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milvus/internal/core/unittest/test_c_api.cpp
zhenshan.cao 2ec573d2f9 Recover delete docs 
Signed-off-by: zhenshan.cao <zhenshan.cao@zilliz.com>
2021-01-20 09:36:50 +08:00

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// Copyright (C) 2019-2020 Zilliz. All rights reserved.
//
// Licensed 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 <iostream>
#include <string>
#include <random>
#include <gtest/gtest.h>
#include <chrono>
#include <google/protobuf/text_format.h>
#include "pb/service_msg.pb.h"
#include "segcore/reduce_c.h"
#include <index/knowhere/knowhere/index/vector_index/helpers/IndexParameter.h>
#include <index/knowhere/knowhere/index/vector_index/adapter/VectorAdapter.h>
#include <index/knowhere/knowhere/index/vector_index/VecIndexFactory.h>
#include <index/knowhere/knowhere/index/vector_index/IndexIVFPQ.h>
#include <common/LoadInfo.h>
#include <utils/Types.h>
#include <segcore/Collection.h>
#include "test_utils/DataGen.h"
namespace chrono = std::chrono;
using namespace milvus;
using namespace milvus::segcore;
using namespace milvus::proto;
using namespace milvus::knowhere;
TEST(CApiTest, CollectionTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
DeleteCollection(collection);
}
TEST(CApiTest, GetCollectionNameTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto name = GetCollectionName(collection);
assert(strcmp(name, "default-collection") == 0);
DeleteCollection(collection);
}
TEST(CApiTest, SegmentTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, InsertTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
std::vector<char> raw_data;
std::vector<uint64_t> timestamps;
std::vector<int64_t> uids;
int N = 10000;
std::default_random_engine e(67);
for (int i = 0; i < N; ++i) {
uids.push_back(100000 + i);
timestamps.push_back(0);
// append vec
float vec[16];
for (auto& x : vec) {
x = e() % 2000 * 0.001 - 1.0;
}
raw_data.insert(raw_data.end(), (const char*)std::begin(vec), (const char*)std::end(vec));
int age = e() % 100;
raw_data.insert(raw_data.end(), (const char*)&age, ((const char*)&age) + sizeof(age));
}
auto line_sizeof = (sizeof(int) + sizeof(float) * 16);
auto offset = PreInsert(segment, N);
auto res = Insert(segment, offset, N, uids.data(), timestamps.data(), raw_data.data(), (int)line_sizeof, N);
assert(res.error_code == Success);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, DeleteTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
long delete_row_ids[] = {100000, 100001, 100002};
unsigned long delete_timestamps[] = {0, 0, 0};
auto offset = PreDelete(segment, 3);
auto del_res = Delete(segment, offset, 3, delete_row_ids, delete_timestamps);
assert(del_res.error_code == Success);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, SearchTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
std::vector<char> raw_data;
std::vector<uint64_t> timestamps;
std::vector<int64_t> uids;
int N = 10000;
std::default_random_engine e(67);
for (int i = 0; i < N; ++i) {
uids.push_back(100000 + i);
timestamps.push_back(0);
// append vec
float vec[16];
for (auto& x : vec) {
x = e() % 2000 * 0.001 - 1.0;
}
raw_data.insert(raw_data.end(), (const char*)std::begin(vec), (const char*)std::end(vec));
int age = e() % 100;
raw_data.insert(raw_data.end(), (const char*)&age, ((const char*)&age) + sizeof(age));
}
auto line_sizeof = (sizeof(int) + sizeof(float) * 16);
auto offset = PreInsert(segment, N);
auto ins_res = Insert(segment, offset, N, uids.data(), timestamps.data(), raw_data.data(), (int)line_sizeof, N);
ASSERT_EQ(ins_res.error_code, Success);
const char* dsl_string = R"(
{
"bool": {
"vector": {
"fakevec": {
"metric_type": "L2",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 10
}
}
}
})";
namespace ser = milvus::proto::service;
int num_queries = 10;
int dim = 16;
std::normal_distribution<double> dis(0, 1);
ser::PlaceholderGroup raw_group;
auto value = raw_group.add_placeholders();
value->set_tag("$0");
value->set_type(ser::PlaceholderType::VECTOR_FLOAT);
for (int i = 0; i < num_queries; ++i) {
std::vector<float> vec;
for (int d = 0; d < dim; ++d) {
vec.push_back(dis(e));
}
// std::string line((char*)vec.data(), (char*)vec.data() + vec.size() * sizeof(float));
value->add_values(vec.data(), vec.size() * sizeof(float));
}
auto blob = raw_group.SerializeAsString();
void* plan = nullptr;
auto status = CreatePlan(collection, dsl_string, &plan);
ASSERT_EQ(status.error_code, Success);
void* placeholderGroup = nullptr;
status = ParsePlaceholderGroup(plan, blob.data(), blob.length(), &placeholderGroup);
ASSERT_EQ(status.error_code, Success);
std::vector<CPlaceholderGroup> placeholderGroups;
placeholderGroups.push_back(placeholderGroup);
timestamps.clear();
timestamps.push_back(1);
CQueryResult search_result;
auto res = Search(segment, plan, placeholderGroups.data(), timestamps.data(), 1, &search_result);
ASSERT_EQ(res.error_code, Success);
DeletePlan(plan);
DeletePlaceholderGroup(placeholderGroup);
DeleteQueryResult(search_result);
DeleteCollection(collection);
DeleteSegment(segment);
}
// TEST(CApiTest, BuildIndexTest) {
// auto schema_tmp_conf = "";
// auto collection = NewCollection(schema_tmp_conf);
// auto segment = NewSegment(collection, 0, Growing);
//
// std::vector<char> raw_data;
// std::vector<uint64_t> timestamps;
// std::vector<int64_t> uids;
// int N = 10000;
// std::default_random_engine e(67);
// for (int i = 0; i < N; ++i) {
// uids.push_back(100000 + i);
// timestamps.push_back(0);
// // append vec
// float vec[16];
// for (auto& x : vec) {
// x = e() % 2000 * 0.001 - 1.0;
// }
// raw_data.insert(raw_data.end(), (const char*)std::begin(vec), (const char*)std::end(vec));
// int age = e() % 100;
// raw_data.insert(raw_data.end(), (const char*)&age, ((const char*)&age) + sizeof(age));
// }
//
// auto line_sizeof = (sizeof(int) + sizeof(float) * 16);
//
// auto offset = PreInsert(segment, N);
//
// auto ins_res = Insert(segment, offset, N, uids.data(), timestamps.data(), raw_data.data(), (int)line_sizeof, N);
// assert(ins_res == 0);
//
