相关度评分 TF&IDF算法
Elasticsearch的相关度评分（relevance score）算法采用的是term frequency/inverse document frequency算法，简称为TF/IDF算法。
算法介绍：
- relevance score算法：简单来说就是，就是计算出一个索引中的文本，与搜索文本，它们之间的关联匹配程度。
- TF/IDF算法：分为两个部分，IF 和IDF
- Term Frequency(TF)： 搜索文本中的各个词条在field文本中出现了多少次，出现的次数越多，就越相关
例如：
搜索请求：hello world
doc1: hello you, and world is very good
doc2: hello, how are you
那么此时根据TF算法，doc1的相关度要比doc2的要高 - Inverse Document Frequency(IDF)：搜索文本中的各个词条在整个索引的所有文档中出现的次数，出现的次数越多，就越不相关。
搜索请求： hello world
doc1: hello, today is very good.
doc2: hi world, how are you.
比如在index中有1万条document, hello这个单词在所有的document中，一共出现了1000次，world这个单词在所有的document中一共出现100次。那么根据IDF算法此时doc2的相关度要比doc1要高。 - field-length norm：field-length norm就是field长度越长，相关度就越弱
搜索请求：hello world
doc1: {"title": "hello article", "content": "1万个单词"}
doc2: {"title": "my article", "content": "1万个单词， hi world"}
此时hello world在整个index中出现的次数是一样多的。但是根据Field-length norm此时doc1比doc2相关度要高。因为title字段更短。
_score是如何被计算出来的
GET /test_index/test_type/_search?explain { "query": { "match": { "test_field": "test hello" } } }
{ "took": 1, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 3, "max_score": 0.843298, "hits": [ { "_shard": "[test_index][2]", "_node": "1LdqLFqxQQq4xg2MphI_gw", "_index": "test_index", "_type": "test_type", "_id": "6", "_score": 0.843298, "_source": { "test_field": "test test" }, "_explanation": { "value": 0.843298, "description": "sum of:", "details": [ { "value": 0.843298, "description": "sum of:", "details": [ { "value": 0.843298, "description": "weight(test_field:test in 0) [PerFieldSimilarity], result of:", "details": [ { "value": 0.843298, "description": "score(doc=0,freq=2.0 = termFreq=2.0 ), product of:", "details": [ { "value": 0.6931472, "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:", "details": [ { "value": 2, "description": "docFreq", "details": [] }, { "value": 4, "description": "docCount", "details": [] } ] }, { "value": 1.2166219, "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:", "details": [ { "value": 2, "description": "termFreq=2.0", "details": [] }, { "value": 1.2, "description": "parameter k1", "details": [] }, { "value": 0.75, "description": "parameter b", "details": [] }, { "value": 1.75, "description": "avgFieldLength", "details": [] }, { "value": 2.56, "description": "fieldLength", "details": [] } ] } ] } ] } ] }, { "value": 0, "description": "match on required clause, product of:", "details": [ { "value": 0, "description": "# clause", "details": [] }, { "value": 1, "description": "_type:test_type, product of:", "details": [ { "value": 1, "description": "boost", "details": [] }, { "value": 1, "description": "queryNorm", "details": [] } ] } ] } ] } }, { "_shard": "[test_index][1]", "_node": "1LdqLFqxQQq4xg2MphI_gw", "_index": "test_index", "_type": "test_type", "_id": "8", "_score": 0.43445712, "_source": { "test_field": "test client 2" }, "_explanation": { "value": 0.43445715, "description": "sum of:", "details": [ { "value": 0.43445715, "description": "sum of:", "details": [ { "value": 0.43445715, "description": "weight(test_field:test in 0) [PerFieldSimilarity], result of:", "details": [ { "value": 0.43445715, "description": "score(doc=0,freq=1.0 = termFreq=1.0 ), product of:", "details": [ { "value": 0.47000363, "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:", "details": [ { "value": 2, "description": "docFreq", "details": [] }, { "value": 3, "description": "docCount", "details": [] } ] }, { "value": 