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doris/be/test/olap/comparison_predicate_test.cpp
HuangWei 10f822eb43 [MemTracker] make all MemTrackers shared (#4135) 
We make all MemTrackers shared, in order to show MemTracker real-time consumptions on the web.
As follows:
1. nearly all MemTracker raw ptr -> shared_ptr
2. Use CreateTracker() to create new MemTracker(in order to add itself to its parent)
3. RowBatch & MemPool still use raw ptrs of MemTracker, it's easy to ensure RowBatch & MemPool destructor exec 
     before MemTracker's destructor. So we don't change these code.
4. MemTracker can use RuntimeProfile's counter to calc consumption. So RuntimeProfile's counter need to be shared 
    too. We add a shared counter pool to store the shared counter, don't change other counters of RuntimeProfile.
Note that, this PR doesn't change the MemTracker tree structure. So there still have some orphan trackers, e.g. RowBlockV2's MemTracker. If you find some shared MemTrackers are little memory consumption & too time-consuming, you could make them be the orphan, then it's fine to use the raw ptr.
2020-07-31 21:57:21 +08:00

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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.
#include <time.h>
#include <gtest/gtest.h>
#include <google/protobuf/stubs/common.h>
#include "olap/field.h"
#include "olap/wrapper_field.h"
#include "olap/column_predicate.h"
#include "olap/comparison_predicate.h"
#include "runtime/mem_pool.h"
#include "runtime/string_value.hpp"
#include "runtime/vectorized_row_batch.h"
#include "util/logging.h"
namespace doris {
namespace datetime {
static uint24_t to_date_timestamp(const char* date_string) {
tm time_tm;
strptime(date_string, "%Y-%m-%d", &time_tm);
int value = (time_tm.tm_year + 1900) * 16 * 32
+ (time_tm.tm_mon + 1) * 32
+ time_tm.tm_mday;
return uint24_t(value);
}
static uint64_t to_datetime_timestamp(const std::string& value_string) {
tm time_tm;
strptime(value_string.c_str(), "%Y-%m-%d %H:%M:%S", &time_tm);
uint64_t value = ((time_tm.tm_year + 1900) * 10000L
+ (time_tm.tm_mon + 1) * 100L
+ time_tm.tm_mday) * 1000000L
+ time_tm.tm_hour * 10000L
+ time_tm.tm_min * 100L
+ time_tm.tm_sec;
return value;
}
static std::string to_date_string(uint24_t& date_value) {
tm time_tm;
int value = date_value;
memset(&time_tm, 0, sizeof(time_tm));
time_tm.tm_mday = static_cast<int>(value & 31);
time_tm.tm_mon = static_cast<int>(value >> 5 & 15) - 1;
time_tm.tm_year = static_cast<int>(value >> 9) - 1900;
char buf[20] = {'\0'};
strftime(buf, sizeof(buf), "%Y-%m-%d", &time_tm);
return std::string(buf);
}
static std::string to_datetime_string(uint64_t& datetime_value) {
tm time_tm;
int64_t part1 = (datetime_value / 1000000L);
int64_t part2 = (datetime_value - part1 * 1000000L);
time_tm.tm_year = static_cast<int>((part1 / 10000L) % 10000) - 1900;
time_tm.tm_mon = static_cast<int>((part1 / 100) % 100) - 1;
time_tm.tm_mday = static_cast<int>(part1 % 100);
time_tm.tm_hour = static_cast<int>((part2 / 10000L) % 10000);
time_tm.tm_min = static_cast<int>((part2 / 100) % 100);
time_tm.tm_sec = static_cast<int>(part2 % 100);
char buf[20] = {'\0'};
strftime(buf, 20, "%Y-%m-%d %H:%M:%S", &time_tm);
return std::string(buf);
}
};
#define TEST_PREDICATE_DEFINITION(CLASS_NAME) \
class CLASS_NAME : public testing::Test { \
public: \
CLASS_NAME() : _vectorized_batch(NULL) { \
_mem_tracker.reset(new MemTracker(-1)); \
_mem_pool.reset(new MemPool(_mem_tracker.get())); \
} \
~CLASS_NAME() {\
