Spring?Boot集成Milvus快速入門demo示例詳解

更新時(shí)間：2024年09月26日 09:18:17 作者：HBLOGA

Milvus是一種高性能向量數(shù)據(jù)庫,支持從筆記本到大型分布式系統(tǒng)的多環(huán)境運(yùn)行,它以開源和云服務(wù)形式提供,是LFAI & Data Foundation的項(xiàng)目,采用Apache 2.0許可,Milvus特別支持高并行化和解耦的系統(tǒng)架構(gòu),使其能夠隨數(shù)據(jù)增長而擴(kuò)展,支持各種復(fù)雜搜索功能,滿足企業(yè)級(jí)AI應(yīng)用需求

1.什么是Milvus？

Milvus 是一種高性能、高擴(kuò)展性的向量數(shù)據(jù)庫，可在從筆記本電腦到大型分布式系統(tǒng)等各種環(huán)境中高效運(yùn)行。它既可以開源軟件的形式提供，也可以云服務(wù)的形式提供。 Milvus 是 LF AI & Data Foundation 下的一個(gè)開源項(xiàng)目，以 Apache 2.0 許可發(fā)布。大多數(shù)貢獻(xiàn)者都是高性能計(jì)算（HPC）領(lǐng)域的專家，擅長構(gòu)建大型系統(tǒng)和優(yōu)化硬件感知代碼。核心貢獻(xiàn)者包括來自 Zilliz、ARM、NVIDIA、AMD、英特爾、Meta、IBM、Salesforce、阿里巴巴和微軟的專業(yè)人士

是什么讓 Milvus 具有如此高的可擴(kuò)展性

2022 年，Milvus 支持十億級(jí)向量，2023 年，它以持續(xù)穩(wěn)定的方式擴(kuò)展到數(shù)百億級(jí)，為 300 多家大型企業(yè)的大規(guī)模場(chǎng)景提供支持，包括 Salesforce、PayPal、Shopee、Airbnb、eBay、NVIDIA、IBM、AT&T、LINE、ROBLOX、Inflection 等。 Milvus 的云原生和高度解耦的系統(tǒng)架構(gòu)確保了系統(tǒng)可以隨著數(shù)據(jù)的增長而不斷擴(kuò)展：

Milvus 本身是完全無狀態(tài)的，因此可以借助 Kubernetes 或公共云輕松擴(kuò)展。此外，Milvus 的各個(gè)組件都有很好的解耦，其中最關(guān)鍵的三項(xiàng)任務(wù)--搜索、數(shù)據(jù)插入和索引/壓實(shí)--被設(shè)計(jì)為易于并行化的流程，復(fù)雜的邏輯被分離出來。這確保了相應(yīng)的查詢節(jié)點(diǎn)、數(shù)據(jù)節(jié)點(diǎn)和索引節(jié)點(diǎn)可以獨(dú)立地向上和向下擴(kuò)展，從而優(yōu)化了性能和成本效率。

Milvus 支持的搜索類型

Milvus 支持各種類型的搜索功能，以滿足不同用例的需求：

ANN 搜索：查找最接近查詢向量的前 K 個(gè)向量。

過濾搜索：在指定的過濾條件下執(zhí)行 ANN 搜索。

范圍搜索：查找查詢向量指定半徑范圍內(nèi)的向量。

混合搜索：基于多個(gè)向量場(chǎng)進(jìn)行 ANN 搜索。關(guān)鍵詞搜索基于 BM25 的關(guān)鍵詞搜索。

重新排序根據(jù)附加標(biāo)準(zhǔn)或輔助算法調(diào)整搜索結(jié)果的順序，完善最初的 ANN 搜索結(jié)果。

獲取：根據(jù)主鍵檢索數(shù)據(jù)。

查詢使用特定表達(dá)式檢索數(shù)據(jù)。

2.測(cè)試環(huán)境搭建

First, we’ll need an instance of Milvus DB. The easiest and quickest way is to get a fully managed free Milvus DB instance provided by Zilliz Cloud: Vector Database built for enterprise-grade AI applications - Zilliz
For this, we’ll need to register for a Zilliz cloud account and follow the documentation for creating a free DB cluster.