0.92436975, "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:", "details": [ { "value": 1, "description": "termFreq=1.0", "details": [] }, { "value": 1.2, "description": "parameter k1", "details": [] }, { "value": 0.75, "description": "parameter b", "details": [] }, { "value": 3.3333333, "description": "avgFieldLength", "details": [] }, { "value": 4, "description": "fieldLength", "details": [] } ] } ] } ] } ] }, { "value": 0, "description": "match on required clause, product of:", "details": [ { "value": 0, "description": "# clause", "details": [] }, { "value": 1, "description": "_type:test_type, product of:", "details": [ { "value": 1, "description": "boost", "details": [] }, { "value": 1, "description": "queryNorm", "details": [] } ] } ] } ] } }, { "_shard": "[test_index][3]", "_node": "1LdqLFqxQQq4xg2MphI_gw", "_index": "test_index", "_type": "test_type", "_id": "7", "_score": 0.25316024, "_source": { "test_field": "test client 1" }, "_explanation": { "value": 0.25316024, "description": "sum of:", "details": [ { "value": 0.25316024, "description": "sum of:", "details": [ { "value": 0.25316024, "description": "weight(test_field:test in 0) [PerFieldSimilarity], result of:", "details": [ { "value": 0.25316024, "description": "score(doc=0,freq=1.0 = termFreq=1.0 ), product of:", "details": [ { "value": 0.2876821, "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:", "details": [ { "value": 1, "description": "docFreq", "details": [] }, { "value": 1, "description": "docCount", "details": [] } ] }, { "value": 0.88, "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:", "details": [ { "value": 1, "description": "termFreq=1.0", "details": [] }, { "value": 1.2, "description": "parameter k1", "details": [] }, { "value": 0.75, "description": "parameter b", "details": [] }, { "value": 3, "description": "avgFieldLength", "details": [] }, { "value": 4, "description": "fieldLength", "details": [] } ] } ] } ] } ] }, { "value": 0, "description": "match on required clause, product of:", "details": [ { "value": 0, "description": "# clause", "details": [] }, { "value": 1, "description": "*:*, product of:", "details": [ { "value": 1, "description": "boost", "details": [] }, { "value": 1, "description": "queryNorm", "details": [] } ] } ] } ] } } ] } }
doc value正排索引
在我们搜索的时候，要依靠倒排索引，但是当我们排序的时候，需要依靠正排索引。通过倒排索引锁定文档document之后，看到每个document的每个field，然后进行排序，所谓的正排索引就是doc values。
对于ES而言，在建立索引的时候，一方面会建立倒排索引，以供搜索使用；一方面会建立正排索引，也就是doc values,以供排序，聚合，过滤等使用。
doc values是被保存在磁盘上的，此时如果内存足够，OS操作系统会自动将其缓存在内存中，性能还是会很高的，如果内存不够用，OS操作系统会将其写入磁盘。
下面举个例子描述正排索引和倒排索引
假设某个index有两个doc
doc1 : hello world you and me
doc2 : hi world, how are you
建立倒排索引
word doc1 doc2 hello * world * * you * * and * me * hi * how * are *
假设某个index有两个doc
doc1: {"name": "jack", "age": 27}
doc2: {"name": "tom", "age": 30}
建立正排索引
document name age doc1 jack 27 doc2 tom 30
解密query、fetch phrase原理
query pharse
基本原理：
- 搜索请求发送到某一个coordinate node协调节点，会构建一个priority queue，长度以paging操作from和size为准，默认是10
- coordinate node将请求转发到所有的shard,每个shard本地搜索，并构建一个本地的priority queue
- 各个shard将自己的priority queue返回给coordinate node,并构建一个全局的priority queue
fetch phrase
基本原理：
- coordinate node协调节点构建完priority queue之后，就发送mget请求去所有shard上获取对应的document
- 各个shard将document返回给coordinate node
- coordinate node将合并后的document结果返回给客户端。
也就是ES的query pharse是根据priority queue去构建搜索结果的
示例
比如总共有60000条数据，三个primary shard,每个shard上分了20000条数据，每页是10条数据，这个时候，你要搜索到第1000页，实际上要拿到的是10001~10010，也就是会构建一个10010大小的priority queue。
