Elasticsearch高手进阶篇(13)
深度探秘搜索技术_案例实战基于multi_match语法实现dis_max+tie_breaker
GET /waws/article/_search
{
"query": {
"multi_match": {
"query": "java solution",
"type": "best_fields",
"fields": [ "title^2", "content" ],
"tie_breaker": 0.3,
"minimum_should_match": "50%"
}
}
}
{
"took": 64,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 4,
"max_score": 0.8055487,
"hits": [
{
"_index": "waws",
"_type": "article",
"_id": "2",
"_score": 0.8055487,
"_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"
}
},
{
"_index": "waws",
"_type": "article",
"_id": "4",
"_score": 0.6498223,
"_source": {
"articleID": "QQPX-R-3956-#aD8",
"userID": 2,
"hidden": true,
"postDate": "2017-01-02",
"tag": [
"java",
"elasticsearch"
],
"tag_cnt": 2,
"view_cnt": 80,
"title": "this is java, elasticsearch, hadoop blog",
"content": "elasticsearch and hadoop are all very good solution, i am a beginner"
}
},
{
"_index": "waws",
"_type": "article",
"_id": "5",
"_score": 0.56008905,
"_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"
}
},
{
"_index": "waws",
"_type": "article",
"_id": "1",
"_score": 0.53484553,
"_source": {
"articleID": "XHDK-A-1293-#fJ3",
"userID": 1,
"hidden": false,
"postDate": "2017-01-01",
"tag": [
"java",
"hadoop"
],
"tag_cnt": 2,
"view_cnt": 30,
"title": "this is java and elasticsearch blog",
"content": "i like to write best elasticsearch article"
}
}
]
}
}
- 去长尾
GET /forum/article/_search
{
"query": {
"dis_max": {
"queries": [
{
"match": {
"title": {
"query": "java beginner",
"minimum_should_match": "50%",
"boost": 2
}
}
},
{
"match": {
"body": {
"query": "java beginner",
"minimum_should_match": "30%"
}
}
}
],
"tie_breaker": 0.3
}
}
}
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 0.53484553,
"hits": [
{
"_index": "waws",
"_type": "article",
"_id": "1",
"_score": 0.53484553,
"_source": {
"articleID": "XHDK-A-1293-#fJ3",
"userID": 1,
"hidden": false,
"postDate": "2017-01-01",
"tag": [
"java",
"hadoop"
],
"tag_cnt": 2,
"view_cnt": 30,
"title": "this is java and elasticsearch blog",
"content": "i like to write best elasticsearch article"
}
},
{
"_index": "waws",
"_type": "article",
"_id": "2",
"_score": 0.3971361,
"_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"
}
},
{
"_index": "waws",
"_type": "article",
"_id": "4",
"_score": 0.310936,
"_source": {
"articleID": "QQPX-R-3956-#aD8",
"userID": 2,
"hidden": true,
"postDate": "2017-01-02",
"tag": [
"java",
"elasticsearch"
],
"tag_cnt": 2,
"view_cnt": 80,
"title": "this is java, elasticsearch, hadoop blog",
"content": "elasticsearch and hadoop are all very good solution, i am a beginner"
}
}
]
}
}
-
minimum_should_match，主要是用来干嘛的
- 去长尾，long tail
-
长尾
- 比如你搜索5个关键词，但是很多结果是只匹配1个关键词的，其实跟你想要的结果相差甚远，这些结果就是长尾
- minimum_should_match，控制搜索结果的精准度，只有匹配一定数量的关键词的数据，才能返回
Elasticsearch高手进阶篇(14)
深度探秘搜索技术_基于multi_match+most fiels策略进行multi-field搜索
-
从best-fields换成most-fields策略
- best-fields策略，主要是说将
某一个field匹配尽可能多的关键词的doc优先返回回来 - most-fields策略，主要是说尽可能返回
更多field匹配到某个关键词的doc，优先返回回来
- best-fields策略，主要是说将
POST /waws/_mapping/article
{
"properties": {
"sub_title": {
"type": "string",
"analyzer": "english",
"fields": {
"std": {
"type": "string",
"analyzer": "standard"
}
}
}
}
}
- 增加数据
POST /waws/article/_bulk
{ "update": { "_id": "1"} }
{ "doc" : {"sub_title" : "learning more courses"}}
