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07aa453270
milvus/internal/core/unittest/test_sealed.cpp
FluorineDog 234954931f
remove deprecated (#6623) 
* remove deprecated

Signed-off-by: fluorinedog <fluorinedog@gmail.com>

* fix timeout

Signed-off-by: fluorinedog <fluorinedog@gmail.com>
2021-07-20 15:18:08 +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
//
// Created by mike on 12/28/20.
//
#include "test_utils/DataGen.h"
#include <gtest/gtest.h>
#include <knowhere/index/vector_index/VecIndex.h>
#include <knowhere/index/vector_index/adapter/VectorAdapter.h>
#include <knowhere/index/vector_index/VecIndexFactory.h>
#include <knowhere/index/vector_index/IndexIVF.h>
#include "segcore/SegmentSealedImpl.h"
using namespace milvus;
using namespace milvus::segcore;
using namespace milvus::query;
TEST(Sealed, without_predicate) {
using namespace milvus::query;
using namespace milvus::segcore;
auto schema = std::make_shared<Schema>();
auto dim = 16;
auto topK = 5;
auto metric_type = MetricType::METRIC_L2;
auto fake_id = schema->AddDebugField("fakevec", DataType::VECTOR_FLOAT, dim, metric_type);
schema->AddDebugField("age", DataType::FLOAT);
std::string dsl = R"({
"bool": {
"must": [
{
"vector": {
"fakevec": {
"metric_type": "L2",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 5
}
}
}
]
}
})";
int64_t N = 1000 * 1000;
auto dataset = DataGen(schema, N);
auto vec_col = dataset.get_col<float>(0);
for (int64_t i = 0; i < 1000 * dim; ++i) {
vec_col.push_back(0);
}
auto query_ptr = vec_col.data() + 4200 * dim;
auto segment = CreateGrowingSegment(schema);
segment->PreInsert(N);
segment->Insert(0, N, dataset.row_ids_.data(), dataset.timestamps_.data(), dataset.raw_);
auto plan = CreatePlan(*schema, dsl);
auto num_queries = 5;
auto ph_group_raw = CreatePlaceholderGroupFromBlob(num_queries, 16, query_ptr);
auto ph_group = ParsePlaceholderGroup(plan.get(), ph_group_raw.SerializeAsString());
SearchResult sr;
Timestamp time = 1000000;
std::vector<const PlaceholderGroup*> ph_group_arr = {ph_group.get()};
sr = segment->Search(plan.get(), *ph_group, time);
auto pre_result = SearchResultToJson(sr);
auto indexing = std::make_shared<knowhere::IVF>();
auto conf = knowhere::Config{{knowhere::meta::DIM, dim},
{knowhere::meta::TOPK, topK},
{knowhere::IndexParams::nlist, 100},
{knowhere::IndexParams::nprobe, 10},
{knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
{knowhere::meta::DEVICEID, 0}};
auto database = knowhere::GenDataset(N, dim, vec_col.data() + 1000 * dim);
indexing->Train(database, conf);
indexing->AddWithoutIds(database, conf);
EXPECT_EQ(indexing->Count(), N);
EXPECT_EQ(indexing->Dim(), dim);
auto query_dataset = knowhere::GenDataset(num_queries, dim, query_ptr);
auto result = indexing->Query(query_dataset, conf, nullptr);
auto ids = result->Get<int64_t*>(milvus::knowhere::meta::IDS); // for comparison
auto dis = result->Get<float*>(milvus::knowhere::meta::DISTANCE); // for comparison
std::vector<int64_t> vec_ids(ids, ids + topK * num_queries);
std::vector<float> vec_dis(dis, dis + topK * num_queries);
sr.internal_seg_offsets_ = vec_ids;
sr.result_distances_ = vec_dis;
auto ref_result = SearchResultToJson(sr);
LoadIndexInfo load_info;
load_info.field_id = fake_id.get();
load_info.index = indexing;
load_info.index_params["metric_type"] = "L2";
auto sealed_segment = SealedCreator(schema, dataset, load_info);
sr = sealed_segment->Search(plan.get(), *ph_group, time);
auto post_result = SearchResultToJson(sr);
std::cout << ref_result.dump(1);
std::cout << post_result.dump(1);
ASSERT_EQ(ref_result.dump(2), post_result.dump(2));
}
TEST(Sealed, with_predicate) {
using namespace milvus::query;
using namespace milvus::segcore;
auto schema = std::make_shared<Schema>();
auto dim = 16;
auto topK = 5;
auto metric_type = MetricType::METRIC_L2;
auto fake_id = schema->AddDebugField("fakevec", DataType::VECTOR_FLOAT, dim, metric_type);
schema->AddDebugField("counter", DataType::INT64);
std::string dsl = R"({
"bool": {
"must": [
{
"range": {
"counter": {
"GE": 420000,
"LT": 420005
}
}
},
{
"vector": {
"fakevec": {
"metric_type": "L2",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 5
}
}
}
]
}
})";
int64_t N = 1000 * 1000;
auto dataset = DataGen(schema, N);
auto vec_col = dataset.get_col<float>(0);
auto query_ptr = vec_col.data() + 420000 * dim;
auto segment = CreateGrowingSegment(schema);
segment->PreInsert(N);
segment->Insert(0, N, dataset.row_ids_.data(), dataset.timestamps_.data(), dataset.raw_);
auto plan = CreatePlan(*schema, dsl);
