal*>(value);
assert_cast<ColumnString&, TypeCheckOnRelease::DISABLE>(*col_to).insert_data(
blob->getBlob(), blob->getBlobLen());
(*vec_null_map_to)[i] = 0;
continue;
}
std::string input_str;
if (context->jsonb_string_as_string() && value->isString()) {
const auto* blob = static_cast<const JsonbBlobVal*>(value);
input_str = std::string(blob->getBlob(), blob->getBlobLen());
} else {
input_str = JsonbToJson::jsonb_to_json_string(val.data, val.size);
}
if (input_str.empty()) {
col_to->insert_default();
(*vec_null_map_to)[i] = 1;
continue;
}
ReadBuffer read_buffer((char*)(input_str.data()), input_str.size());
st = data_type_to->from_string(read_buffer, col_to);
// if parsing failed, will return null
(*vec_null_map_to)[i] = !st.ok();
if (!st.ok()) {
col_to->insert_default();
}
}
block.get_by_position(result).column =
ColumnNullable::create(std::move(col_to), std::move(col_null_map_to));
} else {
return Status::RuntimeError(
"Illegal column {} of first argument of conversion function from string",
col_from.get_name());
}
return Status::OK();
}
};
// Generic conversion of any type to jsonb.
struct ConvertImplGenericToJsonb {
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) {
auto data_type_to = block.get_by_position(result).type;
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
const IDataType& type = *col_with_type_and_name.type;
const IColumn& col_from = *col_with_type_and_name.column;
auto column_string = ColumnString::create();
JsonbWriter writer;
ColumnUInt8::MutablePtr col_null_map_to = ColumnUInt8::create(col_from.size(), 0);
ColumnUInt8::Container* vec_null_map_to = &col_null_map_to->get_data();
DataTypeSerDe::FormatOptions format_options;
format_options.converted_from_string = true;
DataTypeSerDeSPtr from_serde = type.get_serde();
DataTypeSerDeSPtr to_serde = data_type_to->get_serde();
auto col_to = data_type_to->create_column();
auto tmp_col = ColumnString::create();
vectorized::DataTypeSerDe::FormatOptions options;
options.escape_char = '\\';
for (size_t i = 0; i < input_rows_count; i++) {
// convert to string
tmp_col->clear();
VectorBufferWriter write_buffer(*tmp_col.get());
Status st =
from_serde->serialize_column_to_json(col_from, i, i + 1, write_buffer, options);
// if serialized failed, will return null
(*vec_null_map_to)[i] = !st.ok();
if (!st.ok()) {
col_to->insert_default();
continue;
}
write_buffer.commit();
writer.reset();
auto str_ref = tmp_col->get_data_at(0);
Slice data((char*)(str_ref.data), str_ref.size);
// first try to parse string
st = to_serde->deserialize_one_cell_from_json(*col_to, data, format_options);
// if parsing failed, will return null
(*vec_null_map_to)[i] = !st.ok();
if (!st.ok()) {
col_to->insert_default();
}
}
block.replace_by_position(
result, ColumnNullable::create(std::move(col_to), std::move(col_null_map_to)));
return Status::OK();
}
};
struct ConvertNothingToJsonb {
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) {
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
const IColumn& col_from = *col_with_type_and_name.column;
auto data_type_to = block.get_by_position(result).type;
size_t size = col_from.size();
auto col_to = data_type_to->create_column_const_with_default_value(size);
ColumnUInt8::MutablePtr col_null_map_to = ColumnUInt8::create(size, 1);
block.replace_by_position(result, ColumnNullable::create(col_to->assume_mutable(),
std::move(col_null_map_to)));
return Status::OK();
}
};
template <TypeIndex type_index, typename ColumnType, typename ToDataType>
struct ConvertImplFromJsonb {
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) {
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
const IColumn& col_from = *col_with_type_and_name.column;
// result column must set type
DCHECK(block.get_by_position(result).type != nullptr);
auto data_type_to = block.get_by_position(result).type;
if (const ColumnString* column_string = check_and_get_column<ColumnString>(&col_from)) {
auto null_map_col = ColumnUInt8::create(input_rows_count, 0);
auto& null_map = null_map_col->get_data();
auto col_to = ColumnType::create();
col_to->reserve(input_rows_count);
auto& res = col_to->get_data();
res.resize(input_rows_count);
for (size_t i = 0; i < input_rows_count; ++i) {
const auto& val = column_string->get_data_at(i);
if (val.size == 0) {
null_map[i] = 1;
res[i] = 0;
continue;
}
// doc is NOT necessary to be deleted since JsonbDocument will not allocate memory
JsonbDocument* doc = nullptr;
auto st = JsonbDocument::checkAndCreateDocument(val.data, val.size, &doc);
if (!st.ok() || !doc || !doc->getValue()) [[unlikely]] {
null_map[i] = 1;
res[i] = 0;
continue;
}
// value is NOT necessary to be deleted since JsonbValue will not allocate memory
JsonbValue* value = doc->getValue();
if (UNLIKELY(!value)) {
null_map[i] = 1;
res[i] = 0;
continue;
}
if (value->isString()) {
// convert by parse
const auto& data = static_cast<const JsonbBlobVal*>(value)->getBlob();
size_t len = static_cast<const JsonbBlobVal*>(value)->getBlobLen();
ReadBuffer rb((char*)(data), len);
bool parsed = try_parse_impl<ToDataType>(res[i], rb, context);
null_map[i] = !parsed;
continue;
}
if constexpr (type_index == TypeIndex::UInt8) {
// cast from json value to boolean type
if (value->isTrue()) {
res[i] = 1;
} else if (value->isFalse()) {
res[i] = 0;
} else if (value->isInt()) {
res[i] = ((const JsonbIntVal*)value)->val() == 0 ? 0 : 1;
} else if (value->isDouble()) {
res[i] = static_cast<ColumnType::value_type>(
((const JsonbDoubleVal*)value)->val()) == 0
? 0
: 1;
} else {
null_map[i] = 1;
res[i] = 0;
}
} else if constexpr (type_index == TypeIndex::Int8 ||
type_index == TypeIndex::Int16 ||
type_index == TypeIndex::Int32 ||
type_index == TypeIndex::Int64 ||
type_index == TypeIndex::Int128) {
// cast from json value to integer types
if (value->isInt()) {
res[i] = ((const JsonbIntVal*)value)->val();
} else if (value->isDouble()) {
res[i] = static_cast<ColumnType::value_type>(
((const JsonbDoubleVal*)value)->val());
} else if (value->isTrue()) {
res[i] = 1;
} else if (value->isFalse()) {
res[i] = 0;
} else {
null_map[i] = 1;
res[i] = 0;
}
} else if constexpr (type_index == TypeIndex::Float64 ||
type_index == TypeIndex::Float32) {
// cast from json value to floating point types
if (value->isDouble()) {
res[i] = ((const JsonbDoubleVal*)value)->val();
} else if (value->isFloat()) {
res[i] = ((const JsonbFloatVal*)value)->val();
} else if (value->isTrue()) {
res[i] = 1;
} else if (value->isFalse()) {
res[i] = 0;
} else if (value->isInt()) {
res[i] = ((const JsonbIntVal*)value)->val();
} else {
null_map[i] = 1;
res[i] = 0;
}
} else {
LOG(FATAL) << "unsupported type ";
__builtin_unreachable();
}
}
block.replace_by_position(
result, ColumnNullable::create(std::move(col_to), std::move(null_map_col)));
} else {
return Status::RuntimeError(
"Illegal column {} of first argument of conversion function from string",
col_from.get_name());
}
return Status::OK();
}
};
template <typename ToDataType, typename Name>
struct ConvertImpl<DataTypeString, ToDataType, Name> {
template <typename Additions = void*>
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
size_t result, size_t input_rows_count,
Additions additions [[maybe_unused]] = Additions()) {
return Status::RuntimeError("not support convert from string");
}
};
struct NameToString {
static constexpr auto name = "to_string";
};
struct NameToDecimal32 {
static constexpr auto name = "toDecimal32";
};
struct NameToDecimal64 {
static constexpr auto name = "toDecimal64";
};
struct NameToDecimal128 {
static constexpr auto name = "toDecimal128";
};
struct NameToDecimal128V3 {
static constexpr auto name = "toDecimal128V3";
};
struct NameToDecimal256 {
static constexpr auto name = "toDecimal256";
