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apache / doris / refs/heads/vector-index-dev / . / be / src / vec / columns / column_variant.cpp
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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/Columns/ColumnVariant.cpp
// and modified by Doris
#include "vec/columns/column_variant.h"
#include <assert.h>
#include <fmt/core.h>
#include <fmt/format.h>
#include <glog/logging.h>
#include <parallel_hashmap/phmap.h>
#include <rapidjson/stringbuffer.h>
#include <rapidjson/writer.h>
#include <algorithm>
#include <cstdlib>
#include <functional>
#include <limits>
#include <map>
#include <memory>
#include <optional>
#include <sstream>
#include <vector>
#include "common/compiler_util.h" // IWYU pragma: keep
#include "common/exception.h"
#include "common/logging.h"
#include "common/status.h"
#include "exprs/json_functions.h"
#include "olap/olap_common.h"
#include "runtime/primitive_type.h"
#include "util/defer_op.h"
#include "util/jsonb_parser_simd.h"
#include "util/simd/bits.h"
#include "vec/aggregate_functions/aggregate_function.h"
#include "vec/aggregate_functions/helpers.h"
#include "vec/columns/column.h"
#include "vec/columns/column_array.h"
#include "vec/columns/column_nullable.h"
#include "vec/columns/column_string.h"
#include "vec/columns/column_vector.h"
#include "vec/common/arena.h"
#include "vec/common/assert_cast.h"
#include "vec/common/field_visitors.h"
#include "vec/common/schema_util.h"
#include "vec/common/string_buffer.hpp"
#include "vec/common/string_ref.h"
#include "vec/core/column_with_type_and_name.h"
#include "vec/core/field.h"
#include "vec/core/types.h"
#include "vec/data_types/convert_field_to_type.h"
#include "vec/data_types/data_type.h"
#include "vec/data_types/data_type_array.h"
#include "vec/data_types/data_type_decimal.h"
#include "vec/data_types/data_type_factory.hpp"
#include "vec/data_types/data_type_jsonb.h"
#include "vec/data_types/data_type_nothing.h"
#include "vec/data_types/data_type_nullable.h"
#include "vec/data_types/data_type_variant.h"
#include "vec/data_types/get_least_supertype.h"
#include "vec/json/path_in_data.h"
namespace doris::vectorized {
#include "common/compile_check_begin.h"
namespace {
DataTypePtr create_array_of_type(PrimitiveType type, size_t num_dimensions, bool is_nullable) {
if (type == ColumnVariant::MOST_COMMON_TYPE_ID) {
// JSONB type MUST NOT wrapped in ARRAY column, it should be top level.
// So we ignored num_dimensions.
return is_nullable ? make_nullable(std::make_shared<ColumnVariant::MostCommonType>())
: std::make_shared<ColumnVariant::MostCommonType>();
}
DataTypePtr result = type == PrimitiveType::INVALID_TYPE
? is_nullable ? make_nullable(std::make_shared<DataTypeNothing>())
: std::dynamic_pointer_cast<IDataType>(
std::make_shared<DataTypeNothing>())
: DataTypeFactory::instance().create_data_type(type, is_nullable);
for (size_t i = 0; i < num_dimensions; ++i) {
result = std::make_shared<DataTypeArray>(result);
if (is_nullable) {
// wrap array with nullable
result = make_nullable(result);
}
}
return result;
}
DataTypePtr get_base_type_of_array(const DataTypePtr& type) {
/// Get raw pointers to avoid extra copying of type pointers.
const DataTypeArray* last_array = nullptr;
const auto* current_type = type.get();
if (const auto* nullable = typeid_cast<const DataTypeNullable*>(current_type)) {
current_type = nullable->get_nested_type().get();
}
while (const auto* type_array = typeid_cast<const DataTypeArray*>(current_type)) {
current_type = type_array->get_nested_type().get();
last_array = type_array;
if (const auto* nullable = typeid_cast<const DataTypeNullable*>(current_type)) {
current_type = nullable->get_nested_type().get();
}
}
return last_array ? last_array->get_nested_type() : type;
}
size_t get_number_of_dimensions(const IDataType& type) {
int num_dimensions = 0;
const auto* current_type = &type;
if (const auto* nullable = typeid_cast<const DataTypeNullable*>(current_type)) {
current_type = nullable->get_nested_type().get();
}
while (const auto* type_array = typeid_cast<const DataTypeArray*>(current_type)) {
current_type = type_array->get_nested_type().get();
num_dimensions += 1;
if (const auto* nullable = typeid_cast<const DataTypeNullable*>(current_type)) {
current_type = nullable->get_nested_type().get();
}
}
return num_dimensions;
}
/// Calculates number of dimensions in array field.
/// Returns 0 for scalar fields.
class FieldVisitorToNumberOfDimensions : public StaticVisitor<size_t> {
public:
FieldVisitorToNumberOfDimensions() = default;
template <PrimitiveType T>
size_t apply(const typename PrimitiveTypeTraits<T>::NearestFieldType& x) {
if constexpr (T == TYPE_ARRAY) {
const size_t size = x.size();
size_t dimensions = 0;
for (size_t i = 0; i < size; ++i) {
size_t element_dimensions = apply_visitor(*this, x[i]);
dimensions = std::max(dimensions, element_dimensions);
}
return 1 + dimensions;
} else {
return 0;
}
}
};
// Visitor that allows to get type of scalar field
// but exclude fields contain complex field.This is a faster version
// for FieldVisitorToScalarType which does not support complex field.
class SimpleFieldVisitorToScalarType : public StaticVisitor<size_t> {
public:
template <PrimitiveType T>
size_t apply(const typename PrimitiveTypeTraits<T>::NearestFieldType& x) {
if constexpr (T == TYPE_ARRAY) {
throw doris::Exception(ErrorCode::INVALID_ARGUMENT, "Array type is not supported");
} else if constexpr (T == TYPE_BIGINT) {
if (x <= std::numeric_limits<Int8>::max() && x >= std::numeric_limits<Int8>::min()) {
type = PrimitiveType::TYPE_TINYINT;
} else if (x <= std::numeric_limits<Int16>::max() &&
x >= std::numeric_limits<Int16>::min()) {
type = PrimitiveType::TYPE_SMALLINT;
} else if (x <= std::numeric_limits<Int32>::max() &&
x >= std::numeric_limits<Int32>::min()) {
type = PrimitiveType::TYPE_INT;
} else {
type = PrimitiveType::TYPE_BIGINT;
}
return 1;
} else if constexpr (T == TYPE_NULL) {
have_nulls = true;
return 1;
} else {
type = T;
return 1;
}
}
void get_scalar_type(PrimitiveType* data_type) const { *data_type = type; }
bool contain_nulls() const { return have_nulls; }
bool need_convert_field() const { return false; }
private:
PrimitiveType type = PrimitiveType::INVALID_TYPE;
bool have_nulls = false;
};
/// Visitor that allows to get type of scalar field
/// or least common type of scalars in array.
/// More optimized version of FieldToDataType.
class FieldVisitorToScalarType : public StaticVisitor<size_t> {
public:
template <PrimitiveType T>
size_t apply(const typename PrimitiveTypeTraits<T>::NearestFieldType& x) {
if constexpr (T == TYPE_ARRAY) {
size_t size = x.size();
for (size_t i = 0; i < size; ++i) {
apply_visitor(*this, x[i]);
}
return 0;
} else if constexpr (T == TYPE_BIGINT) {
field_types.insert(PrimitiveType::TYPE_BIGINT);
if (x <= std::numeric_limits<Int8>::max() && x >= std::numeric_limits<Int8>::min()) {
type_indexes.insert(PrimitiveType::TYPE_TINYINT);
} else if (x <= std::numeric_limits<Int16>::max() &&
x >= std::numeric_limits<Int16>::min()) {
type_indexes.insert(PrimitiveType::TYPE_SMALLINT);
} else if (x <= std::numeric_limits<Int32>::max() &&
x >= std::numeric_limits<Int32>::min()) {
type_indexes.insert(PrimitiveType::TYPE_INT);
} else {
type_indexes.insert(PrimitiveType::TYPE_BIGINT);
}
return 0;
} else if constexpr (T == TYPE_NULL) {
have_nulls = true;
return 0;
} else {
PrimitiveTypeTraits<PrimitiveType::TYPE_ARRAY>::CppType a;
field_types.insert(T);
type_indexes.insert(T);
return 0;
}
}
void get_scalar_type(PrimitiveType* type) const {
DataTypePtr data_type;
get_least_supertype_jsonb(type_indexes, &data_type);
*type = data_type->get_primitive_type();
}
bool contain_nulls() const { return have_nulls; }
bool need_convert_field() const { return field_types.size() > 1; }
private:
phmap::flat_hash_set<PrimitiveType> type_indexes;
phmap::flat_hash_set<PrimitiveType> field_types;
bool have_nulls = false;
};
/// Visitor that keeps @num_dimensions_to_keep dimensions in arrays
/// and replaces all scalars or nested arrays to @replacement at that level.