auto num_queries = 5;
auto ph_group_raw = CreatePlaceholderGroupFromBlob(num_queries, 16, query_ptr);
auto ph_group = ParsePlaceholderGroup(plan.get(), ph_group_raw.SerializeAsString());
SearchResult sr;
Timestamp time = 10000000;
std::vector<const PlaceholderGroup*> ph_group_arr = {ph_group.get()};
sr = segment->Search(plan.get(), *ph_group, time);
auto pre_sr = sr;
auto indexing = std::make_shared<knowhere::IVF>();
auto conf = knowhere::Config{{knowhere::meta::DIM, dim},
{knowhere::meta::TOPK, topK},
{knowhere::IndexParams::nlist, 100},
{knowhere::IndexParams::nprobe, 10},
{knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
{knowhere::meta::DEVICEID, 0}};
auto database = knowhere::GenDataset(N, dim, vec_col.data());
indexing->Train(database, conf);
indexing->AddWithoutIds(database, conf);
EXPECT_EQ(indexing->Count(), N);
EXPECT_EQ(indexing->Dim(), dim);
auto query_dataset = knowhere::GenDataset(num_queries, dim, query_ptr);
auto result = indexing->Query(query_dataset, conf, nullptr);
LoadIndexInfo load_info;
load_info.field_id = fake_id.get();
load_info.index = indexing;
load_info.index_params["metric_type"] = "L2";
auto sealed_segment = SealedCreator(schema, dataset, load_info);
sr = sealed_segment->Search(plan.get(), *ph_group, time);
auto post_sr = sr;
for (int i = 0; i < num_queries; ++i) {
auto offset = i * topK;
ASSERT_EQ(post_sr.internal_seg_offsets_[offset], 420000 + i);
ASSERT_EQ(post_sr.result_distances_[offset], 0.0);
}
}
TEST(Sealed, LoadFieldData) {
auto dim = 16;
auto topK = 5;
int64_t N = 1000 * 1000;
auto metric_type = MetricType::METRIC_L2;
auto schema = std::make_shared<Schema>();
auto fakevec_id = schema->AddDebugField("fakevec", DataType::VECTOR_FLOAT, dim, metric_type);
auto counter_id = schema->AddDebugField("counter", DataType::INT64);
auto double_id = schema->AddDebugField("double", DataType::DOUBLE);
auto nothing_id = schema->AddDebugField("nothing", DataType::INT32);
auto dataset = DataGen(schema, N);
auto fakevec = dataset.get_col<float>(0);
auto indexing = GenIndexing(N, dim, fakevec.data());
auto segment = CreateSealedSegment(schema);
std::string dsl = R"({
"bool": {
"must": [
{
"range": {
"double": {
"GE": -1,
"LT": 1
}
}
},
{
"vector": {
"fakevec": {
"metric_type": "L2",
"params": {
"nprobe": 10
},
"query": "$0",
"topk": 5
}
}
}
]
}
})";
Timestamp time = 1000000;
auto plan = CreatePlan(*schema, dsl);
auto num_queries = 5;
auto ph_group_raw = CreatePlaceholderGroup(num_queries, 16, 1024);
auto ph_group = ParsePlaceholderGroup(plan.get(), ph_group_raw.SerializeAsString());
ASSERT_ANY_THROW(segment->Search(plan.get(), *ph_group, time));
SealedLoader(dataset, *segment);
segment->DropFieldData(nothing_id);
segment->Search(plan.get(), *ph_group, time);
segment->DropFieldData(fakevec_id);
ASSERT_ANY_THROW(segment->Search(plan.get(), *ph_group, time));
LoadIndexInfo vec_info;
vec_info.field_id = fakevec_id.get();
vec_info.index = indexing;
vec_info.index_params["metric_type"] = milvus::knowhere::Metric::L2;
segment->LoadIndex(vec_info);
ASSERT_EQ(segment->num_chunk(), 1);
auto chunk_span1 = segment->chunk_data<int64_t>(FieldOffset(1), 0);
auto chunk_span2 = segment->chunk_data<double>(FieldOffset(2), 0);
auto ref1 = dataset.get_col<int64_t>(1);
auto ref2 = dataset.get_col<double>(2);
for (int i = 0; i < N; ++i) {
ASSERT_EQ(chunk_span1[i], ref1[i]);
ASSERT_EQ(chunk_span2[i], ref2[i]);
}
auto sr = segment->Search(plan.get(), *ph_group, time);
auto json = SearchResultToJson(sr);
std::cout << json.dump(1);
segment->DropIndex(fakevec_id);
ASSERT_ANY_THROW(segment->Search(plan.get(), *ph_group, time));
segment->LoadIndex(vec_info);
auto sr2 = segment->Search(plan.get(), *ph_group, time);
auto json2 = SearchResultToJson(sr);
ASSERT_EQ(json.dump(-2), json2.dump(-2));
segment->DropFieldData(double_id);
ASSERT_ANY_THROW(segment->Search(plan.get(), *ph_group, time));
auto std_json = Json::parse(R"(
[
[
[
"982->0.000000",
"25315->4.741588",
"551029->5.078479",
"455002->5.134716",
"504754->5.329021"
],
[
"287136->8.409121",
"528353->8.740297",
"935763->9.422906",
"794649->9.436665",
"192031->9.832053"
],
[
"59251->2.542610",
"433044->3.424016",
"797884->3.663446",
"430441->3.692723",
"697705->3.944479"
],
[
"611544->3.463480",
"642941->3.753775",
"967504->3.885163",
"232724->4.574215",
"507245->5.040902"
],
[
"351788->4.453843",
"410227->4.699380",
"501497->4.805948",
"715061->5.166959",
"414882->5.179897"
]
]
])");
ASSERT_EQ(std_json.dump(-2), json.dump(-2));
}
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