};
struct NameToUInt8 {
static constexpr auto name = "toUInt8";
};
struct NameToUInt16 {
static constexpr auto name = "toUInt16";
};
struct NameToUInt32 {
static constexpr auto name = "toUInt32";
};
struct NameToUInt64 {
static constexpr auto name = "toUInt64";
};
struct NameToInt8 {
static constexpr auto name = "toInt8";
};
struct NameToInt16 {
static constexpr auto name = "toInt16";
};
struct NameToInt32 {
static constexpr auto name = "toInt32";
};
struct NameToInt64 {
static constexpr auto name = "toInt64";
};
struct NameToInt128 {
static constexpr auto name = "toInt128";
};
struct NameToFloat32 {
static constexpr auto name = "toFloat32";
};
struct NameToFloat64 {
static constexpr auto name = "toFloat64";
};
struct NameToIPv4 {
static constexpr auto name = "toIPv4";
};
struct NameToIPv6 {
static constexpr auto name = "toIPv6";
};
struct NameToDate {
static constexpr auto name = "toDate";
};
struct NameToDateTime {
static constexpr auto name = "toDateTime";
};
template <typename DataType, typename FromDataType = void*>
bool try_parse_impl(typename DataType::FieldType& x, ReadBuffer& rb, FunctionContext* context,
UInt32 scale [[maybe_unused]] = 0) {
if constexpr (IsDateTimeType<DataType>) {
return try_read_datetime_text(x, rb, context->state()->timezone_obj());
}
if constexpr (IsDateType<DataType>) {
return try_read_date_text(x, rb, context->state()->timezone_obj());
}
if constexpr (IsDateV2Type<DataType>) {
return try_read_date_v2_text(x, rb, context->state()->timezone_obj());
}
if constexpr (IsDateTimeV2Type<DataType>) {
return try_read_datetime_v2_text(x, rb, context->state()->timezone_obj(), scale);
}
if constexpr (IsIPv4Type<DataType>) {
return try_read_ipv4_text(x, rb);
}
if constexpr (IsIPv6Type<DataType>) {
return try_read_ipv6_text(x, rb);
}
if constexpr (std::is_same_v<DataTypeString, FromDataType> &&
std::is_same_v<DataTypeTimeV2, DataType>) {
// cast from string to time(float64)
auto len = rb.count();
auto s = rb.position();
rb.position() = rb.end(); // make is_all_read = true
auto ret = TimeCast::try_parse_time(s, len, x, context->state()->timezone_obj());
x *= (1000 * 1000);
return ret;
}
if constexpr (std::is_floating_point_v<typename DataType::FieldType>) {
return try_read_float_text(x, rb);
}
// uint8_t now use as boolean in doris
if constexpr (std::is_same_v<typename DataType::FieldType, uint8_t>) {
return try_read_bool_text(x, rb);
}
if constexpr (std::is_integral_v<typename DataType::FieldType>) {
return try_read_int_text(x, rb);
}
}
template <typename DataType, typename Additions = void*>
StringParser::ParseResult try_parse_decimal_impl(typename DataType::FieldType& x, ReadBuffer& rb,
Additions additions
[[maybe_unused]] = Additions()) {
if constexpr (IsDataTypeDecimalV2<DataType>) {
UInt32 scale = ((PrecisionScaleArg)additions).scale;
UInt32 precision = ((PrecisionScaleArg)additions).precision;
return try_read_decimal_text<TYPE_DECIMALV2>(x, rb, precision, scale);
}
if constexpr (std::is_same_v<DataTypeDecimal<Decimal32>, DataType>) {
UInt32 scale = ((PrecisionScaleArg)additions).scale;
UInt32 precision = ((PrecisionScaleArg)additions).precision;
return try_read_decimal_text<TYPE_DECIMAL32>(x, rb, precision, scale);
}
if constexpr (std::is_same_v<DataTypeDecimal<Decimal64>, DataType>) {
UInt32 scale = ((PrecisionScaleArg)additions).scale;
UInt32 precision = ((PrecisionScaleArg)additions).precision;
return try_read_decimal_text<TYPE_DECIMAL64>(x, rb, precision, scale);
}
if constexpr (IsDataTypeDecimal128V3<DataType>) {
UInt32 scale = ((PrecisionScaleArg)additions).scale;
UInt32 precision = ((PrecisionScaleArg)additions).precision;
return try_read_decimal_text<TYPE_DECIMAL128I>(x, rb, precision, scale);
}
if constexpr (IsDataTypeDecimal256<DataType>) {
UInt32 scale = ((PrecisionScaleArg)additions).scale;
UInt32 precision = ((PrecisionScaleArg)additions).precision;
return try_read_decimal_text<TYPE_DECIMAL256>(x, rb, precision, scale);
}
}
/// Monotonicity.
struct PositiveMonotonicity {
static bool has() { return true; }
static IFunction::Monotonicity get(const IDataType&, const Field&, const Field&) {
return {true};
}
};
struct UnknownMonotonicity {
static bool has() { return false; }
static IFunction::Monotonicity get(const IDataType&, const Field&, const Field&) {
return {false};
}
};
template <typename T>
struct ToNumberMonotonicity {
static bool has() { return true; }
static UInt64 divide_by_range_of_type(UInt64 x) {
if constexpr (sizeof(T) < sizeof(UInt64))
return x >> (sizeof(T) * 8);
else
return 0;
}
static IFunction::Monotonicity get(const IDataType& type, const Field& left,
const Field& right) {
if (!type.is_value_represented_by_number()) return {};
/// If type is same, the conversion is always monotonic.
/// (Enum has separate case, because it is different data type)
if (check_and_get_data_type<DataTypeNumber<T>>(
&type) /*|| check_and_get_data_type<DataTypeEnum<T>>(&type)*/)
return {true, true, true};
/// Float cases.
/// When converting to Float, the conversion is always monotonic.
if (std::is_floating_point_v<T>) return {true, true, true};
/// If converting from Float, for monotonicity, arguments must fit in range of result type.
if (WhichDataType(type).is_float()) {
if (left.is_null() || right.is_null()) return {};
Float64 left_float = left.get<Float64>();
Float64 right_float = right.get<Float64>();
if (left_float >= std::numeric_limits<T>::min() &&
left_float <= static_cast<Float64>(std::numeric_limits<T>::max()) &&
right_float >= std::numeric_limits<T>::min() &&
right_float <= static_cast<Float64>(std::numeric_limits<T>::max()))
return {true};
return {};
}
/// Integer cases.
const bool from_is_unsigned = type.is_value_represented_by_unsigned_integer();
const bool to_is_unsigned = std::is_unsigned_v<T>;
const size_t size_of_from = type.get_size_of_value_in_memory();
const size_t size_of_to = sizeof(T);
const bool left_in_first_half =
left.is_null() ? from_is_unsigned : (left.get<Int64>() >= 0);
const bool right_in_first_half =
right.is_null() ? !from_is_unsigned : (right.get<Int64>() >= 0);
/// Size of type is the same.
if (size_of_from == size_of_to) {
if (from_is_unsigned == to_is_unsigned) return {true, true, true};
if (left_in_first_half == right_in_first_half) return {true};
return {};
}
/// Size of type is expanded.
if (size_of_from < size_of_to) {
if (from_is_unsigned == to_is_unsigned) return {true, true, true};
if (!to_is_unsigned) return {true, true, true};
/// signed -> unsigned. If arguments from the same half, then function is monotonic.
if (left_in_first_half == right_in_first_half) return {true};
return {};
}
/// Size of type is shrinked.
if (size_of_from > size_of_to) {
/// Function cannot be monotonic on unbounded ranges.
if (left.is_null() || right.is_null()) return {};
if (from_is_unsigned == to_is_unsigned) {
/// all bits other than that fits, must be same.
if (divide_by_range_of_type(left.get<UInt64>()) ==
divide_by_range_of_type(right.get<UInt64>()))
return {true};
return {};
} else {
/// When signedness is changed, it's also required for arguments to be from the same half.
/// And they must be in the same half after converting to the result type.
if (left_in_first_half == right_in_first_half &&
(T(left.get<Int64>()) >= 0) == (T(right.get<Int64>()) >= 0) &&
divide_by_range_of_type(left.get<UInt64>()) ==
divide_by_range_of_type(right.get<UInt64>()))
return {true};
return {};
}
}
LOG(FATAL) << "__builtin_unreachable";
__builtin_unreachable();
}
};
/** The monotonicity for the `to_string` function is mainly determined for test purposes.
* It is doubtful that anyone is looking to optimize queries with conditions `std::to_string(CounterID) = 34`.
