【Elasticsearch系列十九】评分机制详解

💝💝💝欢迎来到我的博客，很高兴能够在这里和您见面！希望您在这里可以感受到一份轻松愉快的氛围，不仅可以获得有趣的内容和知识，也可以畅所欲言、分享您的想法和见解。

• 推荐:kwan 的首页,持续学习,不断总结,共同进步,活到老学到老
• 导航
  • 檀越剑指大厂系列:全面总结 java 核心技术,jvm,并发编程 redis,kafka,Spring,微服务等
  • 常用开发工具系列:常用的开发工具,IDEA,Mac,Alfred,Git,typora 等
  • 数据库系列:详细总结了常用数据库 mysql 技术点,以及工作中遇到的 mysql 问题等
  • 新空间代码工作室:提供各种软件服务,承接各种毕业设计,毕业论文等
  • 懒人运维系列:总结好用的命令,解放双手不香吗?能用一个命令完成绝不用两个操作
  • 数据结构与算法系列:总结数据结构和算法,不同类型针对性训练,提升编程思维,剑指大厂

非常期待和您一起在这个小小的网络世界里共同探索、学习和成长。💝💝💝 ✨✨ 欢迎订阅本专栏 ✨✨

1.评分机制 TF\IDF

TF-IDF（Term Frequency-Inverse Document Frequency）是一种用于信息检索和文本挖掘的统计方法，用以评估一个词在一个文档集中一个特定文档的重要程度。这个评分机制考虑了一个词语在特定文档中的出现频率（Term Frequency，TF）和在整个文档集中的逆文档频率（Inverse Document Frequency，IDF）。

TF（Term Frequency）词频（Term Frequency，TF）表示一个词在一个特定文档中出现的频率。这通常是该词在文档中出现次数与文档的总词数之比。

IDF（Inverse Document Frequency）逆文档频率（Inverse Document Frequency，IDF）是一个词在文档集中的重要性的度量。如果一个词很常见，出现在很多文档中（例如“和”，“是”等），那么它可能不会携带有用的信息。IDF 度量就是为了降低这些常见词在文档相似性度量中的权重。

2.score 是如何被计算出来的

GET /book/_search?explain=true
{"query": {"match": {"description": "java程序员"}}
}

返回

{"took": 5,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 2,"relation": "eq"},"max_score": 2.137549,"hits": [{"_shard": "[book][0]","_node": "MDA45-r6SUGJ0ZyqyhTINA","_index": "book","_type": "_doc","_id": "3","_score": 2.137549,"_source": {"name": "spring开发基础","description": "spring 在java领域非常流行，java程序员都在用。","studymodel": "201001","price": 88.6,"timestamp": "2019-08-24 19:11:35","pic": "group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg","tags": ["spring", "java"]},"_explanation": {"value": 2.137549,"description": "sum of:","details": [{"value": 0.7936629,"description": "weight(description:java in 0) [PerFieldSimilarity], result of:","details": [{"value": 0.7936629,"description": "score(freq=2.0), product of:","details": [{"value": 2.2,"description": "boost","details": []},{"value": 0.47000363,"description": "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:","details": [{"value": 2,"description": "n, number of documents containing term","details": []},{"value": 3,"description": "N, total number of documents with field","details": []}]},{"value": 0.7675597,"description": "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:","details": [{"value": 2.0,"description": "freq, occurrences of term within document","details": []},{"value": 1.2,"description": "k1, term saturation parameter","details": []},{"value": 0.75,"description": "b, length normalization parameter","details": []},{"value": 12.0,"description": "dl, length of field","details": []},{"value": 35.333332,"description": "avgdl, average length of field","details": []}]}]}]},{"value": 1.3438859,"description": "weight(description:程序员 in 0) [PerFieldSimilarity], result of:","details": [{"value": 1.3438859,"description": "score(freq=1.0), product of:","details": [{"value": 2.2,"description": "boost","details": []},{"value": 0.98082924,"description": "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:","details": [{"value": 1,"description": "n, number of documents containing term","details": []},{"value": 3,"description": "N, total number of documents with field","details": []}]},{"value": 0.6227967,"description": "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:","details": [{"value": 1.0,"description": "freq, occurrences of term within document","details": []},{"value": 1.2,"description": "k1, term saturation parameter","details": []},{"value": 0.75,"description": "b, length normalization parameter","details": []},{"value": 12.0,"description": "dl, length of field","details": []},{"value": 35.333332,"description": "avgdl, average length of field","details": []}]}]}]}]}},{"_shard": "[book][0]","_node": "MDA45-r6SUGJ0ZyqyhTINA","_index": "book","_type": "_doc","_id": "2","_score": 0.57961315,"_source": {"name": "java编程思想","description": "java语言是世界第一编程语言，在软件开发领域使用人数最多。","studymodel": "201001","price": 68.6,"timestamp": "2019-08-25 19:11:35","pic": "group1/M00/00/00/wKhlQFs6RCeAY0pHAAJx5ZjNDEM428.jpg","tags": ["java", "dev"]},"_explanation": {"value": 0.57961315,"description": "sum of:","details": [{"value": 0.57961315,"description": "weight(description:java in 0) [PerFieldSimilarity], result of:","details": [{"value": 0.57961315,"description": "score(freq=1.0), product of:","details": [{"value": 2.2,"description": "boost","details": []},{"value": 0.47000363,"description": "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:","details": [{"value": 2,"description": "n, number of documents containing term","details": []},{"value": 3,"description": "N, total number of documents with field","details": []}]},{"value": 0.56055,"description": "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:","details": [{"value": 1.0,"description": "freq, occurrences of term within document","details": []},{"value": 1.2,"description": "k1, term saturation parameter","details": []},{"value": 0.75,"description": "b, length normalization parameter","details": []},{"value": 19.0,"description": "dl, length of field","details": []},{"value": 35.333332,"description": "avgdl, average length of field","details": []}]}]}]}]}}]}
}

