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本篇內容主要講解“怎么使用Elasticsearch中的Match_phrase查詢”，感興趣的朋友不妨來看看。本文介紹的方法操作簡單快捷，實用性強。下面就讓小編來帶大家學習“怎么使用Elasticsearch中的Match_phrase查詢”吧!
新建索引：
PUT test_phrase
設置索引mapping：
PUT /test_phrase/_mapping/_doc
{
"properties": {
"name": {
"type":"text"
}
}
}
結果：
{
"mapping": {
"_doc": {
"properties": {
"name": {
"type": "text"
}
}
}
}
}
插入數據：
PUT test_phrase/_doc/2
{
"name":"我愛北京天安門"
}
查詢數據：
POST test_phrase/_search
{
"query": {"match_all": {}}
}
結果：
{
"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" : "我愛北京天安門"
}
},
{
"_index" : "test_phrase",
"_type" : "_doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "王乃康"
}
}
]
}
}
查看分詞詞項：
POST test_phrase/_analyze
{
"field": "name",
"text": "我愛北京天安門"
}
結果：
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "<IDEOGRAPHIC>",
"position" : 0
},
{
"token" : "愛",
"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" : "門",
"start_offset" : 6,
"end_offset" : 7,
"type" : "<IDEOGRAPHIC>",
"position" : 6
}
]
}POST test_phrase/_search
{
"query": {
"match_phrase": {
"name": {
"query": "我"
}
}
}
}
結果：
{
"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" : "我愛北京天安門"
}
}
]
}
}
分析：
POST test_phrase/_analyze
{
"field": "name",
"text": "我"
}
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "<IDEOGRAPHIC>",
"position" : 0
}
]
}
查詢分詞"我"的position位置是0，首先文檔"我愛北京天安門"的索引分詞中有"我"且position為0，符合短語查詢的要求，因此可以正確返回。POST test_phrase/_search
{
"query": {
"match_phrase": {
"name": {
"query": "我愛"
}
}
}
}
結果：
{
"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" : "我愛北京天安門"
}
}
]
}
}
分析：
POST test_phrase/_analyze
{
"field": "name",
"text": "我愛"
}
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "<IDEOGRAPHIC>",
"position" : 0
},
{
"token" : "愛",
"start_offset" : 1,
"end_offset" : 2,
"type" : "<IDEOGRAPHIC>",
"position" : 1
}
]
}
查詢分詞"我愛"的position分別是"我"-0、"愛"-1，首先索引分詞中也存在"我"、"愛"詞項，其次"我"-0、"愛"-1的position也服務要求，因此可以正確返回。POST test_phrase/_search
{
"query": {
"match_phrase": {
"name": {
"query": "我北"
}
}
}
}
結果：
{
"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
}
]
}
查詢分詞中"我"的position是0，"北"的position是1，索引分詞中"我"的position是0，"北"的position是2，
雖然查詢分詞的詞項在索引分詞的詞項中都存在，但是position并未匹配要求，導致搜索結果不能正確返回。
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" : "我愛北京天安門"
}
}
]
}
}我們可以將一個簡單的 match 查詢作為一個 must 子句。 這個查詢將決定哪些文檔需要被包含到結果集中。 我們可以用 minimum_should_match 參數去除長尾。 然后我們可以以 should 子句的形式添加更多特定查詢。 每一個匹配成功的都會增加匹配文檔的相關度。
GET /my_index/my_type/_search
{
"query": {
"bool": {
"must": {
"match": { #must 子句從結果集中包含或者排除文檔
"title": {
"query": "quick brown fox",
"minimum_should_match": "30%"
}
}
},
"should": {
"match_phrase": { #should 子句增加了匹配到文檔的相關度評分。
"title": {
"query": "quick brown fox",
"slop": 50
}
}
}
}
}
}到此，相信大家對“怎么使用Elasticsearch中的Match_phrase查詢”有了更深的了解，不妨來實際操作一番吧！這里是億速云網站，更多相關內容可以進入相關頻道進行查詢，關注我們，繼續學習！
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