if (_vectorized_batch != NULL) { \
delete _vectorized_batch; \
} \
} \
void SetTabletSchema(std::string name, \
const std::string& type, const std::string& aggregation, \
uint32_t length, bool is_allow_null, bool is_key, TabletSchema* tablet_schema) { \
TabletSchemaPB tablet_schema_pb; \
static int id = 0; \
ColumnPB* column = tablet_schema_pb.add_column(); \
column->set_unique_id(++id); \
column->set_name(name); \
column->set_type(type); \
column->set_is_key(is_key); \
column->set_is_nullable(is_allow_null); \
column->set_length(length); \
column->set_aggregation(aggregation); \
column->set_precision(1000); \
column->set_frac(1000); \
column->set_is_bf_column(false); \
tablet_schema->init_from_pb(tablet_schema_pb); \
} \
void InitVectorizedBatch(const TabletSchema* tablet_schema, \
const std::vector<uint32_t>&ids, \
int size) { \
_vectorized_batch = new VectorizedRowBatch(tablet_schema, ids, size); \
_vectorized_batch->set_size(size); \
} \
std::shared_ptr<MemTracker> _mem_tracker; \
std::unique_ptr<MemPool> _mem_pool; \
VectorizedRowBatch* _vectorized_batch; \
}; \
TEST_PREDICATE_DEFINITION(TestEqualPredicate)
TEST_PREDICATE_DEFINITION(TestLessPredicate)
#define TEST_EQUAL_PREDICATE(TYPE, TYPE_NAME, FIELD_TYPE) \
TEST_F(TestEqualPredicate, TYPE_NAME##_COLUMN) { \
TabletSchema tablet_schema; \
SetTabletSchema(std::string("TYPE_NAME##_COLUMN"), FIELD_TYPE, \
"REPLACE", 1, false, true, &tablet_schema); \
int size = 10; \
std::vector<uint32_t> return_columns; \
for (int i = 0; i < tablet_schema.num_columns(); ++i) { \
return_columns.push_back(i); \
} \
InitVectorizedBatch(&tablet_schema, return_columns, size); \
ColumnVector* col_vector = _vectorized_batch->column(0); \
\
/* for no nulls */ \
col_vector->set_no_nulls(true); \
TYPE* col_data = reinterpret_cast<TYPE*>(_mem_pool->allocate(size * sizeof(TYPE))); \
col_vector->set_col_data(col_data); \
for (int i = 0; i < size; ++i) { \
*(col_data + i) = i; \
} \
TYPE value = 5; \
ColumnPredicate* pred = new EqualPredicate<TYPE>(0, value); \
pred->evaluate(_vectorized_batch); \
ASSERT_EQ(_vectorized_batch->size(), 1); \
uint16_t* sel = _vectorized_batch->selected(); \
ASSERT_EQ(*(col_data + sel[0]), 5); \
\
/* for has nulls */ \
col_vector->set_no_nulls(false); \
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size)); \
memset(is_null, 0, size); \
col_vector->set_is_null(is_null); \
for (int i = 0; i < size; ++i) { \
if (i % 2 == 0) { \
is_null[i] = true; \
} else { \
*(col_data + i) = i; \
} \
} \
_vectorized_batch->set_size(size); \
_vectorized_batch->set_selected_in_use(false); \
pred->evaluate(_vectorized_batch); \
ASSERT_EQ(_vectorized_batch->size(), 1); \
sel = _vectorized_batch->selected(); \
ASSERT_EQ(*(col_data + sel[0]), 5); \
delete pred; \
} \
TEST_EQUAL_PREDICATE(int8_t, TINYINT, "TINYINT")
TEST_EQUAL_PREDICATE(int16_t, SMALLINT, "SMALLINT")
TEST_EQUAL_PREDICATE(int32_t, INT, "INT")
TEST_EQUAL_PREDICATE(int64_t, BIGINT, "BIGINT")
TEST_EQUAL_PREDICATE(int128_t, LARGEINT, "LARGEINT")
TEST_F(TestEqualPredicate, FLOAT_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("FLOAT_COLUMN"), "FLOAT",
"REPLACE", 1, false, true, &tablet_schema);
int size = 10;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
float* col_data = reinterpret_cast<float*>(_mem_pool->allocate(size * sizeof(float)));
col_vector->set_col_data(col_data);
for (int i = 0; i < size; ++i) {
*(col_data + i) = i;
}
float value = 5.0;
ColumnPredicate* pred = new EqualPredicate<float>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
