tencent cloud

腾讯云 Elasticsearch Service 支持以下两种方式配置同义词：上传同义词文件、直接引用同义词。

方式一：上传同义词文件

注意事项

• 上传同义词文件操作将触发集群滚动重启

• 新上传/新变更的同义词文件对老索引不生效，需要重建索引。例如现有的索引myindex使用了synonym.txt同义词文件，当该同义词文件的内容变更并重新上传后，现有的索引myindex不会动态加载更新后的同义词，需要对现有索引进行reindex操作，否则更新后的同义词文件只对新建的索引生效

• 同义词文件要求每行一个同义词表达式（表达式支持Solr规则和WordNet规则），并且文件需要为utf-8编码，扩展名为.txt。例如：

    快乐水,可乐 => 可乐,快乐水
    elasticsearch,es => es

• 同义词文件单个文件最大为10M，上传文件总数最多为10个

操作步骤

1. 登录腾讯云Elasticsearch Service控制台

2. 在集群列表页，单击集群 ID 进入集群详情页

3. 单击【高级配置】，进入【同义词配置】页面

   

4. 单击【更新词典】，在更新同义词页面上传同义词文件

   

5. 上传完成后，单击【保存】

使用同义词文件

以下实例使用filter过滤器配置同义词，使用synonym.txt作为测试文件，文件内容为elasticsearch,es => es

1. 登录已上传同义词文件的集群对应的Kibana控制台。登录控制台的具体步骤请参考通过Kibana访问集群

2. 单击左侧导航栏的Dev Tools。

3. 在Console中执行如下的命令，创建索引

    PUT /my_index
    {
      "settings": {
        "index": {
          "analysis": {
            "analyzer": {
              "my_ik": {
                "type": "custom",
                "tokenizer": "ik_smart",
                "filter": [
                  "my_synonym"
                ]
              }
            },
            "filter": {
              "my_synonym": {
                "type": "synonym",
                "synonyms_path": "analysis/synonym.txt"
              }
            }
          }
        }
      },
      "mappings": {
        "_doc": {
          "properties": {
            "content": {
              "type": "text",
              "analyzer": "my_ik",
              "search_analyzer": "my_ik"
            }
          }
        }
      }
    }

4. 执行如下命令，验证同义词配置

    GET /my_index/_analyze
    {
      "analyzer": "my_ik",
      "text":"tencet elasticsearch service"
    }

   命令执行成功，将返回如下结果

    {
      "tokens": [
        {
          "token": "tencet",
          "start_offset": 0,
          "end_offset": 6,
          "type": "ENGLISH",
          "position": 0
        },
        {
          "token": "es",
          "start_offset": 7,
          "end_offset": 20,
          "type": "SYNONYM",
          "position": 1
        },
        {
          "token": "service",
          "start_offset": 21,
          "end_offset": 28,
          "type": "ENGLISH",
          "position": 2
        }
      ]
    }

   输出结果中，token es的类型是SYNONYM同义词。

5. 执行如下命令，添加一些文档

    POST /my_index/_doc/1
    {
      "content": "tencet elasticsearch service"
    }
   
    POST /my_index/_doc/2
    {
      "content": "hello es"
    }

6. 执行如下命令，搜索同义词

    GET my_index/_search
    {
      "query" : { "match" : { "content" : "es" }},
      "highlight" : {
        "pre_tags" : ["<tag1>", "<tag2>"],
        "post_tags" : ["</tag1>", "</tag2>"],
        "fields" : {"content": {}}
      }
    }

   命令执行成功后，返回如下结果

    {
      "took": 4,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 2,
        "max_score": 0.25811607,
        "hits": [
          {
            "_index": "my_index",
            "_type": "_doc",
            "_id": "2",
            "_score": 0.25811607,
            "_source": {
              "content": "hello es"
            },
            "highlight": {
              "content": [
                "hello <tag1>es</tag1>"
              ]
            }
          },
          {
            "_index": "my_index",
            "_type": "_doc",
            "_id": "1",
            "_score": 0.25316024,
            "_source": {
              "content": "tencet elasticsearch service"
            },
            "highlight": {
              "content": [
                "tencet <tag1>elasticsearch</tag1> service"
              ]
            }
          }
        ]
      }
    }

方式二：直接引用同义词

1. 登录集群对应的Kibana控制台。登录控制台的具体步骤请参考通过Kibana访问集群

2. 单击左侧导航栏的Dev Tools。

3. 在Console中执行如下的命令，创建索引

    PUT /my_index
    {
      "settings": {
        "index": {
          "analysis": {
            "analyzer": {
              "my_ik": {
                "type": "custom",
                "tokenizer": "ik_smart",
                "filter": [
                  "my_synonym"
                ]
              }
            },
            "filter": {
              "my_synonym": {
                "type": "synonym",
                "synonyms": [
                  "elasticsearch,es => es"
                ],
              }
            }
          }
        }
      },
      "mappings": {
        "_doc": {
          "properties": {
            "content": {
              "type": "text",
              "analyzer": "my_ik",
              "search_analyzer": "my_ik"
            }
          }
        }
      }
    }

   这里与使用同义词文件方式的区别是，在filter中定义同义词时，直接引用了同义词，而不是同义词文件："synonyms": ["elasticsearch,es => es"]

4. 执行如下命令，验证同义词配置

    GET /my_index/_analyze
    {
      "analyzer": "my_ik",
      "text":"tencet elasticsearch service"
    }

   命令执行成功，将返回如下结果

    {
      "tokens": [
        {
          "token": "tencet",
          "start_offset": 0,
          "end_offset": 6,
          "type": "ENGLISH",
          "position": 0
        },
        {
          "token": "es",
          "start_offset": 7,
          "end_offset": 20,
          "type": "SYNONYM",
          "position": 1
        },
        {
          "token": "service",
          "start_offset": 21,
          "end_offset": 28,
          "type": "ENGLISH",
          "position": 2
        }
      ]
    }

   输出结果中，token es的类型是SYNONYM同义词。

5. 执行如下命令，添加一些文档

    POST /my_index/_doc/1
    {
      "content": "tencet elasticsearch service"
    }
   
    POST /my_index/_doc/2
    {
      "content": "hello es"
    }

6. 执行如下命令，搜索同义词

    GET my_index/_search
    {
      "query" : { "match" : { "content" : "es" }},
      "highlight" : {
        "pre_tags" : ["<tag1>", "<tag2>"],
        "post_tags" : ["</tag1>", "</tag2>"],
        "fields" : {"content": {}}
      }
    }

   命令执行成功后，返回如下结果

    {
      "took": 4,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 2,
        "max_score": 0.25811607,
        "hits": [
          {
            "_index": "my_index",
            "_type": "_doc",
            "_id": "2",
            "_score": 0.25811607,
            "_source": {
              "content": "hello es"
            },
            "highlight": {
              "content": [
                "hello <tag1>es</tag1>"
              ]
            }
          },
          {
            "_index": "my_index",
            "_type": "_doc",
            "_id": "1",
            "_score": 0.25316024,
            "_source": {
              "content": "tencet elasticsearch service"
            },
            "highlight": {
              "content": [
                "tencet <tag1>elasticsearch</tag1> service"
              ]
            }
          }
        ]
      }
    }