elasticsearch多种搜索方式
简要
1、query string search
2、query DSL
3、query filter
4、full-text search
5、phrase search
6、highlight search
1、query string search
搜索全部商品:GET /ecommerce/product/_search
query string search的由来,因为search参数都是以http请求的query string来附带的。
搜索商品名称中包含yagao的商品,而且按照售价降序排序:GET /ecommerce/product/_search?q=name:yagao&sort=price:desc
适用于临时的在命令行使用一些工具,比如curl,快速的发出请求,来检索想要的信息;
但是如果查询请求很复杂,是很难去构建的,在生产环境中,几乎很少使用query string 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
![](https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif)
{ "took": 2, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 3, "max_score": 1, "hits": [ { "_index": "ecommerce", "_type": "product", "_id": "2", "_score": 1, "_source": { "name": "jiajieshi yagao", "desc": "youxiao fangzhu", "price": 25, "producer": "jiajieshi producer", "tags": [ "fangzhu" ] } }, { "_index": "ecommerce", "_type": "product", "_id": "1", "_score": 1, "_source": { "name": "gaolujie yagao", "desc": "gaoxiao meibai", "price": 30, "producer": "gaolujie producer", "tags": [ "meibai", "fangzhu" ] } }, { "_index": "ecommerce", "_type": "product", "_id": "3", "_score": 1, "_source": { "name": "zhonghua yagao", "desc": "caoben zhiwu", "price": 40, "producer": "zhonghua producer", "tags": [ "qingxin" ] } } ] } }
GET /test_index/test_type/_search?q=test_field:test
GET /test_index/test_type/_search?q=+test_field:test
GET /test_index/test_type/_search?q=-test_field:test
一个是掌握q=field:search content的语法,还有一个是掌握+和-的含义,+是必须包含,-是不包含
_all metadata的原理和作用
GET /test_index/test_type/_search?q=test
直接可以搜索所有的field,任意一个field包含指定的关键字就可以搜索出来。
2、query DSL
DSL:Domain Specified Language,特定领域的语言
http request body:请求体,可以用json的格式来构建查询语法,比较方便,可以构建各种复杂的语法,比query string search肯定强大多了
查询所有的商品
GET /ecommerce/product/_search
{
"query": { "match_all": {} }
}
查询名称包含yagao的商品,同时按照价格降序排序
GET /ecommerce/product/_search
{
"query" : {
"match" : {
"name" : "yagao"
}
},
"sort": [
{ "price": "desc" }
]
}
分页查询
分页查询商品,总共3条商品,假设每页就显示1条商品,现在显示第2页,所以就查出来第2个商品.from://从第几个商品开始查
GET /ecommerce/product/_search
{
"query": { "match_all": {} },
"from": 1,
"size": 1
}
指定要查询出来商品的名称和价格就可以
GET /ecommerce/product/_search
{
"query": { "match_all": {} },
"_source": ["name", "price"]
}
更加适合生产环境的使用,可以构建复杂的查询
Scoll滚动搜索
如果一次性要查出来比如10万条数据,那么性能会很差,此时一般会采取用scoll滚动查询,一批一批的查,直到所有数据都查询完处理完
使用scoll滚动搜索,可以先搜索一批数据,然后下次再搜索一批数据,以此类推,直到搜索出全部的数据来
scoll搜索会在第一次搜索的时候,保存一个当时的视图快照,之后只会基于该旧的视图快照提供数据搜索,如果这个期间数据变更,是不会让用户看到的
采用基于_doc进行排序的方式,性能较高
每次发送scroll请求,我们还需要指定一个scoll参数,指定一个时间窗口,每次搜索请求只要在这个时间窗口内能完成就可以了
每次取3条
GET /test_index/test_type/_search?scroll=1m
{
"query": {
"match_all": {}
},
"sort": [ "_doc" ],
"size": 3
}
获得的结果会有一个scoll_id,下一次再发送scoll请求的时候,必须带上这个scoll_id
GET /_search/scroll
{
"scroll": "1m",
"scroll_id" : "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAACxeFjRvbnNUWVZaVGpHdklqOV9zcFd6MncAAAAAAAAsYBY0b25zVFlWWlRqR3ZJajlfc3BXejJ3AAAAAAAALF8WNG9uc1RZVlpUakd2SWo5X3NwV3oydwAAAAAAACxhFjRvbnNUWVZaVGpHdklqOV9zcFd6MncAAAAAAAAsYhY0b25zVFlWWlRqR3ZJajlfc3BXejJ3"
}
scoll,看起来挺像分页的,但是其实使用场景不一样。分页主要是用来一页一页搜索,给用户看的;scoll主要是用来一批一批检索数据,让系统进行处理的。
组合多个搜索条件
GET /website/article/_search
{
"query": {
"bool": {
"must": [ //title必须包含elasticsearch
{
"match": {
"title": "elasticsearch"
}
}
],
"should": [ //content可以包含elasticsearch也可以不包含
{
"match": {
"content": "elasticsearch"
}
}
],
"must_not": [ //author_id必须不为111
{
"match": {
"author_id": 111
}
}
]
}
}
}
1、match all
GET /_search
{
"query": {
"match_all": {}
}
}
2、match
GET /_search
{
"query": { "match": { "title": "my elasticsearch article" }}
}
3、multi match
GET /test_index/test_type/_search
{
"query": {
"multi_match": {
"query": "test", //搜索的文本
"fields": ["test_field", "test_field1"] //多个field上面搜索
}
}
}
4、range query
GET /company/employee/_search
{
"query": {
"range": {
"age": {
"gte": 30
}
}
}
}
5、term query