*/
struct ToStringMonotonicity {
static bool has() { return true; }
static IFunction::Monotonicity get(const IDataType& type, const Field& left,
const Field& right) {
IFunction::Monotonicity positive(true, true);
IFunction::Monotonicity not_monotonic;
if (left.is_null() || right.is_null()) return {};
if (left.get_type() == Field::Types::UInt64 && right.get_type() == Field::Types::UInt64) {
return (left.get<Int64>() == 0 && right.get<Int64>() == 0) ||
(floor(log10(left.get<UInt64>())) ==
floor(log10(right.get<UInt64>())))
? positive
: not_monotonic;
}
if (left.get_type() == Field::Types::Int64 && right.get_type() == Field::Types::Int64) {
return (left.get<Int64>() == 0 && right.get<Int64>() == 0) ||
(left.get<Int64>() > 0 && right.get<Int64>() > 0 &&
floor(log10(left.get<Int64>())) ==
floor(log10(right.get<Int64>())))
? positive
: not_monotonic;
}
return not_monotonic;
}
};
template <typename ToDataType, typename Name, typename MonotonicityImpl>
class FunctionConvert : public IFunction {
public:
using Monotonic = MonotonicityImpl;
static constexpr auto name = Name::name;
static FunctionPtr create() { return std::make_shared<FunctionConvert>(); }
String get_name() const override { return name; }
bool is_variadic() const override { return true; }
size_t get_number_of_arguments() const override { return 0; }
// This function should not be called for get DateType Ptr
// using the FunctionCast::get_return_type_impl
DataTypePtr get_return_type_impl(const ColumnsWithTypeAndName& arguments) const override {
return std::make_shared<ToDataType>();
}
ColumnNumbers get_arguments_that_are_always_constant() const override { return {1}; }
Status execute_impl(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
size_t result, size_t input_rows_count) const override {
if (!arguments.size()) {
return Status::RuntimeError("Function {} expects at least 1 arguments", get_name());
}
const IDataType* from_type = block.get_by_position(arguments[0]).type.get();
Status ret_status;
/// Generic conversion of any type to String.
if constexpr (std::is_same_v<ToDataType, DataTypeString>) {
return ConvertImplGenericToString::execute(block, arguments, result);
} else {
auto call = [&](const auto& types) -> bool {
using Types = std::decay_t<decltype(types)>;
using LeftDataType = typename Types::LeftType;
using RightDataType = typename Types::RightType;
// now, cast to decimal do not execute the code
if constexpr (IsDataTypeDecimal<RightDataType>) {
if (arguments.size() != 2) {
ret_status = Status::RuntimeError(
"Function {} expects 2 arguments for Decimal.", get_name());
return true;
}
const ColumnWithTypeAndName& scale_column = block.get_by_position(result);
ret_status = ConvertImpl<LeftDataType, RightDataType, Name>::execute(
context, block, arguments, result, input_rows_count,
scale_column.type->get_scale());
} else if constexpr (IsDataTypeDateTimeV2<RightDataType>) {
const ColumnWithTypeAndName& scale_column = block.get_by_position(result);
ret_status = ConvertImpl<LeftDataType, RightDataType, Name>::execute(
context, block, arguments, result, input_rows_count,
scale_column.type->get_scale());
} else {
ret_status = ConvertImpl<LeftDataType, RightDataType, Name>::execute(
context, block, arguments, result, input_rows_count);
}
return true;
};
bool done = call_on_index_and_data_type<ToDataType>(from_type->get_type_id(), call);
if (!done) {
ret_status = Status::RuntimeError(
"Illegal type {} of argument of function {}",
block.get_by_position(arguments[0]).type->get_name(), get_name());
}
return ret_status;
}
}
Monotonicity get_monotonicity_for_range(const IDataType& type, const Field& left,
const Field& right) const override {
return Monotonic::get(type, left, right);
}
};
using FunctionToUInt8 = FunctionConvert<DataTypeUInt8, NameToUInt8, ToNumberMonotonicity<UInt8>>;
using FunctionToUInt16 =
FunctionConvert<DataTypeUInt16, NameToUInt16, ToNumberMonotonicity<UInt16>>;
using FunctionToUInt32 =
FunctionConvert<DataTypeUInt32, NameToUInt32, ToNumberMonotonicity<UInt32>>;
using FunctionToUInt64 =
FunctionConvert<DataTypeUInt64, NameToUInt64, ToNumberMonotonicity<UInt64>>;
using FunctionToInt8 = FunctionConvert<DataTypeInt8, NameToInt8, ToNumberMonotonicity<Int8>>;
using FunctionToInt16 = FunctionConvert<DataTypeInt16, NameToInt16, ToNumberMonotonicity<Int16>>;
using FunctionToInt32 = FunctionConvert<DataTypeInt32, NameToInt32, ToNumberMonotonicity<Int32>>;
using FunctionToInt64 = FunctionConvert<DataTypeInt64, NameToInt64, ToNumberMonotonicity<Int64>>;
using FunctionToInt128 =
FunctionConvert<DataTypeInt128, NameToInt128, ToNumberMonotonicity<Int128>>;
using FunctionToFloat32 =
FunctionConvert<DataTypeFloat32, NameToFloat32, ToNumberMonotonicity<Float32>>;
using FunctionToFloat64 =
FunctionConvert<DataTypeFloat64, NameToFloat64, ToNumberMonotonicity<Float64>>;
using FunctionToTimeV2 =
FunctionConvert<DataTypeTimeV2, NameToFloat64, ToNumberMonotonicity<Float64>>;
using FunctionToString = FunctionConvert<DataTypeString, NameToString, ToStringMonotonicity>;
using FunctionToDecimal32 =
FunctionConvert<DataTypeDecimal<Decimal32>, NameToDecimal32, UnknownMonotonicity>;
using FunctionToDecimal64 =
FunctionConvert<DataTypeDecimal<Decimal64>, NameToDecimal64, UnknownMonotonicity>;
using FunctionToDecimal128 =
FunctionConvert<DataTypeDecimal<Decimal128V2>, NameToDecimal128, UnknownMonotonicity>;
using FunctionToDecimal128V3 =
FunctionConvert<DataTypeDecimal<Decimal128V3>, NameToDecimal128V3, UnknownMonotonicity>;
using FunctionToDecimal256 =
FunctionConvert<DataTypeDecimal<Decimal256>, NameToDecimal256, UnknownMonotonicity>;
using FunctionToIPv4 = FunctionConvert<DataTypeIPv4, NameToIPv4, UnknownMonotonicity>;
using FunctionToIPv6 = FunctionConvert<DataTypeIPv6, NameToIPv6, UnknownMonotonicity>;
using FunctionToDate = FunctionConvert<DataTypeDate, NameToDate, UnknownMonotonicity>;
using FunctionToDateTime = FunctionConvert<DataTypeDateTime, NameToDateTime, UnknownMonotonicity>;
using FunctionToDateV2 = FunctionConvert<DataTypeDateV2, NameToDate, UnknownMonotonicity>;
using FunctionToDateTimeV2 =
FunctionConvert<DataTypeDateTimeV2, NameToDateTime, UnknownMonotonicity>;
template <typename DataType>
struct FunctionTo;
template <>
struct FunctionTo<DataTypeUInt8> {
using Type = FunctionToUInt8;
};
template <>
struct FunctionTo<DataTypeUInt16> {
using Type = FunctionToUInt16;
};
template <>
struct FunctionTo<DataTypeUInt32> {
using Type = FunctionToUInt32;
};
template <>
struct FunctionTo<DataTypeUInt64> {
using Type = FunctionToUInt64;
};
template <>
struct FunctionTo<DataTypeInt8> {
using Type = FunctionToInt8;
};
template <>
struct FunctionTo<DataTypeInt16> {
using Type = FunctionToInt16;
};
template <>
struct FunctionTo<DataTypeInt32> {
using Type = FunctionToInt32;
};
template <>
struct FunctionTo<DataTypeInt64> {
using Type = FunctionToInt64;
};
template <>
struct FunctionTo<DataTypeInt128> {
using Type = FunctionToInt128;
};
template <>
struct FunctionTo<DataTypeFloat32> {
using Type = FunctionToFloat32;
};
template <>
struct FunctionTo<DataTypeFloat64> {
using Type = FunctionToFloat64;
};
template <>
struct FunctionTo<DataTypeDecimal<Decimal32>> {
using Type = FunctionToDecimal32;
};
template <>
struct FunctionTo<DataTypeDecimal<Decimal64>> {
using Type = FunctionToDecimal64;
};
template <>
struct FunctionTo<DataTypeDecimal<Decimal128V2>> {
using Type = FunctionToDecimal128;
};
template <>
struct FunctionTo<DataTypeDecimal<Decimal128V3>> {
using Type = FunctionToDecimal128V3;
};
template <>
struct FunctionTo<DataTypeDecimal<Decimal256>> {
using Type = FunctionToDecimal256;
};
template <>
struct FunctionTo<DataTypeIPv4> {
using Type = FunctionToIPv4;
};
template <>
struct FunctionTo<DataTypeIPv6> {
using Type = FunctionToIPv6;
};
template <>
struct FunctionTo<DataTypeDate> {
using Type = FunctionToDate;
};
template <>
struct FunctionTo<DataTypeDateTime> {
using Type = FunctionToDateTime;
};
template <>
struct FunctionTo<DataTypeDateV2> {
using Type = FunctionToDateV2;
};
template <>
struct FunctionTo<DataTypeDateTimeV2> {
using Type = FunctionToDateTimeV2;
};
template <>
struct FunctionTo<DataTypeTimeV2> {
using Type = FunctionToTimeV2;
};
class PreparedFunctionCast : public PreparedFunctionImpl {
public:
using WrapperType = std::function<Status(FunctionContext* context, Block&, const ColumnNumbers&,
size_t, size_t)>;
explicit PreparedFunctionCast(WrapperType&& wrapper_function_, const char* name_)
: wrapper_function(std::move(wrapper_function_)), name(name_) {}
String get_name() const override { return name; }
protected:
Status execute_impl(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
size_t result, size_t input_rows_count) const override {
return wrapper_function(context, block, arguments, result, input_rows_count);
}
bool use_default_implementation_for_nulls() const override { return false; }
bool use_default_implementation_for_low_cardinality_columns() const override { return false; }
ColumnNumbers get_arguments_that_are_always_constant() const override { return {1}; }
private:
WrapperType wrapper_function;
const char* name;
};
// always from DataTypeString
template <typename ToDataType, typename Name>
struct StringParsing {
using ToFieldType = typename ToDataType::FieldType;
static bool is_all_read(ReadBuffer& in) { return in.eof(); }
template <typename Additions = void*>
static Status execute(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
size_t result, size_t input_rows_count,
Additions additions [[maybe_unused]] = Additions()) {
using ColVecTo = std::conditional_t<IsDecimalNumber<ToFieldType>,
ColumnDecimal<ToFieldType>, ColumnVector<ToFieldType>>;
const IColumn* col_from = block.get_by_position(arguments[0]).column.get();
const auto* col_from_string = check_and_get_column<ColumnString>(col_from);
if (!col_from_string) {
return Status::RuntimeError("Illegal column {} of first argument of function {}",
col_from->get_name(), Name::name);
}
size_t row = input_rows_count;
typename ColVecTo::MutablePtr col_to = nullptr;
if constexpr (IsDataTypeDecimal<ToDataType>) {
UInt32 scale = ((PrecisionScaleArg)additions).scale;
ToDataType::check_type_scale(scale);
col_to = ColVecTo::create(row, scale);
} else {
col_to = ColVecTo::create(row);
}
typename ColVecTo::Container& vec_to = col_to->get_data();
ColumnUInt8::MutablePtr col_null_map_to;
ColumnUInt8::Container* vec_null_map_to [[maybe_unused]] = nullptr;
col_null_map_to = ColumnUInt8::create(row, 0);
vec_null_map_to = &col_null_map_to->get_data();
const ColumnString::Chars* chars = &col_from_string->get_chars();
const IColumn::Offsets* offsets = &col_from_string->get_offsets();
[[maybe_unused]] UInt32 scale = 0;
if constexpr (IsDataTypeDateTimeV2<ToDataType>) {
const auto* type = assert_cast<const DataTypeDateTimeV2*>(
block.get_by_position(result).type.get());
scale = type->get_scale();
}
size_t current_offset = 0;
for (size_t i = 0; i < row; ++i) {
size_t next_offset = (*offsets)[i];
size_t string_size = next_offset - current_offset;
ReadBuffer read_buffer(&(*chars)[current_offset], string_size);
bool parsed;
if constexpr (IsDataTypeDecimal<ToDataType>) {
ToDataType::check_type_precision((PrecisionScaleArg(additions).precision));
StringParser::ParseResult res = try_parse_decimal_impl<ToDataType>(
vec_to[i], read_buffer, PrecisionScaleArg(additions));
parsed = (res == StringParser::PARSE_SUCCESS ||
res == StringParser::PARSE_OVERFLOW ||
res == StringParser::PARSE_UNDERFLOW);
} else if constexpr (IsDataTypeDateTimeV2<ToDataType>) {
parsed = try_parse_impl<ToDataType>(vec_to[i], read_buffer, context, scale);
} else {
parsed =
try_parse_impl<ToDataType, DataTypeString>(vec_to[i], read_buffer, context);
}
(*vec_null_map_to)[i] = !parsed || !is_all_read(read_buffer);
current_offset = next_offset;
}
block.get_by_position(result).column =
ColumnNullable::create(std::move(col_to), std::move(col_null_map_to));
return Status::OK();
}
};
template <typename Name>
struct ConvertImpl<DataTypeString, DataTypeDecimal<Decimal32>, Name>
: StringParsing<DataTypeDecimal<Decimal32>, Name> {};
template <typename Name>
struct ConvertImpl<DataTypeString, DataTypeDecimal<Decimal64>, Name>
: StringParsing<DataTypeDecimal<Decimal64>, Name> {};
template <typename Name>
struct ConvertImpl<DataTypeString, DataTypeDecimal<Decimal128V2>, Name>
: StringParsing<DataTypeDecimal<Decimal128V2>, Name> {};
template <typename Name>
struct ConvertImpl<DataTypeString, DataTypeDecimal<Decimal128V3>, Name>
: StringParsing<DataTypeDecimal<Decimal128V3>, Name> {};
template <typename Name>
struct ConvertImpl<DataTypeString, DataTypeDecimal<Decimal256>, Name>
: StringParsing<DataTypeDecimal<Decimal256>, Name> {};
template <typename Name>
struct ConvertImpl<DataTypeString, DataTypeIPv4, Name> : StringParsing<DataTypeIPv4, Name> {};
template <typename Name>
struct ConvertImpl<DataTypeString, DataTypeIPv6, Name> : StringParsing<DataTypeIPv6, Name> {};
struct NameCast {
static constexpr auto name = "CAST";
};
template <typename ToDataType, typename Name>
class FunctionConvertFromString : public IFunction {
public:
static constexpr auto name = Name::name;
static FunctionPtr create() { return std::make_shared<FunctionConvertFromString>(); }
String get_name() const override { return name; }
bool is_variadic() const override { return true; }
size_t get_number_of_arguments() const override { return 0; }
ColumnNumbers get_arguments_that_are_always_constant() const override { return {1}; }
// This function should not be called for get DateType Ptr
// using the FunctionCast::get_return_type_impl
DataTypePtr get_return_type_impl(const ColumnsWithTypeAndName& arguments) const override {
DataTypePtr res;
if constexpr (IsDataTypeDecimal<ToDataType>) {
auto error_type = std::make_shared<ToDataType>();
throw doris::Exception(ErrorCode::INVALID_ARGUMENT,
"something wrong type in function {}.", get_name(),
error_type->get_name());
} else {
res = std::make_shared<ToDataType>();
}
return res;
}
Status execute_impl(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
size_t result, size_t input_rows_count) const override {
const IDataType* from_type = block.get_by_position(arguments[0]).type.get();
if (check_and_get_data_type<DataTypeString>(from_type)) {
return StringParsing<ToDataType, Name>::execute(context, block, arguments, result,
input_rows_count);
}
return Status::RuntimeError(
"Illegal type {} of argument of function {} . Only String or FixedString "
"argument is accepted for try-conversion function. For other arguments, use "
"function without 'orZero' or 'orNull'.",
block.get_by_position(arguments[0]).type->get_name(), get_name());
}
};
template <typename ToDataType, typename Name>
class FunctionConvertToTimeType : public IFunction {
public:
static constexpr auto name = Name::name;
static FunctionPtr create() { return std::make_shared<FunctionConvertToTimeType>(); }
String get_name() const override { return name; }
bool is_variadic() const override { return true; }
size_t get_number_of_arguments() const override { return 0; }
ColumnNumbers get_arguments_that_are_always_constant() const override { return {1}; }
// This function should not be called for get DateType Ptr
// using the FunctionCast::get_return_type_impl
DataTypePtr get_return_type_impl(const ColumnsWithTypeAndName& arguments) const override {
return std::make_shared<ToDataType>();
}
Status execute_impl(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
size_t result, size_t input_rows_count) const override {
Status ret_status = Status::OK();
const IDataType* from_type = block.get_by_position(arguments[0]).type.get();
auto call = [&](const auto& types) -> bool {
using Types = std::decay_t<decltype(types)>;
using LeftDataType = typename Types::LeftType;
using RightDataType = typename Types::RightType;
ret_status = ConvertImplToTimeType<LeftDataType, RightDataType, Name>::execute(
block, arguments, result, input_rows_count);
return true;
};
bool done = call_on_index_and_number_data_type<ToDataType>(from_type->get_type_id(), call);
if (!done) {
return Status::RuntimeError("Illegal type {} of argument of function {}",
block.get_by_position(arguments[0]).type->get_name(),
get_name());
}
return ret_status;
}
};
class FunctionCast final : public IFunctionBase {
public:
using WrapperType =
std::function<Status(FunctionContext*, Block&, const ColumnNumbers&, size_t, size_t)>;
using ElementWrappers = std::vector<WrapperType>;
using MonotonicityForRange =
std::function<Monotonicity(const IDataType&, const Field&, const Field&)>;
FunctionCast(const char* name_, MonotonicityForRange&& monotonicity_for_range_,
const DataTypes& argument_types_, const DataTypePtr& return_type_)
: name(name_),
monotonicity_for_range(monotonicity_for_range_),
argument_types(argument_types_),
return_type(return_type_) {}
const DataTypes& get_argument_types() const override { return argument_types; }
const DataTypePtr& get_return_type() const override { return return_type; }
PreparedFunctionPtr prepare(FunctionContext* context, const Block& /*sample_block*/,
const ColumnNumbers& /*arguments*/,
size_t /*result*/) const override {
return std::make_shared<PreparedFunctionCast>(
prepare_unpack_dictionaries(context, get_argument_types()[0], get_return_type()),
name);
}
String get_name() const override { return name; }
Monotonicity get_monotonicity_for_range(const IDataType& type, const Field& left,
const Field& right) const override {
return monotonicity_for_range(type, left, right);
}
bool is_use_default_implementation_for_constants() const override { return true; }
private:
const char* name = nullptr;
MonotonicityForRange monotonicity_for_range;
DataTypes argument_types;
DataTypePtr return_type;
template <typename DataType>
WrapperType create_wrapper(const DataTypePtr& from_type, const DataType* const,
bool requested_result_is_nullable) const {
FunctionPtr function;
if (requested_result_is_nullable &&
check_and_get_data_type<DataTypeString>(from_type.get())) {
/// In case when converting to Nullable type, we apply different parsing rule,
/// that will not throw an exception but return NULL in case of malformed input.