class FieldVisitorReplaceScalars : public StaticVisitor<Field> {
public:
FieldVisitorReplaceScalars(const Field& replacement_, size_t num_dimensions_to_keep_)
: replacement(replacement_), num_dimensions_to_keep(num_dimensions_to_keep_) {}
template <PrimitiveType T>
Field apply(const typename PrimitiveTypeTraits<T>::NearestFieldType& x) const {
if constexpr (T == TYPE_ARRAY) {
if (num_dimensions_to_keep == 0) {
return replacement;
}
const size_t size = x.size();
Array res(size);
for (size_t i = 0; i < size; ++i) {
res[i] = apply_visitor(
FieldVisitorReplaceScalars(replacement, num_dimensions_to_keep - 1), x[i]);
}
return Field::create_field<TYPE_ARRAY>(res);
} else {
return replacement;
}
}
private:
const Field& replacement;
size_t num_dimensions_to_keep;
};
} // namespace
template <typename Visitor>
void get_field_info_impl(const Field& field, FieldInfo* info) {
Visitor to_scalar_type_visitor;
apply_visitor(to_scalar_type_visitor, field);
PrimitiveType type_id;
to_scalar_type_visitor.get_scalar_type(&type_id);
// array item's dimension may missmatch, eg. [1, 2, [1, 2, 3]]
FieldVisitorToNumberOfDimensions v;
*info = {
type_id,
to_scalar_type_visitor.contain_nulls(),
to_scalar_type_visitor.need_convert_field(),
apply_visitor(v, field),
};
}
void get_field_info(const Field& field, FieldInfo* info) {
if (field.is_complex_field()) {
get_field_info_impl<FieldVisitorToScalarType>(field, info);
} else {
get_field_info_impl<SimpleFieldVisitorToScalarType>(field, info);
}
}
ColumnVariant::Subcolumn::Subcolumn(MutableColumnPtr&& data_, DataTypePtr type, bool is_nullable_,
bool is_root_)
: least_common_type(type), is_nullable(is_nullable_), is_root(is_root_) {
data.push_back(std::move(data_));
data_types.push_back(type);
}
ColumnVariant::Subcolumn::Subcolumn(size_t size_, bool is_nullable_, bool is_root_)
: least_common_type(std::make_shared<DataTypeNothing>()),
is_nullable(is_nullable_),
num_of_defaults_in_prefix(size_),
is_root(is_root_) {}
size_t ColumnVariant::Subcolumn::Subcolumn::size() const {
size_t res = num_of_defaults_in_prefix;
for (const auto& part : data) {
res += part->size();
}
return res;
}
size_t ColumnVariant::Subcolumn::Subcolumn::byteSize() const {
size_t res = 0;
for (const auto& part : data) {
res += part->byte_size();
}
return res;
}
size_t ColumnVariant::Subcolumn::Subcolumn::allocatedBytes() const {
size_t res = 0;
for (const auto& part : data) {
res += part->allocated_bytes();
}
return res;
}
void ColumnVariant::Subcolumn::insert(Field field) {
FieldInfo info;
get_field_info(field, &info);
insert(std::move(field), std::move(info));
}
void ColumnVariant::Subcolumn::add_new_column_part(DataTypePtr type) {
data.push_back(type->create_column());
least_common_type = LeastCommonType {type};
data_types.push_back(type);
}
void ColumnVariant::Subcolumn::insert(Field field, FieldInfo info) {
auto base_type = info.scalar_type_id;
if (base_type == PrimitiveType::INVALID_TYPE && info.num_dimensions == 0) {
insert_default();
return;
}
auto column_dim = least_common_type.get_dimensions();
auto value_dim = info.num_dimensions;
if (least_common_type.get_base()->get_primitive_type() == INVALID_TYPE) {
column_dim = value_dim;
}
if (base_type == PrimitiveType::INVALID_TYPE) {
value_dim = column_dim;
}
bool type_changed = false;
if (value_dim != column_dim || info.num_dimensions >= 2) {
// Deduce to JSONB
VLOG_DEBUG << fmt::format(
"Dimension of types mismatched between inserted value and column, "
"expected:{}, but meet:{} for type:{}",
column_dim, value_dim, least_common_type.get()->get_name());
base_type = MOST_COMMON_TYPE_ID;
value_dim = 0;
type_changed = true;
}
if (data.empty()) {
add_new_column_part(create_array_of_type(base_type, value_dim, is_nullable));
} else if (least_common_type.get_base_type_id() != base_type &&
base_type != PrimitiveType::INVALID_TYPE) {
if (schema_util::is_conversion_required_between_integers(
base_type, least_common_type.get_base_type_id())) {
VLOG_DEBUG << "Conversion between " << type_to_string(base_type) << " and "
<< type_to_string(least_common_type.get_type_id());
DataTypePtr base_data_type;
PrimitiveType base_data_type_id;
get_least_supertype_jsonb(
PrimitiveTypeSet {base_type, least_common_type.get_base_type_id()},
&base_data_type);
type_changed = true;
base_data_type_id = base_data_type->get_primitive_type();
if (is_nullable) {
base_data_type = make_nullable(base_data_type);
}
if (!least_common_type.get_base()->equals(*base_data_type)) {
add_new_column_part(
create_array_of_type(base_data_type_id, value_dim, is_nullable));
}
}
}
if (type_changed || info.need_convert) {
Field new_field;
convert_field_to_type(field, *least_common_type.get(), &new_field);
field = new_field;
}
data.back()->insert(field);
}
static DataTypePtr create_array(PrimitiveType type, size_t num_dimensions) {
DataTypePtr result_type = type == PrimitiveType::INVALID_TYPE
? make_nullable(std::make_shared<DataTypeNothing>())
: DataTypeFactory::instance().create_data_type(type, true);
for (size_t i = 0; i < num_dimensions; ++i) {
result_type = make_nullable(std::make_shared<DataTypeArray>(result_type));
}
return result_type;
}
Array create_empty_array_field(size_t num_dimensions) {
if (num_dimensions == 0) {
throw doris::Exception(ErrorCode::INVALID_ARGUMENT,
"Cannot create array field with 0 dimensions");
}
Array array;
Array* current_array = &array;
for (size_t i = 1; i < num_dimensions; ++i) {
current_array->push_back(Field::create_field<TYPE_NULL>(Null()));
current_array = &current_array->back().get<Array&>();
}
return array;
}
// Recreates column with default scalar values and keeps sizes of arrays.
static ColumnPtr recreate_column_with_default_values(const ColumnPtr& column,
PrimitiveType scalar_type,
size_t num_dimensions) {
const auto* column_array = check_and_get_column<ColumnArray>(remove_nullable(column).get());
if (column_array != nullptr && num_dimensions != 0) {
return make_nullable(ColumnArray::create(
recreate_column_with_default_values(column_array->get_data_ptr(), scalar_type,
num_dimensions - 1),
IColumn::mutate(column_array->get_offsets_ptr())));
}
return create_array(scalar_type, num_dimensions)
->create_column()
->clone_resized(column->size());
}
ColumnVariant::Subcolumn ColumnVariant::Subcolumn::clone_with_default_values(
const FieldInfo& field_info) const {
Subcolumn new_subcolumn(*this);
new_subcolumn.least_common_type =
LeastCommonType {create_array(field_info.scalar_type_id, field_info.num_dimensions)};
for (int i = 0; i < new_subcolumn.data.size(); ++i) {
new_subcolumn.data[i] = recreate_column_with_default_values(
new_subcolumn.data[i], field_info.scalar_type_id, field_info.num_dimensions);
new_subcolumn.data_types[i] = create_array_of_type(field_info.scalar_type_id,
field_info.num_dimensions, is_nullable);
}
return new_subcolumn;
}
Field ColumnVariant::Subcolumn::get_last_field() const {
if (data.empty()) {
return Field();
}
const auto& last_part = data.back();
assert(!last_part->empty());
return (*last_part)[last_part->size() - 1];
}
void ColumnVariant::Subcolumn::insert_range_from(const Subcolumn& src, size_t start,
size_t length) {
if (start + length > src.size()) {
throw doris::Exception(
ErrorCode::OUT_OF_BOUND,
"Invalid range for insert_range_from: start={}, length={}, src.size={}", start,
length, src.size());
}
size_t end = start + length;
// num_rows += length;
if (data.empty()) {
add_new_column_part(src.get_least_common_type());
} else if (!least_common_type.get()->equals(*src.get_least_common_type())) {
DataTypePtr new_least_common_type;
get_least_supertype_jsonb(DataTypes {least_common_type.get(), src.get_least_common_type()},
&new_least_common_type);
if (!new_least_common_type->equals(*least_common_type.get())) {
add_new_column_part(std::move(new_least_common_type));
}
}
if (end <= src.num_of_defaults_in_prefix) {
data.back()->insert_many_defaults(length);
return;
}
if (start < src.num_of_defaults_in_prefix) {
data.back()->insert_many_defaults(src.num_of_defaults_in_prefix - start);
}
auto insert_from_part = [&](const auto& column, const auto& column_type, size_t from,
size_t n) {
if (from + n > column->size()) {
throw doris::Exception(
ErrorCode::OUT_OF_BOUND,
"Invalid range for insert_range_from: from={}, n={}, column.size={}", from, n,
column->size());
}
if (column_type->equals(*least_common_type.get())) {
data.back()->insert_range_from(*column, from, n);
return;
}
/// If we need to insert large range, there is no sense to cut part of column and cast it.
