3.6 ElasticSearch数据建模之案例

1.需求简介

以一个博客文章需求案例来演示elasticsearch数据建模的过程，博客索引包括如下字段。

• 标题:title
• 发布日期:publish_date
• 作者:author
• 摘要:abstract
• 网络地址:url

2.blog_index的Mapping

PUT /blog_index      

{
  "settings": {
    "number_of_shards": 5,
    "number_of_replicas": 1
  }
}      

PUT /blog_index/_mapping      

{
  "properties": {
    "title": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      }
    },
    "publish_date": {
      "type": "date"
    },
    "author": {
      "type": "keyword"
    },
    "abstract": {
      "type": "text"
    },
    "url": {
      "enabled": false
    }
  }
}      

对text类型的文档进行查询或者聚合时，该字段会被elasticsearch分词，如果需要实现完全匹配的功能，这时就需要对该字段设置一个keyword属性，然后将keyword属性的type设置为keyword。

3.需求变更

如果需要新增一个内容content字段，并且这个字段会很大，占据非常多的磁盘空间，这时可对Mapping做如下修改。

PUT /blog_index      

{
  "settings": {
    "number_of_shards": 5,
    "number_of_replicas": 1
  }
}      

PUT /blog_index/_mapping      

{
  "_source": {
    "enabled": false
  },
  "properties": {
    "title": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      },
      "store": true
    },
    "publish_date": {
      "type": "date",
      "store": true
    },
    "author": {
      "type": "keyword",
      "store": true
    },
    "abstract": {
      "type": "text",
      "store": true
    },
    "content": {
      "type": "text",
      "store": true
    },
    "url": {
      "type": "keyword",
      "ignore_above": 100,
      "store": true
    }
  }
}      

4.查询

(1).插入文档

POST /blog_index/_doc      

{
  "title": "hello world",
  "author": "steven",
  "publish_date": "2021-01-01",
  "abstract": "hello my world",
  "content": "hello my world",
  "url": "https://www.world.com/"
}      

POST /blog_index/_search      

{
  "query": {
    "match": {
      "title": "hello"
    }
  }
}      

{
  "took" : 464,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 0.2876821,
    "hits" : [
      {
        "_index" : "blog_index",
        "_type" : "_doc",
        "_id" : "yoldm3sBEsHOdz1YP8pk",
        "_score" : 0.2876821
      }
    ]
  }
}      

POST /blog_index/_search      

{
  "stored_fields": ["title", "content"],
  "query": {
    "match": {
      "title": "hello"
    }
  },
  "highlight": {
    "fields": {
      "title": {}
    }
  }
}      

{
  "took" : 106,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 0.2876821,
    "hits" : [
      {
        "_index" : "blog_index",
        "_type" : "_doc",
        "_id" : "yoldm3sBEsHOdz1YP8pk",
        "_score" : 0.2876821,
        "fields" : {
          "title" : [
            "hello world"
          ],
          "content" : [
            "hello my world"
          ]
        },
        "highlight" : {
          "title" : [
            "<em>hello</em> world"
          ]
        }
      }
    ]
  }
}