多索引多type搜索
分页搜索
每页5条 查询一到3页数据
第一页:http://127.0.0.1:9200/blogs2/product/_search?size=5&from=0
第二页:http://127.0.0.1:9200/blogs2/product/_search?size=5&from=5
第三页:http://127.0.0.1:9200/blogs2/product/_search?size=5&from=10
size参数为每页显示数量 from为跳过前面数量
搜索文档的一部分
或者通过指定排除属性和包含属性 支持匹配
http://localhost:9200/twitter/tweet/1?_source_include=*.id&_source_exclude=entities
搜索多个索引（type）文档
参数
{ "docs":[{ "_index":"blogs", "_type":"product", "_id":"1" },{ "_index":"blogs2", "_type":"product", "_id":"2" }] }
结果
{ "docs": [ { "_index": "blogs", "_type": "product", "_id": "1", "_version": 13, "_seq_no": 10, "_primary_term": 1, "found": true, "_source": { "productName": "测试修改", "price": 11, "remark": "不错的床垫", "tags": [ "家具", "床垫", "棉花", null ], "videw": 1 } }, { "_index": "blogs2", "_type": "product", "_id": "2", "_version": 1, "_seq_no": 0, "_primary_term": 1, "found": true, "_source": { "productName": "法瑞思 天然椰棕床垫棕垫硬床垫定做 薄乳胶棕榈榻榻米床垫定制折叠1.2/1.5/1.8", "price": 10, "remark": "不错的床垫", "tags": [ "家具", "床垫", "棉花" ] } } ] }
如果index相同type不同
简易搜索
查询产品名字包含沙发同时品牌id为1的产品信息
or搜索？
文档是and搜索我测试时or搜索
http://127.0.0.1:9200/blogs2/product/_search?q=productName:大幅度 +price:10 如果一个字段多只查询 http://127.0.0.1:9200/blogs2/product/_search?q=productName:(大幅度 床垫) +price:10
所有字段中查询
查询所有字段里面包含床垫的文档
http://127.0.0.1:9200/blogs2/product/_search?q=测试
ES会将文档里面的所有值都拼接成一个串来查找
结构化查询DSL
term(=)过滤
主要用于精确匹配 比如年龄 bool
{ "query":{ "term":{ "price":12 } } }
terms(in)过滤
跟term相同 terms支持多值搜索 下面会搜索出单价为12和13的搜索出来
{ "query":{ "terms":{ "price":[12,13] } } }
range(between)过滤
{ "query": { "range": { "price": { "gt": 10 } } } }
将会查出价格大于10的 gt大于lt小于 gte大于等于 lte小于等于
大于等于小余等于
{ "query": { "range": { "price": { "gt": 10, "lt":13 } } } }
exist
查询不存在指定字段的所有文档
{ "query": { "missing": { "field":"price" } } }
字段不存在或为空判断
{ "query": { "bool": { "must_not": { "exists": { "field": "price" } } } } }
存在指定字段查询
{ "query": { "bool": { "filter": { "exists": { "field": "productSortItemIds" } } } } }
bool过滤
合并多个查询条件的bool值
must:多个条件完全匹配相当于and
must_not 多个条件取想法 相当于<>
should: 至少有一个条件匹配相当于or
如下面例子
{ "query": { "bool": { "must":[ {"term":{"price":10}}, {"term":{"orgId":1}} ], "numst_not":{ "term":{"id":1} }, "should":[ {"term":{"id":10}}, {"term":{"id":20}} ] } } }
(price=10 and orgId=1) and (id<>1) and (id=10 or id=20)
match_all
空查询
{ "query": { "match_all":{} } }
match(like)
在搜索确定值类型字段 将会按照完全匹配。如果是full text则是采用分词
{ "query": { "match":{"price":10} } }
mult_match( like and)
与match相同 只是允许同时搜索多个字段 operator 是默认搜索字段会分词，只是区分分词匹配是and全部匹配或者or部分匹配
{ "query": { "multi_match": { "query": "不错的", "fields": [ "productName","remark" ], "operator": "AND"//OR AND } } }
filter
filter将不进行_score打分 性能会高一点
{ "query": { "bool": { "must": { "match": { "productName": "床垫" } }, "filter": { "match": { "remark": "床垫" } } } } }
is null查询
需要在创建mapping的时候 设置字段的null_value 然后match搜索就搜索 设置的value就行了
验证查询
验证一个查询是否有效
查询搜索分词索引情况
http://127.0.0.1:9200/blogs2/product/_validate/query?explain
脚本字段
可以通过脚本计算结果 但是不会返回其他字段 到时有需求可以研究一下
#localhost:9200/test_3/doc/_search { "script_fields":{ "new_filed":{ "script":{ "lang":"painless", "source":"doc['isActived'].value+1" } } } }
响应