/// Casting of all column and inserting from it can be faster.
/// Threshold is just a guess.
if (n * 3 >= column->size()) {
ColumnPtr casted_column;
Status st = schema_util::cast_column({column, column_type, ""}, least_common_type.get(),
&casted_column);
if (!st.ok()) {
throw doris::Exception(ErrorCode::INVALID_ARGUMENT, st.to_string());
}
data.back()->insert_range_from(*casted_column, from, n);
return;
}
auto casted_column = column->cut(from, n);
Status st = schema_util::cast_column({casted_column, column_type, ""},
least_common_type.get(), &casted_column);
if (!st.ok()) {
throw doris::Exception(ErrorCode::INVALID_ARGUMENT, st.to_string());
}
data.back()->insert_range_from(*casted_column, 0, n);
};
size_t pos = 0;
size_t processed_rows = src.num_of_defaults_in_prefix;
/// Find the first part of the column that intersects the range.
while (pos < src.data.size() && processed_rows + src.data[pos]->size() < start) {
processed_rows += src.data[pos]->size();
++pos;
}
/// Insert from the first part of column.
if (pos < src.data.size() && processed_rows < start) {
size_t part_start = start - processed_rows;
size_t part_length = std::min(src.data[pos]->size() - part_start, end - start);
insert_from_part(src.data[pos], src.data_types[pos], part_start, part_length);
processed_rows += src.data[pos]->size();
++pos;
}
/// Insert from the parts of column in the middle of range.
while (pos < src.data.size() && processed_rows + src.data[pos]->size() < end) {
insert_from_part(src.data[pos], src.data_types[pos], 0, src.data[pos]->size());
processed_rows += src.data[pos]->size();
++pos;
}
/// Insert from the last part of column if needed.
if (pos < src.data.size() && processed_rows < end) {
size_t part_end = end - processed_rows;
insert_from_part(src.data[pos], src.data_types[pos], 0, part_end);
}
}
bool ColumnVariant::Subcolumn::is_finalized() const {
return num_of_defaults_in_prefix == 0 && (data.empty() || (data.size() == 1));
}
template <typename Func>
MutableColumnPtr ColumnVariant::apply_for_subcolumns(Func&& func) const {
if (!is_finalized()) {
auto finalized = clone_finalized();
auto& finalized_object = assert_cast<ColumnVariant&>(*finalized);
return finalized_object.apply_for_subcolumns(std::forward<Func>(func));
}
auto res = ColumnVariant::create(is_nullable, false);
for (const auto& subcolumn : subcolumns) {
auto new_subcolumn = func(subcolumn->data.get_finalized_column());
res->add_sub_column(subcolumn->path, new_subcolumn->assume_mutable(),
subcolumn->data.get_least_common_type());
}
check_consistency();
return res;
}
void ColumnVariant::resize(size_t n) {
if (n == num_rows) {
return;
}
if (n > num_rows) {
insert_many_defaults(n - num_rows);
} else {
for (auto& subcolumn : subcolumns) {
subcolumn->data.pop_back(num_rows - n);
}
}
num_rows = n;
}
bool ColumnVariant::Subcolumn::check_if_sparse_column(size_t arg_num_rows) {
if (arg_num_rows < config::variant_threshold_rows_to_estimate_sparse_column) {
return false;
}
std::vector<double> defaults_ratio;
for (size_t i = 0; i < data.size(); ++i) {
defaults_ratio.push_back(data[i]->get_ratio_of_default_rows());
}
double default_ratio = std::accumulate(defaults_ratio.begin(), defaults_ratio.end(), 0.0) /
static_cast<double>(defaults_ratio.size());
return default_ratio >= config::variant_ratio_of_defaults_as_sparse_column;
}
void ColumnVariant::Subcolumn::finalize(FinalizeMode mode) {
if (is_finalized()) {
return;
}
if (data.size() == 1 && num_of_defaults_in_prefix == 0) {
data[0] = data[0]->convert_to_full_column_if_const();
return;
}
DataTypePtr to_type = least_common_type.get();
if (mode == FinalizeMode::WRITE_MODE && is_root) {
// Root always JSONB type in write mode
to_type = is_nullable ? make_nullable(std::make_shared<MostCommonType>())
: std::make_shared<MostCommonType>();
least_common_type = LeastCommonType {to_type};
}
auto result_column = to_type->create_column();
if (num_of_defaults_in_prefix) {
result_column->insert_many_defaults(num_of_defaults_in_prefix);
}
for (size_t i = 0; i < data.size(); ++i) {
auto& part = data[i];
auto from_type = data_types[i];
part = part->convert_to_full_column_if_const();
size_t part_size = part->size();
if (!from_type->equals(*to_type)) {
ColumnPtr ptr;
Status st = schema_util::cast_column({part, from_type, ""}, to_type, &ptr);
if (!st.ok()) {
throw doris::Exception(ErrorCode::INVALID_ARGUMENT, st.to_string());
}
part = ptr->convert_to_full_column_if_const();
}
result_column->insert_range_from(*part, 0, part_size);
}
data = {std::move(result_column)};
data_types = {std::move(to_type)};
num_of_defaults_in_prefix = 0;
}
void ColumnVariant::Subcolumn::insert_default() {
if (data.empty()) {
++num_of_defaults_in_prefix;
} else {
data.back()->insert_default();
}
}
void ColumnVariant::Subcolumn::insert_many_defaults(size_t length) {
if (data.empty()) {
num_of_defaults_in_prefix += length;
} else {
data.back()->insert_many_defaults(length);
}
}
void ColumnVariant::Subcolumn::pop_back(size_t n) {
if (n > size()) {
throw doris::Exception(ErrorCode::OUT_OF_BOUND,
"Invalid number of elements to pop: {}, size: {}", n, size());
}
size_t num_removed = 0;
for (auto it = data.rbegin(); it != data.rend(); ++it) {
if (n == 0) {
break;
}
auto& column = *it;
if (n < column->size()) {
column->pop_back(n);
n = 0;
} else {
++num_removed;
n -= column->size();
}
}
size_t sz = data.size() - num_removed;
data.resize(sz);
data_types.resize(sz);
// need to update least_common_type when pop_back a column from the last
least_common_type = sz > 0 ? LeastCommonType {data_types[sz - 1]}
: LeastCommonType {std::make_shared<DataTypeNothing>()};
num_of_defaults_in_prefix -= n;
}
IColumn& ColumnVariant::Subcolumn::get_finalized_column() {
if (!is_finalized()) {
throw doris::Exception(ErrorCode::INTERNAL_ERROR, "Subcolumn is not finalized");
}
return *data[0];
}
const IColumn& ColumnVariant::Subcolumn::get_finalized_column() const {
if (!is_finalized()) {
throw doris::Exception(ErrorCode::INTERNAL_ERROR, "Subcolumn is not finalized");
}
return *data[0];
}
const ColumnPtr& ColumnVariant::Subcolumn::get_finalized_column_ptr() const {
if (!is_finalized()) {
throw doris::Exception(ErrorCode::INTERNAL_ERROR, "Subcolumn is not finalized");
}
return data[0];
}
ColumnPtr& ColumnVariant::Subcolumn::get_finalized_column_ptr() {
if (!is_finalized()) {
throw doris::Exception(ErrorCode::INTERNAL_ERROR, "Subcolumn is not finalized");
}
return data[0];
}
void ColumnVariant::Subcolumn::remove_nullable() {
if (!is_finalized()) {
throw doris::Exception(ErrorCode::INTERNAL_ERROR, "Subcolumn is not finalized");
}
data[0] = doris::vectorized::remove_nullable(data[0]);
least_common_type.remove_nullable();
}
ColumnVariant::Subcolumn::LeastCommonType::LeastCommonType(DataTypePtr type_)
: type(std::move(type_)),
base_type(get_base_type_of_array(type)),
num_dimensions(get_number_of_dimensions(*type)) {
least_common_type_serder = type->get_serde();
type_id = type->get_primitive_type();
base_type_id = base_type->get_primitive_type();
}
ColumnVariant::ColumnVariant(bool is_nullable_, bool create_root_)
: is_nullable(is_nullable_), num_rows(0) {
if (create_root_) {
subcolumns.create_root(Subcolumn(0, is_nullable, true /*root*/));
}
}
ColumnVariant::ColumnVariant(bool is_nullable_, DataTypePtr type, MutableColumnPtr&& column)
: is_nullable(is_nullable_) {
add_sub_column({}, std::move(column), type);
}
ColumnVariant::ColumnVariant(Subcolumns&& subcolumns_, bool is_nullable_)
: is_nullable(is_nullable_),
subcolumns(std::move(subcolumns_)),
num_rows(subcolumns.empty() ? 0 : (*subcolumns.begin())->data.size()) {
check_consistency();
}
void ColumnVariant::check_consistency() const {
if (subcolumns.empty()) {
return;
}
for (const auto& leaf : subcolumns) {
if (num_rows != leaf->data.size()) {
throw doris::Exception(doris::ErrorCode::INTERNAL_ERROR,
"unmatched column: {}, expeted rows: {}, but meet: {}",
leaf->path.get_path(), num_rows, leaf->data.size());
}
}
}
size_t ColumnVariant::size() const {
#ifndef NDEBUG
check_consistency();
#endif
return num_rows;
}
MutableColumnPtr ColumnVariant::clone_resized(size_t new_size) const {
if (new_size == 0) {
return ColumnVariant::create(is_nullable);
}
// If subcolumns are empty, then res will be empty but new_size > 0
if (subcolumns.empty()) {
// Add an emtpy column with new_size rows
auto res = ColumnVariant::create(true, false);
res->set_num_rows(new_size);
return res;
}
auto res = apply_for_subcolumns(
[&](const auto& subcolumn) { return subcolumn.clone_resized(new_size); });
return res;
}
size_t ColumnVariant::byte_size() const {
size_t res = 0;
for (const auto& entry : subcolumns) {
res += entry->data.byteSize();
}
return res;
}
size_t ColumnVariant::allocated_bytes() const {
size_t res = 0;
for (const auto& entry : subcolumns) {
res += entry->data.allocatedBytes();
}
return res;
}
void ColumnVariant::for_each_subcolumn(ColumnCallback callback) {
for (auto& entry : subcolumns) {
for (auto& part : entry->data.data) {
callback(part);
}
}
}
void ColumnVariant::insert_from(const IColumn& src, size_t n) {
const auto* src_v = check_and_get_column<ColumnVariant>(src);
// optimize when src and this column are scalar variant, since try_insert is inefficiency
if (src_v != nullptr && src_v->is_scalar_variant() && is_scalar_variant() &&
src_v->get_root_type()->equals(*get_root_type()) && src_v->is_finalized() &&
is_finalized()) {
assert_cast<ColumnNullable&, TypeCheckOnRelease::DISABLE>(*get_root())
.insert_from(*src_v->get_root(), n);
++num_rows;
return;
}
return try_insert(src[n]);
}
void ColumnVariant::try_insert(const Field& field) {
if (field.get_type() != PrimitiveType::TYPE_VARIANT) {
if (field.is_null()) {
insert_default();
return;
}
auto* root = get_subcolumn({});
// Insert to an emtpy ColumnVariant may result root null,
// so create a root column of Variant is expected.
