es索引数据过滤查询_es 跨索引过滤

1.我们往kibana插入数据,来进行查询

POST /t1/_doc/
{
  "name":"cat",
  "age":"18",
  "address":"BJ",
  "job":"dev"
}
POST /t1/_doc/
{
  "name":"dog",
  "age":"19",
  "address":"SD",
  "job":"ops"
}
POST /t1/_doc/
{
  "name":"alice",
  "age":"20",
  "address":"BJ",
  "job":"dev"
}
POST /t1/_doc/
{
  "name":"tigger",
  "age":"20",
  "address":"AH",
  "job":"ops"
}
POST /t1/_doc/
{
  "name":"fix",
  "age":"21",
  "address":"HN",
  "job":"dev"
}
POST /t1/_doc/
{
  "name":"jack",
  "age":"18",
  "address":"BJ",
  "job":"dev"
}
POST /t1/_doc/
{
  "name":"Tom",
  "age":"21",
  "address":"HN",
  "job":"ops"
}
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2.查询

#term查询，是根据具体的字段进行查询
#查询所有
GET t1/_search

#查询name为cat的
GET t1/_search
{
  "query": {
    "term": {
      "name.keyword": {
        "value": "cat"
      }
    }
  }
  
}
#查询地区为北京的
GET t1/_search
{
  "query": {
    "term": {
      "address.keyword": {
        "value": "BJ"
      }
    }
  }
  
}


#bool查询 逻辑判断是否
bool组合语法
must：必须符合，包含    与AND等价
should：满足任意的条件，  与or等价
filter：必须满足，是否命中，不计算分值，等同must
must_not:  必须不符合不包含   与not等价

#查询工作为ops的，且年龄为19到20岁的
GET t1/_search
{
  "query": {
    "bool": {
      "must": [
        {"match": {    #我要查询job必须是包含ops的
          "job": "ops"
        }}
      ],
      "filter": [
        {
          "range": {
            "age.keyword": {
              "gte": 19,
              "lte": 20
            }
          }
        }
      ]
    }
  }
}
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结果

3通过elassearch插件查询，找出北京，年纪为20岁的