3.分析如何被匹配上

分析一个 document 是如何被匹配上的

• 最终得分
• IDF 得分

GET /book/_explain/3
{"query": {"match": {"description": "java程序员"}}
}

4.Doc value

搜索的时候，要依靠倒排索引；排序的时候，需要依靠正排索引，看到每个 document 的每个 field，然后进行排序，所谓的正排索引，其实就是 doc values

在建立索引的时候，一方面会建立倒排索引，以供搜索用；一方面会建立正排索引，也就是 doc values，以供排序，聚合，过滤等操作使用

doc values 是被保存在磁盘上的，此时如果内存足够，os 会自动将其缓存在内存中，性能还是会很高；如果内存不足够，os 会将其写入磁盘上

倒排索引

doc1: hello world you and me

doc2: hi, world, how are you

term	doc1	doc2
hello	*
world	*	*
you	*	*
and	*
me	*
hi		*
how		*
are		*

搜索时：

hello you --> hello, you

hello --> doc1

you --> doc1,doc2

doc1: hello world you and me

doc2: hi, world, how are you

sort by 出现问题

正排索引

doc1: { “name”: “jack”, “age”: 27 }

doc2: { “name”: “tom”, “age”: 30 }

document	name	age
doc1	jack	27
doc2	tom	30

5.query phase

1. 搜索请求发送到某一个 coordinate node，构构建一个 priority queue，长度以 paging 操作 from 和 size 为准，默认为 10

2. coordinate node 将请求转发到所有 shard，每个 shard 本地搜索，并构建一个本地的 priority queue

3. 各个 shard 将自己的 priority queue 返回给 coordinate node，并构建一个全局的 priority queue

6.replica shard 提升吞吐量

replica shard 如何提升搜索吞吐量

一次请求要打到所有 shard 的一个 replica/primary 上去，如果每个 shard 都有多个 replica，那么同时并发过来的搜索请求可以同时打到其他的 replica 上去

7.fetch phbase 工作流程

1. coordinate node 构建完 priority queue 之后，就发送 mget 请求去所有 shard 上获取对应的 document

2. 各个 shard 将 document 返回给 coordinate node

3. coordinate node 将合并后的 document 结果返回给 client 客户端

一般搜索，如果不加 from 和 size，就默认搜索前 10 条，按照_score 排序

8.搜索参数小总结

preference:

决定了哪些 shard 会被用来执行搜索操作

_primary, _primary_first, _local, _only_node:xyz, _prefer_node:xyz, _shards:2,3

bouncing results 问题，两个 document 排序，field 值相同；不同的 shard 上，可能排序不同；每次请求轮询打到不同的 replica shard 上；每次页面上看到的搜索结果的排序都不一样。这就是 bouncing result，也就是跳跃的结果。

搜索的时候，是轮询将搜索请求发送到每一个 replica shard（primary shard），但是在不同的 shard 上，可能 document 的排序不同

解决方案就是将 preference 设置为一个字符串，比如说 user_id，让每个 user 每次搜索的时候，都使用同一个 replica shard 去执行，就不会看到 bouncing results 了

timeout:

主要就是限定在一定时间内，将部分获取到的数据直接返回，避免查询耗时过长

routing:

document 文档路由，_id 路由，routing=user_id，这样的话可以让同一个 user 对应的数据到一个 shard 上去

search_type:

default：query_then_fetch

dfs_query_then_fetch，可以提升 revelance sort 精准度

9.bucket 和 metric

bucket:一个数据分组

city name
北京 张三
北京 李四
天津 王五
天津 赵六

天津 王麻子

划分出来两个 bucket，一个是北京 bucket，一个是天津 bucket
北京 bucket：包含了 2 个人，张三，李四
上海 bucket：包含了 3 个人，王五，赵六，王麻子

metric:对一个数据分组执行的统计

metric，就是对一个 bucket 执行的某种聚合分析的操作，比如说求平均值，求最大值，求最小值

select count(*) from book group by studymodel

• bucket：group by studymodel --> 那些 studymodel 相同的数据，就会被划分到一个 bucket 中
• metric：count(*)，对每个 user_id bucket 中所有的数据，计算一个数量。还有 avg()，sum()，max()，min()

觉得有用的话点个赞 👍🏻 呗。
❤️❤️❤️本人水平有限，如有纰漏，欢迎各位大佬评论批评指正！😄😄😄

💘💘💘如果觉得这篇文对你有帮助的话，也请给个点赞、收藏下吧，非常感谢!👍 👍 👍

🔥🔥🔥Stay Hungry Stay Foolish 道阻且长,行则将至,让我们一起加油吧！🌙🌙🌙