es-09模糊查询

模糊查询

• 前缀搜索：prefix

  概念：以xx开头的搜索，不计算相关度评分。

  注意：

  • 前缀搜索匹配的是term，而不是field。
  • 前缀搜索的性能很差
  • 前缀搜索没有缓存
  • 前缀搜索尽可能把前缀长度设置的更长

  语法：

  GET <index>/_search
  {"query": {"prefix": {"<field>": {"value": "<word_prefix>"}}}
  }
  index_prefixes: 默认   "min_chars" : 2,   "max_chars" : 5 
  

#prefix: 前缀搜索
DELETE my_index
# elasticsearch stack
# elasticsearch search
# el
# ela 
# elas elasticsearch
PUT my_index
{"mappings": {"properties": {"text": {"analyzer": "ik_max_word","type": "text","index_prefixes":{"min_chars":2,"max_chars":4},"fields": {"keyword": {"type": "keyword","ignore_above": 256}}}}}
}
GET my_index/_mapping
POST /my_index/_bulk?filter_path=items.*.error
{"index":{"_id":"1"}}
{"text":"城管打电话喊商贩去摆摊摊"}
{"index":{"_id":"2"}}
{"text":"笑果文化回应商贩老农去摆摊"}
{"index":{"_id":"3"}}
{"text":"老农耗时17年种出椅子树"}
{"index":{"_id":"4"}}
{"text":"夫妻结婚30多年AA制,被城管抓"}
{"index":{"_id":"5"}}
{"text":"黑人见义勇为阻止抢劫反被铐住"}
GET my_index/_search
GET my_index/_mapping
GET _analyze
{"text": ["夫妻结婚30多年AA制,被城管抓"]
}
GET my_index/_search
{"query": {"prefix": {"text": {"value": "城管"}}}
}

• 通配符：wildcard

  概念：通配符运算符是匹配一个或多个字符的占位符。例如，*通配符运算符匹配零个或多个字符。您可以将通配符运算符与其他字符结合使用以创建通配符模式。

  注意：

  • 通配符匹配的也是term，而不是field

  语法：

  GET <index>/_search
  {"query": {"wildcard": {"<field>": {"value": "<word_with_wildcard>"}}}
  }
  

# 通配符
DELETE my_index
POST /my_index/_bulk
{ "index": { "_id": "1"} }
{ "text": "my english" }
{ "index": { "_id": "2"} }
{ "text": "my english is good" }
{ "index": { "_id": "3"} }
{ "text": "my chinese is good" }
{ "index": { "_id": "4"} }
{ "text": "my japanese is nice" }
{ "index": { "_id": "5"} }
{ "text": "my disk is full" }
DELETE product_en
POST /product_en/_bulk
{ "index": { "_id": "1"} }
{ "title": "my english","desc" :  "shouji zhong de zhandouji","price" :  3999, "tags": [ "xingjiabi", "fashao", "buka", "1"]}
{ "index": { "_id": "2"} }
{ "title": "xiaomi nfc phone","desc" :  "zhichi quangongneng nfc,shouji zhong de jianjiji","price" :  4999, "tags": [ "xingjiabi", "fashao", "gongjiaoka" , "asd2fgas"]}
{ "index": { "_id": "3"} }
{ "title": "nfc phone","desc" :  "shouji zhong de hongzhaji","price" :  2999, "tags": [ "xingjiabi", "fashao", "menjinka" , "as345"]}
{ "title": { "_id": "4"} }
{ "text": "xiaomi erji","desc" :  "erji zhong de huangmenji","price" :  999, "tags": [ "low", "bufangshui", "yinzhicha", "4dsg" ]}
{ "index": { "_id": "5"} }
{ "title": "hongmi erji","desc" :  "erji zhong de kendeji","price" :  399, "tags": [ "lowbee", "xuhangduan", "zhiliangx" , "sdg5"]}
GET my_index/_search
GET product_en/_searchGET my_index/_search
{"query": {"wildcard": {"text.keyword": {"value": "my eng*ish"}}}
}
GET product_en/_mapping
#exact value
GET product_en/_search
{"query": {"wildcard": {"tags.keyword": {"value": "men*inka"}}}
}

• 正则：regexp

  概念：regexp查询的性能可以根据提供的正则表达式而有所不同。为了提高性能，应避免使用通配符模式，如.或 .?+未经前缀或后缀

  语法：

  GET <index>/_search
  {"query": {"regexp": {"<field>": {"value": "<regex>","flags": "ALL",}}}
  }
  

#正则
GET product_en/_search
GET product_en/_search
{"query": {"regexp": {"title": "[\\s\\S]*nfc[\\s\\S]*"}}
}
GET product_en/_search
GET product_en/_search
{"query": {"regexp": {"desc": {"value": "zh~dng","flags": "COMPLEMENT"}}}
}
GET product_en/_search
{"query": {"regexp": {"tags.keyword": {"value": ".*<2-3>.*","flags": "INTERVAL"}}}
}

flags

• ALL

  启用所有可选操作符。

• COMPLEMENT

  启用_{操作符。可以使用}对下面最短的模式进行否定。例如

  a~bc # matches ‘adc’ and ‘aec’ but not ‘abc’