uint16_t* sel = _vectorized_batch->selected();
ASSERT_FLOAT_EQ(*(col_data + sel[0]), 5.0);
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
*(col_data + i) = i;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
sel = _vectorized_batch->selected();
ASSERT_FLOAT_EQ(*(col_data + sel[0]), 5.0);
delete pred;
}
TEST_F(TestEqualPredicate, DOUBLE_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("DOUBLE_COLUMN"), "DOUBLE",
"REPLACE", 1, false, true, &tablet_schema);
int size = 10;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
double* col_data = reinterpret_cast<double*>(_mem_pool->allocate(size * sizeof(double)));
col_vector->set_col_data(col_data);
for (int i = 0; i < size; ++i) {
*(col_data + i) = i;
}
double value = 5.0;
ColumnPredicate* pred = new EqualPredicate<double>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
uint16_t* sel = _vectorized_batch->selected();
ASSERT_DOUBLE_EQ(*(col_data + sel[0]), 5.0);
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
*(col_data + i) = i;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
sel = _vectorized_batch->selected();
ASSERT_DOUBLE_EQ(*(col_data + sel[0]), 5.0);
delete pred;
}
TEST_F(TestEqualPredicate, DECIMAL_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("DECIMAL_COLUMN"), "DECIMAL",
"REPLACE", 1, false, true, &tablet_schema);
int size = 10;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
decimal12_t* col_data = reinterpret_cast<decimal12_t*>(_mem_pool->allocate(size * sizeof(decimal12_t)));
col_vector->set_col_data(col_data);
for (int i = 0; i < size; ++i) {
(*(col_data + i)).integer = i;
(*(col_data + i)).fraction = i;
}
decimal12_t value(5, 5);
ColumnPredicate* pred = new EqualPredicate<decimal12_t>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
uint16_t* sel = _vectorized_batch->selected();
ASSERT_EQ(*(col_data + sel[0]), value);
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
(*(col_data + i)).integer = i;
(*(col_data + i)).fraction = i;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
sel = _vectorized_batch->selected();
ASSERT_EQ(*(col_data + sel[0]), value);
delete pred;
}
TEST_F(TestEqualPredicate, STRING_COLUMN) {
TabletSchema char_tablet_schema;
SetTabletSchema(std::string("STRING_COLUMN"), "CHAR",
"REPLACE", 5, false, true, &char_tablet_schema);
// test WrapperField.from_string() for char type
WrapperField* field = WrapperField::create(char_tablet_schema.column(0));
ASSERT_EQ(OLAP_SUCCESS, field->from_string("true"));
const std::string tmp = field->to_string();
ASSERT_EQ(5, tmp.size());
ASSERT_EQ('t', tmp[0]);
ASSERT_EQ('r', tmp[1]);
ASSERT_EQ('u', tmp[2]);
ASSERT_EQ('e', tmp[3]);
ASSERT_EQ(0, tmp[4]);
TabletSchema tablet_schema;
SetTabletSchema(std::string("STRING_COLUMN"), "VARCHAR",
"REPLACE", 1, false, true, &tablet_schema);
int size = 10;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
StringValue* col_data = reinterpret_cast<StringValue*>(_mem_pool->allocate(size * sizeof(StringValue)));
col_vector->set_col_data(col_data);
char* string_buffer = reinterpret_cast<char*>(_mem_pool->allocate(55));
for (int i = 0; i < size; ++i) {
for (int j = 0; j <= i; ++j) {
string_buffer[j] = 'a' + i;
}
(*(col_data + i)).len = i + 1;
(*(col_data + i)).ptr = string_buffer;
string_buffer += i + 1;
}
StringValue value;
const char* value_buffer = "dddd";
value.len = 4;
value.ptr = const_cast<char*>(value_buffer);
ColumnPredicate* pred = new EqualPredicate<StringValue>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
