18_ElasticSearch 基于slop参数实现近似匹配
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18_ElasticSearch 基于slop参数实现近似匹配
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概述
slop的含义
- query string，搜索文本，中的几个term，要经过几次移动才能与一个document匹配，这个移动的次数，就是slop
实际举例
一个query string经过几次移动之后可以匹配到一个document，然后设置slop
hello world, java is very good, spark is also very good.
- 以上doc 使用 java spark 搜索，方式 match phrase，无法搜索到 因为 java和 spark 中间还有间隔包含其他
例子一 slop 查询
- slop的phrase match，就是proximity match，近似匹配
- 如果我们指定了slop，那么就允许java spark进行移动，来尝试与doc进行匹配
- ava spark，可以有一定的距离，但是靠的越近，越先搜索出来，proximity match
GET /forum/article/_search
{
"query": {
"match_phrase": {
"title": {
"query": "java spark",
"slop": 1
}
}
}
}
原理解析：移动规则
- 将spark 往前移动了3此匹配上doc
- 这里的slop，就是3，因为java spark这个短语，spark移动了3次，就可以跟一个doc匹配上了
- slop的含义，不仅仅是说一个query string terms移动几次，跟一个doc匹配上。一个query string terms，最多可以移动几次去尝试跟一个doc匹配上
java is very good spark is
java spark
java --> spark
java --> spark
java --> spark
- slop 查询 就可以把刚才那个doc匹配上，那个doc会作为结果返回
- 但是如果slop设置的是2，那么java spark，spark最多只能移动2次，此时跟doc是匹配不上的，那个doc是不会作为结果返回的
GET /forum/article/_search
{
"query": {
"match_phrase": {
"title": {
"query": "java spark",
"slop": 3
}
}
}
}
例子二
spark is best big data
data spark
--> data/spark
spark <--data
spark --> data
spark --> data
spark --> data
- 移动了5次才搜索到
GET /forum/article/_search
{
"query": {
"match_phrase": {
"content": {
"query": "data spark",
"slop": 5
}
}
}
}
例子三
slop搜索下，关键词离的越近，relevance score就会越高
GET /forum/article/_search
{
"query": {
"match_phrase": {
"content": {
"query": "java best",
"slop": 15
}
}
}
}
返回结果:
- 1、java spark，可以有一定的距离，但是靠的越近分数越高，越先搜索出来，proximity match
{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.65380025,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "2",
"_score": 0.65380025,
"_source": {
"articleID": "KDKE-B-9947-#kL5",
"userID": 1,
"hidden": false,
"postDate": "2017-01-02",
"tag": [
"java"
],
"tag_cnt": 1,
"view_cnt": 50,
"title": "this is java blog",
"content": "i think java is the best programming language",
"sub_title": "learned a lot of course",
"author_first_name": "Smith",
"author_last_name": "Williams",
"new_author_last_name": "Williams",
"new_author_first_name": "Smith"
}
},
{
"_index": "forum",
"_type": "article",
"_id": "5",
"_score": 0.07111243,
"_source": {
"articleID": "DHJK-B-1395-#Ky5",
"userID": 3,
"hidden": false,
"postDate": "2017-03-01",
"tag": [
"elasticsearch"
],
"tag_cnt": 1,
"view_cnt": 10,
"title": "this is spark blog",
"content": "spark is best big data solution based on scala ,an programming language similar to java spark",
"sub_title": "haha, hello world",
"author_first_name": "Tonny",
"author_last_name": "Peter Smith",
"new_author_last_name": "Peter Smith",
"new_author_first_name": "Tonny"
}
}
]
}
}
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