function = FunctionConvertFromString<DataType, NameCast>::create();
} else if (requested_result_is_nullable &&
(IsTimeType<DataType> || IsTimeV2Type<DataType>)&&!(
check_and_get_data_type<DataTypeDateTime>(from_type.get()) ||
check_and_get_data_type<DataTypeDate>(from_type.get()) ||
check_and_get_data_type<DataTypeDateV2>(from_type.get()) ||
check_and_get_data_type<DataTypeDateTimeV2>(from_type.get()))) {
function = FunctionConvertToTimeType<DataType, NameCast>::create();
} else {
function = FunctionTo<DataType>::Type::create();
}
/// Check conversion using underlying function
{ function->get_return_type(ColumnsWithTypeAndName(1, {nullptr, from_type, ""})); }
return [function](FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) {
return function->execute(context, block, arguments, result, input_rows_count);
};
}
WrapperType create_string_wrapper(const DataTypePtr& from_type) const {
FunctionPtr function = FunctionToString::create();
/// Check conversion using underlying function
{ function->get_return_type(ColumnsWithTypeAndName(1, {nullptr, from_type, ""})); }
return [function](FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) {
return function->execute(context, block, arguments, result, input_rows_count);
};
}
template <typename FieldType>
WrapperType create_decimal_wrapper(const DataTypePtr& from_type,
const DataTypeDecimal<FieldType>* to_type) const {
using ToDataType = DataTypeDecimal<FieldType>;
TypeIndex type_index = from_type->get_type_id();
UInt32 precision = to_type->get_precision();
UInt32 scale = to_type->get_scale();
WhichDataType which(type_index);
bool ok = which.is_int() || which.is_native_uint() || which.is_decimal() ||
which.is_float() || which.is_date_or_datetime() ||
which.is_date_v2_or_datetime_v2() || which.is_string_or_fixed_string();
if (!ok) {
return create_unsupport_wrapper(from_type->get_name(), to_type->get_name());
}
return [type_index, precision, scale](FunctionContext* context, Block& block,
const ColumnNumbers& arguments, const size_t result,
size_t input_rows_count) {
auto res = call_on_index_and_data_type<ToDataType>(
type_index, [&](const auto& types) -> bool {
using Types = std::decay_t<decltype(types)>;
using LeftDataType = typename Types::LeftType;
using RightDataType = typename Types::RightType;
auto state = ConvertImpl<LeftDataType, RightDataType, NameCast>::execute(
context, block, arguments, result, input_rows_count,
PrecisionScaleArg {precision, scale});
if (!state) {
throw Exception(state.code(), state.to_string());
}
return true;
});
/// Additionally check if call_on_index_and_data_type wasn't called at all.
if (!res) {
auto to = DataTypeDecimal<FieldType>(precision, scale);
return Status::RuntimeError("Conversion from {} to {} is not supported",
getTypeName(type_index), to.get_name());
}
return Status::OK();
};
}
WrapperType create_identity_wrapper(const DataTypePtr&) const {
return [](FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t /*input_rows_count*/) {
block.get_by_position(result).column = block.get_by_position(arguments.front()).column;
return Status::OK();
};
}
WrapperType create_nothing_wrapper(const IDataType* to_type) const {
ColumnPtr res = to_type->create_column_const_with_default_value(1);
return [res](FunctionContext* context, Block& block, const ColumnNumbers&,
const size_t result, size_t input_rows_count) {
/// Column of Nothing type is trivially convertible to any other column
block.get_by_position(result).column =
res->clone_resized(input_rows_count)->convert_to_full_column_if_const();
return Status::OK();
};
}
WrapperType create_unsupport_wrapper(const String error_msg) const {
return [error_msg](FunctionContext* /*context*/, Block& /*block*/,
const ColumnNumbers& /*arguments*/, const size_t /*result*/,
size_t /*input_rows_count*/) {
return Status::InvalidArgument(error_msg);
};
}
WrapperType create_unsupport_wrapper(const String from_type_name,
const String to_type_name) const {
const String error_msg = fmt::format("Conversion from {} to {} is not supported",
from_type_name, to_type_name);
return create_unsupport_wrapper(error_msg);
}
WrapperType create_hll_wrapper(FunctionContext* context, const DataTypePtr& from_type_untyped,
const DataTypeHLL& to_type) const {
/// Conversion from String through parsing.
if (check_and_get_data_type<DataTypeString>(from_type_untyped.get())) {
return &ConvertImplGenericFromString::execute;
}
//TODO if from is not string, it must be HLL?
const auto* from_type = check_and_get_data_type<DataTypeHLL>(from_type_untyped.get());
if (!from_type) {
return create_unsupport_wrapper(
"CAST AS HLL can only be performed between HLL, String "
"types");
}
return nullptr;
}
WrapperType create_bitmap_wrapper(FunctionContext* context,
const DataTypePtr& from_type_untyped,
const DataTypeBitMap& to_type) const {
/// Conversion from String through parsing.
if (check_and_get_data_type<DataTypeString>(from_type_untyped.get())) {
return &ConvertImplGenericFromString::execute;
}
//TODO if from is not string, it must be BITMAP?
const auto* from_type = check_and_get_data_type<DataTypeBitMap>(from_type_untyped.get());
if (!from_type) {
return create_unsupport_wrapper(
"CAST AS BITMAP can only be performed between BITMAP, String "
"types");
}
return nullptr;
}
WrapperType create_array_wrapper(FunctionContext* context, const DataTypePtr& from_type_untyped,
const DataTypeArray& to_type) const {
/// Conversion from String through parsing.
if (check_and_get_data_type<DataTypeString>(from_type_untyped.get())) {
return &ConvertImplGenericFromString::execute;
}
const auto* from_type = check_and_get_data_type<DataTypeArray>(from_type_untyped.get());
if (!from_type) {
return create_unsupport_wrapper(
"CAST AS Array can only be performed between same-dimensional Array, String "
"types");
}
DataTypePtr from_nested_type = from_type->get_nested_type();
/// In query SELECT CAST([] AS Array(Array(String))) from type is Array(Nothing)
bool from_empty_array = is_nothing(from_nested_type);
if (from_type->get_number_of_dimensions() != to_type.get_number_of_dimensions() &&
!from_empty_array) {
return create_unsupport_wrapper(
"CAST AS Array can only be performed between same-dimensional array types");
}
const DataTypePtr& to_nested_type = to_type.get_nested_type();
/// Prepare nested type conversion
const auto nested_function =
prepare_unpack_dictionaries(context, from_nested_type, to_nested_type);
return [nested_function, from_nested_type, to_nested_type](
FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t /*input_rows_count*/) -> Status {
ColumnPtr from_column = block.get_by_position(arguments.front()).column;
const ColumnArray* from_col_array =
check_and_get_column<ColumnArray>(from_column.get());
if (from_col_array) {
/// create columns for converting nested column containing original and result columns
ColumnWithTypeAndName from_nested_column {from_col_array->get_data_ptr(),
from_nested_type, ""};
/// convert nested column
ColumnNumbers new_arguments {block.columns()};
block.insert(from_nested_column);
size_t nested_result = block.columns();
block.insert({to_nested_type, ""});
RETURN_IF_ERROR(nested_function(context, block, new_arguments, nested_result,
from_col_array->get_data_ptr()->size()));
auto nested_result_column = block.get_by_position(nested_result).column;
/// set converted nested column to result
block.get_by_position(result).column = ColumnArray::create(
nested_result_column, from_col_array->get_offsets_ptr());
} else {
return Status::RuntimeError("Illegal column {} for function CAST AS Array",
from_column->get_name());
}
return Status::OK();
};
}
// check jsonb value type and get to_type value
WrapperType create_jsonb_wrapper(const DataTypeJsonb& from_type, const DataTypePtr& to_type,
bool jsonb_string_as_string) const {
switch (to_type->get_type_id()) {
case TypeIndex::UInt8:
return &ConvertImplFromJsonb<TypeIndex::UInt8, ColumnUInt8, DataTypeUInt8>::execute;
case TypeIndex::Int8:
return &ConvertImplFromJsonb<TypeIndex::Int8, ColumnInt8, DataTypeInt8>::execute;
case TypeIndex::Int16:
return &ConvertImplFromJsonb<TypeIndex::Int16, ColumnInt16, DataTypeInt16>::execute;
case TypeIndex::Int32:
return &ConvertImplFromJsonb<TypeIndex::Int32, ColumnInt32, DataTypeInt32>::execute;
case TypeIndex::Int64:
return &ConvertImplFromJsonb<TypeIndex::Int64, ColumnInt64, DataTypeInt64>::execute;
case TypeIndex::Int128:
return &ConvertImplFromJsonb<TypeIndex::Int128, ColumnInt128, DataTypeInt128>::execute;
case TypeIndex::Float64:
return &ConvertImplFromJsonb<TypeIndex::Float64, ColumnFloat64,
DataTypeFloat64>::execute;
case TypeIndex::String:
if (!jsonb_string_as_string) {
// Conversion from String through parsing.
