  const orm = require('elasticsearch-orm');
  const instance = orm({
      'domain':'127.0.0.1',
      'port':9200
  });

  instance.on('connected',() =>{
      console.log('连接成功');
  });

  instance.on('error',(e) =>{
    console.log('连接异常',e);
  });

索引相关

创建一个索引

生成一个索引类型

  
  const demoIndex = instance.register('demoIndex',{
      'index':'demoindex',
      'type':'demotype'
    },{
        'title':{
            'type':'text'
        },
        'age':{
            'type':'integer'
        },
        'location':{
            'type':'geo_point'
        }
      },{
        'number_of_shards': 2,
        'number_of_replicas': 4
      });

同步索引：如果索引还未被创建，就会按照 mappings 和 settings 创建索引，如果索引已经创建，则会自动判断哪些 mappings 是新增的，并将这些新增的 mappings 添加到索引中。sync 方法返回一个 Promise 对象，所以可以使用 await 关键字。

   await demoIndex.sync();

索引健康值

    const health = await demoIndex.health();

索引状态

    const stat = await demoIndex.stat();

索引统计

    const state = await demoIndex.state();

设置索引别名

    const result = await demoIndex.alias(['alias_name']);

删除别名

    const result = await demoIndex.removeAlias(['alias_name']);

刷新

    const result = await demoIndex.refresh();

冲洗

    const result = await demoIndex.flush();

强制合并

    const result = await demoIndex.forceMerge();

测试分词器

    const result = await demoIndex.analyze('我爱北京天安门','ik_max_word');

开启一个索引

    const result = await demoIndex.open();

关闭一个索引

    const result = await demoIndex.close();

文档相关

创建文档

create 方法返回一个 Promise 对象，可以使用 await 关键字，最终返回新建的文档 ID

let id = await demoIndex.create({
    'title':'Demo Title',
    'age',12,
    'location':{
      'lon':100.1,
      'lat':40.2
    }
  });

指定文档 ID 创建文档

  await demoIndex.create({
    'title':'Demo Title',
    'age',12,
    'location':{
      'lon':100.1,
      'lat':40.2
    }
  },'document_id');

指定文档 routing

  await demoIndex.create({
    'title':'Demo Title',
    'age',12,
    'location':{
      'lon':100.1,
      'lat':40.2
    }
  },'document_id','routing_hash');

指定父节点

  await demoIndex.create({
    'title':'Title',
    'age':123
    },null,null,{
      'parent':'parent_id'
    })

更新文档

  await demoIndex.update('docuemnt_id',{
    'title':"Demo Title 2",
    'age':13
  })

指定文档 routing

  await demoIndex.update('document_id',{
    'title':'Demo Title 2',
    'age':14
    },'routing_hash')

删除文档

  await demoIndex.delete(id);
  await demoIndex.delete(['id1','id2'])

通过 id 获取文档

如果 id 不存在，会返回一个 Error

  let doc = await demoIndex.get(id);

查询相关

构建简单查询

    let ret = await demoIndex.query();

ret 对象返回连个子对象，一个是list，是将结果提取好的_source 数组，另一个是 orgResult，是 es 返回的原始内容

查询条件

单一查询条件，全部查询条件列表请参看查询 API

  let ret = await demoIndex.term('age',12).query();

多查询条件

  let ret = await demoIndex
      .term('age',12)
      .match('title','')
      .query();

must,should,not 查询

  const Condition = require("elasticsearch-orm").Condition;
  let ret = await demoIndex
    .must(new Condition().term('age',12))
    .should(new Condition().match('title','Tiel'))
    .not(new Condition().exists('age'))
    .query();

filter 查询

  const Condition = require("elasticsearch-orm").Condition;
  let ret = await demoIndex
            .filter(new Condition().matchAll())
            .query();

构建嵌套查询

const Condition = require("elasticsearch-orm").Condition;
let condition = new Condition();
condition.term('age',12)
    .match('title','Title')
    .not(new Conditio()
    .range('age',0,10));
let ret = await demoIndex
    .should(condition)
    .exists('location')
    .query();

使用聚合

使用基本聚合

通过 orgResult 对象的原始返回值，可以拿到聚合的结果，完整的聚合 API 请参看聚合 API

  const Aggs = require('elasticsearch-orm').Aggs;
  let ret = await demoIndex
      .exists('age')
      .aggs(new Aggs('avg_age').avg('age'))
      .query();