3.代碼工程

實(shí)驗(yàn)?zāi)康?/h3>

實(shí)現(xiàn)對(duì)Milvus向量數(shù)據(jù)庫的的crud

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>3.2.1</version>
    </parent>
    <modelVersion>4.0.0</modelVersion>
    <artifactId>Milvus</artifactId>
    <properties>
        <maven.compiler.source>17</maven.compiler.source>
        <maven.compiler.target>17</maven.compiler.target>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-autoconfigure</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>io.milvus</groupId>
            <artifactId>milvus-sdk-java</artifactId>
            <version>2.4.1</version>
        </dependency>
    </dependencies>
    <build>
        <pluginManagement>
            <plugins>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-compiler-plugin</artifactId>
                    <version>3.8.1</version>
                    <configuration>
                        <fork>true</fork>
                        <failOnError>false</failOnError>
                    </configuration>
                </plugin>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-surefire-plugin</artifactId>
                    <version>2.22.2</version>
                    <configuration>
                        <forkCount>0</forkCount>
                        <failIfNoTests>false</failIfNoTests>
                    </configuration>
                </plugin>
            </plugins>
        </pluginManagement>
    </build>
</project>

controller

package com.et.controller;
import com.et.service.HelloZillizVectorDBService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
import java.util.HashMap;
import java.util.Map;
@RestController
public class HelloWorldController {
   @Autowired
   HelloZillizVectorDBService helloZillizVectorDBService;
    @RequestMapping("/hello")
    public Map<String, Object> showHelloWorld(){
        Map<String, Object> map = new HashMap<>();
      helloZillizVectorDBService.search();
        map.put("msg", "HelloWorld");
        return map;
    }
}