return &ConvertImplGenericToString::execute2;
} else {
return ConvertImplGenericFromJsonb::execute;
}
default:
return ConvertImplGenericFromJsonb::execute;
}
}
// create cresponding jsonb value with type to_type
// use jsonb writer to create jsonb value
WrapperType create_jsonb_wrapper(const DataTypePtr& from_type, const DataTypeJsonb& to_type,
bool string_as_jsonb_string) const {
switch (from_type->get_type_id()) {
case TypeIndex::UInt8:
return &ConvertImplNumberToJsonb<ColumnUInt8>::execute;
case TypeIndex::Int8:
return &ConvertImplNumberToJsonb<ColumnInt8>::execute;
case TypeIndex::Int16:
return &ConvertImplNumberToJsonb<ColumnInt16>::execute;
case TypeIndex::Int32:
return &ConvertImplNumberToJsonb<ColumnInt32>::execute;
case TypeIndex::Int64:
return &ConvertImplNumberToJsonb<ColumnInt64>::execute;
case TypeIndex::Int128:
return &ConvertImplNumberToJsonb<ColumnInt128>::execute;
case TypeIndex::Float64:
return &ConvertImplNumberToJsonb<ColumnFloat64>::execute;
case TypeIndex::String:
if (string_as_jsonb_string) {
// We convert column string to jsonb type just add a string jsonb field to dst column instead of parse
// each line in original string column.
return &ConvertImplStringToJsonbAsJsonbString::execute;
} else {
return &ConvertImplGenericFromString::execute;
}
case TypeIndex::Nothing:
return &ConvertNothingToJsonb::execute;
default:
return &ConvertImplGenericToJsonb::execute;
}
}
struct ConvertImplGenericFromVariant {
static Status execute(const FunctionCast* fn, FunctionContext* context, Block& block,
const ColumnNumbers& arguments, const size_t result,
size_t input_rows_count) {
auto& data_type_to = block.get_by_position(result).type;
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
auto& col_from = col_with_type_and_name.column;
auto& variant = assert_cast<const ColumnObject&>(*col_from);
ColumnPtr col_to = data_type_to->create_column();
if (!variant.is_finalized()) {
variant.assume_mutable()->finalize();
}
// It's important to convert as many elements as possible in this context. For instance,
// if the root of this variant column is a number column, converting it to a number column
// is acceptable. However, if the destination type is a string and root is none scalar root, then
// we should convert the entire tree to a string.
bool is_root_valuable =
variant.is_scalar_variant() ||
(!variant.is_null_root() &&
!WhichDataType(remove_nullable(variant.get_root_type())).is_nothing() &&
!WhichDataType(data_type_to).is_string() &&
!WhichDataType(data_type_to).is_json());
if (is_root_valuable) {
ColumnPtr nested = variant.get_root();
auto nested_from_type = variant.get_root_type();
// DCHECK(nested_from_type->is_nullable());
DCHECK(!data_type_to->is_nullable());
auto new_context = context->clone();
new_context->set_jsonb_string_as_string(true);
// dst type nullable has been removed, so we should remove the inner nullable of root column
auto wrapper = fn->prepare_impl(
new_context.get(), remove_nullable(nested_from_type), data_type_to, true);
Block tmp_block {{remove_nullable(nested), remove_nullable(nested_from_type), ""}};
tmp_block.insert({nullptr, data_type_to, ""});
/// Perform the requested conversion.
Status st = wrapper(new_context.get(), tmp_block, {0}, 1, input_rows_count);
if (!st.ok()) {
// Fill with default values, which is null
col_to->assume_mutable()->insert_many_defaults(input_rows_count);
col_to = make_nullable(col_to, true);
} else {
col_to = tmp_block.get_by_position(1).column;
// Note: here we should return the nullable result column
col_to = wrap_in_nullable(
col_to,
Block({{nested, nested_from_type, ""}, {col_to, data_type_to, ""}}),
{0}, 1, input_rows_count);
}
} else {
if (variant.empty()) {
// TODO not found root cause, a tmp fix
col_to->assume_mutable()->insert_many_defaults(input_rows_count);
col_to = make_nullable(col_to, true);
} else if (WhichDataType(data_type_to).is_string()) {
// serialize to string
return ConvertImplGenericToString::execute2(context, block, arguments, result,
input_rows_count);
} else if (WhichDataType(data_type_to).is_json()) {
// serialize to json by parsing
return ConvertImplGenericToJsonb::execute(context, block, arguments, result,
input_rows_count);
} else if (!data_type_to->is_nullable() &&
!WhichDataType(data_type_to).is_string()) {
// other types
col_to->assume_mutable()->insert_many_defaults(input_rows_count);
col_to = make_nullable(col_to, true);
} else {
assert_cast<ColumnNullable&>(*col_to->assume_mutable())
.insert_many_defaults(input_rows_count);
}
}
if (col_to->size() != input_rows_count) {
return Status::InternalError("Unmatched row count {}, expected {}", col_to->size(),
input_rows_count);
}
block.replace_by_position(result, std::move(col_to));
return Status::OK();
}
};
struct ConvertImplGenericToVariant {
static Status execute(FunctionContext* context, Block& block,
const ColumnNumbers& arguments, const size_t result,
size_t input_rows_count) {
auto& data_type_to = block.get_by_position(result).type;
const auto& col_with_type_and_name = block.get_by_position(arguments[0]);
auto& from_type = col_with_type_and_name.type;
auto& col_from = col_with_type_and_name.column;
// set variant root column/type to from column/type
const auto& data_type_object = assert_cast<const DataTypeObject&>(*data_type_to);
auto variant = ColumnObject::create(data_type_object.variant_max_subcolumns_count());
variant->create_root(from_type, col_from->assume_mutable());
block.replace_by_position(result, std::move(variant));
return Status::OK();
}
};
// create cresponding variant value to wrap from_type
WrapperType create_variant_wrapper(const DataTypePtr& from_type,
const DataTypeObject& to_type) const {
return &ConvertImplGenericToVariant::execute;
}
// create cresponding type convert from variant
WrapperType create_variant_wrapper(const DataTypeObject& from_type,
const DataTypePtr& to_type) const {
return [this](FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t input_rows_count) -> Status {
return ConvertImplGenericFromVariant::execute(this, context, block, arguments, result,
input_rows_count);
};
}
//TODO(Amory) . Need support more cast for key , value for map
WrapperType create_map_wrapper(FunctionContext* context, const DataTypePtr& from_type,
const DataTypeMap& to_type) const {
if (from_type->get_type_id() == TypeIndex::String) {
return &ConvertImplGenericFromString::execute;
}
auto from = check_and_get_data_type<DataTypeMap>(from_type.get());
if (!from) {
return create_unsupport_wrapper(
fmt::format("CAST AS Map can only be performed between Map types or from "
"String. from type: {}, to type: {}",
from_type->get_name(), to_type.get_name()));
}
DataTypes from_kv_types;
DataTypes to_kv_types;
from_kv_types.reserve(2);
to_kv_types.reserve(2);
from_kv_types.push_back(from->get_key_type());
from_kv_types.push_back(from->get_value_type());
to_kv_types.push_back(to_type.get_key_type());
to_kv_types.push_back(to_type.get_value_type());
auto kv_wrappers = get_element_wrappers(context, from_kv_types, to_kv_types);
return [kv_wrappers, from_kv_types, to_kv_types](
FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t /*input_rows_count*/) -> Status {
auto& from_column = block.get_by_position(arguments.front()).column;
auto from_col_map = check_and_get_column<ColumnMap>(from_column.get());
if (!from_col_map) {
return Status::RuntimeError("Illegal column {} for function CAST AS MAP",
from_column->get_name());
}
Columns converted_columns(2);
ColumnsWithTypeAndName columnsWithTypeAndName(2);
columnsWithTypeAndName[0] = {from_col_map->get_keys_ptr(), from_kv_types[0], ""};
columnsWithTypeAndName[1] = {from_col_map->get_values_ptr(), from_kv_types[1], ""};
for (size_t i = 0; i < 2; ++i) {
ColumnNumbers element_arguments {block.columns()};
block.insert(columnsWithTypeAndName[i]);
size_t element_result = block.columns();
block.insert({to_kv_types[i], ""});
RETURN_IF_ERROR(kv_wrappers[i](context, block, element_arguments, element_result,
columnsWithTypeAndName[i].column->size()));
converted_columns[i] = block.get_by_position(element_result).column;
}
block.get_by_position(result).column = ColumnMap::create(
converted_columns[0], converted_columns[1], from_col_map->get_offsets_ptr());
