Elasticsearch5.5.1 自定义评分插件开发

Posted 2023-03-03 山塘小鱼儿

文本相似度插件开发，本文基于Elasticsearch5.5.1，Kibana5.5.1

下载地址为：

Past Releases of Elastic Stack Software | Elastic

本地启动两个服务后，localhost:5601打开Kibana界面，点击devTools，效果图

创建索引 PUT index

添加数据 GET index/doc_1，json

"title":"11111",

"feature":"搭建好ES之后，想用命令行简单测试一下，涉及到了下面几个命令，也遇到了一些问题，记录一下"

查询语句 GET index/doc_1/_search 必须有_search,不然就变插入或更新了

 

  "from": 0,
  "size": 15,
  "min_score": 0.3,
  "query": 
    "function_score": 
     
      "functions": [
        
          "script_score": 
            "script": 
              "inline": "icon_hash",
              "lang": "native",
              "params": 
                "feature": "想"
              
            
          
        
      ]
    
  

记录一下插件的写法：

1.相似度比较算法，pom

<dependency>
  <groupId>com.janeluo</groupId>
  <artifactId>ikanalyzer</artifactId>
  <version>2012_u6</version>
</dependency>

算法代码：

import org.wltea.analyzer.core.IKSegmenter;
import org.wltea.analyzer.core.Lexeme;

import java.io.IOException;
import java.io.StringReader;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
import java.util.Vector;
public class TextComparator 

    public static double YUZHI = 0.1;

    public TextComparator() 
    

    public static double getSimilarity(Vector<String> T1, Vector<String> T2) throws Exception 

        if (T1 != null &&T1.size() > 0 && T2 != null && T2.size() > 0) 
            Map<String, double[]> T = new HashMap();
            String index = null;

            int i;
            double[] c;
            for(i = 0; i < T1.size(); ++i) 
                index = (String)T1.get(i);
                if (index != null) 
                    c = (double[])T.get(index);
                    c = new double[]1.0, YUZHI;
                    T.put(index, c);
                
            

            for(i = 0; i < T2.size(); ++i) 
                index = (String)T2.get(i);
                if (index != null) 
                    c = (double[])T.get(index);
                    if (c != null && c.length == 2) 
                        c[1] = 1.0;
                     else 
                        c = new double[]YUZHI, 1.0;
                        T.put(index, c);
                    
                
            

            Iterator<String> it = T.keySet().iterator();
            double s1 = 0.0;
            double s2 = 0.0;

            double Ssum;

            for(Ssum = 0.0; it.hasNext(); s2 += c[1] * c[1]) 
                c = (double[])T.get(it.next());
                Ssum += c[0] * c[1];
                s1 += c[0] * c[0];
            

            return Ssum / Math.sqrt(s1 * s2);
         else 
            throw new Exception("传入参数有问题！");
        
    

    public static Vector<String> participle(String str) 
        Vector<String> str1 = new Vector();

        try 
            StringReader reader = new StringReader(str);
            IKSegmenter ik = new IKSegmenter(reader, true);
            Lexeme lexeme = null;

            while((lexeme = ik.next()) != null) 
                str1.add(lexeme.getLexemeText());
            

            if (str1.size() == 0) 
                return null;
            

            System.out.println("str分词后：" + str1);
         catch (IOException var5) 
            System.out.println();
        

        return str1;
    

    public static void main(String[] args) 
        String s1 = "想";
        String s2 = "搭建好ES之后，想用命令行简单测试一下，涉及到了下面几个命令，也遇到了一些问题，记录一下";

        Double score;
        try 
            score = getSimilarity(participle(s1), participle(s2));
         catch (Exception var5) 
            throw new RuntimeException(var5);
        

        System.out.println(score);
    

    public static Double getScore(String s1, String s2) 
        try 
            return getSimilarity(participle(s1), participle(s2));
         catch (Exception var3) 
            throw new RuntimeException(var3);
        
    

Elasticsearch插件代码

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.elasticsearch.common.Nullable;
import org.elasticsearch.common.xcontent.support.XContentMapValues;
import org.elasticsearch.plugins.ActionPlugin;
import org.elasticsearch.plugins.Plugin;
import org.elasticsearch.plugins.ScriptPlugin;
import org.elasticsearch.script.AbstractDoubleSearchScript;
import org.elasticsearch.script.ExecutableScript;
import org.elasticsearch.script.NativeScriptFactory;

import java.util.Collections;
import java.util.List;
import java.util.Map;

public class IconHashPlugin extends Plugin implements ActionPlugin, ScriptPlugin 
    private final static Logger LOGGER = LogManager.getLogger(IconHashPlugin.class);

    public IconHashPlugin() 
        super();
        LOGGER.warn("Create the Basic Plugin and installed it into elasticsearch");
    


    @Override
    public List<NativeScriptFactory> getNativeScripts() 
        return Collections.singletonList(new MyNativeScriptFactory());
    

    public static class MyNativeScriptFactory implements NativeScriptFactory 
        private final static Logger LOGGER = LogManager.getLogger(MyNativeScriptFactory.class);

        @Override
        public ExecutableScript newScript(@Nullable Map<String, Object> params) 
            LOGGER.info("MyNativeScriptFactory  run new Script ");
            String featureStr = params == null ? null : XContentMapValues.nodeStringValue(params.get("feature"), null);
            if (featureStr == null) 
                LOGGER.error("Missing the field parameter ");
            
            return new MyScript(featureStr);
        

        @Override
        public boolean needsScores() 
            return false;
        

        @Override
        public String getName() 
            return "icon_hash";
        
    

    public static class MyScript extends AbstractDoubleSearchScript 

        private final static Logger LOGGER = LogManager.getLogger(MyScript.class);
        private final String featureStr;

        public MyScript(String featureStr) 
            this.featureStr = featureStr;
        

        @Override
        public double runAsDouble() 
            LOGGER.info("my run As begining ");
            String strSrcFeature = (String) source().get("feature");
            String f1 = featureStr;
            String f2 = strSrcFeature;

            LOGGER.info("featureStr------> "+featureStr);
            LOGGER.info("strSrcFeature------> "+strSrcFeature);

            Double score = MyTextComparator.getScore(featureStr,strSrcFeature);
            LOGGER.info("score------> "+score);
            return score;
        
    

2.部署插件

打包啥的见我的另一个代码源码：

https://download.csdn.net/download/airyearth/87435594

本次主要就是替换了算法

3.部署插件，非常重要的一点就是把一些冲突的jar包删掉，copy进Elasticsearch的\\elasticsearch-5.5.1\\plugins后，手动删掉lucene所有的包，不然会和es冲突

重启es就可以了