service

package com.et.service;
import io.milvus.client.MilvusServiceClient;
import io.milvus.grpc.DataType;
import io.milvus.grpc.DescribeCollectionResponse;
import io.milvus.grpc.MutationResult;
import io.milvus.grpc.SearchResults;
import io.milvus.param.*;
import io.milvus.param.collection.*;
import io.milvus.param.dml.InsertParam;
import io.milvus.param.dml.SearchParam;
import io.milvus.param.index.CreateIndexParam;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;
import java.util.*;
@Service
public class HelloZillizVectorDBService {
   @Value("${uri}")
   public String uri;
   @Value("${token}")
   public String token;
    public  void search() {
        // connect to milvus
        final MilvusServiceClient milvusClient = new MilvusServiceClient(
                ConnectParam.newBuilder()
                        .withUri(uri)
                        .withToken(token)
                        .build());
        System.out.println("Connecting to DB: " + uri);
        // Check if the collection exists
        String collectionName = "book";
        R<DescribeCollectionResponse> responseR =
                milvusClient.describeCollection(DescribeCollectionParam.newBuilder().withCollectionName(collectionName).build());
        if (responseR.getData() != null) {
            milvusClient.dropCollection(DropCollectionParam.newBuilder().withCollectionName(collectionName).build());
        }
        System.out.println("Success!");
        // create a collection with customized primary field: book_id_field
        int dim = 64;
        FieldType bookIdField = FieldType.newBuilder()
                .withName("book_id")
                .withDataType(DataType.Int64)
                .withPrimaryKey(true)
                .withAutoID(false)
                .build();
        FieldType wordCountField = FieldType.newBuilder()
                .withName("word_count")
                .withDataType(DataType.Int64)
                .build();
        FieldType bookIntroField = FieldType.newBuilder()
                .withName("book_intro")
                .withDataType(DataType.FloatVector)
                .withDimension(dim)
                .build();
        CreateCollectionParam createCollectionParam = CreateCollectionParam.newBuilder()
                .withCollectionName(collectionName)
                .withDescription("my first collection")
                .withShardsNum(2)
                .addFieldType(bookIdField)
                .addFieldType(wordCountField)
                .addFieldType(bookIntroField)
                .build();
        System.out.println("Creating example collection: " + collectionName);
        System.out.println("Schema: " + createCollectionParam);
        milvusClient.createCollection(createCollectionParam);
        System.out.println("Success!");
        //insert data with customized ids
        Random ran = new Random();
        int singleNum = 1000;
        int insertRounds = 2;
        long insertTotalTime = 0L;
        System.out.println("Inserting " + singleNum * insertRounds + " entities... ");
        for (int r = 0; r < insertRounds; r++) {
            List<Long> book_id_array = new ArrayList<>();
            List<Long> word_count_array = new ArrayList<>();
            List<List<Float>> book_intro_array = new ArrayList<>();
            for (long i = r * singleNum; i < (r + 1) * singleNum; ++i) {
                book_id_array.add(i);
                word_count_array.add(i + 10000);
                List<Float> vector = new ArrayList<>();
                for (int k = 0; k < dim; ++k) {
                    vector.add(ran.nextFloat());
                }
                book_intro_array.add(vector);
            }
            List<InsertParam.Field> fields = new ArrayList<>();
            fields.add(new InsertParam.Field(bookIdField.getName(), book_id_array));
            fields.add(new InsertParam.Field(wordCountField.getName(), word_count_array));
            fields.add(new InsertParam.Field(bookIntroField.getName(), book_intro_array));
            InsertParam insertParam = InsertParam.newBuilder()
                    .withCollectionName(collectionName)
                    .withFields(fields)
                    .build();
            long startTime = System.currentTimeMillis();
            R<MutationResult> insertR = milvusClient.insert(insertParam);
            long endTime = System.currentTimeMillis();
            insertTotalTime += (endTime - startTime) / 1000.00;
        }
        System.out.println("Succeed in " + insertTotalTime + " seconds!");
        // flush data
        System.out.println("Flushing...");
        long startFlushTime = System.currentTimeMillis();
        milvusClient.flush(FlushParam.newBuilder()
                .withCollectionNames(Collections.singletonList(collectionName))
                .withSyncFlush(true)
                .withSyncFlushWaitingInterval(50L)
                .withSyncFlushWaitingTimeout(30L)
                .build());
        long endFlushTime = System.currentTimeMillis();
        System.out.println("Succeed in " + (endFlushTime - startFlushTime) / 1000.00 + " seconds!");
        // build index
        System.out.println("Building AutoIndex...");
        final IndexType INDEX_TYPE = IndexType.AUTOINDEX;   // IndexType
        long startIndexTime = System.currentTimeMillis();
        R<RpcStatus> indexR = milvusClient.createIndex(
                CreateIndexParam.newBuilder()
                        .withCollectionName(collectionName)
                        .withFieldName(bookIntroField.getName())
                        .withIndexType(INDEX_TYPE)
                        .withMetricType(MetricType.L2)
                        .withSyncMode(Boolean.TRUE)
                        .withSyncWaitingInterval(500L)
                        .withSyncWaitingTimeout(30L)
                        .build());
        long endIndexTime = System.currentTimeMillis();
        System.out.println("Succeed in " + (endIndexTime - startIndexTime) / 1000.00 + " seconds!");
        // load collection
        System.out.println("Loading collection...");
        long startLoadTime = System.currentTimeMillis();
        milvusClient.loadCollection(LoadCollectionParam.newBuilder()
                .withCollectionName(collectionName)
                .withSyncLoad(true)
                .withSyncLoadWaitingInterval(500L)
                .withSyncLoadWaitingTimeout(100L)
                .build());
        long endLoadTime = System.currentTimeMillis();
        System.out.println("Succeed in " + (endLoadTime - startLoadTime) / 1000.00 + " seconds");
        // search
        final Integer SEARCH_K = 2;                       // TopK
        final String SEARCH_PARAM = "{\"nprobe\":10}";    // Params
        List<String> search_output_fields = Arrays.asList("book_id", "word_count");
        for (int i = 0; i < 10; i++) {
            List<Float> floatList = new ArrayList<>();
            for (int k = 0; k < dim; ++k) {
                floatList.add(ran.nextFloat());
            }
            List<List<Float>> search_vectors = Collections.singletonList(floatList);
            SearchParam searchParam = SearchParam.newBuilder()
                    .withCollectionName(collectionName)
                    .withMetricType(MetricType.L2)
                    .withOutFields(search_output_fields)
                    .withTopK(SEARCH_K)
                    .withVectors(search_vectors)
                    .withVectorFieldName(bookIntroField.getName())
                    .withParams(SEARCH_PARAM)
                    .build();
            long startSearchTime = System.currentTimeMillis();
            R<SearchResults> search = milvusClient.search(searchParam);
            long endSearchTime = System.currentTimeMillis();
            System.out.println("Searching vector: " + search_vectors);
            System.out.println("Result: " + search.getData().getResults().getFieldsDataList());
            System.out.println("search " + i + " latency: " + (endSearchTime - startSearchTime) / 1000.00 + " seconds");
        }
        milvusClient.close();
    }
}

application.yaml

用實(shí)際的值替換下面的參數(shù)

uri = https://in01-XXXXXXXXXXXXX.aws-us-west-2.vectordb.zillizcloud.com:XXXXX
token = db_admin:password (or ApiKey)