return Status::OK();
};
}
ElementWrappers get_element_wrappers(FunctionContext* context,
const DataTypes& from_element_types,
const DataTypes& to_element_types) const {
DCHECK(from_element_types.size() == to_element_types.size());
ElementWrappers element_wrappers;
element_wrappers.reserve(from_element_types.size());
for (size_t i = 0; i < from_element_types.size(); ++i) {
const DataTypePtr& from_element_type = from_element_types[i];
const DataTypePtr& to_element_type = to_element_types[i];
element_wrappers.push_back(
prepare_unpack_dictionaries(context, from_element_type, to_element_type));
}
return element_wrappers;
}
// check struct value type and get to_type value
// TODO: need handle another type to cast struct
WrapperType create_struct_wrapper(FunctionContext* context, const DataTypePtr& from_type,
const DataTypeStruct& to_type) const {
// support CAST AS Struct from string
if (from_type->get_type_id() == TypeIndex::String) {
return &ConvertImplGenericFromString::execute;
}
// only support CAST AS Struct from struct or string types
auto from = check_and_get_data_type<DataTypeStruct>(from_type.get());
if (!from) {
return create_unsupport_wrapper(
fmt::format("CAST AS Struct can only be performed between struct types or from "
"String. Left type: {}, right type: {}",
from_type->get_name(), to_type.get_name()));
}
const auto& from_element_types = from->get_elements();
const auto& to_element_types = to_type.get_elements();
// only support CAST AS Struct from struct type with same number of elements
if (from_element_types.size() != to_element_types.size()) {
return create_unsupport_wrapper(
fmt::format("CAST AS Struct can only be performed between struct types with "
"the same number of elements. Left type: {}, right type: {}",
from_type->get_name(), to_type.get_name()));
}
auto element_wrappers = get_element_wrappers(context, from_element_types, to_element_types);
return [element_wrappers, from_element_types, to_element_types](
FunctionContext* context, Block& block, const ColumnNumbers& arguments,
const size_t result, size_t /*input_rows_count*/) -> Status {
auto& from_column = block.get_by_position(arguments.front()).column;
auto from_col_struct = check_and_get_column<ColumnStruct>(from_column.get());
if (!from_col_struct) {
return Status::RuntimeError("Illegal column {} for function CAST AS Struct",
from_column->get_name());
}
size_t elements_num = to_element_types.size();
Columns converted_columns(elements_num);
for (size_t i = 0; i < elements_num; ++i) {
ColumnWithTypeAndName from_element_column {from_col_struct->get_column_ptr(i),
from_element_types[i], ""};
ColumnNumbers element_arguments {block.columns()};
block.insert(from_element_column);
size_t element_result = block.columns();
block.insert({to_element_types[i], ""});
RETURN_IF_ERROR(element_wrappers[i](context, block, element_arguments,
element_result,
from_col_struct->get_column(i).size()));
converted_columns[i] = block.get_by_position(element_result).column;
}
block.get_by_position(result).column = ColumnStruct::create(converted_columns);
return Status::OK();
};
}
WrapperType prepare_unpack_dictionaries(FunctionContext* context, const DataTypePtr& from_type,
const DataTypePtr& to_type) const {
const auto& from_nested = from_type;
const auto& to_nested = to_type;
if (from_type->is_null_literal()) {
if (!to_nested->is_nullable()) {
return create_unsupport_wrapper("Cannot convert NULL to a non-nullable type");
}
return [](FunctionContext* context, Block& block, const ColumnNumbers&,
const size_t result, size_t input_rows_count) {
auto& res = block.get_by_position(result);
res.column = res.type->create_column_const_with_default_value(input_rows_count)
->convert_to_full_column_if_const();
return Status::OK();
};
}
bool skip_not_null_check = false;
auto wrapper =
prepare_remove_nullable(context, from_nested, to_nested, skip_not_null_check);
return wrapper;
}
static bool need_replace_null_data_to_default(FunctionContext* context,
const DataTypePtr& from_type,
const DataTypePtr& to_type) {
if (from_type->equals(*to_type)) {
return false;
}
auto make_default_wrapper = [&](const auto& types) -> bool {
using Types = std::decay_t<decltype(types)>;
using ToDataType = typename Types::LeftType;
if constexpr (!(IsDataTypeDecimalOrNumber<ToDataType> || IsTimeType<ToDataType> ||
IsTimeV2Type<ToDataType> ||
std::is_same_v<ToDataType, DataTypeTimeV2>)) {
return false;
}
return call_on_index_and_data_type<
ToDataType>(from_type->get_type_id(), [&](const auto& types2) -> bool {
using Types2 = std::decay_t<decltype(types2)>;
using FromDataType = typename Types2::LeftType;
if constexpr (!(IsDataTypeDecimalOrNumber<FromDataType> ||
IsTimeType<FromDataType> || IsTimeV2Type<FromDataType> ||
std::is_same_v<FromDataType, DataTypeTimeV2>)) {
return false;
}
if constexpr (IsDataTypeDecimal<FromDataType> || IsDataTypeDecimal<ToDataType>) {
using FromFieldType = typename FromDataType::FieldType;
using ToFieldType = typename ToDataType::FieldType;
UInt32 from_precision = NumberTraits::max_ascii_len<FromFieldType>();
UInt32 from_scale = 0;
if constexpr (IsDataTypeDecimal<FromDataType>) {
const auto* from_decimal_type =
check_and_get_data_type<FromDataType>(from_type.get());
from_precision =
NumberTraits::max_ascii_len<typename FromFieldType::NativeType>();
from_scale = from_decimal_type->get_scale();
}
UInt32 to_max_digits = 0;
UInt32 to_precision = 0;
UInt32 to_scale = 0;
ToFieldType max_result {0};
ToFieldType min_result {0};
if constexpr (IsDataTypeDecimal<ToDataType>) {
to_max_digits =
NumberTraits::max_ascii_len<typename ToFieldType::NativeType>();
const auto* to_decimal_type =
check_and_get_data_type<ToDataType>(to_type.get());
to_precision = to_decimal_type->get_precision();
ToDataType::check_type_precision(to_precision);
to_scale = to_decimal_type->get_scale();
ToDataType::check_type_scale(to_scale);
max_result = ToDataType::get_max_digits_number(to_precision);
min_result = -max_result;
}
if constexpr (std::is_integral_v<ToFieldType> ||
std::is_floating_point_v<ToFieldType>) {
max_result = type_limit<ToFieldType>::max();
min_result = type_limit<ToFieldType>::min();
to_max_digits = NumberTraits::max_ascii_len<ToFieldType>();
to_precision = to_max_digits;
}
bool narrow_integral =
context->check_overflow_for_decimal() &&
(to_precision - to_scale) <= (from_precision - from_scale);
bool multiply_may_overflow = context->check_overflow_for_decimal();
if (to_scale > from_scale) {
multiply_may_overflow &=
(from_precision + to_scale - from_scale) >= to_max_digits;
}
return narrow_integral || multiply_may_overflow;
}
return false;
});
};
return call_on_index_and_data_type<void>(to_type->get_type_id(), make_default_wrapper);
}
WrapperType prepare_remove_nullable(FunctionContext* context, const DataTypePtr& from_type,
const DataTypePtr& to_type,
bool skip_not_null_check) const {
/// Determine whether pre-processing and/or post-processing must take place during conversion.
bool result_is_nullable = to_type->is_nullable();
if (result_is_nullable) {
return [this, from_type, to_type](FunctionContext* context, Block& block,
const ColumnNumbers& arguments, const size_t result,
size_t input_rows_count) {
auto from_type_not_nullable = remove_nullable(from_type);
auto to_type_not_nullable = remove_nullable(to_type);
bool replace_null_data_to_default = need_replace_null_data_to_default(
context, from_type_not_nullable, to_type_not_nullable);
auto nested_result_index = block.columns();
block.insert(block.get_by_position(result).get_nested());
auto nested_source_index = block.columns();
block.insert(block.get_by_position(arguments[0])
.get_nested(replace_null_data_to_default));
RETURN_IF_ERROR(prepare_impl(context, from_type_not_nullable, to_type_not_nullable,
true)(context, block, {nested_source_index},
nested_result_index, input_rows_count));
block.get_by_position(result).column =
wrap_in_nullable(block.get_by_position(nested_result_index).column, block,
arguments, result, input_rows_count);
block.erase(nested_source_index);
block.erase(nested_result_index);
return Status::OK();
};
} else {
return prepare_impl(context, from_type, to_type, false);
}
}
/// 'from_type' and 'to_type' are nested types in case of Nullable.