uint16_t* sel = _vectorized_batch->selected();
ASSERT_EQ(sel[0], 3);
ASSERT_EQ(*(col_data + sel[0]), value);
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
string_buffer = reinterpret_cast<char*>(_mem_pool->allocate(55));
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
for (int j = 0; j <= i; ++j) {
string_buffer[j] = 'a' + i;
}
(*(col_data + i)).len = i + 1;
(*(col_data + i)).ptr = string_buffer;
}
string_buffer += i + 1;
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
sel = _vectorized_batch->selected();
ASSERT_EQ(*(col_data + sel[0]), value);
delete field;
delete pred;
}
TEST_F(TestEqualPredicate, DATE_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("DATE_COLUMN"), "DATA",
"REPLACE", 1, false, true, &tablet_schema);
int size = 6;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
uint24_t* col_data = reinterpret_cast<uint24_t*>(_mem_pool->allocate(size * sizeof(uint24_t)));
col_vector->set_col_data(col_data);
std::vector<std::string> date_array;
date_array.push_back("2017-09-07");
date_array.push_back("2017-09-08");
date_array.push_back("2017-09-09");
date_array.push_back("2017-09-10");
date_array.push_back("2017-09-11");
date_array.push_back("2017-09-12");
for (int i = 0; i < size; ++i) {
uint24_t timestamp = datetime::to_date_timestamp(date_array[i].c_str());
*(col_data + i) = timestamp;
}
uint24_t value = datetime::to_date_timestamp("2017-09-10");
ColumnPredicate* pred = new EqualPredicate<uint24_t>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
uint16_t* sel = _vectorized_batch->selected();
ASSERT_EQ(sel[0], 3);
ASSERT_EQ(*(col_data + sel[0]), value);
ASSERT_EQ(datetime::to_date_string(*(col_data + sel[0])), "2017-09-10");
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
uint24_t timestamp = datetime::to_date_timestamp(date_array[i].c_str());
*(col_data + i) = timestamp;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
sel = _vectorized_batch->selected();
ASSERT_EQ(*(col_data + sel[0]), value);
ASSERT_EQ(datetime::to_date_string(*(col_data + sel[0])), "2017-09-10");
delete pred;
}
TEST_F(TestEqualPredicate, DATETIME_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("DATETIME_COLUMN"), "DATETIME",
"REPLACE", 1, false, true, &tablet_schema);
int size = 6;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
uint64_t* col_data = reinterpret_cast<uint64_t*>(_mem_pool->allocate(size * sizeof(uint64_t)));
col_vector->set_col_data(col_data);
std::vector<std::string> date_array;
date_array.push_back("2017-09-07 00:00:00");
date_array.push_back("2017-09-08 00:01:00");
date_array.push_back("2017-09-09 00:00:01");
date_array.push_back("2017-09-10 01:00:00");
date_array.push_back("2017-09-11 01:01:00");
date_array.push_back("2017-09-12 01:01:01");
for (int i = 0; i < size; ++i) {
uint64_t timestamp = datetime::to_datetime_timestamp(date_array[i].c_str());
*(col_data + i) = timestamp;
}
uint64_t value = datetime::to_datetime_timestamp("2017-09-10 01:00:00");
ColumnPredicate* pred = new EqualPredicate<uint64_t>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
uint16_t* sel = _vectorized_batch->selected();
ASSERT_EQ(sel[0], 3);
ASSERT_EQ(*(col_data + sel[0]), value);
ASSERT_EQ(datetime::to_datetime_string(*(col_data + sel[0])), "2017-09-10 01:00:00");
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
uint64_t timestamp = datetime::to_datetime_timestamp(date_array[i].c_str());
*(col_data + i) = timestamp;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
sel = _vectorized_batch->selected();
ASSERT_EQ(*(col_data + sel[0]), value);