聚合的子聚合

  const Aggs = require('elasticsearch-orm').Aggs;
  let aggs = new Aggs('test_aggs').terms('title');
  aggs.aggs(new Aggs('sub_aggs').valueCount('age'));
  let ret = await demoIndex
      .exist('age')
      .aggs(aggs)
      .query();

分页相关

分页

  let ret = await demoIndex
      .from(0)
      .size(15)
      .query();

使用滚动

发起一个滚动

    await demoIndex.query({
        'scroll':'1m'
    })

执行滚动

    await demoIndex.scroll(scrollId,{
        'scroll':'1m'
    });

清除一个滚动

    await demoIndex.clearScroll(scrollId);

排序

  let ret = await demoIndex
      .sort('age','asc')
      .sort('title','asc','min')
      .query();

或者

  let ret = await demoIndex
      .sort({
          'age':{
              'order':'desc',
              'mode':'min'
          }
      })
      .query();

设置

如果设置了 debug 为 true，则每次请求的请求体、url和返回值都会被打印出来

  let instance = orm({
    'domain':'127.0.0.1',
    'port':9200
  });
  instance.set("debug",true);

可以设置 debug 的方法

  instance.set("log",console.log);

设置请求超时时间，以毫秒为单位（默认是30s）

  instance.set('timeout',5000);

集群相关接口

获取集群健康值

    const health = await instance.health();

获取集群状态

    const state = await instance.state();

获取集群统计

    const stat = await instance.stat();

获取索引列表

    const result = await instance.indices();

节点信息

    const result = await instance.nodes();

节点状态

    const result = await instance.nodeStat('node_id');

关闭一个节点

    const result = await instance.shutDown('node_id');

查询API

文本匹配

match 查询

  let condition = new Condition();
  condition.match('title','content1 content2');
  condition.match('title','content1 content2',{
    'operator':'and'
    });

生成的查询json 为

  {
    "match":{
        "title":"content1 content2",
        "operator":"and"
    }
  }

field 参数可以是数组

  condition.match(['title','description'],'content1 content2');
  condition.match(['title','description'],'content1 content2',{
      'type':'best_fields'
    });

生成的查询 json 为

  {
    "multi_match":{
        "query":"content1 content2",
        "type":"best_fields",
        "fields":["title","description"]
    }
  }

短语查询 matchPhrase 和 matchPhrasePrefix

condition.matchPhrase('title','content1 content2');
condition.matchPrasePrefix('title','content1 content2');
condition.matchPhrase('title','content1 content2',{
  'analyzer':'test_analyzer'
  });

生成查询 json

  {
    "match_phrase":{
      "title":{
        "query":"content1 content2",
        "analyzer":"test_analyzer"
      }
    }
  }
  {
    "match_phrase_prefix":{
      "title":{
        "query":"content1 content2"
      }
    }
  }

精确值

term 查询

condition.term('age',13);
condition.term('age',[13,15]);

生成查询 json

  {
    "term":{
        "age":13
    }
  }
  {
    "terms":{
        "age":[13,15]
    }
  }

exists 查询

condition.exists('age');
condition.exists(['age','title']);

生成json

{
  "exists":{
    "field":"age"
  }
}
{
  "exists":{
    "fields":["age","title"]
  }
}

range 查询

condition.range('age',1);
condition.range('age',1,10);
condition.range('age',null,10);
condition.range('age',1,10,true,false);

生成json

  {
    "range":{
        "age":{
            "gt":1
        }
    }
  }
  {
    "range":{
        "age":{
            "gt":1,
            "lt":10
        }
    }
  }
  {
    "range":{
        "age":{
            "lt":10
        }
    }
  }
  {
    "range":{
        "age":{
            "gte":1,
            "lt":10
        }
    }
  }

使用 Range 对象

const Range = require('elasticsearch-orm').Range();
let range = new Range(1);
range = new Range(1,10);
range = new Range(1,10,false,true);
range = new Range().gt(1,true).lt(10,false);
condition.range(range);

prefix、wildcard 和fuzzy

condition.prefix('title','Tre');
condition.wildcard('title','Tre*hao');
condition.fuzzy('title',{
  'value':'ki',
  'boost':1.0
})

生成 json 文件

{
  "prefix":{
    "title":"Tre"
  }
}
{
  "wildcard":{
    "title":"Tre*hao"
  }
}
{
  "fuzzy":{
    "title":{
        "value":"ki",
        "boost":1.0
    }
  }
}

