ElasticSearch6.2.4(3)——简单的搜索方式

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/haozhishang/article/details/80060233

1.准备工作，添加数据

    
    
PUT /zoo/product/1
{
"name":"monkey",
"age":10,
"content":"xiao small but ke ai"
}

PUT /zoo/product/2
{
"name":"monkey",
"age":13,
"content":"xiao small but very big"
}

PUT /zoo/product/3
{
"name":"dog",
"age":13,
"content":"xiao big but very small"
}

PUT /zoo/product/4
{
"name":"cat",
"age":18,
"content":"xiao big but very small"
}

PUT /zoo/product/5
{
"name":"tig",
"age":30,
"content":"xiao big but very big"
}
    
    

2.搜索zoo下面product的所有document

GET /zoo/product/_search
took：耗费了几毫秒
timed_out：是否超时，这里是没有
_shards：数据拆成了5个分片，所以对于搜索请求，会打到所有的primary shard（或者是它的某个replica shard也可以）
hits.total：查询结果的数量，3个document
hits.max_score：score的含义，就是document对于一个search的相关度的匹配分数，越相关，就越匹配，分数也高

hits.hits：包含了匹配搜索的document的详细数据

3.查询所有动物

    
    
GET /zoo/product/_search
{
"query": {
"match_all": {}
}
}
    
    

4.查询包含big的内容，并且按照age降序(desc是降序，asc是升序)

    
    
GET /zoo/product/_search
{
"query": {
"match": {
"content": "big"
}
},
"sort": [
{
"age": {
"order": "desc"
}
}
]
}
    
    

5.从0下标开始查询出2包含big的动物

    
    
GET /zoo/product/_search
{
"query": {
"match": {
"content": "big"
}
},
"size": 2,
"from": 0
}
    
    

6.查询出来的数据只要age和content属性

    
    
GET /zoo/product/_search
{
"_source": ["age","content"]
}
    
    

7.查询出包含big but整段短语

    
    
GET /zoo/product/_search
{
"query": {
"match_phrase": {
"content": "big but"
}
}
}
    
    

8.查询出来的数据进行高亮处理

    
    
GET /zoo/product/_search
{
"query": {
"match": {
"content": "big but"
}
},
"highlight": {
"fields": {
"content": {}
}
}
}
    
    

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/haozhishang/article/details/80060233

1.准备工作，添加数据

  
  
PUT /zoo/product/1
{
"name":"monkey",
"age":10,
"content":"xiao small but ke ai"
}

PUT /zoo/product/2
{
"name":"monkey",
"age":13,
"content":"xiao small but very big"
}

PUT /zoo/product/3
{
"name":"dog",
"age":13,
"content":"xiao big but very small"
}

PUT /zoo/product/4
{
"name":"cat",
"age":18,
"content":"xiao big but very small"
}

PUT /zoo/product/5
{
"name":"tig",
"age":30,
"content":"xiao big but very big"
}
  
  

2.搜索zoo下面product的所有document

GET /zoo/product/_search
took：耗费了几毫秒
timed_out：是否超时，这里是没有
_shards：数据拆成了5个分片，所以对于搜索请求，会打到所有的primary shard（或者是它的某个replica shard也可以）
hits.total：查询结果的数量，3个document
hits.max_score：score的含义，就是document对于一个search的相关度的匹配分数，越相关，就越匹配，分数也高

hits.hits：包含了匹配搜索的document的详细数据

3.查询所有动物

  
  
GET /zoo/product/_search
{
"query": {
"match_all": {}
}
}
  
  

4.查询包含big的内容，并且按照age降序(desc是降序，asc是升序)

  
  
GET /zoo/product/_search
{
"query": {
"match": {
"content": "big"
}
},
"sort": [
{
"age": {
"order": "desc"
}
}
]
}
  
  

5.从0下标开始查询出2包含big的动物

  
  
GET /zoo/product/_search
{
"query": {
"match": {
"content": "big"
}
},
"size": 2,
"from": 0
}
  
  

6.查询出来的数据只要age和content属性

  
  
GET /zoo/product/_search
{
"_source": ["age","content"]
}
  
  

7.查询出包含big but整段短语

  
  
GET /zoo/product/_search
{
"query": {
"match_phrase": {
"content": "big but"
}
}
}
  
  

8.查询出来的数据进行高亮处理

  
  
GET /zoo/product/_search
{
"query": {
"match": {
"content": "big but"
}
},
"highlight": {
"fields": {
"content": {}
}
}
}