ASSERT_EQ(datetime::to_datetime_string(*(col_data + sel[0])), "2017-09-10 01:00:00");
delete pred;
}
#define TEST_LESS_PREDICATE(TYPE, TYPE_NAME, FIELD_TYPE) \
TEST_F(TestLessPredicate, TYPE_NAME##_COLUMN) { \
TabletSchema tablet_schema; \
SetTabletSchema(std::string("TYPE_NAME_COLUMN"), FIELD_TYPE, \
"REPLACE", 1, false, true, &tablet_schema); \
int size = 10; \
std::vector<uint32_t> return_columns; \
for (int i = 0; i < tablet_schema.num_columns(); ++i) { \
return_columns.push_back(i); \
} \
InitVectorizedBatch(&tablet_schema, return_columns, size); \
ColumnVector* col_vector = _vectorized_batch->column(0); \
\
/* for no nulls */ \
col_vector->set_no_nulls(true); \
TYPE* col_data = reinterpret_cast<TYPE*>(_mem_pool->allocate(size * sizeof(TYPE))); \
col_vector->set_col_data(col_data); \
for (int i = 0; i < size; ++i) { \
*(col_data + i) = i; \
} \
TYPE value = 5; \
ColumnPredicate* pred = new LessPredicate<TYPE>(0, value); \
pred->evaluate(_vectorized_batch); \
ASSERT_EQ(_vectorized_batch->size(), 5); \
uint16_t* sel = _vectorized_batch->selected(); \
TYPE sum = 0; \
for (int i = 0; i < _vectorized_batch->size(); ++i) { \
sum += *(col_data + sel[i]); \
} \
ASSERT_EQ(sum, 10); \
\
/* for has nulls */ \
col_vector->set_no_nulls(false); \
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size)); \
memset(is_null, 0, size); \
col_vector->set_is_null(is_null); \
for (int i = 0;i < size; ++i) {\
if (i % 2 == 0) { \
is_null[i] = true; \
} else { \
*(col_data + i) = i; \
} \
} \
_vectorized_batch->set_size(size); \
_vectorized_batch->set_selected_in_use(false); \
pred->evaluate(_vectorized_batch); \
ASSERT_EQ(_vectorized_batch->size(), 2); \
sel = _vectorized_batch->selected(); \
sum = 0; \
for (int i = 0; i < _vectorized_batch->size(); ++i) { \
sum += *(col_data + sel[i]); \
} \
ASSERT_EQ(sum, 4); \
delete pred; \
} \
TEST_LESS_PREDICATE(int8_t, TINYINT, "TINYINT")
TEST_LESS_PREDICATE(int16_t, SMALLINT, "SMALLINT")
TEST_LESS_PREDICATE(int32_t, INT, "INT")
TEST_LESS_PREDICATE(int64_t, BIGINT, "BIGINT")
TEST_LESS_PREDICATE(int128_t, LARGEINT, "LARGEINT")
TEST_F(TestLessPredicate, FLOAT_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("FLOAT_COLUMN"), "FLOAT",
"REPLACE", 1, false, true, &tablet_schema);
int size = 10;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
float* col_data = reinterpret_cast<float*>(_mem_pool->allocate(size * sizeof(float)));
col_vector->set_col_data(col_data);
for (int i = 0; i < size; ++i) {
*(col_data + i) = i;
}
float value = 5.0;
ColumnPredicate* pred = new LessPredicate<float>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 5);
uint16_t* sel = _vectorized_batch->selected();
float sum = 0;
for (int i = 0; i < _vectorized_batch->size(); ++i) {
sum += *(col_data + sel[i]);
}
ASSERT_FLOAT_EQ(sum, 10.0);
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
*(col_data + i) = i;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 2);
sel = _vectorized_batch->selected(); \
sum = 0;
for (int i = 0; i < _vectorized_batch->size(); ++i) {
sum += *(col_data + sel[i]);
}
ASSERT_FLOAT_EQ(sum, 4.0);
delete pred;
}
TEST_F(TestLessPredicate, DOUBLE_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("DOUBLE_COLUMN"), "DOUBLE",
"REPLACE", 1, false, true, &tablet_schema);
int size = 10;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
double* col_data = reinterpret_cast<double*>(_mem_pool->allocate(size * sizeof(double)));
col_vector->set_col_data(col_data);
for (int i = 0; i < size; ++i) {
