本篇内容主要讲解“怎么使用Elasticsearch中的Match_phrase查询”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“怎么使用Elasticsearch中的Match_phrase查询”吧!
新建索引：
PUT test_phrase
设置索引mapping：
PUT /test_phrase/_mapping/_doc
{
"properties": {
"name": {
"type":"text"
}
}
}
结果：
{
"mapping": {
"_doc": {
"properties": {
"name": {
"type": "text"
}
}
}
}
}
插入数据：
PUT test_phrase/_doc/2
{
"name":"我爱北京天安门"
}
查询数据：
POST test_phrase/_search
{
"query": {"match_all": {}}
}
结果：
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.0,
"hits" : [
{
"_index" : "test_phrase",
"_type" : "_doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"name" : "我爱北京天安门"
}
},
{
"_index" : "test_phrase",
"_type" : "_doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "王乃康"
}
}
]
}
}
查看分词词项：
POST test_phrase/_analyze
{
"field": "name",
"text": "我爱北京天安门"
}
结果：
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "<IDEOGRAPHIC>",
"position" : 0
},
{
"token" : "爱",
"start_offset" : 1,
"end_offset" : 2,
"type" : "<IDEOGRAPHIC>",
"position" : 1
},
{
"token" : "北",
"start_offset" : 2,
"end_offset" : 3,
"type" : "<IDEOGRAPHIC>",
"position" : 2
},
{
"token" : "京",
"start_offset" : 3,
"end_offset" : 4,
"type" : "<IDEOGRAPHIC>",
"position" : 3
},
{
"token" : "天",
"start_offset" : 4,
"end_offset" : 5,
"type" : "<IDEOGRAPHIC>",
"position" : 4
},
{
"token" : "安",
"start_offset" : 5,
"end_offset" : 6,
"type" : "<IDEOGRAPHIC>",
"position" : 5
},
{
"token" : "门",
"start_offset" : 6,
"end_offset" : 7,
"type" : "<IDEOGRAPHIC>",
"position" : 6
}
]
}POST test_phrase/_search
{
"query": {
"match_phrase": {
"name": {
"query": "我"
}
}
}
}
结果：
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test_phrase",
"_type" : "_doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"name" : "我爱北京天安门"
}
}
]
}
}
分析：
POST test_phrase/_analyze
{
"field": "name",
"text": "我"
}
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "<IDEOGRAPHIC>",
"position" : 0
}
]
}
查询分词"我"的position位置是0，首先文档"我爱北京天安门"的索引分词中有"我"且position为0，符合短语查询的要求，因此可以正确返回。POST test_phrase/_search
{
"query": {
"match_phrase": {
"name": {
"query": "我爱"
}
}
}
}
结果：
{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.5753642,
"hits" : [
{
"_index" : "test_phrase",
"_type" : "_doc",
"_id" : "2",
"_score" : 0.5753642,
"_source" : {
"name" : "我爱北京天安门"
}
}
]
}
}
分析：
POST test_phrase/_analyze
{
"field": "name",
"text": "我爱"
}
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "<IDEOGRAPHIC>",
"position" : 0
},
{
"token" : "爱",
"start_offset" : 1,
"end_offset" : 2,
"type" : "<IDEOGRAPHIC>",
"position" : 1
}
]
}
查询分词"我爱"的position分别是"我"-0、"爱"-1，首先索引分词中也存在"我"、"爱"词项，其次"我"-0、"爱"-1的position也服务要求，因此可以正确返回。POST test_phrase/_search
{
"query": {
"match_phrase": {
"name": {
"query": "我北"
}
}
}
}
结果：
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 0,
"max_score" : null,
"hits" : [ ]
}
}
分析：
POST test_phrase/_analyze
{
"field": "name",
"text": "我北"
}
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "<IDEOGRAPHIC>",
"position" : 0
},
{
"token" : "北",
"start_offset" : 1,
"end_offset" : 2,
"type" : "<IDEOGRAPHIC>",
"position" : 1
}
]
}
查询分词中"我"的position是0，"北"的position是1，索引分词中"我"的position是0，"北"的position是2，
虽然查询分词的词项在索引分词的词项中都存在，但是position并未匹配要求，导致搜索结果不能正确返回。
修正："slop": 1
POST test_phrase/_search
{
"query": {
"match_phrase": {
"name": {
"query": "我北",
"slop": 1
}
}
}
}
{
"took" : 5,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.37229446,
"hits" : [
{
"_index" : "test_phrase",
"_type" : "_doc",
"_id" : "2",
"_score" : 0.37229446,
"_source" : {
"name" : "我爱北京天安门"
}
}
]
}
}我们可以将一个简单的 match 查询作为一个 must 子句。 这个查询将决定哪些文档需要被包含到结果集中。 我们可以用 minimum_should_match 参数去除长尾。 然后我们可以以 should 子句的形式添加更多特定查询。 每一个匹配成功的都会增加匹配文档的相关度。
GET /my_index/my_type/_search
{
"query": {
"bool": {
"must": {
"match": { #must 子句从结果集中包含或者排除文档
"title": {
"query": "quick brown fox",
"minimum_should_match": "30%"
}
}
},
"should": {
"match_phrase": { #should 子句增加了匹配到文档的相关度评分。
"title": {
"query": "quick brown fox",
"slop": 50
}
}
}
}
}
}到此，相信大家对“怎么使用Elasticsearch中的Match_phrase查询”有了更深的了解，不妨来实际操作一番吧！这里是亿速云网站，更多相关内容可以进入相关频道进行查询，关注我们，继续学习！
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