地理位置查询

geoShape

condition.geoShape('location','circle',
  [{
  'lon':100.0,
  'lat':41.0
  }],
  {
    'radius':"100m",
    "relation":"within"
    })

生成json

  {
    "geo_shape":{
        "location":{
            "shape":{
              "type":"circle",
              "coordinates":[{
                "lon":100.0,
                "lat":41.0
              }],
              "relation":"within"
            }
        }
    }
  }

geoDistance

  condition.geoDistance('location',{
    'lon':100.0,
    'lat':31.0
    },'100m');

生成 json

  {
    "geo_distance":{
      "distance":"100m",
      "location":{
        "lon":100.0,
        "lat":31.0
      }
    }
  }

geoPolygon

condition.geoPolygon('location',[{
  'lon':100.0,
  'lat':41.1
  },{
    'lon':101.0,
    'lat':42.1
   },{
     'lot':102.3,
     'lat':42.4
    }])

生成 json

{
  "geo_polygon":{
      "location":{
          "points":[{
                  "lon":100.0,
                  "lat":41.1
                },{
                  "lon":101.0,
                  "lat":42.1
                },{
                  "lot":102.3,
                  "lat":42.4
                }]
      }
  }
}

geoBoundingBox

  condition.geoBoundingBox('location',{
    'top_left':{
        'lon':100.1,
        'lat':31.3
    },
    'bottom_right':{
      'lon':100.3,
      'lat':32.1
    }
    });

生成 json

{
  "geo_bounding_box":{
    "location":{
        "top_left":{
          "lon":100.0,
          "lat":31.3
        },
        "bottom_right":{
          "lon":103.3,
          "lat":31.3
        }
    }
  }
}

关系查询

hasParent

  condition.hasParent('parentType',new Condition().matchAll(),{
    'score':1
    });

生成 json

  {
    "has_parent":{
      "parent_type":"parentType",
      "query":{
          "match_all":{}
      }
    }
  }

hasChild

  condition.hasChild('childType',new Condition().matchAll(),{
    'min_children':10
    });

生成 json

  {
    "has_child":{
      "type":"childType",
      "query":{
        "match_all":{}
      }
    }
  }

parentId

condition.parentId('parent_id_1','type');

生成 json

{
  "parent_id":{
    "type":"type",
    "id":"parent_id_1"
  }
}

聚合API

基本的数值聚合

  const Aggs = require('elasticsearch-orm').Aggs;
  aggs = new Aggs('test').avg('age');
  aggs = new Aggs('test').cardinality('age');
  aggs = new Aggs('test').max('age');
  aggs = new Aggs('test').min('age');
  aggs = new Aggs('test').sum('age');
  aggs = new Aggs('test').valueCount('age');
  aggs = new Aggs('test').stats('age');
  aggs = new Aggs('test').percentiles('age');
  aggs = new Aggs('test').percentileRanks('age');

分组聚合

terms

aggs = new Aggs('test').terms('age',{
  'order':{
      'field':"age",
      'type':'desc'
  },
  'size':10
  })

histogram

aggs = new Aggs('test').histogram('age',10);

dateHistogram

aggs= new Aggs('test').dateHistogram('date','month',{
  'format':"yyyy-MM",
  'offset':'+1h'
  });

dateRange

const Range = require('elasticsearch-orm').Range;
aggs = new Aggs('test').dateRange('date',[new Range()],{
  'format':"yyyy-MM"
});

range

aggs = new Aggs('test').ranges('age',[new Range(1,10)]);

filter

aggs = new Aggs('test').filter('age',new Condition().matchAll());

missing

aggs = new Aggs('test').missing('age')

sampler

aggs =new Aggs('test').sampler(100,{
  'max_doc_per_value':10
});

children

  aggs = new Aggs('test').children('childrenType');

significantTerms

  aggs = new Aggs('test').significantTerms('age');

地理相关的聚合

geoBounds

aggs = new Aggs('test').geoBounds('location',{
  'wrap_longtitude':true
})

geoDistance

aggs = new Aggs('test').geoDistance('location',{
  'lon':100.0,
  'lat':13.1
},[new Range(1,10)],{
  'unit':'m'
  });

geoCentroid

aggs = new Aggs('test').geoCentroid('location');

Keywords

FAQs

What is elasticsearch-orm?

Is elasticsearch-orm popular?

Is elasticsearch-orm well maintained?

Last updated on 05 Mar 2019

Did you know?

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

elasticsearch-orm