*(col_data + i) = i;
}
double value = 5.0;
ColumnPredicate* pred = new LessPredicate<double>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 5);
uint16_t* sel = _vectorized_batch->selected();
double sum = 0;
for (int i = 0; i < _vectorized_batch->size(); ++i) {
sum += *(col_data + sel[i]);
}
ASSERT_DOUBLE_EQ(sum, 10.0);
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
*(col_data + i) = i;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 2);
sel = _vectorized_batch->selected(); \
sum = 0;
for (int i = 0; i < _vectorized_batch->size(); ++i) {
sum += *(col_data + sel[i]);
}
ASSERT_DOUBLE_EQ(sum, 4.0);
delete pred;
}
TEST_F(TestLessPredicate, DECIMAL_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("DECIMAL_COLUMN"), "DECIMAL",
"REPLACE", 1, false, true, &tablet_schema);
int size = 10;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
decimal12_t* col_data = reinterpret_cast<decimal12_t*>(_mem_pool->allocate(size * sizeof(decimal12_t)));
col_vector->set_col_data(col_data);
for (int i = 0; i < size; ++i) {
(*(col_data + i)).integer = i;
(*(col_data + i)).fraction = i;
}
decimal12_t value(5, 5);
ColumnPredicate* pred = new LessPredicate<decimal12_t>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 5);
uint16_t* sel = _vectorized_batch->selected();
decimal12_t sum(0, 0);
for (int i = 0; i < _vectorized_batch->size(); ++i) {
sum += *(col_data + sel[i]);
}
ASSERT_EQ(sum.integer, 10);
ASSERT_EQ(sum.fraction, 10);
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
(*(col_data + i)).integer = i;
(*(col_data + i)).fraction = i;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 2);
sum.integer = 0;
sum.fraction = 0;
for (int i = 0; i < _vectorized_batch->size(); ++i) {
sum += *(col_data + sel[i]);
}
ASSERT_EQ(sum.integer, 4);
ASSERT_EQ(sum.fraction, 4);
delete pred;
}
TEST_F(TestLessPredicate, STRING_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("STRING_COLUMN"), "VARCHAR",
"REPLACE", 1, false, true, &tablet_schema);
int size = 10;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
StringValue* col_data = reinterpret_cast<StringValue*>(_mem_pool->allocate(size * sizeof(StringValue)));
col_vector->set_col_data(col_data);
char* string_buffer = reinterpret_cast<char*>(_mem_pool->allocate(55));
for (int i = 0; i < size; ++i) {
for (int j = 0; j <= i; ++j) {
string_buffer[j] = 'a' + i;
}
(*(col_data + i)).len = i + 1;
(*(col_data + i)).ptr = string_buffer;
string_buffer += i + 1;
}
StringValue value;
const char* value_buffer = "dddd";
value.len = 4;
value.ptr = const_cast<char*>(value_buffer);
ColumnPredicate* pred = new LessPredicate<StringValue>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 3);
uint16_t* sel = _vectorized_batch->selected();
ASSERT_TRUE(strncmp((*(col_data + sel[0])).ptr, "a", 1) == 0);
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
string_buffer = reinterpret_cast<char*>(_mem_pool->allocate(55));
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
for (int j = 0; j <= i; ++j) {
string_buffer[j] = 'a' + i;
}
(*(col_data + i)).len = i + 1;
(*(col_data + i)).ptr = string_buffer;
}
string_buffer += i + 1;
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
sel = _vectorized_batch->selected();
ASSERT_TRUE(strncmp((*(col_data + sel[0])).ptr, "bb", 2) == 0);
delete pred;
}
TEST_F(TestLessPredicate, DATE_COLUMN) {
TabletSchema tablet_schema;
SetTabletSchema(std::string("DATE_COLUMN"), "DATE",
"REPLACE", 1, false, true, &tablet_schema);
int size = 6;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