只是一些關(guān)鍵代碼，所有代碼請(qǐng)參見下面代碼倉庫

代碼倉庫

GitHub - Harries/springboot-demo: a simple springboot demo with some components for example: redis,solr,rockmq and so on.(Milvus)

4.測(cè)試

啟動(dòng)Spring Boot應(yīng)用，訪問http://127.0.0.1:8080/hello，查看控制臺(tái)輸出日志

Connecting to DB: https://in03-258dd5ae260ce1b.serverless.gcp-us-west1.cloud.zilliz.com
2024-09-25T21:29:30.636+08:00 ERROR 52861 --- [nio-8080-exec-2] i.m.client.AbstractMilvusGrpcClient : DescribeCollectionRequest collectionName:book failed, error code: 100, reason: can't find collection[database=db_258dd5ae260ce1b][collection=book]
2024-09-25T21:29:30.641+08:00 ERROR 52861 --- [nio-8080-exec-2] i.m.client.AbstractMilvusGrpcClient : DescribeCollectionRequest collectionName:book failed! Exception:{}
io.milvus.exception.ServerException: can't find collection[database=db_258dd5ae260ce1b][collection=book]
 at io.milvus.client.AbstractMilvusGrpcClient.handleResponse(AbstractMilvusGrpcClient.java:347) ~[milvus-sdk-java-2.3.4.jar:na]
 at io.milvus.client.AbstractMilvusGrpcClient.describeCollection(AbstractMilvusGrpcClient.java:655) ~[milvus-sdk-java-2.3.4.jar:na]
 at io.milvus.client.MilvusServiceClient.lambda$describeCollection$9(MilvusServiceClient.java:402) ~[milvus-sdk-java-2.3.4.jar:na]
 at io.milvus.client.MilvusServiceClient.retry(MilvusServiceClient.java:285) ~[milvus-sdk-java-2.3.4.jar:na]
 at io.milvus.client.MilvusServiceClient.describeCollection(MilvusServiceClient.java:402) ~[milvus-sdk-java-2.3.4.jar:na]
 at com.et.service.HelloZillizVectorDBService.search(HelloZillizVectorDBService.java:37) ~[classes/:na]
 at com.et.controller.HelloWorldController.showHelloWorld(HelloWorldController.java:18) ~[classes/:na]
 at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) ~[na:na]
 at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77) ~[na:na]
 at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) ~[na:na]
 at java.base/java.lang.reflect.Method.invoke(Method.java:568) ~[na:na]
 at org.springframework.web.method.support.InvocableHandlerMethod.doInvoke(InvocableHandlerMethod.java:262) ~[spring-web-6.1.2.jar:6.1.2]
 at org.springframework.web.method.support.InvocableHandlerMethod.invokeForRequest(InvocableHandlerMethod.java:190) ~[spring-web-6.1.2.jar:6.1.2]
 at org.springframework.web.servlet.mvc.method.annotation.ServletInvocableHandlerMethod.invokeAndHandle(ServletInvocableHandlerMethod.java:118) ~[spring-webmvc-6.1.2.jar:6.1.2]
 at org.springframework.web.servlet.mvc.method.annotation.RequestMappingHandlerAdapter.invokeHandlerMethod(RequestMappingHandlerAdapter.java:917) ~[spring-webmvc-6.1.2.jar:6.1.2]
 at org.springframework.web.servlet.mvc.method.annotation.RequestMappingHandlerAdapter.handleInternal(RequestMappingHandlerAdapter.java:829) ~[spring-webmvc-6.1.2.jar:6.1.2]
 at org.springframework.web.servlet.mvc.method.AbstractHandlerMethodAdapter.handle(AbstractHandlerMethodAdapter.java:87) ~[spring-webmvc-6.1.2.jar:6.1.2]
 at org.springframework.web.servlet.DispatcherServlet.doDispatch(DispatcherServlet.java:1089) ~[spring-webmvc-6.1.2.jar:6.1.2]
 at org.springframework.web.servlet.DispatcherServlet.doService(DispatcherServlet.java:979) ~[spring-webmvc-6.1.2.jar:6.1.2]
 at org.springframework.web.servlet.FrameworkServlet.processRequest(FrameworkServlet.java:1014) ~[spring-webmvc-6.1.2.jar:6.1.2]
 at org.springframework.web.servlet.FrameworkServlet.doGet(FrameworkServlet.java:903) ~[spring-webmvc-6.1.2.jar:6.1.2]
 at jakarta.servlet.http.HttpServlet.service(HttpServlet.java:564) ~[tomcat-embed-core-10.1.17.jar:6.0]
