返回包含与搜索字词相似的字词的文档 编辑距离是将一个术语转换为另一个术语所需的一个字符更改的次数。这些更改可以包括

• 更改字符（box → fox）
• 删除字符（black → lack）
• 插入字符（sic → sick）
• 转置两个相邻字符（act → cat）

为了找到相似的术语，fuzzy查询会在指定的编辑距离内创建一组搜索词的所有可能的变体或扩展。然后查询返回每个扩展的完全匹配

通过 fuzziness 修改编辑距离。一般使用默认值 AUTO，根据术语的长度生成编辑距离

在 Postman 中，向 ES 服务器发 GET 请求

GET http://localhost:9200/student/_search

body

{
    "query": {
        "fuzzy": {
            "name": {
                "value": "zhangsan"
            }
        }
    }
}

response

• 查询到name与zhangsan类似的记录

{
    "took": 34,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 3,
            "relation": "eq"
        },
        "max_score": 1.3862942,
        "hits": [
            {
                "_index": "student",
                "_type": "_doc",
                "_id": "1001",
                "_score": 1.3862942,
                "_source": {
                    "name": "zhangsan",
                    "nickname": "zhangsan",
                    "sex": "男",
                    "age": 30
                }
            },
            {
                "_index": "student",
                "_type": "_doc",
                "_id": "1004",
                "_score": 1.2130076,
                "_source": {
                    "name": "zhangsan1",
                    "nickname": "zhangsan1",
                    "sex": "女",
                    "age": 50
                }
            },
            {
                "_index": "student",
                "_type": "_doc",
                "_id": "1005",
                "_score": 1.2130076,
                "_source": {
                    "name": "zhangsan2",
                    "nickname": "zhangsan2",
                    "sex": "女",
                    "age": 30
                }
            }
        ]
    }
}