if (root == nullptr) {
bool succ = add_sub_column({}, num_rows);
if (!succ) {
throw doris::Exception(doris::ErrorCode::INVALID_ARGUMENT,
"Failed to add root sub column {}");
}
root = get_subcolumn({});
}
root->insert(field);
++num_rows;
return;
}
const auto& object = field.get<const VariantMap&>();
size_t old_size = size();
for (const auto& [key_str, value] : object) {
PathInData key;
if (!key_str.empty()) {
key = PathInData(key_str);
}
if (!has_subcolumn(key)) {
bool succ = add_sub_column(key, old_size);
if (!succ) {
throw doris::Exception(doris::ErrorCode::INVALID_ARGUMENT,
"Failed to add sub column {}", key.get_path());
}
}
auto* subcolumn = get_subcolumn(key);
if (!subcolumn) {
doris::Exception(doris::ErrorCode::INVALID_ARGUMENT,
fmt::format("Failed to find sub column {}", key.get_path()));
}
subcolumn->insert(value);
}
for (auto& entry : subcolumns) {
if (old_size == entry->data.size()) {
bool inserted = try_insert_default_from_nested(entry);
if (!inserted) {
entry->data.insert_default();
}
}
}
++num_rows;
}
void ColumnVariant::insert_default() {
for (auto& entry : subcolumns) {
entry->data.insert_default();
}
++num_rows;
}
void ColumnVariant::Subcolumn::get(size_t n, Field& res) const {
if (least_common_type.get_base_type_id() == PrimitiveType::INVALID_TYPE) {
res = Field();
return;
}
if (is_finalized()) {
if (least_common_type.get_base_type_id() == PrimitiveType::TYPE_JSONB) {
// JsonbFiled is special case
res = Field::create_field<TYPE_JSONB>(JsonbField());
}
get_finalized_column().get(n, res);
return;
}
size_t ind = n;
if (ind < num_of_defaults_in_prefix) {
res = least_common_type.get()->get_default();
return;
}
ind -= num_of_defaults_in_prefix;
for (size_t i = 0; i < data.size(); ++i) {
const auto& part = data[i];
const auto& part_type = data_types[i];
if (ind < part->size()) {
res = vectorized::remove_nullable(part_type)->get_default();
part->get(ind, res);
Field new_field;
convert_field_to_type(res, *least_common_type.get(), &new_field);
res = new_field;
return;
}
ind -= part->size();
}
throw doris::Exception(ErrorCode::OUT_OF_BOUND, "Index ({}) for getting field is out of range",
n);
}
Field ColumnVariant::operator[](size_t n) const {
Field object;
get(n, object);
return object;
}
void ColumnVariant::get(size_t n, Field& res) const {
if (UNLIKELY(n >= size())) {
throw doris::Exception(ErrorCode::OUT_OF_BOUND,
"Index ({}) for getting field is out of range for size {}", n,
size());
}
res = Field::create_field<TYPE_VARIANT>(VariantMap());
auto& object = res.get<VariantMap&>();
for (const auto& entry : subcolumns) {
Field field;
entry->data.get(n, field);
// Notice: we treat null as empty field, since we do not distinguish null and empty for Variant type.
if (field.get_type() != PrimitiveType::TYPE_NULL) {
object.try_emplace(entry->path.get_path(), field);
}
}
if (object.empty()) {
res = Field();
}
}
void ColumnVariant::add_nested_subcolumn(const PathInData& key, const FieldInfo& field_info,
size_t new_size) {
if (!key.has_nested_part()) {
throw doris::Exception(ErrorCode::INTERNAL_ERROR,
"Cannot add Nested subcolumn, because path doesn't contain Nested");
}
bool inserted = false;
/// We find node that represents the same Nested type as @key.
const auto* nested_node = subcolumns.find_best_match(key);
// TODO a better way to handle following case:
// {"a" : {"b" : [{"x" : 10}]}}
// {"a" : {"b" : {"c": [{"y" : 10}]}}}
// maybe a.b.c.y should not follow from a.b's nested data
// If we have a nested subcolumn and it contains nested node in it's path
if (nested_node &&
Subcolumns::find_parent(nested_node, [](const auto& node) { return node.is_nested(); })) {
/// Find any leaf of Nested subcolumn.
const auto* leaf = Subcolumns::find_leaf(nested_node, [&](const auto&) { return true; });
assert(leaf);
/// Recreate subcolumn with default values and the same sizes of arrays.
auto new_subcolumn = leaf->data.clone_with_default_values(field_info);
/// It's possible that we have already inserted value from current row
/// to this subcolumn. So, adjust size to expected.
if (new_subcolumn.size() > new_size) {
new_subcolumn.pop_back(new_subcolumn.size() - new_size);
}
assert(new_subcolumn.size() == new_size);
inserted = subcolumns.add(key, new_subcolumn);
} else {
/// If node was not found just add subcolumn with empty arrays.
inserted = subcolumns.add(key, Subcolumn(new_size, is_nullable));
}
if (!inserted) {
throw doris::Exception(ErrorCode::INTERNAL_ERROR, "Subcolumn '{}' already exists",
key.get_path());
}
if (num_rows == 0) {
num_rows = new_size;
} else if (new_size != num_rows) {
throw doris::Exception(
ErrorCode::INTERNAL_ERROR,
"Required size of subcolumn {} ({}) is inconsistent with column size ({})",
key.get_path(), new_size, num_rows);
}
}
void ColumnVariant::insert_range_from(const IColumn& src, size_t start, size_t length) {
#ifndef NDEBUG
check_consistency();
#endif
const auto& src_object = assert_cast<const ColumnVariant&>(src);
for (const auto& entry : src_object.subcolumns) {
if (!has_subcolumn(entry->path)) {
if (entry->path.has_nested_part()) {
FieldInfo field_info {
.scalar_type_id = entry->data.least_common_type.get_base_type_id(),
.have_nulls = false,
.need_convert = false,
.num_dimensions = entry->data.get_dimensions()};
add_nested_subcolumn(entry->path, field_info, num_rows);
} else {
add_sub_column(entry->path, num_rows);
}
}
auto* subcolumn = get_subcolumn(entry->path);
subcolumn->insert_range_from(entry->data, start, length);
}
for (auto& entry : subcolumns) {
if (!src_object.has_subcolumn(entry->path)) {
bool inserted = try_insert_many_defaults_from_nested(entry);
if (!inserted) {
entry->data.insert_many_defaults(length);
}
}
}
num_rows += length;
finalize(FinalizeMode::READ_MODE);
#ifndef NDEBUG
check_consistency();
#endif
}
ColumnPtr ColumnVariant::replicate(const Offsets& offsets) const {
if (num_rows == 0 || subcolumns.empty()) {
// Add an emtpy column with offsets.back rows
auto res = ColumnVariant::create(true, false);
res->set_num_rows(offsets.back());
}
return apply_for_subcolumns(
[&](const auto& subcolumn) { return subcolumn.replicate(offsets); });
}
ColumnPtr ColumnVariant::permute(const Permutation& perm, size_t limit) const {
if (num_rows == 0 || subcolumns.empty()) {
if (limit == 0) {
limit = num_rows;
} else {
limit = std::min(num_rows, limit);
}
if (perm.size() < limit) {
throw doris::Exception(ErrorCode::INTERNAL_ERROR,
"Size of permutation is less than required.");
}
// Add an emtpy column with limit rows
auto res = ColumnVariant::create(true, false);
res->set_num_rows(limit);
return res;
}
return apply_for_subcolumns(
[&](const auto& subcolumn) { return subcolumn.permute(perm, limit); });
}
void ColumnVariant::pop_back(size_t length) {
for (auto& entry : subcolumns) {
entry->data.pop_back(length);
}
num_rows -= length;
}
const ColumnVariant::Subcolumn* ColumnVariant::get_subcolumn(const PathInData& key) const {
const auto* node = subcolumns.find_leaf(key);
if (node == nullptr) {
VLOG_DEBUG << "There is no subcolumn " << key.get_path();
return nullptr;
}
return &node->data;
}
const ColumnVariant::Subcolumn* ColumnVariant::get_subcolumn_with_cache(const PathInData& key,
size_t key_index) const {
// Optimization by caching the order of fields (which is almost always the same)
// and a quick check to match the next expected field, instead of searching the hash table.