 at org.springframework.web.servlet.FrameworkServlet.service(FrameworkServlet.java:885) ~[spring-webmvc-6.1.2.jar:6.1.2]
 at jakarta.servlet.http.HttpServlet.service(HttpServlet.java:658) ~[tomcat-embed-core-10.1.17.jar:6.0]
 at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:205) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:149) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.tomcat.websocket.server.WsFilter.doFilter(WsFilter.java:51) ~[tomcat-embed-websocket-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:174) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:149) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.springframework.web.filter.RequestContextFilter.doFilterInternal(RequestContextFilter.java:100) ~[spring-web-6.1.2.jar:6.1.2]
 at org.springframework.web.filter.OncePerRequestFilter.doFilter(OncePerRequestFilter.java:116) ~[spring-web-6.1.2.jar:6.1.2]
 at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:174) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:149) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.springframework.web.filter.FormContentFilter.doFilterInternal(FormContentFilter.java:93) ~[spring-web-6.1.2.jar:6.1.2]
 at org.springframework.web.filter.OncePerRequestFilter.doFilter(OncePerRequestFilter.java:116) ~[spring-web-6.1.2.jar:6.1.2]
 at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:174) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:149) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.springframework.web.filter.CharacterEncodingFilter.doFilterInternal(CharacterEncodingFilter.java:201) ~[spring-web-6.1.2.jar:6.1.2]
 at org.springframework.web.filter.OncePerRequestFilter.doFilter(OncePerRequestFilter.java:116) ~[spring-web-6.1.2.jar:6.1.2]
 at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:174) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:149) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:167) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:90) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.authenticator.AuthenticatorBase.invoke(AuthenticatorBase.java:482) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:115) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:93) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:74) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:340) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.coyote.http11.Http11Processor.service(Http11Processor.java:391) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.coyote.AbstractProcessorLight.process(AbstractProcessorLight.java:63) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.coyote.AbstractProtocol$ConnectionHandler.process(AbstractProtocol.java:896) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.tomcat.util.net.NioEndpoint$SocketProcessor.doRun(NioEndpoint.java:1744) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.tomcat.util.net.SocketProcessorBase.run(SocketProcessorBase.java:52) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.tomcat.util.threads.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1191) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.tomcat.util.threads.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:659) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61) ~[tomcat-embed-core-10.1.17.jar:10.1.17]
 at java.base/java.lang.Thread.run(Thread.java:840) ~[na:na]
Success!
Creating example collection: book