• INTERVAL

  启用<>操作符。可以使用<>匹配数值范围。例如

  foo<1-100> # matches ‘foo1’, ‘foo2’ … ‘foo99’, ‘foo100’

  foo<01-100> # matches ‘foo01’, ‘foo02’ … ‘foo99’, ‘foo100’

• INTERSECTION

  启用&操作符，它充当AND操作符。如果左边和右边的模式都匹配，则匹配成功。例如:

  aaa.+&.+bbb # matches ‘aaabbb’

• ANYSTRING

  启用@操作符。您可以使用@来匹配任何整个字符串。
  您可以将@操作符与&和~操作符组合起来，创建一个“everything except”逻辑。例如:

  @&~(abc.+) # matches everything except terms beginning with ‘abc’

• 模糊查询：fuzzy

  混淆字符 (box → fox) 缺少字符 (black → lack)

  多出字符 (sic → sick) 颠倒次序 (act → cat)

  语法

  GET <index>/_search
  {"query": {"fuzzy": {"<field>": {"value": "<keyword>"}}}
  }
  

# fuzzy:模糊查询
GET product_en/_search
GET product_en/_search
{"query": {"fuzzy": {"desc": {"value": "quangongneng nfc","fuzziness": "2"}}}
}GET product_en/_search
{"query": {"match": {"desc": {"query": "nfe quasdasdasdasd","fuzziness": 1}}}
}

参数：

• value：（必须，关键词）

• fuzziness：编辑距离，（0，1，2）并非越大越好，召回率高但结果不准确

  1. 两段文本之间的Damerau-Levenshtein距离是使一个字符串与另一个字符串匹配所需的插入、删除、替换和调换的数量

  2. 距离公式：Levenshtein是lucene的，es改进版：Damerau-Levenshtein，

  axe=>aex Levenshtein=2 Damerau-Levenshtein=1

• transpositions：（可选，布尔值）指示编辑是否包括两个相邻字符的变位（ab→ba）。默认为true。

• 短语前缀：match_phrase_prefix

  match_phrase：

  • match_phrase会分词
  • 被检索字段必须包含match_phrase中的所有词项并且顺序必须是相同的
  • 被检索字段包含的match_phrase中的词项之间不能有其他词项

  概念：

  ​ match_phrase_prefix与match_phrase相同,但是它多了一个特性,就是它允许在文本的最后一个词项(term)上的前缀匹配,如果 是一个单词,比如a,它会匹配文档字段所有以a开头的文档,如果是一个短语,比如 “this is ma” ,他会先在倒排索引中做以ma做前缀搜索,然后在匹配到的doc中做match_phrase查询,(网上有的说是先match_phrase,然后再进行前缀搜索, 是不对的)

  参数

  • analyzer 指定何种分析器来对该短语进行分词处理
  • max_expansions 限制匹配的最大词项
  • boost 用于设置该查询的权重
  • slop 允许短语间的词项(term)间隔：slop 参数告诉 match_phrase 查询词条相隔多远时仍然能将文档视为匹配 什么是相隔多远？ 意思是说为了让查询和文档匹配你需要移动词条多少次？

  原理解析：https://www.elastic.co/cn/blog/found-fuzzy-search#performance-considerations

# match_phrase_prefix
GET product_en/_search
{"query": {"match_phrase": {"desc": "shouji zhong de"}}
}GET product_en/_search
{"query": {"match_phrase_prefix": {"desc": {"query": "de zhong shouji hongzhaji","max_expansions": 50,"slop":3}}}
}GET product_en/_search
{"query": {"match_phrase_prefix": {"desc": {"query": "zhong hongzhaji","max_expansions": 50,"slop": 3}}}
}# source: zhong de hongzhaji
# query:  zhong >  hongzhaji# source: shouji zhong de hongzhaji
# query:  de zhong shouji hongzhaji# de shouji/zhong  hongzhaji  1次
# shouji/de zhong  hongzhaji  2次
# shouji zhong/de  hongzhaji  3次
# shouji zhong de  hongzhaji  4次

• N-gram和edge ngram

  tokenizer

  GET _analyze
  {"tokenizer": "ngram","text": "reba always loves me"
  }
  

  token filter

  GET _analyze
  {"tokenizer": "ik_max_word","filter": [ "ngram" ],"text": "reba always loves me"
  }
  

  min_gram：创建索引所拆分字符的最小阈值

  max_gram：创建索引所拆分字符的最大阈值

  ngram：从每一个字符开始,按照步长,进行分词,适合前缀中缀检索

  edge_ngram：从第一个字符开始,按照步长,进行分词,适合前缀匹配场景

  # ngram 和 edge-ngram