if (_prev_positions.size() > key_index && _prev_positions[key_index].second != nullptr &&
key == _prev_positions[key_index].first) {
return _prev_positions[key_index].second;
}
const auto* subcolumn = get_subcolumn(key);
if (key_index >= _prev_positions.size()) {
_prev_positions.resize(key_index + 1);
}
if (subcolumn != nullptr) {
_prev_positions[key_index] = std::make_pair(key, subcolumn);
}
return subcolumn;
}
ColumnVariant::Subcolumn* ColumnVariant::get_subcolumn(const PathInData& key, size_t key_index) {
return const_cast<ColumnVariant::Subcolumn*>(get_subcolumn_with_cache(key, key_index));
}
const ColumnVariant::Subcolumn* ColumnVariant::get_subcolumn(const PathInData& key,
size_t key_index) const {
return get_subcolumn_with_cache(key, key_index);
}
ColumnVariant::Subcolumn* ColumnVariant::get_subcolumn(const PathInData& key) {
const auto* node = subcolumns.find_leaf(key);
if (node == nullptr) {
VLOG_DEBUG << "There is no subcolumn " << key.get_path();
return nullptr;
}
return &const_cast<Subcolumns::Node*>(node)->data;
}
bool ColumnVariant::has_subcolumn(const PathInData& key) const {
return subcolumns.find_leaf(key) != nullptr;
}
bool ColumnVariant::add_sub_column(const PathInData& key, MutableColumnPtr&& subcolumn,
DataTypePtr type) {
size_t new_size = subcolumn->size();
doc_structure = nullptr;
if (key.empty() && subcolumns.empty()) {
// create root
subcolumns.create_root(Subcolumn(std::move(subcolumn), type, is_nullable, true));
num_rows = new_size;
return true;
}
if (key.empty() && ((!subcolumns.get_root()->is_scalar()) ||
subcolumns.get_root()->data.get_least_common_type()->get_primitive_type() ==
INVALID_TYPE)) {
bool root_it_scalar = subcolumns.get_root()->is_scalar();
// update root to scalar
subcolumns.get_mutable_root()->modify_to_scalar(
Subcolumn(std::move(subcolumn), type, is_nullable, true));
if (!root_it_scalar) {
subcolumns.add_leaf(subcolumns.get_root_ptr());
}
if (num_rows == 0) {
num_rows = new_size;
}
return true;
}
bool inserted = subcolumns.add(key, Subcolumn(std::move(subcolumn), type, is_nullable));
if (!inserted) {
VLOG_DEBUG << "Duplicated sub column " << key.get_path();
return false;
}
if (num_rows == 0) {
num_rows = new_size;
} else if (new_size != num_rows) {
VLOG_DEBUG << "Size of subcolumn is in consistent with column";
return false;
}
return true;
}
bool ColumnVariant::add_sub_column(const PathInData& key, size_t new_size) {
if (key.empty() && subcolumns.empty()) {
// create root
subcolumns.create_root(Subcolumn(new_size, is_nullable, true));
num_rows = new_size;
return true;
}
if (key.empty() && (!subcolumns.get_root()->is_scalar())) {
// update none scalar root column to scalar node
subcolumns.get_mutable_root()->modify_to_scalar(Subcolumn(new_size, is_nullable, true));
subcolumns.add_leaf(subcolumns.get_root_ptr());
if (num_rows == 0) {
num_rows = new_size;
}
return true;
}
doc_structure = nullptr;
bool inserted = subcolumns.add(key, Subcolumn(new_size, is_nullable));
if (!inserted) {
VLOG_DEBUG << "Duplicated sub column " << key.get_path();
return false;
}
if (num_rows == 0) {
num_rows = new_size;
} else if (new_size != num_rows) {
VLOG_DEBUG << "Size of subcolumn is in consistent with column";
return false;
}
return true;
}
PathsInData ColumnVariant::getKeys() const {
PathsInData keys;
keys.reserve(subcolumns.size());
for (const auto& entry : subcolumns) {
keys.emplace_back(entry->path);
}
return keys;
}
bool ColumnVariant::is_finalized() const {
return std::all_of(subcolumns.begin(), subcolumns.end(),
[](const auto& entry) { return entry->data.is_finalized(); });
}
void ColumnVariant::Subcolumn::wrapp_array_nullable() {
// Wrap array with nullable, treat empty array as null to elimate conflict at present
auto& result_column = get_finalized_column_ptr();
if (is_column<vectorized::ColumnArray>(result_column.get()) && !result_column->is_nullable()) {
auto new_null_map = ColumnUInt8::create();
new_null_map->reserve(result_column->size());
auto& null_map_data = new_null_map->get_data();
const auto* array = static_cast<const ColumnArray*>(result_column.get());
for (size_t i = 0; i < array->size(); ++i) {
null_map_data.push_back(array->is_default_at(i));
}
result_column = ColumnNullable::create(std::move(result_column), std::move(new_null_map));
data_types[0] = make_nullable(data_types[0]);
least_common_type = LeastCommonType {data_types[0]};
}
}
rapidjson::Value* find_leaf_node_by_path(rapidjson::Value& json, const PathInData& path,
int idx = 0) {
if (idx >= path.get_parts().size()) {
return &json;
}
std::string_view current_key = path.get_parts()[idx].key;
if (!json.IsObject()) {
return nullptr;
}
/*! RapidJSON uses 32-bit array/string indices even on 64-bit platforms,
instead of using \c size_t. Users may override the SizeType by defining
\ref RAPIDJSON_NO_SIZETYPEDEFINE.
*/
rapidjson::Value name(current_key.data(), cast_set<unsigned>(current_key.size()));
auto it = json.FindMember(name);
if (it == json.MemberEnd()) {
return nullptr;
}
rapidjson::Value& current = it->value;
// if (idx == path.get_parts().size() - 1) {
// return &current;
// }
return find_leaf_node_by_path(current, path, idx + 1);
}
// skip empty json:
// 1. null value as empty json, todo: think a better way to disinguish empty json and null json.
// 2. nested array with only nulls, eg. [null. null],todo: think a better way to deal distinguish array null value and real null value.