Schema: CreateCollectionParam(collectionName=book, shardsNum=2, description=my first collection, fieldTypes=[FieldType{name='book_id', type='Int64', elementType='None', primaryKey=true, partitionKey=false, autoID=false, params={}}, FieldType{name='word_count', type='Int64', elementType='None', primaryKey=false, partitionKey=false, autoID=false, params={}}, FieldType{name='book_intro', type='FloatVector', elementType='None', primaryKey=false, partitionKey=false, autoID=false, params={dim=64}}], partitionsNum=0, consistencyLevel=BOUNDED, databaseName=null, enableDynamicField=false)
Success!
Inserting 2000 entities... 
Succeed in 1 seconds!
Flushing...
Succeed in 3.019 seconds!
Building AutoIndex...
Succeed in 3.092 seconds!
Loading collection...
Succeed in 1.915 seconds
Searching vector: [[0.9944244, 0.693386, 0.23255765, 0.25712538, 0.8882742, 0.31521088, 0.10415316, 0.63687223, 0.23532659, 0.9057376, 0.23146588, 0.7598192, 0.18651527, 0.22246557, 0.71210265, 0.9853572, 0.7285811, 0.49920946, 0.5757665, 0.9907576, 0.6746712, 0.53398585, 0.3538667, 0.90167266, 0.09474373, 0.24573046, 0.19975495, 0.9486663, 0.38426965, 0.94270027, 0.0389961, 0.3591758, 0.092348695, 0.35975152, 0.0286901, 0.39673442, 0.59137595, 0.9894001, 0.7167906, 0.92300236, 0.95593584, 0.041143477, 0.26055408, 0.37320316, 0.64292294, 0.9690605, 0.8847745, 0.78260505, 0.92037845, 0.3789205, 0.13551015, 0.30201513, 0.49969083, 0.9982949, 0.066904426, 0.2057795, 0.58327323, 0.5174314, 0.14389539, 0.5790425, 0.9743603, 0.50154245, 0.6616634, 0.91518396]]
Result: [type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 1901
 data: 420
 }
}
field_id: 100
, type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 11901
 data: 10420
 }
}
field_id: 101
]
search 0 latency: 0.884 seconds
Searching vector: [[0.012040913, 0.26888567, 0.8862797, 0.6432027, 0.72606975, 0.9214382, 0.8760127, 0.91277415, 0.6635162, 0.41956168, 0.21806747, 0.7068079, 0.562168, 0.28791857, 0.33324522, 0.83323824, 0.25118947, 0.8217658, 0.8863152, 0.5157847, 0.49335915, 0.39096528, 0.47110444, 0.9944315, 0.66889536, 0.98959273, 0.59788084, 0.7379387, 0.2901945, 0.6934935, 0.031101823, 0.93097365, 0.9596271, 0.32376093, 0.34571892, 0.8362285, 0.99687576, 0.7937399, 0.78078747, 0.34658694, 0.10428548, 0.2792738, 0.8640791, 0.57405174, 0.9725894, 0.20060968, 0.10779828, 0.101415455, 0.09755844, 0.7593852, 0.28356135, 0.6208364, 0.55110157, 0.42079234, 0.27782845, 0.06248188, 0.37675542, 0.7703235, 0.41342628, 0.07876682, 0.3361001, 0.8730568, 0.9582374, 0.38860106]]
Result: [type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 118
 data: 776
 }
}
field_id: 100
, type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 10118
 data: 10776
 }
}
field_id: 101
]
search 1 latency: 0.193 seconds
Searching vector: [[0.17430764, 0.9034548, 0.59125566, 0.13526762, 0.48277777, 0.9429901, 0.07483035, 0.09548402, 0.7757748, 0.5250427, 0.70900095, 0.46090156, 0.055408716, 0.5705429, 0.19141757, 0.5524303, 0.9825838, 0.6484894, 0.84965557, 0.41863292, 0.69617915, 0.123098195, 0.3800782, 0.8989199, 0.30235797, 0.33991778, 0.9502303, 0.6279421, 0.3922888, 0.94838214, 0.98462456, 0.23758143, 0.5195748, 0.16518217, 0.044407308, 0.9360681, 0.086349785, 0.9243839, 0.7705846, 0.85942554, 0.33542854, 0.6248715, 0.9321932, 0.3886962, 0.23936534, 0.5275571, 0.7020884, 0.68789816, 0.70815116, 0.7435949, 0.5740872, 0.36369282, 0.22608125, 0.42592448, 0.9893665, 0.59022135, 0.37368262, 0.9808166, 0.113725126, 0.63403445, 0.6192569, 0.3703097, 0.11013746, 0.19765383]]
Result: [type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 11983
 data: 10315
 }
}
field_id: 101
, type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 1983