// // TODO: add index ptr
// Close(segment);
// BuildIndex(collection, segment);
//
// const char* dsl_string = R"(
// {
// "bool": {
// "vector": {
// "fakevec": {
// "metric_type": "L2",
// "params": {
// "nprobe": 10
// },
// "query": "$0",
// "topk": 10
// }
// }
// }
// })";
//
// namespace ser = milvus::proto::service;
// int num_queries = 10;
// int dim = 16;
// std::normal_distribution<double> dis(0, 1);
// ser::PlaceholderGroup raw_group;
// auto value = raw_group.add_placeholders();
// value->set_tag("$0");
// value->set_type(ser::PlaceholderType::VECTOR_FLOAT);
// for (int i = 0; i < num_queries; ++i) {
// std::vector<float> vec;
// for (int d = 0; d < dim; ++d) {
// vec.push_back(dis(e));
// }
// // std::string line((char*)vec.data(), (char*)vec.data() + vec.size() * sizeof(float));
// value->add_values(vec.data(), vec.size() * sizeof(float));
// }
// auto blob = raw_group.SerializeAsString();
//
// auto plan = CreatePlan(collection, dsl_string);
// auto placeholderGroup = ParsePlaceholderGroup(plan, blob.data(), blob.length());
// std::vector<CPlaceholderGroup> placeholderGroups;
// placeholderGroups.push_back(placeholderGroup);
// timestamps.clear();
// timestamps.push_back(1);
//
// auto search_res = Search(segment, plan, placeholderGroups.data(), timestamps.data(), 1);
//
// DeletePlan(plan);
// DeletePlaceholderGroup(placeholderGroup);
// DeleteQueryResult(search_res);
// DeleteCollection(collection);
// DeleteSegment(segment);
//}
TEST(CApiTest, IsOpenedTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
auto is_opened = IsOpened(segment);
assert(is_opened);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, CloseTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
auto status = Close(segment);
assert(status == 0);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, GetMemoryUsageInBytesTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
auto old_memory_usage_size = GetMemoryUsageInBytes(segment);
std::cout << "old_memory_usage_size = " << old_memory_usage_size << std::endl;
std::vector<char> raw_data;
std::vector<uint64_t> timestamps;
std::vector<int64_t> uids;
int N = 10000;
std::default_random_engine e(67);
for (int i = 0; i < N; ++i) {
uids.push_back(100000 + i);
timestamps.push_back(0);
// append vec
float vec[16];
for (auto& x : vec) {
x = e() % 2000 * 0.001 - 1.0;
}
raw_data.insert(raw_data.end(), (const char*)std::begin(vec), (const char*)std::end(vec));
int age = e() % 100;
raw_data.insert(raw_data.end(), (const char*)&age, ((const char*)&age) + sizeof(age));
}
auto line_sizeof = (sizeof(int) + sizeof(float) * 16);
auto offset = PreInsert(segment, N);
auto res = Insert(segment, offset, N, uids.data(), timestamps.data(), raw_data.data(), (int)line_sizeof, N);
assert(res.error_code == Success);
auto memory_usage_size = GetMemoryUsageInBytes(segment);
std::cout << "new_memory_usage_size = " << memory_usage_size << std::endl;
assert(memory_usage_size == 2785280);
DeleteCollection(collection);
DeleteSegment(segment);
}
namespace {
auto
generate_data(int N) {
std::vector<char> raw_data;
std::vector<uint64_t> timestamps;
std::vector<int64_t> uids;
std::default_random_engine er(42);
std::normal_distribution<> distribution(0.0, 1.0);
std::default_random_engine ei(42);
for (int i = 0; i < N; ++i) {
uids.push_back(10 * N + i);
timestamps.push_back(0);
// append vec
float vec[16];
for (auto& x : vec) {
x = distribution(er);
}
raw_data.insert(raw_data.end(), (const char*)std::begin(vec), (const char*)std::end(vec));
int age = ei() % 100;
raw_data.insert(raw_data.end(), (const char*)&age, ((const char*)&age) + sizeof(age));
}
return std::make_tuple(raw_data, timestamps, uids);
}
std::string
generate_collection_shema(std::string metric_type, std::string dim, bool is_binary) {
schema::CollectionSchema collection_schema;
collection_schema.set_name("collection_test");
collection_schema.set_autoid(true);
auto vec_field_schema = collection_schema.add_fields();
vec_field_schema->set_name("fakevec");
vec_field_schema->set_fieldid(100);
if (is_binary) {
vec_field_schema->set_data_type(schema::DataType::VECTOR_BINARY);
} else {
vec_field_schema->set_data_type(schema::DataType::VECTOR_FLOAT);
}
auto metric_type_param = vec_field_schema->add_index_params();
metric_type_param->set_key("metric_type");
metric_type_param->set_value(metric_type);
auto dim_param = vec_field_schema->add_type_params();
dim_param->set_key("dim");
dim_param->set_value(dim);
auto other_field_schema = collection_schema.add_fields();
;
other_field_schema->set_name("counter");
other_field_schema->set_fieldid(101);
other_field_schema->set_data_type(schema::DataType::INT64);
std::string schema_string;
auto marshal = google::protobuf::TextFormat::PrintToString(collection_schema, &schema_string);
assert(marshal == true);
return schema_string;
}
VecIndexPtr
generate_index(
void* raw_data, milvus::knowhere::Config conf, int64_t dim, int64_t topK, int64_t N, std::string index_type) {
auto indexing = knowhere::VecIndexFactory::GetInstance().CreateVecIndex(index_type, knowhere::IndexMode::MODE_CPU);
auto database = milvus::knowhere::GenDataset(N, dim, raw_data);
indexing->Train(database, conf);
indexing->AddWithoutIds(database, conf);
EXPECT_EQ(indexing->Count(), N);
EXPECT_EQ(indexing->Dim(), dim);
EXPECT_EQ(indexing->Count(), N);
EXPECT_EQ(indexing->Dim(), dim);
return indexing;
}
} // namespace
// TEST(CApiTest, TestSearchPreference) {
// auto schema_tmp_conf = "";
// auto collection = NewCollection(schema_tmp_conf);
// auto segment = NewSegment(collection, 0, Growing);
//
// auto beg = chrono::high_resolution_clock::now();
// auto next = beg;
// int N = 1000 * 1000 * 10;
// auto [raw_data, timestamps, uids] = generate_data(N);
// auto line_sizeof = (sizeof(int) + sizeof(float) * 16);
//
// next = chrono::high_resolution_clock::now();
// std::cout << "generate_data: " << chrono::duration_cast<chrono::milliseconds>(next - beg).count() << "ms"
// << std::endl;
// beg = next;
//
// auto offset = PreInsert(segment, N);