uint24_t* col_data = reinterpret_cast<uint24_t*>(_mem_pool->allocate(size * sizeof(uint24_t)));
col_vector->set_col_data(col_data);
std::vector<std::string> date_array;
date_array.push_back("2017-09-07");
date_array.push_back("2017-09-08");
date_array.push_back("2017-09-09");
date_array.push_back("2017-09-10");
date_array.push_back("2017-09-11");
date_array.push_back("2017-09-12");
for (int i = 0; i < size; ++i) {
uint24_t timestamp = datetime::to_date_timestamp(date_array[i].c_str());
*(col_data + i) = timestamp;
}
uint24_t value = datetime::to_date_timestamp("2017-09-10");
ColumnPredicate* pred = new LessPredicate<uint24_t>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 3);
uint16_t* sel = _vectorized_batch->selected();
ASSERT_EQ(datetime::to_date_string(*(col_data + sel[0])), "2017-09-07");
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
uint24_t timestamp = datetime::to_date_timestamp(date_array[i].c_str());
*(col_data + i) = timestamp;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
sel = _vectorized_batch->selected();
ASSERT_EQ(datetime::to_date_string(*(col_data + sel[0])), "2017-09-08");
delete pred;
}
TEST_F(TestLessPredicate, DATETIME_COLUMN) {
TabletSchema tablet_schema;
TabletColumn tablet_column;
SetTabletSchema(std::string("DATETIME_COLUMN"), "DATETIME",
"REPLACE", 1, false, true, &tablet_schema);
int size = 6;
std::vector<uint32_t> return_columns;
for (int i = 0; i < tablet_schema.num_columns(); ++i) {
return_columns.push_back(i);
}
InitVectorizedBatch(&tablet_schema, return_columns, size);
ColumnVector* col_vector = _vectorized_batch->column(0);
// for no nulls
col_vector->set_no_nulls(true);
uint64_t* col_data = reinterpret_cast<uint64_t*>(_mem_pool->allocate(size * sizeof(uint64_t)));
col_vector->set_col_data(col_data);
std::vector<std::string> date_array;
date_array.push_back("2017-09-07 00:00:00");
date_array.push_back("2017-09-08 00:01:00");
date_array.push_back("2017-09-09 00:00:01");
date_array.push_back("2017-09-10 01:00:00");
date_array.push_back("2017-09-11 01:01:00");
date_array.push_back("2017-09-12 01:01:01");
for (int i = 0; i < size; ++i) {
uint64_t timestamp = datetime::to_datetime_timestamp(date_array[i].c_str());
*(col_data + i) = timestamp;
}
uint64_t value = datetime::to_datetime_timestamp("2017-09-10 01:00:00");
ColumnPredicate* pred = new LessPredicate<uint64_t>(0, value);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 3);
uint16_t* sel = _vectorized_batch->selected();
ASSERT_EQ(datetime::to_datetime_string(*(col_data + sel[0])), "2017-09-07 00:00:00");
// for has nulls
col_vector->set_no_nulls(false);
bool* is_null = reinterpret_cast<bool*>(_mem_pool->allocate(size));
memset(is_null, 0, size);
col_vector->set_is_null(is_null);
for (int i = 0; i < size; ++i) {
if (i % 2 == 0) {
is_null[i] = true;
} else {
uint64_t timestamp = datetime::to_datetime_timestamp(date_array[i].c_str());
*(col_data + i) = timestamp;
}
}
_vectorized_batch->set_size(size);
_vectorized_batch->set_selected_in_use(false);
pred->evaluate(_vectorized_batch);
ASSERT_EQ(_vectorized_batch->size(), 1);
sel = _vectorized_batch->selected();
ASSERT_EQ(datetime::to_datetime_string(*(col_data + sel[0])), "2017-09-08 00:01:00");
delete pred;
}
} // namespace doris
int main(int argc, char** argv) {
int ret = doris::OLAP_SUCCESS;
testing::InitGoogleTest(&argc, argv);
doris::CpuInfo::init();
ret = RUN_ALL_TESTS();
google::protobuf::ShutdownProtobufLibrary();
return ret;
}
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