 data: 315
 }
}
field_id: 100
]
search 2 latency: 0.21 seconds
Searching vector: [[0.78146076, 0.4127342, 0.19565648, 0.7598609, 0.0024149418, 0.20477176, 0.2239834, 0.40071744, 0.34123564, 0.69816893, 0.16385543, 0.8746263, 0.93988293, 0.14641893, 0.77087975, 0.74109954, 0.44419652, 0.686166, 0.8017639, 0.62660855, 0.12844962, 0.09513128, 0.0733701, 0.50885594, 0.19533062, 0.6489767, 0.5061082, 0.76457673, 0.556717, 0.62532073, 0.44644332, 0.47170228, 0.17381704, 0.031323075, 0.1744278, 0.61520636, 0.7062351, 0.48471195, 0.659993, 0.8125965, 0.8243918, 0.2953208, 0.6301986, 0.48987025, 0.44276655, 0.87922597, 0.59580773, 0.03247899, 0.2750023, 0.91016316, 0.109629214, 0.8797219, 0.26653385, 0.55840206, 0.5943634, 0.28695053, 0.4747029, 0.7866449, 0.19065481, 0.15052843, 0.017061412, 0.73239404, 0.29625612, 0.86883324]]
Result: [type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 11674
 data: 11420
 }
}
field_id: 101
, type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 1674
 data: 1420
 }
}
field_id: 100
]
search 3 latency: 0.234 seconds
Searching vector: [[0.5506256, 0.4888333, 0.55734605, 0.33249807, 0.9612315, 0.14942867, 0.74893725, 0.32297194, 0.19399291, 0.3686679, 0.16189837, 0.51538646, 0.4324317, 0.24171656, 0.17148066, 0.98530066, 0.70252293, 0.36399698, 0.67156136, 0.30545527, 0.8341777, 0.10815537, 0.04843366, 0.5751087, 0.030209243, 0.5765392, 0.56931233, 0.7783963, 0.87335634, 0.24376905, 0.15837216, 0.8376091, 0.8253407, 0.90347946, 0.91477525, 0.3711217, 0.34623754, 0.3486765, 0.7336032, 0.1333232, 0.9737603, 0.9348897, 0.56583005, 0.19536161, 0.66466415, 0.25348592, 0.1945247, 0.30146033, 0.6067432, 0.0488891, 0.61155295, 0.32729924, 0.58033705, 0.9833621, 0.805477, 0.0863865, 0.88150877, 0.13743609, 0.8735751, 0.730011, 0.2418676, 0.24961507, 0.15848696, 0.90943843]]
Result: [type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 632
 data: 71
 }
}
field_id: 100
, type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 10632
 data: 10071
 }
}
field_id: 101
]
search 4 latency: 0.246 seconds
Searching vector: [[0.097262084, 0.67786944, 0.5956394, 0.599271, 0.80228144, 0.20551127, 0.60712826, 0.7927536, 0.8707964, 0.92879516, 0.8600838, 0.088315904, 0.28391147, 0.13210869, 0.6447914, 0.79745305, 0.1630668, 0.87363887, 0.8451723, 0.56345624, 0.30312908, 0.8302696, 0.7499766, 0.42542475, 0.9217818, 0.16116339, 0.76864123, 0.7699597, 0.55911744, 0.88660645, 0.4057927, 0.05212158, 0.9849399, 0.6994747, 0.25052422, 0.5464197, 0.6989017, 0.6539669, 0.8416681, 0.60720086, 0.67637247, 0.74851876, 0.87226254, 0.015863419, 0.2851053, 0.741167, 0.5423461, 0.4004978, 0.21673638, 0.32579643, 0.90930575, 0.5031407, 0.11341238, 0.042031705, 0.72256076, 0.20273715, 0.67203254, 0.49100053, 0.5708503, 0.78067535, 0.053472757, 0.8504045, 0.77535784, 0.43355346]]
Result: [type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 1240
 data: 1074
 }
}
field_id: 100
, type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 11240
 data: 11074
 }
}
field_id: 101
]
search 5 latency: 0.233 seconds
Searching vector: [[0.6666099, 0.60750645, 0.5930602, 0.62844944, 0.91999257, 0.40563875, 0.16784662, 0.58380336, 0.49874693, 0.9921237, 0.70105964, 0.109129846, 0.62001497, 0.29218578, 0.38023782, 0.80481774, 0.61428535, 0.45924222, 0.2801816, 0.40553528, 0.9678988, 0.4772452, 0.26234365, 0.934155, 0.6174237, 0.14148414, 0.5784021, 0.54518217, 0.11126441, 0.41204536, 0.61628705, 0.09558219, 0.12766111, 0.6261982, 0.899587, 0.8454346, 0.46918148, 0.5451731, 0.904986, 0.41042298, 0.82801545, 0.8856106, 0.5411191, 0.45282567, 0.448133, 0.8004736, 0.73305, 0.28807688, 0.99202037, 0.69817233, 0.67967457, 0.9214035, 0.97179586, 0.05739242, 0.15004623, 0.2254278, 0.6256416, 0.25962013, 0.015357256, 0.37749702, 0.037437856, 0.43823433, 0.88566333, 0.03802377]]
