milvus/internal/core/unittest/bench/bench_search.cpp
zhenshan.cao 60e88fb833
fix: Restore the MVCC functionality. (#29749)
When the TimeTravel functionality was previously removed, it
inadvertently affected the MVCC functionality within the system. This PR
aims to reintroduce the internal MVCC functionality as follows:

1. Add MvccTimestamp to the requests of Search/Query and the results of
Search internally.
2. When the delegator receives a Query/Search request and there is no
MVCC timestamp set in the request, set the delegator's current tsafe as
the MVCC timestamp of the request. If the request already has an MVCC
timestamp, do not modify it.
3. When the Proxy handles Search and triggers the second phase ReQuery,
divide the ReQuery into different shards and pass the MVCC timestamp to
the corresponding Query requests.

issue: #29656

Signed-off-by: zhenshan.cao <zhenshan.cao@zilliz.com>
2024-01-09 11:38:48 +08:00

138 lines
4.8 KiB
C++

// 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 <cstdint>
#include <benchmark/benchmark.h>
#include <string>
#include "segcore/SegmentGrowing.h"
#include "segcore/SegmentSealed.h"
#include "test_utils/DataGen.h"
using namespace milvus;
using namespace milvus::query;
using namespace milvus::segcore;
static int dim = 768;
const auto schema = []() {
auto schema = std::make_shared<Schema>();
schema->AddDebugField(
"fakevec", DataType::VECTOR_FLOAT, dim, knowhere::metric::L2);
auto i64_fid = schema->AddDebugField("age", DataType::INT64);
schema->set_primary_field_id(i64_fid);
return schema;
}();
const auto search_plan = [] {
const char* raw_plan = R"(vector_anns: <
field_id: 100
query_info: <
topk: 5
round_decimal: -1
metric_type: "L2"
search_params: "{\"nprobe\": 10}"
>
placeholder_tag: "$0"
>)";
auto plan_str = translate_text_plan_to_binary_plan(raw_plan);
auto plan =
CreateSearchPlanByExpr(*schema, plan_str.data(), plan_str.size());
return plan;
}();
auto ph_group = [] {
auto num_queries = 10;
auto ph_group_raw = CreatePlaceholderGroup(num_queries, dim, 1024);
auto ph_group = ParsePlaceholderGroup(search_plan.get(),
ph_group_raw.SerializeAsString());
return ph_group;
}();
static void
Search_GrowingIndex(benchmark::State& state) {
// schema->AddDebugField("age", DataType::FLOAT);
static int64_t N = 1024 * 32;
const auto dataset_ = [] {
auto dataset_ = DataGen(schema, N);
return dataset_;
}();
auto chunk_rows = state.range(1) * 1024;
auto segconf = SegcoreConfig::default_config();
segconf.set_chunk_rows(chunk_rows);
std::map<std::string, std::string> index_params = {
{"index_type", "IVF_FLAT"}, {"metric_type", "L2"}, {"nlist", "128"}};
std::map<std::string, std::string> type_params = {{"dim", "128"}};
FieldIndexMeta fieldIndexMeta(schema->get_field_id(FieldName("fakevec")),
std::move(index_params),
std::move(type_params));
segconf.set_enable_interim_segment_index(true);
std::map<FieldId, FieldIndexMeta> filedMap = {
{schema->get_field_id(FieldName("fakevec")), fieldIndexMeta}};
IndexMetaPtr metaPtr =
std::make_shared<CollectionIndexMeta>(226985, std::move(filedMap));
auto segment = CreateGrowingSegment(schema, metaPtr, -1, segconf);
segment->PreInsert(N);
segment->Insert(0,
N,
dataset_.row_ids_.data(),
dataset_.timestamps_.data(),
dataset_.raw_);
Timestamp ts = 10000000;
for (auto _ : state) {
auto qr = segment->Search(search_plan.get(), ph_group.get(), ts);
}
}
BENCHMARK(Search_GrowingIndex)
->MinTime(5)
->ArgsProduct({{true, false}, {8, 16, 32}});
static void
Search_Sealed(benchmark::State& state) {
auto segment = CreateSealedSegment(schema);
static int64_t N = 1024 * 1024;
const auto dataset_ = [] {
auto dataset_ = DataGen(schema, N);
return dataset_;
}();
SealedLoadFieldData(dataset_, *segment);
auto choice = state.range(0);
if (choice == 0) {
// Brute Force
} else if (choice == 1) {
// hnsw
auto vec = dataset_.get_col<float>(milvus::FieldId(100));
auto indexing =
GenVecIndexing(N, dim, vec.data(), knowhere::IndexEnum::INDEX_HNSW);
segcore::LoadIndexInfo info;
info.index = std::move(indexing);
info.field_id = (*schema)[FieldName("fakevec")].get_id().get();
info.index_params["index_type"] = "HNSW";
info.index_params["metric_type"] = knowhere::metric::L2;
segment->DropFieldData(milvus::FieldId(100));
segment->LoadIndex(info);
}
Timestamp ts = 10000000;
for (auto _ : state) {
auto qr = segment->Search(search_plan.get(), ph_group.get(), ts);
}
}
BENCHMARK(Search_Sealed)->MinTime(5)->Arg(1)->Arg(0);