// auto res = Insert(segment, offset, N, uids.data(), timestamps.data(), raw_data.data(), (int)line_sizeof, N);
// assert(res == 0);
// next = chrono::high_resolution_clock::now();
// std::cout << "insert: " << chrono::duration_cast<chrono::milliseconds>(next - beg).count() << "ms" << std::endl;
// beg = next;
//
// auto N_del = N / 100;
// std::vector<uint64_t> del_ts(N_del, 100);
// auto pre_off = PreDelete(segment, N_del);
// Delete(segment, pre_off, N_del, uids.data(), del_ts.data());
//
// next = chrono::high_resolution_clock::now();
// std::cout << "delete1: " << chrono::duration_cast<chrono::milliseconds>(next - beg).count() << "ms" << std::endl;
// beg = next;
//
// auto row_count = GetRowCount(segment);
// assert(row_count == N);
//
// std::vector<long> result_ids(10 * 16);
// std::vector<float> result_distances(10 * 16);
//
// CQueryInfo queryInfo{1, 10, "fakevec"};
// auto sea_res =
// Search(segment, queryInfo, 104, (float*)raw_data.data(), 16, result_ids.data(), result_distances.data());
//
// // ASSERT_EQ(sea_res, 0);
// // ASSERT_EQ(result_ids[0], 10 * N);
// // ASSERT_EQ(result_distances[0], 0);
//
// next = chrono::high_resolution_clock::now();
// std::cout << "query1: " << chrono::duration_cast<chrono::milliseconds>(next - beg).count() << "ms" << std::endl;
// beg = next;
// sea_res = Search(segment, queryInfo, 104, (float*)raw_data.data(), 16, result_ids.data(),
// result_distances.data());
//
// // ASSERT_EQ(sea_res, 0);
// // ASSERT_EQ(result_ids[0], 10 * N);
// // ASSERT_EQ(result_distances[0], 0);
//
// next = chrono::high_resolution_clock::now();
// std::cout << "query2: " << chrono::duration_cast<chrono::milliseconds>(next - beg).count() << "ms" << std::endl;
// beg = next;
//
// // Close(segment);
// // BuildIndex(segment);
//
// next = chrono::high_resolution_clock::now();
// std::cout << "build index: " << chrono::duration_cast<chrono::milliseconds>(next - beg).count() << "ms"
// << std::endl;
// beg = next;
//
// std::vector<int64_t> result_ids2(10);
// std::vector<float> result_distances2(10);
//
// sea_res =
// Search(segment, queryInfo, 104, (float*)raw_data.data(), 16, result_ids2.data(), result_distances2.data());
//
// // sea_res = Search(segment, nullptr, 104, result_ids2.data(),
// // result_distances2.data());
//
// next = chrono::high_resolution_clock::now();
// std::cout << "search10: " << chrono::duration_cast<chrono::milliseconds>(next - beg).count() << "ms" << std::endl;
// beg = next;
//
// sea_res =
// Search(segment, queryInfo, 104, (float*)raw_data.data(), 16, result_ids2.data(), result_distances2.data());
//
// next = chrono::high_resolution_clock::now();
// std::cout << "search11: " << chrono::duration_cast<chrono::milliseconds>(next - beg).count() << "ms" << std::endl;
// beg = next;
//
// // std::cout << "case 1" << std::endl;
// // for (int i = 0; i < 10; ++i) {
// // std::cout << result_ids[i] << "->" << result_distances[i] << std::endl;
// // }
// // std::cout << "case 2" << std::endl;
// // for (int i = 0; i < 10; ++i) {
// // std::cout << result_ids2[i] << "->" << result_distances2[i] << std::endl;
// // }
// //
// // for (auto x : result_ids2) {
// // ASSERT_GE(x, 10 * N + N_del);
// // ASSERT_LT(x, 10 * N + N);
// // }
//
// // auto iter = 0;
// // for(int i = 0; i < result_ids.size(); ++i) {
// // auto uid = result_ids[i];
// // auto dis = result_distances[i];
// // if(uid >= 10 * N + N_del) {
// // auto uid2 = result_ids2[iter];
// // auto dis2 = result_distances2[iter];
// // ASSERT_EQ(uid, uid2);
// // ASSERT_EQ(dis, dis2);
// // ++iter;
// // }
// // }
//
// DeleteCollection(collection);
// DeleteSegment(segment);
//}
TEST(CApiTest, GetDeletedCountTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
long delete_row_ids[] = {100000, 100001, 100002};
unsigned long delete_timestamps[] = {0, 0, 0};
auto offset = PreDelete(segment, 3);
auto del_res = Delete(segment, offset, 3, delete_row_ids, delete_timestamps);
assert(del_res.error_code == Success);
// TODO: assert(deleted_count == len(delete_row_ids))
auto deleted_count = GetDeletedCount(segment);
assert(deleted_count == 0);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, GetRowCountTest) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
int N = 10000;
auto [raw_data, timestamps, uids] = generate_data(N);
auto line_sizeof = (sizeof(int) + sizeof(float) * 16);
auto offset = PreInsert(segment, N);
auto res = Insert(segment, offset, N, uids.data(), timestamps.data(), raw_data.data(), (int)line_sizeof, N);
assert(res.error_code == Success);
auto row_count = GetRowCount(segment);
assert(row_count == N);
DeleteCollection(collection);
DeleteSegment(segment);
}
// TEST(CApiTest, SchemaTest) {
// std::string schema_string =
// "id: 6873737669791618215\nname: \"collection0\"\nschema: \u003c\n "
// "field_metas: \u003c\n field_name: \"age\"\n type: INT32\n dim: 1\n \u003e\n "
// "field_metas: \u003c\n field_name: \"field_1\"\n type: VECTOR_FLOAT\n dim: 16\n \u003e\n"
// "\u003e\ncreate_time: 1600416765\nsegment_ids: 6873737669791618215\npartition_tags: \"default\"\n";
//
// auto collection = NewCollection(schema_string.data());
// auto segment = NewSegment(collection, 0, Growing);
// DeleteCollection(collection);
// DeleteSegment(segment);
//}
TEST(CApiTest, MergeInto) {
std::vector<int64_t> uids;
std::vector<float> distance;
std::vector<int64_t> new_uids;
std::vector<float> new_distance;
int64_t num_queries = 1;
int64_t topk = 2;
uids.push_back(1);
uids.push_back(2);
distance.push_back(5);
distance.push_back(1000);
new_uids.push_back(3);
new_uids.push_back(4);
new_distance.push_back(2);
new_distance.push_back(6);
auto res = MergeInto(num_queries, topk, distance.data(), uids.data(), new_distance.data(), new_uids.data());
ASSERT_EQ(res, 0);
ASSERT_EQ(uids[0], 3);
ASSERT_EQ(distance[0], 2);
ASSERT_EQ(uids[1], 1);
ASSERT_EQ(distance[1], 5);
}
TEST(CApiTest, Reduce) {
auto schema_tmp_conf = "";
auto collection = NewCollection(schema_tmp_conf);
auto segment = NewSegment(collection, 0, Growing);
std::vector<char> raw_data;
std::vector<uint64_t> timestamps;
std::vector<int64_t> uids;