// 3. empty root jsonb value(not null)
// 4. type is nothing
bool skip_empty_json(const ColumnNullable* nullable, const DataTypePtr& type,
PrimitiveType base_type_id, size_t row, const PathInData& path) {
// skip nulls
if (nullable && nullable->is_null_at(row)) {
return true;
}
// check if it is empty nested json array, then skip
if (base_type_id == PrimitiveType::TYPE_VARIANT && type->equals(*ColumnVariant::NESTED_TYPE)) {
Field field = (*nullable)[row];
if (field.get_type() == PrimitiveType::TYPE_ARRAY) {
const auto& array = field.get<Array>();
bool only_nulls_inside = true;
for (const auto& elem : array) {
if (elem.get_type() != PrimitiveType::TYPE_NULL) {
only_nulls_inside = false;
break;
}
}
// if only nulls then skip
return only_nulls_inside;
}
}
// skip empty jsonb value
if ((path.empty() && nullable && nullable->get_data_at(row).empty())) {
return true;
}
// skip nothing type
if (base_type_id == PrimitiveType::INVALID_TYPE) {
return true;
}
return false;
}
Status find_and_set_leave_value(const IColumn* column, const PathInData& path,
const DataTypeSerDeSPtr& type_serde, const DataTypePtr& type,
PrimitiveType base_type_index, rapidjson::Value& root,
rapidjson::Document::AllocatorType& allocator, Arena& mem_pool,
size_t row) {
#ifndef NDEBUG
// sanitize type and column
if (column->get_name() != type->create_column()->get_name()) {
return Status::InternalError(
"failed to set value for path {}, expected type {}, but got {} at row {}",
path.get_path(), type->get_name(), column->get_name(), row);
}
#endif
const auto* nullable = check_and_get_column<ColumnNullable>(column);
if (skip_empty_json(nullable, type, base_type_index, row, path)) {
return Status::OK();
}
// TODO could cache the result of leaf nodes with it's path info
rapidjson::Value* target = find_leaf_node_by_path(root, path);
if (UNLIKELY(!target)) {
rapidjson::StringBuffer buffer;
rapidjson::Writer<rapidjson::StringBuffer> writer(buffer);
root.Accept(writer);
LOG(WARNING) << "could not find path " << path.get_path()
<< ", root: " << std::string(buffer.GetString(), buffer.GetSize());
return Status::NotFound("Not found path {}", path.get_path());
}
RETURN_IF_ERROR(type_serde->write_one_cell_to_json(*column, *target, allocator, mem_pool, row));
return Status::OK();
}
// compact null values
// {"a" : {"b" : "d" {"n" : null}, "e" : null}, "c" : 10 }
// after compact -> {"a" : {"c"} : 10}
void compact_null_values(rapidjson::Value& json, rapidjson::Document::AllocatorType& allocator) {
if (!json.IsObject() || json.IsNull()) {
return;
}
rapidjson::Value::MemberIterator it = json.MemberBegin();
while (it != json.MemberEnd()) {
rapidjson::Value& value = it->value;
if (value.IsNull()) {
it = json.EraseMember(it);
continue;
}
compact_null_values(value, allocator);
if (value.IsObject() && value.ObjectEmpty()) {
it = json.EraseMember(it);
continue;
}
++it;
}
}
// Construct rapidjson value from Subcolumns
void get_json_by_column_tree(rapidjson::Value& root, rapidjson::Document::AllocatorType& allocator,
const ColumnVariant::Subcolumns::Node* node_root) {
if (node_root == nullptr || node_root->children.empty()) {
root.SetNull();
return;
}
root.SetObject();
// sort to make output stable
std::vector<StringRef> sorted_keys = node_root->get_sorted_chilren_keys();
for (const StringRef& key : sorted_keys) {
rapidjson::Value value(rapidjson::kObjectType);
get_json_by_column_tree(value, allocator, node_root->get_child_node(key).get());
root.AddMember(rapidjson::StringRef(key.data, key.size), value, allocator);
}
}
Status ColumnVariant::serialize_one_row_to_string(size_t row, std::string* output) const {
if (!is_finalized()) {
const_cast<ColumnVariant*>(this)->finalize(FinalizeMode::READ_MODE);
}
rapidjson::StringBuffer buf;
if (is_scalar_variant()) {
auto type = get_root_type();
*output = type->to_string(*get_root(), row);
return Status::OK();
}
RETURN_IF_ERROR(serialize_one_row_to_json_format(row, &buf, nullptr));
// TODO avoid copy
*output = std::string(buf.GetString(), buf.GetSize());
return Status::OK();
}
Status ColumnVariant::serialize_one_row_to_string(size_t row, BufferWritable& output) const {
if (!is_finalized()) {
const_cast<ColumnVariant*>(this)->finalize(FinalizeMode::READ_MODE);
}
if (is_scalar_variant()) {
auto type = get_root_type();
type->to_string(*get_root(), row, output);
return Status::OK();
}
rapidjson::StringBuffer buf;
RETURN_IF_ERROR(serialize_one_row_to_json_format(row, &buf, nullptr));
output.write(buf.GetString(), buf.GetLength());
return Status::OK();
}
Status ColumnVariant::serialize_one_row_to_json_format(size_t row, rapidjson::StringBuffer* output,
bool* is_null) const {
CHECK(is_finalized());
if (subcolumns.empty()) {
if (is_null != nullptr) {
*is_null = true;
} else {
rapidjson::Value root(rapidjson::kNullType);
rapidjson::Writer<rapidjson::StringBuffer> writer(*output);
if (!root.Accept(writer)) {
return Status::InternalError("Failed to serialize json value");
}
}
return Status::OK();
}
CHECK(size() > row);
rapidjson::StringBuffer buffer;
rapidjson::Value root(rapidjson::kNullType);
if (doc_structure == nullptr) {
doc_structure = std::make_shared<rapidjson::Document>();
rapidjson::Document::AllocatorType& allocator = doc_structure->GetAllocator();
get_json_by_column_tree(*doc_structure, allocator, subcolumns.get_root());
}
if (!doc_structure->IsNull()) {
root.CopyFrom(*doc_structure, doc_structure->GetAllocator());
}
Arena mem_pool;
bool serialize_root = true; // Assume all subcolumns are null by default
for (const auto& subcolumn : subcolumns) {
if (subcolumn->data.is_root) {
continue; // Skip the root column
}
// If any non-root subcolumn is NOT null, set serialize_root to false and exit early
if (!assert_cast<const ColumnNullable&, TypeCheckOnRelease::DISABLE>(
*subcolumn->data.get_finalized_column_ptr())
.is_null_at(row)) {
serialize_root = false;
break;
}
}
#ifndef NDEBUG
VLOG_DEBUG << "dump structure " << JsonFunctions::print_json_value(*doc_structure);
#endif
if (serialize_root && subcolumns.get_root()->is_scalar()) {
// only serialize root when all other subcolumns is null
RETURN_IF_ERROR(
subcolumns.get_root()->data.get_least_common_type_serde()->write_one_cell_to_json(
subcolumns.get_root()->data.get_finalized_column(), root,
doc_structure->GetAllocator(), mem_pool, row));
output->Clear();
compact_null_values(root, doc_structure->GetAllocator());
rapidjson::Writer<rapidjson::StringBuffer> writer(*output);
root.Accept(writer);
return Status::OK();
}
// handle subcolumns exclude root node
for (const auto& subcolumn : subcolumns) {
if (subcolumn->data.is_root) {
continue;
}
RETURN_IF_ERROR(find_and_set_leave_value(
subcolumn->data.get_finalized_column_ptr().get(), subcolumn->path,
subcolumn->data.get_least_common_type_serde(),
subcolumn->data.get_least_common_type(),
subcolumn->data.least_common_type.get_base_type_id(), root,
doc_structure->GetAllocator(), mem_pool, row));
}
compact_null_values(root, doc_structure->GetAllocator());
if (root.IsNull() && is_null != nullptr) {
// Fast path
*is_null = true;
} else {
output->Clear();
rapidjson::Writer<rapidjson::StringBuffer> writer(*output);
if (!root.Accept(writer)) {
return Status::InternalError("Failed to serialize json value");
}
}
return Status::OK();
}
Status ColumnVariant::merge_sparse_to_root_column() {
CHECK(is_finalized());
if (sparse_columns.empty()) {
return Status::OK();
}
ColumnPtr src = subcolumns.get_mutable_root()->data.get_finalized_column_ptr();
MutableColumnPtr mresult = src->clone_empty();
const ColumnNullable* src_null = assert_cast<const ColumnNullable*>(src.get());
const ColumnString* src_column_ptr =
assert_cast<const ColumnString*>(&src_null->get_nested_column());
rapidjson::StringBuffer buffer;
doc_structure = std::make_shared<rapidjson::Document>();
rapidjson::Document::AllocatorType& allocator = doc_structure->GetAllocator();
get_json_by_column_tree(*doc_structure, allocator, sparse_columns.get_root());
#ifndef NDEBUG
VLOG_DEBUG << "dump structure " << JsonFunctions::print_json_value(*doc_structure);
#endif
ColumnNullable* result_column_nullable =
assert_cast<ColumnNullable*>(mresult->assume_mutable().get());
ColumnString* result_column_ptr =
assert_cast<ColumnString*>(&result_column_nullable->get_nested_column());
result_column_nullable->reserve(num_rows);
// parse each row to jsonb
for (size_t i = 0; i < num_rows; ++i) {
// root is not null, store original value, eg. the root is scalar type like '[1]'
if (!src_null->empty() && !src_null->is_null_at(i)) {