/// 'requested_result_is_nullable' is true if CAST to Nullable type is requested.
WrapperType prepare_impl(FunctionContext* context, const DataTypePtr& from_type,
const DataTypePtr& to_type, bool requested_result_is_nullable) const {
if (from_type->equals(*to_type)) {
return create_identity_wrapper(from_type);
}
// variant needs to be judged first
if (to_type->get_type_id() == TypeIndex::VARIANT) {
return create_variant_wrapper(from_type, assert_cast<const DataTypeObject&>(*to_type));
}
if (from_type->get_type_id() == TypeIndex::VARIANT) {
return create_variant_wrapper(assert_cast<const DataTypeObject&>(*from_type), to_type);
}
switch (from_type->get_type_id()) {
case TypeIndex::Nothing:
return create_nothing_wrapper(to_type.get());
case TypeIndex::JSONB:
return create_jsonb_wrapper(static_cast<const DataTypeJsonb&>(*from_type), to_type,
context ? context->jsonb_string_as_string() : false);
default:
break;
}
WrapperType ret;
auto make_default_wrapper = [&](const auto& types) -> bool {
using Types = std::decay_t<decltype(types)>;
using ToDataType = typename Types::LeftType;
if constexpr (std::is_same_v<ToDataType, DataTypeUInt8> ||
std::is_same_v<ToDataType, DataTypeUInt16> ||
std::is_same_v<ToDataType, DataTypeUInt32> ||
std::is_same_v<ToDataType, DataTypeUInt64> ||
std::is_same_v<ToDataType, DataTypeInt8> ||
std::is_same_v<ToDataType, DataTypeInt16> ||
std::is_same_v<ToDataType, DataTypeInt32> ||
std::is_same_v<ToDataType, DataTypeInt64> ||
std::is_same_v<ToDataType, DataTypeInt128> ||
std::is_same_v<ToDataType, DataTypeFloat32> ||
std::is_same_v<ToDataType, DataTypeFloat64> ||
std::is_same_v<ToDataType, DataTypeDate> ||
std::is_same_v<ToDataType, DataTypeDateTime> ||
std::is_same_v<ToDataType, DataTypeDateV2> ||
std::is_same_v<ToDataType, DataTypeDateTimeV2> ||
std::is_same_v<ToDataType, DataTypeTimeV2> ||
std::is_same_v<ToDataType, DataTypeIPv4> ||
std::is_same_v<ToDataType, DataTypeIPv6>) {
ret = create_wrapper(from_type, check_and_get_data_type<ToDataType>(to_type.get()),
requested_result_is_nullable);
return true;
}
if constexpr (std::is_same_v<ToDataType, DataTypeDecimal<Decimal32>> ||
std::is_same_v<ToDataType, DataTypeDecimal<Decimal64>> ||
std::is_same_v<ToDataType, DataTypeDecimal<Decimal128V2>> ||
std::is_same_v<ToDataType, DataTypeDecimal<Decimal128V3>> ||
std::is_same_v<ToDataType, DataTypeDecimal<Decimal256>>) {
ret = create_decimal_wrapper(from_type,
check_and_get_data_type<ToDataType>(to_type.get()));
return true;
}
return false;
};
if (call_on_index_and_data_type<void>(to_type->get_type_id(), make_default_wrapper)) {
return ret;
}
switch (to_type->get_type_id()) {
case TypeIndex::String:
return create_string_wrapper(from_type);
case TypeIndex::Array:
return create_array_wrapper(context, from_type,
static_cast<const DataTypeArray&>(*to_type));
case TypeIndex::Struct:
return create_struct_wrapper(context, from_type,
static_cast<const DataTypeStruct&>(*to_type));
case TypeIndex::Map:
return create_map_wrapper(context, from_type,
static_cast<const DataTypeMap&>(*to_type));
case TypeIndex::HLL:
return create_hll_wrapper(context, from_type,
static_cast<const DataTypeHLL&>(*to_type));
case TypeIndex::BitMap:
return create_bitmap_wrapper(context, from_type,
static_cast<const DataTypeBitMap&>(*to_type));
case TypeIndex::JSONB:
return create_jsonb_wrapper(from_type, static_cast<const DataTypeJsonb&>(*to_type),
context ? context->string_as_jsonb_string() : false);
default:
break;
}
return create_unsupport_wrapper(from_type->get_name(), to_type->get_name());
}
};
class FunctionBuilderCast : public FunctionBuilderImpl {
public:
using MonotonicityForRange = FunctionCast::MonotonicityForRange;
static constexpr auto name = "CAST";
static FunctionBuilderPtr create() { return std::make_shared<FunctionBuilderCast>(); }
FunctionBuilderCast() = default;
String get_name() const override { return name; }
size_t get_number_of_arguments() const override { return 2; }
ColumnNumbers get_arguments_that_are_always_constant() const override { return {1}; }
protected:
FunctionBasePtr build_impl(const ColumnsWithTypeAndName& arguments,
const DataTypePtr& return_type) const override {
DataTypes data_types(arguments.size());
for (size_t i = 0; i < arguments.size(); ++i) data_types[i] = arguments[i].type;
auto monotonicity = get_monotonicity_information(arguments.front().type, return_type.get());
return std::make_shared<FunctionCast>(name, std::move(monotonicity), data_types,
return_type);
}
DataTypePtr get_return_type_impl(const ColumnsWithTypeAndName& arguments) const override {
DataTypePtr type = arguments[1].type;
DCHECK(type != nullptr);
bool need_to_be_nullable = false;
// 1. from_type is nullable
need_to_be_nullable |= arguments[0].type->is_nullable();
// 2. from_type is string, to_type is not string
need_to_be_nullable |= (arguments[0].type->get_type_id() == TypeIndex::String) &&
(type->get_type_id() != TypeIndex::String);
// 3. from_type is not DateTime/Date, to_type is DateTime/Date
need_to_be_nullable |= (arguments[0].type->get_type_id() != TypeIndex::Date &&
arguments[0].type->get_type_id() != TypeIndex::DateTime) &&
(type->get_type_id() == TypeIndex::Date ||
type->get_type_id() == TypeIndex::DateTime);
// 4. from_type is not DateTimeV2/DateV2, to_type is DateTimeV2/DateV2
need_to_be_nullable |= (arguments[0].type->get_type_id() != TypeIndex::DateV2 &&
arguments[0].type->get_type_id() != TypeIndex::DateTimeV2) &&
(type->get_type_id() == TypeIndex::DateV2 ||
type->get_type_id() == TypeIndex::DateTimeV2);
if (need_to_be_nullable && !type->is_nullable()) {
return make_nullable(type);
}
return type;
}
bool use_default_implementation_for_nulls() const override { return false; }
bool use_default_implementation_for_low_cardinality_columns() const override { return false; }
private:
template <typename DataType>
static auto monotonicity_for_type(const DataType* const) {
return FunctionTo<DataType>::Type::Monotonic::get;
}
MonotonicityForRange get_monotonicity_information(const DataTypePtr& from_type,
const IDataType* to_type) const {
if (const auto type = check_and_get_data_type<DataTypeUInt8>(to_type))
return monotonicity_for_type(type);
if (const auto type = check_and_get_data_type<DataTypeUInt16>(to_type))
return monotonicity_for_type(type);
if (const auto type = check_and_get_data_type<DataTypeUInt32>(to_type))
return monotonicity_for_type(type);
if (const auto type = check_and_get_data_type<DataTypeUInt64>(to_type))
return monotonicity_for_type(type);
if (const auto type = check_and_get_data_type<DataTypeInt8>(to_type))
return monotonicity_for_type(type);
if (const auto type = check_and_get_data_type<DataTypeInt16>(to_type))
return monotonicity_for_type(type);
if (const auto type = check_and_get_data_type<DataTypeInt32>(to_type))
return monotonicity_for_type(type);
if (const auto type = check_and_get_data_type<DataTypeInt64>(to_type))
return monotonicity_for_type(type);
if (const auto type = check_and_get_data_type<DataTypeFloat32>(to_type))
return monotonicity_for_type(type);
if (const auto type = check_and_get_data_type<DataTypeFloat64>(to_type))
return monotonicity_for_type(type);
/// other types like Null, FixedString, Array and Tuple have no monotonicity defined
return {};
}
};
} // namespace doris::vectorized