{ "update": { "_id": "2"} }
{ "doc" : {"sub_title" : "learned a lot of course"}}
{ "update": { "_id": "3"} }
{ "doc" : {"sub_title" : "we have a lot of fun"}}
{ "update": { "_id": "4"} }
{ "doc" : {"sub_title" : "both of them are good"}}
{ "update": { "_id": "5"} }
{ "doc" : {"sub_title" : "haha, hello world"}}
- 获取数据(问题部分演示)
GET /waws/article/_search
{
"query": {
"match": {
"sub_title": "learning courses"
}
}
}
# 这个部分我们看到下面的搜索结果中learned a lot of course排在了learning more courses的前面，但是我们的搜索的数据更想和字段"sub_title": "learning courses" 更加接近，所以我们使用下面的方式
{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1.219939,
"hits": [
{
"_index": "waws",
"_type": "article",
"_id": "2",
"_score": 1.219939,
"_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"
}
},
{
"_index": "waws",
"_type": "article",
"_id": "1",
"_score": 0.5063205,
"_source": {
"articleID": "XHDK-A-1293-#fJ3",
"userID": 1,
"hidden": false,
"postDate": "2017-01-01",
"tag": [
"java",
"hadoop"
],
"tag_cnt": 2,
"view_cnt": 30,
"title": "this is java and elasticsearch blog",
"content": "i like to write best elasticsearch article",
"sub_title": "learning more courses"
}
}
]
}
}
sub_title用的是enligsh analyzer，所以还原了单词
因为如果我们用的是类似于english analyzer这种分词器的话，就会将单词还原为其最基本的形态，stemmer learning --> learn learned --> learn courses --> course
sub_titile: learning coureses --> learn course
{ "doc" : {"sub_title" : "learned a lot of course"} }，就排在了{ "doc" : {"sub_title" : "learning more courses"} }的前面
-
设置了两个字段和两种分词器
- 其实就是我们设置了两种分词器，第一种是english分词器，会将词语进行规则化(同义词、单复数、时态等)，第二种是stardand分词器，不会将词语进行规则化，按照词语更加原始的状态进行匹配
GET /waws/article/_search
{
"query": {
"multi_match": {
"query": "learning courses",
"type": "most_fields",
"fields": [ "sub_title", "sub_title.std" ]
}
}
}
# 虽然我们没有达到预想的效果，但是我们在使用"type": "most_fields"之后，我们的"sub_title": "learning more courses"的"_score": 0.5063205 上涨到 "_score": 1.012641 在整体的所搜中的比重更大，之所以learned a lot of course排在最前面，可能是去除了停用词后，句式更短，计算出来的TF/IDF更大，所以排在前面
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1.219939,
"hits": [
{
"_index": "waws",
"_type": "article",
"_id": "2",
"_score": 1.219939,
"_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"
}
},
{
"_index": "waws",
"_type": "article",
"_id": "1",
"_score": 1.012641,
"_source": {
"articleID": "XHDK-A-1293-#fJ3",
"userID": 1,
"hidden": false,
"postDate": "2017-01-01",
"tag": [
"java",
"hadoop"
],
"tag_cnt": 2,
"view_cnt": 30,
"title": "this is java and elasticsearch blog",
"content": "i like to write best elasticsearch article",
"sub_title": "learning more courses"
}
}
]
}
}
具体的分数怎么算出来的，很难说，因为这个东西很复杂， 还不只是TF/IDF算法。因为不同的query，不同的语法，都有不同的计算score的细节
- best_fields
best_fields，是对多个field进行搜索，挑选某个field匹配度最高的那个分数，同时在多个query最高分相同的情况下，在一定程度上考虑其他query的分数。简单来说，你对多个field进行搜索，就想搜索到某一个field尽可能包含更多关键字的数据
- 优点：通过best_fields策略，以及综合考虑其他field，还有minimum_should_match支持，可以尽可能精准地将匹配的结果推送到最前面
- 缺点：除了那些精准匹配的结果，其他差不多大的结果，排序结果不是太均匀，没有什么区分度了
实际的例子：百度之类的搜索引擎，最匹配的到最前面，但是其他的就没什么区分度了
- most_fields
most_fields，综合多个field一起进行搜索，尽可能多地让所有field的query参与到总分数的计算中来，此时就会是个大杂烩，出现类似best_fields案例最开始的那个结果，结果不一定精准，某一个document的一个field包含更多的关键字，但是因为其他document有更多field匹配到了，所以排在了前面；所以需要建立类似sub_title.std这样的field，尽可能让某一个field精准匹配query string，贡献更高的分数，将更精准匹配的数据排到前面
- 优点：将尽可能匹配更多field的结果推送到最前面，整个排序结果是比较均匀的
- 缺点：可能那些精准匹配的结果，无法推送到最前面
实际的例子：wiki，明显的most_fields策略，搜索结果比较均匀，但是的确要翻好几页才能找到最匹配的结果