result_column_ptr->insert_data(src_column_ptr->get_data_at(i).data,
src_column_ptr->get_data_at(i).size);
result_column_nullable->get_null_map_data().push_back(0);
continue;
}
// parse and encode sparse columns
buffer.Clear();
rapidjson::Value root(rapidjson::kNullType);
if (!doc_structure->IsNull()) {
root.CopyFrom(*doc_structure, doc_structure->GetAllocator());
}
size_t null_count = 0;
Arena mem_pool;
for (const auto& subcolumn : sparse_columns) {
auto& column = subcolumn->data.get_finalized_column_ptr();
if (assert_cast<const ColumnNullable&, TypeCheckOnRelease::DISABLE>(*column).is_null_at(
i)) {
++null_count;
continue;
}
bool succ = find_and_set_leave_value(
column.get(), subcolumn->path, subcolumn->data.get_least_common_type_serde(),
subcolumn->data.get_least_common_type(),
subcolumn->data.least_common_type.get_base_type_id(), root,
doc_structure->GetAllocator(), mem_pool, i);
if (succ && subcolumn->path.empty() && !root.IsObject()) {
// root was modified, only handle root node
break;
}
}
// all null values, store null to sparse root
if (null_count == sparse_columns.size()) {
result_column_ptr->insert_default();
result_column_nullable->get_null_map_data().push_back(1);
continue;
}
// encode sparse columns into jsonb format
compact_null_values(root, doc_structure->GetAllocator());
// parse as jsonb value and put back to rootnode
// TODO, we could convert to jsonb directly from rapidjson::Value for better performance, instead of parsing
JsonBinaryValue jsonb_value;
rapidjson::Writer<rapidjson::StringBuffer> writer(buffer);
root.Accept(writer);
RETURN_IF_ERROR(jsonb_value.from_json_string(buffer.GetString(), buffer.GetSize()));
result_column_ptr->insert_data(jsonb_value.value(), jsonb_value.size());
result_column_nullable->get_null_map_data().push_back(0);
}
subcolumns.get_mutable_root()->data.get_finalized_column().clear();
// assign merged column, do insert_range_from to make a copy, instead of replace the ptr itselft
// to make sure the root column ptr is not changed
subcolumns.get_mutable_root()->data.get_finalized_column().insert_range_from(
*mresult->get_ptr(), 0, num_rows);
return Status::OK();
}
void ColumnVariant::unnest(Subcolumns::NodePtr& entry, Subcolumns& arg_subcolumns) const {
entry->data.finalize();
auto nested_column = entry->data.get_finalized_column_ptr()->assume_mutable();
auto* nested_column_nullable = assert_cast<ColumnNullable*>(nested_column.get());
auto* nested_column_array =
assert_cast<ColumnArray*>(nested_column_nullable->get_nested_column_ptr().get());
auto& offset = nested_column_array->get_offsets_ptr();
auto* nested_object_nullable = assert_cast<ColumnNullable*>(
nested_column_array->get_data_ptr()->assume_mutable().get());
auto& nested_object_column =
assert_cast<ColumnVariant&>(nested_object_nullable->get_nested_column());
PathInData nested_path = entry->path;
for (auto& nested_entry : nested_object_column.subcolumns) {
if (nested_entry->data.least_common_type.get_base_type_id() ==
PrimitiveType::INVALID_TYPE) {
continue;
}
nested_entry->data.finalize();
PathInDataBuilder path_builder;
// format nested path
path_builder.append(nested_path.get_parts(), false);
path_builder.append(nested_entry->path.get_parts(), true);
auto subnested_column = ColumnArray::create(
ColumnNullable::create(nested_entry->data.get_finalized_column_ptr(),
nested_object_nullable->get_null_map_column_ptr()),
offset);
auto nullable_subnested_column = ColumnNullable::create(
subnested_column, nested_column_nullable->get_null_map_column_ptr());
auto type = make_nullable(
std::make_shared<DataTypeArray>(nested_entry->data.least_common_type.get()));
Subcolumn subcolumn(nullable_subnested_column->assume_mutable(), type, is_nullable);
arg_subcolumns.add(path_builder.build(), subcolumn);
}
}
void ColumnVariant::finalize(FinalizeMode mode) {
Subcolumns new_subcolumns;
// finalize root first
if (mode == FinalizeMode::WRITE_MODE || !is_null_root()) {
new_subcolumns.create_root(subcolumns.get_root()->data);
new_subcolumns.get_mutable_root()->data.finalize(mode);
}
for (auto&& entry : subcolumns) {
const auto& least_common_type = entry->data.get_least_common_type();
/// Do not add subcolumns, which consists only from NULLs
if (get_base_type_of_array(least_common_type)->get_primitive_type() == INVALID_TYPE) {
continue;
}
// unnest all nested columns, add them to new_subcolumns
if (mode == FinalizeMode::WRITE_MODE &&
least_common_type->equals(*ColumnVariant::NESTED_TYPE)) {
unnest(entry, new_subcolumns);
continue;
}
entry->data.finalize(mode);
entry->data.wrapp_array_nullable();
if (entry->data.is_root) {
continue;
}
// Check and spilit sparse subcolumns, not support nested array at present
if (mode == FinalizeMode::WRITE_MODE && (entry->data.check_if_sparse_column(num_rows)) &&
!entry->path.has_nested_part()) {
// TODO seperate ambiguous path
sparse_columns.add(entry->path, entry->data);
continue;
}
new_subcolumns.add(entry->path, entry->data);
}
std::swap(subcolumns, new_subcolumns);
doc_structure = nullptr;
_prev_positions.clear();
}
void ColumnVariant::finalize() {
finalize(FinalizeMode::READ_MODE);
}
void ColumnVariant::ensure_root_node_type(const DataTypePtr& expected_root_type) {
auto& root = subcolumns.get_mutable_root()->data;
if (!root.get_least_common_type()->equals(*expected_root_type)) {
// make sure the root type is alawys as expected
ColumnPtr casted_column;
THROW_IF_ERROR(
schema_util::cast_column(ColumnWithTypeAndName {root.get_finalized_column_ptr(),
root.get_least_common_type(), ""},
expected_root_type, &casted_column));
root.data[0] = casted_column;
root.data_types[0] = expected_root_type;
root.least_common_type = Subcolumn::LeastCommonType {expected_root_type};
}
}
bool ColumnVariant::empty() const {
return subcolumns.empty() || subcolumns.begin()->get()->path.get_path() == COLUMN_NAME_DUMMY;
}
ColumnPtr get_base_column_of_array(const ColumnPtr& column) {
if (const auto* column_array = check_and_get_column<ColumnArray>(column.get())) {
return column_array->get_data_ptr();
}
return column;
}
ColumnPtr ColumnVariant::filter(const Filter& filter, ssize_t count) const {
if (!is_finalized()) {
auto finalized = clone_finalized();
auto& finalized_object = assert_cast<ColumnVariant&>(*finalized);
return finalized_object.filter(filter, count);
}
if (num_rows == 0 || subcolumns.empty()) {
// Add an emtpy column with filtered rows
auto res = ColumnVariant::create(true, false);
res->set_num_rows(count_bytes_in_filter(filter));
return res;
}
auto new_column = ColumnVariant::create(true, false);
for (auto& entry : subcolumns) {
auto subcolumn = entry->data.get_finalized_column().filter(filter, -1);
new_column->add_sub_column(entry->path, subcolumn->assume_mutable(),
entry->data.get_least_common_type());
}
return new_column;
}
Status ColumnVariant::filter_by_selector(const uint16_t* sel, size_t sel_size, IColumn* col_ptr) {
if (!is_finalized()) {
finalize();
}
if (num_rows == 0 || subcolumns.empty()) {
assert_cast<ColumnVariant*>(col_ptr)->insert_many_defaults(sel_size);
return Status::OK();
}
auto* res = assert_cast<ColumnVariant*>(col_ptr);
for (const auto& subcolumn : subcolumns) {
auto new_subcolumn = subcolumn->data.get_least_common_type()->create_column();
RETURN_IF_ERROR(subcolumn->data.get_finalized_column().filter_by_selector(
sel, sel_size, new_subcolumn.get()));
res->add_sub_column(subcolumn->path, new_subcolumn->assume_mutable(),
subcolumn->data.get_least_common_type());
}
return Status::OK();
}
size_t ColumnVariant::filter(const Filter& filter) {
if (!is_finalized()) {
finalize();
}
size_t count = filter.size() - simd::count_zero_num((int8_t*)filter.data(), filter.size());
if (count == 0) {
for_each_subcolumn([](auto& part) { part->clear(); });
} else {
for_each_subcolumn([&](auto& part) {
if (part->size() != count) {
if (part->is_exclusive()) {
const auto result_size = part->filter(filter);
if (result_size != count) {
throw Exception(ErrorCode::INTERNAL_ERROR,
"result_size not euqal with filter_size, result_size={}, "
"filter_size={}",
result_size, count);
}
CHECK_EQ(result_size, count);
} else {
part = part->filter(filter, count);
}
}
});
}
num_rows = count;
#ifndef NDEBUG
check_consistency();
#endif
return count;
}
void ColumnVariant::clear_subcolumns_data() {
for (auto& entry : subcolumns) {
for (auto& part : entry->data.data) {
DCHECK_EQ(part->use_count(), 1);
(*std::move(part)).clear();
}
entry->data.num_of_defaults_in_prefix = 0;
}
num_rows = 0;
}
void ColumnVariant::clear() {
Subcolumns empty;