int N = 10000;
std::default_random_engine e(67);
for (int i = 0; i < N; ++i) {
uids.push_back(100000 + i);
timestamps.push_back(0);
// append vec
float vec[16];
for (auto& x : vec) {
x = e() % 2000 * 0.001 - 1.0;
}
raw_data.insert(raw_data.end(), (const char*)std::begin(vec), (const char*)std::end(vec));
int age = e() % 100;
raw_data.insert(raw_data.end(), (const char*)&age, ((const char*)&age) + sizeof(age));
}
auto line_sizeof = (sizeof(int) + sizeof(float) * 16);
auto offset = PreInsert(segment, N);
auto ins_res = Insert(segment, offset, N, uids.data(), timestamps.data(), raw_data.data(), (int)line_sizeof, N);
assert(ins_res.error_code == Success);
const char* dsl_string = R"(
{
"bool": {
"vector": {
"fakevec": {
"metric_type": "L2",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 10
}
}
}
})";
namespace ser = milvus::proto::service;
int num_queries = 10;
int dim = 16;
std::normal_distribution<double> dis(0, 1);
ser::PlaceholderGroup raw_group;
auto value = raw_group.add_placeholders();
value->set_tag("$0");
value->set_type(ser::PlaceholderType::VECTOR_FLOAT);
for (int i = 0; i < num_queries; ++i) {
std::vector<float> vec;
for (int d = 0; d < dim; ++d) {
vec.push_back(dis(e));
}
// std::string line((char*)vec.data(), (char*)vec.data() + vec.size() * sizeof(float));
value->add_values(vec.data(), vec.size() * sizeof(float));
}
auto blob = raw_group.SerializeAsString();
void* plan = nullptr;
auto status = CreatePlan(collection, dsl_string, &plan);
assert(status.error_code == Success);
void* placeholderGroup = nullptr;
status = ParsePlaceholderGroup(plan, blob.data(), blob.length(), &placeholderGroup);
assert(status.error_code == Success);
std::vector<CPlaceholderGroup> placeholderGroups;
placeholderGroups.push_back(placeholderGroup);
timestamps.clear();
timestamps.push_back(1);
std::vector<CQueryResult> results;
CQueryResult res1;
CQueryResult res2;
auto res = Search(segment, plan, placeholderGroups.data(), timestamps.data(), 1, &res1);
assert(res.error_code == Success);
res = Search(segment, plan, placeholderGroups.data(), timestamps.data(), 1, &res2);
assert(res.error_code == Success);
results.push_back(res1);
results.push_back(res2);
bool is_selected[1] = {false};
status = ReduceQueryResults(results.data(), 1, is_selected);
assert(status.error_code == Success);
FillTargetEntry(segment, plan, res1);
void* reorganize_search_result = nullptr;
status = ReorganizeQueryResults(&reorganize_search_result, placeholderGroups.data(), 1, results.data(), is_selected,
1, plan);
assert(status.error_code == Success);
auto hits_blob_size = GetHitsBlobSize(reorganize_search_result);
assert(hits_blob_size > 0);
std::vector<char> hits_blob;
hits_blob.resize(hits_blob_size);
GetHitsBlob(reorganize_search_result, hits_blob.data());
assert(hits_blob.data() != nullptr);
auto num_queries_group = GetNumQueriesPeerGroup(reorganize_search_result, 0);
assert(num_queries_group == 10);
std::vector<int64_t> hit_size_peer_query;
hit_size_peer_query.resize(num_queries_group);
GetHitSizePeerQueries(reorganize_search_result, 0, hit_size_peer_query.data());
assert(hit_size_peer_query[0] > 0);
DeletePlan(plan);
DeletePlaceholderGroup(placeholderGroup);
DeleteQueryResult(res1);
DeleteQueryResult(res2);
DeleteMarshaledHits(reorganize_search_result);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, LoadIndexInfo) {
// generator index
constexpr auto DIM = 16;
constexpr auto K = 10;
auto N = 1024 * 10;
auto [raw_data, timestamps, uids] = generate_data(N);
auto indexing = std::make_shared<milvus::knowhere::IVFPQ>();
auto conf = milvus::knowhere::Config{{milvus::knowhere::meta::DIM, DIM},
{milvus::knowhere::meta::TOPK, K},
{milvus::knowhere::IndexParams::nlist, 100},
{milvus::knowhere::IndexParams::nprobe, 4},
{milvus::knowhere::IndexParams::m, 4},
{milvus::knowhere::IndexParams::nbits, 8},
{milvus::knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
{milvus::knowhere::meta::DEVICEID, 0}};
auto database = milvus::knowhere::GenDataset(N, DIM, raw_data.data());
indexing->Train(database, conf);
indexing->AddWithoutIds(database, conf);
EXPECT_EQ(indexing->Count(), N);
EXPECT_EQ(indexing->Dim(), DIM);
auto binary_set = indexing->Serialize(conf);
CBinarySet c_binary_set = (CBinarySet)&binary_set;
void* c_load_index_info = nullptr;
auto status = NewLoadIndexInfo(&c_load_index_info);
assert(status.error_code == Success);
std::string index_param_key1 = "index_type";
std::string index_param_value1 = "IVF_PQ";
status = AppendIndexParam(c_load_index_info, index_param_key1.data(), index_param_value1.data());
std::string index_param_key2 = "index_mode";
std::string index_param_value2 = "cpu";
status = AppendIndexParam(c_load_index_info, index_param_key2.data(), index_param_value2.data());
assert(status.error_code == Success);
std::string field_name = "field0";
status = AppendFieldInfo(c_load_index_info, field_name.data(), 0);
assert(status.error_code == Success);
status = AppendIndex(c_load_index_info, c_binary_set);
assert(status.error_code == Success);
DeleteLoadIndexInfo(c_load_index_info);
}
TEST(CApiTest, LoadIndex_Search) {
// generator index
constexpr auto DIM = 16;
constexpr auto K = 10;
auto N = 1024 * 1024 * 10;
auto num_query = 100;
auto [raw_data, timestamps, uids] = generate_data(N);
auto indexing = std::make_shared<milvus::knowhere::IVFPQ>();
auto conf = milvus::knowhere::Config{{milvus::knowhere::meta::DIM, DIM},
{milvus::knowhere::meta::TOPK, K},
{milvus::knowhere::IndexParams::nlist, 100},
{milvus::knowhere::IndexParams::nprobe, 4},
{milvus::knowhere::IndexParams::m, 4},
{milvus::knowhere::IndexParams::nbits, 8},
{milvus::knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
{milvus::knowhere::meta::DEVICEID, 0}};
auto database = milvus::knowhere::GenDataset(N, DIM, raw_data.data());
indexing->Train(database, conf);
indexing->AddWithoutIds(database, conf);
EXPECT_EQ(indexing->Count(), N);
EXPECT_EQ(indexing->Dim(), DIM);
// serializ index to binarySet
auto binary_set = indexing->Serialize(conf);
// fill loadIndexInfo
LoadIndexInfo load_index_info;