//把这个字段当成exact value去查询(前提条件:手动创建mapping的时候需要指定no_analy不分词去建立索引,这样才可以用test hello在term搜到)
GET /test_index/test_type/_search
{
"query": {
"term": {
"test_field": "test hello"
}
}
}
6、terms query
GET /_search
{
"query": { "terms": { "tag": [ "search", "full_text", "nosql" ] }} //对tag字段指定多个搜索词
}
3、query filter
搜索商品名称包含yagao,而且售价大于25元的商品
GET /ecommerce/product/_search
{
"query" : {
"bool" : {
"must" : {
"match" : {
"name" : "yagao"
}
},
"filter" : {
"range" : {
"price" : { "gt" : 25 }
}
}
}
}
}
{
"bool": {
"must": { "match": { "title": "how to make millions" }},
"must_not": { "match": { "tag": "spam" }},
"should": [
{ "match": { "tag": "starred" }}
],
"filter": {
"range": { "date": { "gte": "2014-01-01" }}
}
}
}
{
"bool": {
"must": { "match": { "title": "how to make millions" }},
"must_not": { "match": { "tag": "spam" }},
"should": [
{ "match": { "tag": "starred" }}
],
"filter": {
"bool": {
"must": [
{ "range": { "date": { "gte": "2014-01-01" }}},
{ "range": { "price": { "lte": 29.99 }}}
],
"must_not": [
{ "term": { "category": "ebooks" }}
]
}
}
}
}
GET /company/employee/_search
{
"query": {
"constant_score": { //constant_score是固定语法单纯使用filter的时候需要加上的
"filter": {
"range": {
"age": {
"gte": 30
}
}
}
}
}
}
4、full-text search
GET /ecommerce/product/_search
{
"query" : {
"match" : {
"producer" : "yagao producer"
}
}
}
5、phrase search(短语搜索)
跟全文检索相对应,相反,全文检索会将输入的搜索串拆解开来,去倒排索引里面去一一匹配,只要能匹配上任意一个拆解后的单词,就可以作为结果返回
phrase search,要求输入的搜索串,必须在指定的字段文本中,完全包含一模一样的,才可以算匹配,才能作为结果返回
GET /ecommerce/product/_search
{
"query" : {
"match_phrase" : {
"producer" : "yagao producer"
}
}
}
6、highlight search
GET /ecommerce/product/_search
{
"query" : {
"match" : {
"producer" : "producer"
}
},
"highlight": {
"fields" : {
"producer" : {}
}
}
}
7、判断搜索是否合法
//判断搜索是否合法,如果不合法问题在哪里
GET /test_index/test_type/_validate/query?explain
{
"query": {
"math": {
"test_field": "test"
}
}
}
{
"valid": false,
"error": "org.elasticsearch.common.ParsingException: no [query] registered for [math]"
}
8、排序
1、默认排序规则
默认情况下,是按照_score降序排序的
然而,某些情况下,可能没有有用的_score,比如说filter
GET /_search
{
"query" : {
"bool" : {
"filter" : {
"term" : {
"author_id" : 1
}
}
}
}
}
当然,也可以是constant_score
GET /_search
{
"query" : {
"constant_score" : {
"filter" : {
"term" : {
"author_id" : 1
}
}
}
}
}
2、定制排序规则
GET /company/employee/_search
{
"query": {
"constant_score": {
"filter": {
"range": {
"age": {
"gte": 30
}
}
}
}
},
"sort": [
{
"join_date": {
"order": "asc"
}
}
]
}
问题:如果对一个string field进行排序,结果往往不准确,因为分词后是多个单词,再排序就不是我们想要的结果了
通常解决方案是,将一个string field建立两次索引,一个分词,用来进行搜索;一个不分词,用来进行排序
PUT /website
{
"mappings": {
"article": {
"properties": {
"title": {
"type": "text", //分词索引
"fields": {
"raw": { //不分词索引
"type": "string",
"index": "not_analyzed"
}
},
"fielddata": true //正排索引
},
"content": {
"type": "text"
},
"post_date": {
"type": "date"
},
"author_id": {
"type": "long"
}
}
}
}
}
PUT /website/article/1
{
"title": "first article",
"content": "this is my second article",
"post_date": "2017-01-01",
"author_id": 110
}
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 1,
"hits": [
{
"_index": "website",
"_type": "article",
"_id": "2",
"_score": 1,
"_source": {
"title": "first article",
"content": "this is my first article",
"post_date": "2017-02-01",
"author_id": 110
}
},
{
"_index": "website",
"_type": "article",
"_id": "1",
"_score": 1,
"_source": {
"title": "second article",
"content": "this is my second article",
"post_date": "2017-01-01",
"author_id": 110
}
},
{
"_index": "website",
"_type": "article",
"_id": "3",
"_score": 1,
"_source": {
"title": "third article",
"content": "this is my third article",
"post_date": "2017-03-01",
"author_id": 110
}
}
]
}
}
GET /website/article/_search
{
"query": {
"match_all": {}
},
"sort": [
{
"title.raw": { //拿未分词索引的去排,上面有创建了
"order": "desc"
}
}
]
}
作者:Kero小柯
出处:http://www.cnblogs.com/kesimin/
本文版权归作者和博客园共有,欢迎转载,但必需注明出处,并且在文章页面明显位置给出原文连接。