Result: [type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 840
 data: 758
 }
}
field_id: 100
, type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 10840
 data: 10758
 }
}
field_id: 101
]
search 6 latency: 0.204 seconds
Searching vector: [[0.11513382, 0.46449423, 0.74709874, 0.45208257, 0.09473729, 0.254663, 0.34211916, 0.9703239, 0.93299186, 0.9023329, 0.3711759, 0.8269761, 0.032090902, 0.7948698, 0.5412331, 0.18797356, 0.52153504, 0.9574906, 0.86989266, 0.9373586, 0.95098126, 0.74318993, 0.25189924, 0.7419808, 0.94729316, 0.5275025, 0.08891839, 0.100687325, 0.71073514, 0.915546, 0.20827055, 0.21283334, 0.29924893, 0.7821449, 0.4894712, 0.10083097, 0.43401027, 0.16695005, 0.6697829, 0.8920079, 0.09012061, 0.073818564, 0.005478561, 0.046307027, 0.77760696, 0.7813985, 0.57121813, 0.53898925, 0.112255454, 0.57960665, 0.43604386, 0.14278823, 0.79370797, 0.38984406, 0.16432655, 0.46382785, 0.29833382, 0.50633746, 0.6599931, 0.87258536, 0.6334079, 0.47853124, 0.3620978, 0.9284326]]
Result: [type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 11609
 data: 10760
 }
}
field_id: 101
, type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 1609
 data: 760
 }
}
field_id: 100
]
search 7 latency: 0.201 seconds
Searching vector: [[0.14196914, 0.72220755, 0.97196966, 0.95760065, 0.17740268, 0.84783286, 0.96259207, 0.6342059, 0.14442027, 0.49199748, 0.008492172, 0.397287, 0.9179648, 0.960828, 0.5513112, 0.36929417, 0.5710163, 0.5876399, 0.05758536, 0.8194362, 0.061840713, 0.98121184, 0.1594106, 0.72564113, 0.52452654, 0.42187476, 0.027037859, 0.3591584, 0.8754618, 0.4417299, 0.68962467, 0.16419351, 0.9725907, 0.0099541545, 0.98403805, 0.29020512, 0.7518712, 0.9071815, 0.71259576, 0.6060254, 0.90245855, 0.502836, 0.97288334, 0.67282623, 0.34302354, 0.7001372, 0.48336947, 0.15780938, 0.21866882, 0.7550309, 0.7676532, 0.35363984, 0.37162405, 0.74286896, 0.9311386, 0.5419771, 0.34793264, 0.8912447, 0.32318318, 0.75553536, 0.41343224, 0.8355903, 0.93488806, 0.29507792]]
Result: [type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 443
 data: 1132
 }
}
field_id: 100
, type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 10443
 data: 11132
 }
}
field_id: 101
]
search 8 latency: 0.227 seconds
Searching vector: [[0.77036685, 0.11818504, 0.9503699, 0.029416382, 0.02396065, 0.23895812, 0.41341382, 0.82225484, 0.8573582, 0.09137666, 0.6607858, 0.3046307, 0.07275897, 0.25788748, 0.6302589, 0.95142424, 0.46371943, 0.99155724, 0.7007737, 0.9712237, 0.043981254, 0.6340834, 0.6424302, 0.7176585, 0.6954333, 0.091201425, 0.026640236, 0.15468991, 0.36369103, 0.9462943, 0.0063298345, 0.72643405, 0.15416229, 0.9989488, 0.3602597, 0.8026867, 0.52236784, 0.8901442, 0.69515526, 0.59382325, 0.7458346, 0.95818526, 0.5083475, 0.09321368, 0.8753361, 0.26350176, 0.6790923, 0.6423713, 0.833, 0.5457574, 0.8288555, 0.1950497, 0.52434474, 0.7186958, 0.77294624, 0.24331266, 0.10928261, 0.8677729, 0.084388554, 0.7637791, 0.6102848, 0.8594893, 0.16542625, 0.9389486]]
Result: [type: Int64
field_name: "book_id"
scalars {
 long_data {
 data: 1938
 data: 668
 }
}
field_id: 100
, type: Int64
field_name: "word_count"
scalars {
 long_data {
 data: 11938
 data: 10668
 }
}
field_id: 101
]
search 9 latency: 1.076 seconds