auto& index_params = load_index_info.index_params;
index_params["index_type"] = "IVF_PQ";
index_params["index_mode"] = "CPU";
auto mode = milvus::knowhere::IndexMode::MODE_CPU;
load_index_info.index =
milvus::knowhere::VecIndexFactory::GetInstance().CreateVecIndex(index_params["index_type"], mode);
load_index_info.index->Load(binary_set);
// search
auto query_dataset = milvus::knowhere::GenDataset(num_query, DIM, raw_data.data() + DIM * 4200);
auto result = indexing->Query(query_dataset, conf, nullptr);
auto ids = result->Get<int64_t*>(milvus::knowhere::meta::IDS);
auto dis = result->Get<float*>(milvus::knowhere::meta::DISTANCE);
for (int i = 0; i < std::min(num_query * K, 100); ++i) {
std::cout << ids[i] << "->" << dis[i] << std::endl;
}
}
TEST(CApiTest, UpdateSegmentIndex_Without_Predicate) {
// insert data to segment
constexpr auto DIM = 16;
constexpr auto K = 5;
std::string schema_string = generate_collection_shema("L2", "16", false);
auto collection = NewCollection(schema_string.c_str());
auto schema = ((segcore::Collection*)collection)->get_schema();
auto segment = NewSegment(collection, 0, Growing);
auto N = 1000 * 1000;
auto dataset = DataGen(schema, N);
auto vec_col = dataset.get_col<float>(0);
auto query_ptr = vec_col.data() + 420000 * DIM;
PreInsert(segment, N);
auto ins_res = Insert(segment, 0, N, dataset.row_ids_.data(), dataset.timestamps_.data(), dataset.raw_.raw_data,
dataset.raw_.sizeof_per_row, dataset.raw_.count);
assert(ins_res.error_code == Success);
const char* dsl_string = R"(
{
"bool": {
"vector": {
"fakevec": {
"metric_type": "L2",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 5
}
}
}
})";
// create place_holder_group
int num_queries = 5;
auto raw_group = CreatePlaceholderGroupFromBlob(num_queries, 16, query_ptr);
auto blob = raw_group.SerializeAsString();
// search on segment's small index
void* plan = nullptr;
auto status = CreatePlan(collection, dsl_string, &plan);
assert(status.error_code == Success);
void* placeholderGroup = nullptr;
status = ParsePlaceholderGroup(plan, blob.data(), blob.length(), &placeholderGroup);
assert(status.error_code == Success);
std::vector<CPlaceholderGroup> placeholderGroups;
placeholderGroups.push_back(placeholderGroup);
Timestamp time = 10000000;
CQueryResult c_search_result_on_smallIndex;
auto res_before_load_index =
Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_smallIndex);
assert(res_before_load_index.error_code == Success);
// load index to segment
auto conf = milvus::knowhere::Config{{milvus::knowhere::meta::DIM, DIM},
{milvus::knowhere::meta::TOPK, K},
{milvus::knowhere::IndexParams::nlist, 100},
{milvus::knowhere::IndexParams::nprobe, 10},
{milvus::knowhere::IndexParams::m, 4},
{milvus::knowhere::IndexParams::nbits, 8},
{milvus::knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
{milvus::knowhere::meta::DEVICEID, 0}};
auto indexing = generate_index(vec_col.data(), conf, DIM, K, N, IndexEnum::INDEX_FAISS_IVFPQ);
// gen query dataset
auto query_dataset = milvus::knowhere::GenDataset(num_queries, DIM, query_ptr);
auto result_on_index = indexing->Query(query_dataset, conf, nullptr);
auto ids = result_on_index->Get<int64_t*>(milvus::knowhere::meta::IDS);
auto dis = result_on_index->Get<float*>(milvus::knowhere::meta::DISTANCE);
std::vector<int64_t> vec_ids(ids, ids + K * num_queries);
std::vector<float> vec_dis;
for (int j = 0; j < K * num_queries; ++j) {
vec_dis.push_back(dis[j] * -1);
}
auto search_result_on_raw_index = (QueryResult*)c_search_result_on_smallIndex;
search_result_on_raw_index->internal_seg_offsets_ = vec_ids;
search_result_on_raw_index->result_distances_ = vec_dis;
auto binary_set = indexing->Serialize(conf);
void* c_load_index_info = nullptr;
status = NewLoadIndexInfo(&c_load_index_info);
assert(status.error_code == Success);
std::string index_type_key = "index_type";
std::string index_type_value = "IVF_PQ";
std::string index_mode_key = "index_mode";
std::string index_mode_value = "cpu";
std::string metric_type_key = "metric_type";
std::string metric_type_value = "L2";
AppendIndexParam(c_load_index_info, index_type_key.c_str(), index_type_value.c_str());
AppendIndexParam(c_load_index_info, index_mode_key.c_str(), index_mode_value.c_str());
AppendIndexParam(c_load_index_info, metric_type_key.c_str(), metric_type_value.c_str());
AppendFieldInfo(c_load_index_info, "fakevec", 0);
AppendIndex(c_load_index_info, (CBinarySet)&binary_set);
status = UpdateSegmentIndex(segment, c_load_index_info);
assert(status.error_code == Success);
CQueryResult c_search_result_on_bigIndex;
auto res_after_load_index = Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_bigIndex);
assert(res_after_load_index.error_code == Success);
auto search_result_on_raw_index_json = QueryResultToJson(*search_result_on_raw_index);
auto search_result_on_bigIndex_json = QueryResultToJson((*(QueryResult*)c_search_result_on_bigIndex));
std::cout << search_result_on_raw_index_json.dump(1) << std::endl;
std::cout << search_result_on_bigIndex_json.dump(1) << std::endl;
ASSERT_EQ(search_result_on_raw_index_json.dump(1), search_result_on_bigIndex_json.dump(1));
DeleteLoadIndexInfo(c_load_index_info);
DeletePlan(plan);
DeletePlaceholderGroup(placeholderGroup);
DeleteQueryResult(c_search_result_on_smallIndex);
DeleteQueryResult(c_search_result_on_bigIndex);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, UpdateSegmentIndex_With_float_Predicate_Range) {
// insert data to segment
constexpr auto DIM = 16;
constexpr auto K = 5;
std::string schema_string = generate_collection_shema("L2", "16", false);
auto collection = NewCollection(schema_string.c_str());
auto schema = ((segcore::Collection*)collection)->get_schema();
auto segment = NewSegment(collection, 0, Growing);
auto N = 1000 * 1000;
auto dataset = DataGen(schema, N);
auto vec_col = dataset.get_col<float>(0);
auto query_ptr = vec_col.data() + 420000 * DIM;
PreInsert(segment, N);
auto ins_res = Insert(segment, 0, N, dataset.row_ids_.data(), dataset.timestamps_.data(), dataset.raw_.raw_data,
dataset.raw_.sizeof_per_row, dataset.raw_.count);