std::swap(empty, subcolumns);
num_rows = 0;
_prev_positions.clear();
}
void ColumnVariant::create_root() {
auto type = is_nullable ? make_nullable(std::make_shared<MostCommonType>())
: std::make_shared<MostCommonType>();
add_sub_column({}, type->create_column(), type);
}
void ColumnVariant::create_root(const DataTypePtr& type, MutableColumnPtr&& column) {
if (num_rows == 0) {
num_rows = column->size();
}
add_sub_column({}, std::move(column), type);
}
DataTypePtr ColumnVariant::get_most_common_type() const {
auto type = is_nullable ? make_nullable(std::make_shared<MostCommonType>())
: std::make_shared<MostCommonType>();
return type;
}
bool ColumnVariant::is_null_root() const {
auto* root = subcolumns.get_root();
if (root == nullptr) {
return true;
}
if (root->data.num_of_defaults_in_prefix == 0 &&
(root->data.data.empty() ||
root->data.get_least_common_type()->get_primitive_type() == INVALID_TYPE)) {
return true;
}
return false;
}
bool ColumnVariant::is_scalar_variant() const {
// Only root itself
return !is_null_root() && subcolumns.get_leaves().size() == 1 &&
subcolumns.get_root()->is_scalar();
}
const DataTypePtr ColumnVariant::NESTED_TYPE = std::make_shared<vectorized::DataTypeNullable>(
std::make_shared<vectorized::DataTypeArray>(std::make_shared<vectorized::DataTypeNullable>(
std::make_shared<vectorized::DataTypeVariant>())));
DataTypePtr ColumnVariant::get_root_type() const {
return subcolumns.get_root()->data.get_least_common_type();
}
#define SANITIZE_ROOT() \
if (is_null_root()) { \
return Status::InternalError("No root column, path {}", path.get_path()); \
} \
if (subcolumns.get_root()->data.get_least_common_type()->get_primitive_type() != TYPE_JSONB) { \
return Status::InternalError( \
"Root column is not jsonb type but {}, path {}", \
subcolumns.get_root()->data.get_least_common_type()->get_name(), path.get_path()); \
}
Status ColumnVariant::extract_root(const PathInData& path, MutableColumnPtr& dst) const {
SANITIZE_ROOT();
if (!path.empty()) {
RETURN_IF_ERROR(schema_util::extract(subcolumns.get_root()->data.get_finalized_column_ptr(),
path, dst));
} else {
if (!dst) {
dst = subcolumns.get_root()->data.get_finalized_column_ptr()->clone_empty();
dst->reserve(num_rows);
}
dst->insert_range_from(*subcolumns.get_root()->data.get_finalized_column_ptr(), 0,
num_rows);
}
return Status::OK();
}
void ColumnVariant::insert_indices_from(const IColumn& src, const uint32_t* indices_begin,
const uint32_t* indices_end) {
// optimize when src and this column are scalar variant, since try_insert is inefficiency
const auto* src_v = check_and_get_column<ColumnVariant>(src);
bool src_can_do_quick_insert =
src_v != nullptr && src_v->is_scalar_variant() && src_v->is_finalized();
// num_rows == 0 means this column is empty, we not need to check it type
if (num_rows == 0 && src_can_do_quick_insert) {
// add a new root column, and insert from src root column
clear();
add_sub_column({}, src_v->get_root()->clone_empty(), src_v->get_root_type());
get_root()->insert_indices_from(*src_v->get_root(), indices_begin, indices_end);
num_rows += indices_end - indices_begin;
} else if (src_can_do_quick_insert && is_scalar_variant() &&
src_v->get_root_type()->equals(*get_root_type())) {
get_root()->insert_indices_from(*src_v->get_root(), indices_begin, indices_end);
num_rows += indices_end - indices_begin;
} else {
for (const auto* x = indices_begin; x != indices_end; ++x) {
try_insert(src[*x]);
}
}
finalize();
}
void ColumnVariant::for_each_imutable_subcolumn(ImutableColumnCallback callback) const {
if (!is_finalized()) {
auto finalized = clone_finalized();
auto& finalized_object = assert_cast<ColumnVariant&>(*finalized);
finalized_object.for_each_imutable_subcolumn(callback);
return;
}
for (const auto& entry : subcolumns) {
for (auto& part : entry->data.data) {
callback(*part);
}
}
}
bool ColumnVariant::is_exclusive() const {
bool is_exclusive = IColumn::is_exclusive();
for_each_imutable_subcolumn([&](const auto& subcolumn) {
if (!subcolumn.is_exclusive()) {
is_exclusive = false;
}
});
return is_exclusive;
}
void ColumnVariant::update_hash_with_value(size_t n, SipHash& hash) const {
for_each_imutable_subcolumn(
[&](const auto& subcolumn) { return subcolumn.update_hash_with_value(n, hash); });
}
void ColumnVariant::update_hashes_with_value(uint64_t* __restrict hashes,
const uint8_t* __restrict null_data) const {
for_each_imutable_subcolumn([&](const auto& subcolumn) {
return subcolumn.update_hashes_with_value(hashes, nullptr);
});
}
void ColumnVariant::update_xxHash_with_value(size_t start, size_t end, uint64_t& hash,
const uint8_t* __restrict null_data) const {
for_each_imutable_subcolumn([&](const auto& subcolumn) {
return subcolumn.update_xxHash_with_value(start, end, hash, nullptr);
});
}
void ColumnVariant::update_crcs_with_value(uint32_t* __restrict hash, PrimitiveType type,
uint32_t rows, uint32_t offset,
const uint8_t* __restrict null_data) const {
for_each_imutable_subcolumn([&](const auto& subcolumn) {
return subcolumn.update_crcs_with_value(hash, type, rows, offset, nullptr);
});
}
void ColumnVariant::update_crc_with_value(size_t start, size_t end, uint32_t& hash,
const uint8_t* __restrict null_data) const {
for_each_imutable_subcolumn([&](const auto& subcolumn) {
return subcolumn.update_crc_with_value(start, end, hash, nullptr);
});
}
std::string ColumnVariant::debug_string() const {
std::stringstream res;
res << get_name() << "(num_row = " << num_rows;
for (auto& entry : subcolumns) {
if (entry->data.is_finalized()) {
res << "[column:" << entry->data.data[0]->dump_structure()
<< ",type:" << entry->data.data_types[0]->get_name()
<< ",path:" << entry->path.get_path() << "],";
}
}
res << ")";
return res.str();
}
Status ColumnVariant::sanitize() const {
#ifndef NDEBUG
RETURN_IF_CATCH_EXCEPTION(check_consistency());
for (const auto& subcolumn : subcolumns) {
if (subcolumn->data.is_finalized()) {
auto column = subcolumn->data.get_least_common_type()->create_column();
std::string original = subcolumn->data.get_finalized_column().get_name();
std::string expected = column->get_name();
if (original != expected) {
return Status::InternalError("Incompatible type between {} and {}, debug_info:",
original, expected, debug_string());
}
}
}
VLOG_DEBUG << "sanitized " << debug_string();
#endif
return Status::OK();
}
ColumnVariant::Subcolumn ColumnVariant::Subcolumn::cut(size_t start, size_t length) const {
Subcolumn new_subcolumn(0, is_nullable);
new_subcolumn.insert_range_from(*this, start, length);
return new_subcolumn;
}
const ColumnVariant::Subcolumns::Node* ColumnVariant::get_leaf_of_the_same_nested(
const Subcolumns::NodePtr& entry) const {
const auto* leaf = subcolumns.get_leaf_of_the_same_nested(
entry->path,
[&](const Subcolumns::Node& node) { return node.data.size() > entry->data.size(); });
if (leaf && leaf->data.get_least_common_typeBase()->get_primitive_type() == INVALID_TYPE) {
return nullptr;
}
return leaf;
}
bool ColumnVariant::try_insert_many_defaults_from_nested(const Subcolumns::NodePtr& entry) const {
const auto* leaf = get_leaf_of_the_same_nested(entry);
if (!leaf) {
return false;
}
size_t old_size = entry->data.size();
FieldInfo field_info = {
.scalar_type_id = entry->data.least_common_type.get_base_type_id(),
.have_nulls = false,
.need_convert = false,
.num_dimensions = entry->data.get_dimensions(),
};
/// Cut the needed range from the found leaf
/// and replace scalar values to the correct
/// default values for given entry.
auto new_subcolumn = leaf->data.cut(old_size, leaf->data.size() - old_size)
.clone_with_default_values(field_info);
entry->data.insert_range_from(new_subcolumn, 0, new_subcolumn.size());
return true;
}
bool ColumnVariant::try_insert_default_from_nested(const Subcolumns::NodePtr& entry) const {
const auto* leaf = get_leaf_of_the_same_nested(entry);
if (!leaf) {
return false;
}
auto last_field = leaf->data.get_last_field();
if (last_field.is_null()) {
return false;
}
size_t leaf_num_dimensions = leaf->data.get_dimensions();
size_t entry_num_dimensions = entry->data.get_dimensions();
auto default_scalar = entry_num_dimensions > leaf_num_dimensions
? Field::create_field<TYPE_ARRAY>(create_empty_array_field(
entry_num_dimensions - leaf_num_dimensions))
: entry->data.get_least_common_type()->get_default();
auto default_field = apply_visitor(
FieldVisitorReplaceScalars(default_scalar, leaf_num_dimensions), last_field);
entry->data.insert(std::move(default_field));
return true;
}
} // namespace doris::vectorized