注意这里千万不要理解成每个shard都是返回10条数据。这样理解是错误的！
下面做一下详细的分析：
请求首先可能是打到一个不包含这个index的shard的node上去，这个node就是一个协调节点coordinate node，那么这个coordinate node就会将搜索请求转发到index的三个shard所在的node上去。比如说我们之前说的情况下，要搜索60000条数据中的第1000页，实际上每个shard都要将内部的20000条数据中的第10001~10010条数据，拿出来，不是才10条，是10010条数据。3个shard的每个shard都返回10010条数据给协调节点coordinate node，coordinate node会收到总共30030条数据,此时会构建一个30030大小的priority queue，然后在这些数据中进行排序，根据_score相关度分数，然后取到10001~10010这10条数据，就是我们要的第1000页的10条数据。
如下图所示：
Bouncing Results问题
想象一下有两个文档有同样值的时间戳字段，搜索结果用 timestamp 字段来排序。 由于搜索请求是在所有有效的分片副本间轮询的，那就有可能发生主分片处理请求时，这两个文档是一种顺序， 而副本分片处理请求时又是另一种顺序。
-
bouncing results 问题:：每次用户刷新页面，搜索结果表现是不同的顺序。 让同一个用户始终使用同一个分片，这样可以避免这种问题， 可以设置 preference 参数为一个特定的任意值比如用户会话ID来解决。
偏好这个参数 preference 允许 用来控制由哪些分片或节点来处理搜索请求。 它接受像 _primary, _primary_first, _local, _only_node:xyz, _prefer_node:xyz, 和 _shards:2,3 这样的值, 这些值在 search preference 文档页面被详细解释。
但是最有用的值是某些随机字符串，它可以避免 bouncing results 问题。 - timeout：已经讲解过原理了，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：默认default：query_then_fetch，dfs_query_then_fetch可以提升revelance sort精准度
基于scoll技术滚动搜索大量数据
在实际应用中，通过from+size不可避免会出现深分页的瓶颈，那么通过scoll技术就是一个很好的解决深分页的方法。比如如果我们一次性要查出10万条数据，那么使用from+size很显然性能会非常的差，priority queue会非常的大。此时如果采用scroll滚动查询，就可以一批一批的查，直到所有数据都查询完。
scroll原理
scoll搜索会在第一次搜索的时候，保存一个当时的视图快照，之后只会基于该旧的视图快照提供数据搜索，如果这个期间数据变更，是不会让用户看到的。而且ES内部是基于_doc进行排序的方式，性能较高。
示例：
# 使用scroll POST /test_index/_search?scroll=1m { "query": { "match_all": {} }, "sort": [ "_doc" ], "size": 3 }
获取到scroll_id
{ "_scroll_id": "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAI-sFjFMZHFMRnF4UVFxNHhnMk1waElfZ3cAAAAAAACPqxYxTGRxTEZxeFFRcTR4ZzJNcGhJX2d3AAAAAAAAj68WMUxkcUxGcXhRUXE0eGcyTXBoSV9ndwAAAAAAAI-tFjFMZHFMRnF4UVFxNHhnMk1waElfZ3cAAAAAAACPrhYxTGRxTEZxeFFRcTR4ZzJNcGhJX2d3", "took": 3, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 12, "max_score": null, "hits": [ { "_index": "test_index", "_type": "test_type", "_id": "AWypxxLYFCl_S-ox4wvd", "_score": null, "_source": { "test_content": "my test" }, "sort": [ 0 ] }, { "_index": "test_index", "_type": "test_type", "_id": "6", "_score": null, "_source": { "test_field": "test test" }, "sort": [ 0 ] }, { "_index": "test_index", "_type": "test_type", "_id": "7", "_score": null, "_source": { "test_field": "test client 1" }, "sort": [ 0 ] } ] } }
滚动搜索
# 滚动搜索 POST _search/scroll { "scroll":"1m", "scroll_id":"DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAJDMFjFMZHFMRnF4UVFxNHhnMk1waElfZ3cAAAAAAACQzRYxTGRxTEZxeFFRcTR4ZzJNcGhJX2d3AAAAAAAAkM8WMUxkcUxGcXhRUXE0eGcyTXBoSV9ndwAAAAAAAJDOFjFMZHFMRnF4UVFxNHhnMk1waElfZ3cAAAAAAACQ0BYxTGRxTEZxeFFRcTR4ZzJNcGhJX2d3" }
搜索结果
{ "_scroll_id": "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAJDMFjFMZHFMRnF4UVFxNHhnMk1waElfZ3cAAAAAAACQzRYxTGRxTEZxeFFRcTR4ZzJNcGhJX2d3AAAAAAAAkM8WMUxkcUxGcXhRUXE0eGcyTXBoSV9ndwAAAAAAAJDOFjFMZHFMRnF4UVFxNHhnMk1waElfZ3cAAAAAAACQ0BYxTGRxTEZxeFFRcTR4ZzJNcGhJX2d3", "took": 1, "timed_out": false, "terminated_early": true, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 12, "max_score": null, "hits": [ { "_index": "test_index", "_type": "test_type", "_id": "11", "_score": null, "_source": { "num": 0, "tags": [] }, "sort": [ 0 ] }, { "_index": "test_index", "_type": "test_type", "_id": "8", "_score": null, "_source": { "test_field": "test client 2" }, "sort": [ 1 ] }, { "_index": "test_index", "_type": "test_type", "_id": "4", "_score": null, "_source": { "test_field": "test4" }, "sort": [ 1 ] } ] } }