assert(ins_res.error_code == Success);
const char* dsl_string = R"({
"bool": {
"must": [
{
"range": {
"counter": {
"GE": 420000,
"LT": 420010
}
}
},
{
"vector": {
"fakevec": {
"metric_type": "L2",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 5
}
}
}
]
}
})";
// create place_holder_group
int num_queries = 10;
auto raw_group = CreatePlaceholderGroupFromBlob(num_queries, DIM, query_ptr);
auto blob = raw_group.SerializeAsString();
// search on segment's small index
void* plan = nullptr;
auto status = CreatePlan(collection, dsl_string, &plan);
assert(status.error_code == Success);
void* placeholderGroup = nullptr;
status = ParsePlaceholderGroup(plan, blob.data(), blob.length(), &placeholderGroup);
assert(status.error_code == Success);
std::vector<CPlaceholderGroup> placeholderGroups;
placeholderGroups.push_back(placeholderGroup);
Timestamp time = 10000000;
CQueryResult c_search_result_on_smallIndex;
auto res_before_load_index =
Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_smallIndex);
assert(res_before_load_index.error_code == Success);
// load index to segment
auto conf = milvus::knowhere::Config{{milvus::knowhere::meta::DIM, DIM},
{milvus::knowhere::meta::TOPK, K},
{milvus::knowhere::IndexParams::nlist, 100},
{milvus::knowhere::IndexParams::nprobe, 10},
{milvus::knowhere::IndexParams::m, 4},
{milvus::knowhere::IndexParams::nbits, 8},
{milvus::knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
{milvus::knowhere::meta::DEVICEID, 0}};
auto indexing = generate_index(vec_col.data(), conf, DIM, K, N, IndexEnum::INDEX_FAISS_IVFPQ);
// gen query dataset
auto query_dataset = milvus::knowhere::GenDataset(num_queries, DIM, query_ptr);
auto result_on_index = indexing->Query(query_dataset, conf, nullptr);
auto ids = result_on_index->Get<int64_t*>(milvus::knowhere::meta::IDS);
auto dis = result_on_index->Get<float*>(milvus::knowhere::meta::DISTANCE);
std::vector<int64_t> vec_ids(ids, ids + K * num_queries);
std::vector<float> vec_dis;
for (int j = 0; j < K * num_queries; ++j) {
vec_dis.push_back(dis[j] * -1);
}
auto search_result_on_raw_index = (QueryResult*)c_search_result_on_smallIndex;
search_result_on_raw_index->internal_seg_offsets_ = vec_ids;
search_result_on_raw_index->result_distances_ = vec_dis;
auto binary_set = indexing->Serialize(conf);
void* c_load_index_info = nullptr;
status = NewLoadIndexInfo(&c_load_index_info);
assert(status.error_code == Success);
std::string index_type_key = "index_type";
std::string index_type_value = "IVF_PQ";
std::string index_mode_key = "index_mode";
std::string index_mode_value = "cpu";
std::string metric_type_key = "metric_type";
std::string metric_type_value = "L2";
AppendIndexParam(c_load_index_info, index_type_key.c_str(), index_type_value.c_str());
AppendIndexParam(c_load_index_info, index_mode_key.c_str(), index_mode_value.c_str());
AppendIndexParam(c_load_index_info, metric_type_key.c_str(), metric_type_value.c_str());
AppendFieldInfo(c_load_index_info, "fakevec", 0);
AppendIndex(c_load_index_info, (CBinarySet)&binary_set);
status = UpdateSegmentIndex(segment, c_load_index_info);
assert(status.error_code == Success);
CQueryResult c_search_result_on_bigIndex;
auto res_after_load_index = Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_bigIndex);
assert(res_after_load_index.error_code == Success);
auto search_result_on_bigIndex = (*(QueryResult*)c_search_result_on_bigIndex);
for (int i = 0; i < num_queries; ++i) {
auto offset = i * K;
ASSERT_EQ(search_result_on_bigIndex.internal_seg_offsets_[offset], 420000 + i);
ASSERT_EQ(search_result_on_bigIndex.result_distances_[offset],
search_result_on_raw_index->result_distances_[offset]);
}
DeleteLoadIndexInfo(c_load_index_info);
DeletePlan(plan);
DeletePlaceholderGroup(placeholderGroup);
DeleteQueryResult(c_search_result_on_smallIndex);
DeleteQueryResult(c_search_result_on_bigIndex);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, UpdateSegmentIndex_With_float_Predicate_Term) {
// insert data to segment
constexpr auto DIM = 16;
constexpr auto K = 5;
std::string schema_string = generate_collection_shema("L2", "16", false);
auto collection = NewCollection(schema_string.c_str());
auto schema = ((segcore::Collection*)collection)->get_schema();
auto segment = NewSegment(collection, 0, Growing);
auto N = 1000 * 1000;
auto dataset = DataGen(schema, N);
auto vec_col = dataset.get_col<float>(0);
auto query_ptr = vec_col.data() + 420000 * DIM;
PreInsert(segment, N);
auto ins_res = Insert(segment, 0, N, dataset.row_ids_.data(), dataset.timestamps_.data(), dataset.raw_.raw_data,
dataset.raw_.sizeof_per_row, dataset.raw_.count);
assert(ins_res.error_code == Success);
const char* dsl_string = R"({
"bool": {
"must": [
{
"term": {
"counter": {
"values": [420000, 420001, 420002, 420003, 420004]
}
}
},
{
"vector": {
"fakevec": {
"metric_type": "L2",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 5
}
}
}
]
}
})";
// create place_holder_group
int num_queries = 5;
auto raw_group = CreatePlaceholderGroupFromBlob(num_queries, DIM, query_ptr);
auto blob = raw_group.SerializeAsString();
// search on segment's small index
void* plan = nullptr;
auto status = CreatePlan(collection, dsl_string, &plan);
assert(status.error_code == Success);
void* placeholderGroup = nullptr;
status = ParsePlaceholderGroup(plan, blob.data(), blob.length(), &placeholderGroup);
assert(status.error_code == Success);
std::vector<CPlaceholderGroup> placeholderGroups;
placeholderGroups.push_back(placeholderGroup);
Timestamp time = 10000000;
CQueryResult c_search_result_on_smallIndex;
auto res_before_load_index =
Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_smallIndex);
assert(res_before_load_index.error_code == Success);
// load index to segment
auto conf = milvus::knowhere::Config{{milvus::knowhere::meta::DIM, DIM},
{milvus::knowhere::meta::TOPK, K},
{milvus::knowhere::IndexParams::nlist, 100},
{milvus::knowhere::IndexParams::nprobe, 10},
{milvus::knowhere::IndexParams::m, 4},
{milvus::knowhere::IndexParams::nbits, 8},
{milvus::knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
