Elasticsearch对数组字段的自动完成搜索

一尘不染

Elasticsearch对数组字段的自动完成搜索

elasticsearch

我正在对具有字符串类型数组的文档字段进行自动完成建议。我的文件如下所示；

{

    "title": "Product1",
    "sales": "6",
    "rating": "0.0",
    "cost": "45.00",
    "tags": [
        "blog",
        "magazine",
        "responsive",
        "two columns",
        "wordpress"
    ],
    "category": "wordpress",
    "description": "Product1 Description",
    "createDate": "2013-12-19"
}

{

    "title": "Product1",
    "sales": "6",
    "rating": "0.0",
    "cost": "45.00",
    "tags": [
        "blog",
        "paypal",
        "responsive",
        "skrill",
        "wordland"
    ],
    "category": "wordpress",
    "description": "Product1 Description",
    "createDate": "2013-12-19"
}

我正在标签字段上执行自动完成搜索。我的查询就像；

query: {
                    query_string: {
                        query: "word*",
                        fields: ["tags"]
                    }
                },
                facets: {
                    tags: {
                        terms: {
                            field: "tags"
                        }
                    }
                }

当用户键入“ word”时，我要显示“ wordland”和“ wordpress”。但是，我无法做到这一点。

您能帮上忙吗？

谢谢

阅读 229

2020-06-22

共1个答案

一尘不染

您是否尝试过完成建议？解决问题的一种方法如下：

1）创建索引：

curl -XPUT "http://localhost:9200/test_index/"

2）使用完成建议者类型创建映射：

curl -XPUT "http://localhost:9200/test_index/product/_mapping" -d'
{
   "product": {
      "properties": {
         "category": {
            "type": "string"
         },
         "cost": {
            "type": "string"
         },
         "createDate": {
            "type": "date",
            "format": "dateOptionalTime"
         },
         "description": {
            "type": "string"
         },
         "rating": {
            "type": "string"
         },
         "sales": {
            "type": "string"
         },
         "tags": {
            "type": "string"
         },
         "title": {
            "type": "string"
         },
         "suggest": {
            "type": "completion",
            "index_analyzer": "simple",
            "search_analyzer": "simple",
            "payloads": false
         }
      }
   }
}'

3）添加文件：

curl -XPUT "http://localhost:9200/test_index/product/1" -d'
{
   "title": "Product1",
   "sales": "6",
   "rating": "0.0",
   "cost": "45.00",
   "tags": [
      "blog",
      "magazine",
      "responsive",
      "two columns",
      "wordpress"
   ],
   "suggest": {
      "input": [
         "blog",
         "magazine",
         "responsive",
         "two columns",
         "wordpress"
      ]
   },
   "category": "wordpress",
   "description": "Product1 Description",
   "createDate": "2013-12-19"
}'

curl -XPUT "http://localhost:9200/test_index/product/2" -d'
{

    "title": "Product2",
    "sales": "6",
    "rating": "0.0",
    "cost": "45.00",
    "tags": [
        "blog",
        "paypal",
        "responsive",
        "skrill",
        "wordland"
    ],
   "suggest": {
      "input": [
         "blog",
        "paypal",
        "responsive",
        "skrill",
        "wordland"
      ]
   },
    "category": "wordpress",
    "description": "Product1 Description",
    "createDate": "2013-12-19"
}'

4）然后使用_suggest端点进行查询：

curl -XPOST "http://localhost:9200/test_index/_suggest" -d'
{
    "product_suggest":{
        "text":"word",
        "completion": {
            "field" : "suggest"
        }
    }
}'

您将获得预期的结果：

{
   "_shards": {
      "total": 2,
      "successful": 2,
      "failed": 0
   },
   "product_suggest": [
      {
         "text": "word",
         "offset": 0,
         "length": 4,
         "options": [
            {
               "text": "wordland",
               "score": 1
            },
            {
               "text": "wordpress",
               "score": 1
            }
         ]
      }
   ]
}

当然，可以稍微改进此解决方案，特别是通过修剪一些重复的数据，但这应该为您指明正确的方向。

2020-06-22