{ "took": 4, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 62342, "max_score": 1.0, "hits": [ { "_index": "test_3", "_type": "doc", "_id": "843_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } }, { "_index": "test_3", "_type": "doc", "_id": "250_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } }, { "_index": "test_3", "_type": "doc", "_id": "1040_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } }, { "_index": "test_3", "_type": "doc", "_id": "984_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } }, { "_index": "test_3", "_type": "doc", "_id": "284_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } }, { "_index": "test_3", "_type": "doc", "_id": "486_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } }, { "_index": "test_3", "_type": "doc", "_id": "414_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } }, { "_index": "test_3", "_type": "doc", "_id": "1190_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } }, { "_index": "test_3", "_type": "doc", "_id": "553_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } }, { "_index": "test_3", "_type": "doc", "_id": "867_CB407", "_score": 1.0, "fields": { "new_filed": [ 2 ] } } ] } }
查询权重分计算
get http://127.0.0.1:9200/index/type/id/_explain
body
{ "query":{ "match":{ "productName":"jw" } } }
返回结果
{ "_index": "test", "_type": "doc", "_id": "560_406", "matched": true, "explanation": { "value": 5.3691883, "description": "sum of:", "details": [ { "value": 2.6373672, "description": "weight(Synonym(productName:j productName:jw) in 0) [PerFieldSimilarity], result of:", "details": [ { "value": 2.6373672, "description": "score(doc=0,freq=1.0 = termFreq=1.0 ), product of:", "details": [ { "value": 2.0502589, "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:", "details": [ { "value": 56, "description": "docFreq", "details": [] }, { "value": 438, "description": "docCount", "details": [] } ] }, { "value": 1.2863581, "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:", "details": [ { "value": 1, "description": "termFreq=1.0", "details": [] }, { "value": 1.2, "description": "parameter k1", "details": [] }, { "value": 0.75, "description": "parameter b", "details": [] }, { "value": 32.90639, "description": "avgFieldLength", "details": [] }, { "value": 15, "description": "fieldLength", "details": [] } ] } ] } ] }, { "value": 2.731821, "description": "weight(productName:w in 0) [PerFieldSimilarity], result of:", "details": [ { "value": 2.731821, "description": "score(doc=0,freq=1.0 = termFreq=1.0 ), product of:", "details": [ { "value": 2.1236863, "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:", "details": [ { "value": 52, "description": "docFreq", "details": [] }, { "value": 438, "description": "docCount", "details": [] } ] }, { "value": 1.2863581, "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:", "details": [ { "value": 1, "description": "termFreq=1.0", "details": [] }, { "value": 1.2, "description": "parameter k1", "details": [] }, { "value": 0.75, "description": "parameter b", "details": [] }, { "value": 32.90639, "description": "avgFieldLength", "details": [] }, { "value": 15, "description": "fieldLength", "details": [] } ] } ] } ] } ] } }
查询explain分析器
http://127.0.0.1:9200/index/_search?_source
{ "profile":true, "query":{ "match":{ "productName":"来啦" } } }
count查询
通过url设置?_source=false 不返回_source 或者 get /index/type/_count