{milvus::knowhere::meta::DEVICEID, 0}};
auto indexing = generate_index(vec_col.data(), conf, DIM, K, N, IndexEnum::INDEX_FAISS_IVFPQ);
// gen query dataset
auto query_dataset = milvus::knowhere::GenDataset(num_queries, DIM, query_ptr);
auto result_on_index = indexing->Query(query_dataset, conf, nullptr);
auto ids = result_on_index->Get<int64_t*>(milvus::knowhere::meta::IDS);
auto dis = result_on_index->Get<float*>(milvus::knowhere::meta::DISTANCE);
std::vector<int64_t> vec_ids(ids, ids + K * num_queries);
std::vector<float> vec_dis;
for (int j = 0; j < K * num_queries; ++j) {
vec_dis.push_back(dis[j] * -1);
}
auto search_result_on_raw_index = (QueryResult*)c_search_result_on_smallIndex;
search_result_on_raw_index->internal_seg_offsets_ = vec_ids;
search_result_on_raw_index->result_distances_ = vec_dis;
auto binary_set = indexing->Serialize(conf);
void* c_load_index_info = nullptr;
status = NewLoadIndexInfo(&c_load_index_info);
assert(status.error_code == Success);
std::string index_type_key = "index_type";
std::string index_type_value = "IVF_PQ";
std::string index_mode_key = "index_mode";
std::string index_mode_value = "cpu";
std::string metric_type_key = "metric_type";
std::string metric_type_value = "L2";
AppendIndexParam(c_load_index_info, index_type_key.c_str(), index_type_value.c_str());
AppendIndexParam(c_load_index_info, index_mode_key.c_str(), index_mode_value.c_str());
AppendIndexParam(c_load_index_info, metric_type_key.c_str(), metric_type_value.c_str());
AppendFieldInfo(c_load_index_info, "fakevec", 0);
AppendIndex(c_load_index_info, (CBinarySet)&binary_set);
status = UpdateSegmentIndex(segment, c_load_index_info);
assert(status.error_code == Success);
CQueryResult c_search_result_on_bigIndex;
auto res_after_load_index = Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_bigIndex);
assert(res_after_load_index.error_code == Success);
auto search_result_on_bigIndex = (*(QueryResult*)c_search_result_on_bigIndex);
for (int i = 0; i < num_queries; ++i) {
auto offset = i * K;
ASSERT_EQ(search_result_on_bigIndex.internal_seg_offsets_[offset], 420000 + i);
ASSERT_EQ(search_result_on_bigIndex.result_distances_[offset],
search_result_on_raw_index->result_distances_[offset]);
}
DeleteLoadIndexInfo(c_load_index_info);
DeletePlan(plan);
DeletePlaceholderGroup(placeholderGroup);
DeleteQueryResult(c_search_result_on_smallIndex);
DeleteQueryResult(c_search_result_on_bigIndex);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, UpdateSegmentIndex_With_binary_Predicate_Range) {
// insert data to segment
constexpr auto DIM = 16;
constexpr auto K = 5;
std::string schema_string = generate_collection_shema("JACCARD", "16", true);
auto collection = NewCollection(schema_string.c_str());
auto schema = ((segcore::Collection*)collection)->get_schema();
auto segment = NewSegment(collection, 0, Growing);
auto N = 1000 * 1000;
auto dataset = DataGen(schema, N);
auto vec_col = dataset.get_col<uint8_t>(0);
auto query_ptr = vec_col.data() + 420000 * DIM / 8;
PreInsert(segment, N);
auto ins_res = Insert(segment, 0, N, dataset.row_ids_.data(), dataset.timestamps_.data(), dataset.raw_.raw_data,
dataset.raw_.sizeof_per_row, dataset.raw_.count);
assert(ins_res.error_code == Success);
const char* dsl_string = R"({
"bool": {
"must": [
{
"range": {
"counter": {
"GE": 420000,
"LT": 420010
}
}
},
{
"vector": {
"fakevec": {
"metric_type": "JACCARD",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 5
}
}
}
]
}
})";
// create place_holder_group
int num_queries = 5;
auto raw_group = CreateBinaryPlaceholderGroupFromBlob(num_queries, DIM, query_ptr);
auto blob = raw_group.SerializeAsString();
// search on segment's small index
void* plan = nullptr;
auto status = CreatePlan(collection, dsl_string, &plan);
assert(status.error_code == Success);
void* placeholderGroup = nullptr;
status = ParsePlaceholderGroup(plan, blob.data(), blob.length(), &placeholderGroup);
assert(status.error_code == Success);
std::vector<CPlaceholderGroup> placeholderGroups;
placeholderGroups.push_back(placeholderGroup);
Timestamp time = 10000000;
CQueryResult c_search_result_on_smallIndex;
auto res_before_load_index =
Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_smallIndex);
assert(res_before_load_index.error_code == Success);
// load index to segment
auto conf = milvus::knowhere::Config{
{milvus::knowhere::meta::DIM, DIM},
{milvus::knowhere::meta::TOPK, K},
{milvus::knowhere::IndexParams::nprobe, 10},
{milvus::knowhere::IndexParams::nlist, 100},
{milvus::knowhere::IndexParams::m, 4},
{milvus::knowhere::IndexParams::nbits, 8},
{milvus::knowhere::Metric::TYPE, milvus::knowhere::Metric::JACCARD},
};
auto indexing = generate_index(vec_col.data(), conf, DIM, K, N, IndexEnum::INDEX_FAISS_BIN_IVFFLAT);
// gen query dataset
auto query_dataset = milvus::knowhere::GenDataset(num_queries, DIM, query_ptr);
auto result_on_index = indexing->Query(query_dataset, conf, nullptr);
auto ids = result_on_index->Get<int64_t*>(milvus::knowhere::meta::IDS);
auto dis = result_on_index->Get<float*>(milvus::knowhere::meta::DISTANCE);
std::vector<int64_t> vec_ids(ids, ids + K * num_queries);
std::vector<float> vec_dis;
for (int j = 0; j < K * num_queries; ++j) {
vec_dis.push_back(dis[j] * -1);
}
auto search_result_on_raw_index = (QueryResult*)c_search_result_on_smallIndex;
search_result_on_raw_index->internal_seg_offsets_ = vec_ids;
search_result_on_raw_index->result_distances_ = vec_dis;
auto binary_set = indexing->Serialize(conf);
void* c_load_index_info = nullptr;
status = NewLoadIndexInfo(&c_load_index_info);
assert(status.error_code == Success);
std::string index_type_key = "index_type";
std::string index_type_value = "BIN_IVF_FLAT";
std::string index_mode_key = "index_mode";
std::string index_mode_value = "cpu";
std::string metric_type_key = "metric_type";
std::string metric_type_value = "JACCARD";
AppendIndexParam(c_load_index_info, index_type_key.c_str(), index_type_value.c_str());
AppendIndexParam(c_load_index_info, index_mode_key.c_str(), index_mode_value.c_str());
AppendIndexParam(c_load_index_info, metric_type_key.c_str(), metric_type_value.c_str());
AppendFieldInfo(c_load_index_info, "fakevec", 0);
AppendIndex(c_load_index_info, (CBinarySet)&binary_set);
status = UpdateSegmentIndex(segment, c_load_index_info);
assert(status.error_code == Success);
CQueryResult c_search_result_on_bigIndex;
auto res_after_load_index = Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_bigIndex);
assert(res_after_load_index.error_code == Success);
auto search_result_on_bigIndex = (*(QueryResult*)c_search_result_on_bigIndex);
for (int i = 0; i < num_queries; ++i) {
auto offset = i * K;
ASSERT_EQ(search_result_on_bigIndex.internal_seg_offsets_[offset], 420000 + i);
ASSERT_EQ(search_result_on_bigIndex.result_distances_[offset],
search_result_on_raw_index->result_distances_[offset]);
}
DeleteLoadIndexInfo(c_load_index_info);
DeletePlan(plan);
DeletePlaceholderGroup(placeholderGroup);
DeleteQueryResult(c_search_result_on_smallIndex);
DeleteQueryResult(c_search_result_on_bigIndex);
DeleteCollection(collection);
DeleteSegment(segment);
}
TEST(CApiTest, UpdateSegmentIndex_With_binary_Predicate_Term) {
// insert data to segment
constexpr auto DIM = 16;
constexpr auto K = 5;
std::string schema_string = generate_collection_shema("JACCARD", "16", true);
auto collection = NewCollection(schema_string.c_str());
auto schema = ((segcore::Collection*)collection)->get_schema();
auto segment = NewSegment(collection, 0, Growing);
auto N = 1000 * 1000;
auto dataset = DataGen(schema, N);
auto vec_col = dataset.get_col<uint8_t>(0);
auto query_ptr = vec_col.data() + 420000 * DIM / 8;
PreInsert(segment, N);
auto ins_res = Insert(segment, 0, N, dataset.row_ids_.data(), dataset.timestamps_.data(), dataset.raw_.raw_data,
dataset.raw_.sizeof_per_row, dataset.raw_.count);
assert(ins_res.error_code == Success);
const char* dsl_string = R"({
"bool": {
"must": [
{
"term": {
"counter": {
"values": [420000, 420001, 420002, 420003, 420004]
}
}
},
{
"vector": {
"fakevec": {
"metric_type": "JACCARD",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 5
}
}
}
]
}
})";
// create place_holder_group
int num_queries = 5;
auto raw_group = CreateBinaryPlaceholderGroupFromBlob(num_queries, DIM, query_ptr);
auto blob = raw_group.SerializeAsString();
// search on segment's small index
void* plan = nullptr;
auto status = CreatePlan(collection, dsl_string, &plan);
assert(status.error_code == Success);
void* placeholderGroup = nullptr;
status = ParsePlaceholderGroup(plan, blob.data(), blob.length(), &placeholderGroup);
assert(status.error_code == Success);
std::vector<CPlaceholderGroup> placeholderGroups;
placeholderGroups.push_back(placeholderGroup);
Timestamp time = 10000000;
CQueryResult c_search_result_on_smallIndex;
auto res_before_load_index =
Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_smallIndex);
assert(res_before_load_index.error_code == Success);
// load index to segment
auto conf = milvus::knowhere::Config{
{milvus::knowhere::meta::DIM, DIM},
{milvus::knowhere::meta::TOPK, K},
{milvus::knowhere::IndexParams::nprobe, 10},
{milvus::knowhere::IndexParams::nlist, 100},
{milvus::knowhere::IndexParams::m, 4},
{milvus::knowhere::IndexParams::nbits, 8},
{milvus::knowhere::Metric::TYPE, milvus::knowhere::Metric::JACCARD},
};
auto indexing = generate_index(vec_col.data(), conf, DIM, K, N, IndexEnum::INDEX_FAISS_BIN_IVFFLAT);
// gen query dataset
auto query_dataset = milvus::knowhere::GenDataset(num_queries, DIM, query_ptr);
auto result_on_index = indexing->Query(query_dataset, conf, nullptr);
auto ids = result_on_index->Get<int64_t*>(milvus::knowhere::meta::IDS);
auto dis = result_on_index->Get<float*>(milvus::knowhere::meta::DISTANCE);
std::vector<int64_t> vec_ids(ids, ids + K * num_queries);
std::vector<float> vec_dis;
for (int j = 0; j < K * num_queries; ++j) {
vec_dis.push_back(dis[j] * -1);
}
auto search_result_on_raw_index = (QueryResult*)c_search_result_on_smallIndex;
search_result_on_raw_index->internal_seg_offsets_ = vec_ids;
search_result_on_raw_index->result_distances_ = vec_dis;
auto binary_set = indexing->Serialize(conf);
void* c_load_index_info = nullptr;
status = NewLoadIndexInfo(&c_load_index_info);
assert(status.error_code == Success);
std::string index_type_key = "index_type";
std::string index_type_value = "BIN_IVF_FLAT";
std::string index_mode_key = "index_mode";
std::string index_mode_value = "cpu";
std::string metric_type_key = "metric_type";
std::string metric_type_value = "JACCARD";
AppendIndexParam(c_load_index_info, index_type_key.c_str(), index_type_value.c_str());
AppendIndexParam(c_load_index_info, index_mode_key.c_str(), index_mode_value.c_str());
AppendIndexParam(c_load_index_info, metric_type_key.c_str(), metric_type_value.c_str());
AppendFieldInfo(c_load_index_info, "fakevec", 0);
AppendIndex(c_load_index_info, (CBinarySet)&binary_set);
status = UpdateSegmentIndex(segment, c_load_index_info);
assert(status.error_code == Success);
CQueryResult c_search_result_on_bigIndex;
auto res_after_load_index = Search(segment, plan, placeholderGroups.data(), &time, 1, &c_search_result_on_bigIndex);
assert(res_after_load_index.error_code == Success);
std::vector<CQueryResult> results;
results.push_back(c_search_result_on_bigIndex);
bool is_selected[1] = {false};
status = ReduceQueryResults(results.data(), 1, is_selected);
assert(status.error_code == Success);
FillTargetEntry(segment, plan, c_search_result_on_bigIndex);
auto search_result_on_bigIndex = (*(QueryResult*)c_search_result_on_bigIndex);
for (int i = 0; i < num_queries; ++i) {
auto offset = i * K;
ASSERT_EQ(search_result_on_bigIndex.internal_seg_offsets_[offset], 420000 + i);
ASSERT_EQ(search_result_on_bigIndex.result_distances_[offset],
search_result_on_raw_index->result_distances_[offset]);
}
DeleteLoadIndexInfo(c_load_index_info);
DeletePlan(plan);
DeletePlaceholderGroup(placeholderGroup);
DeleteQueryResult(c_search_result_on_smallIndex);
DeleteQueryResult(c_search_result_on_bigIndex);
DeleteCollection(collection);
DeleteSegment(segment);
}
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