分析器(Analyzer) Elasticsearch 无论是内置分析器还是自定义分析器，都由三部分组成：字符过滤器(Character Filters)、分词器(Tokenizer)、词元过滤器(Token Filters)。 分析器Analyzer工作流程： Input Text => Character Filters(如果有多个，按顺序应用) => Tokenizer => Token Filters(如果有多个，按顺序应用) => Output Token 字符过滤器(Character Filters) 字符过滤器：对原始文本预处理，如去除HTML标签，”&”转成”and”等。 注意：一个分析器同时有多个字符过滤器时，按顺序应用。 分词器(Tokenizer) 分词器：将字符串分解成一系列的词元Token。如根据空格将英文单词分开。 词元过滤器(Token Filters) 词元过滤器：对分词器分出来的词元Token做进一步处理，如转换大小写、移除停用词、单复数转换、同义词转换等。 注意：一个分析器同时有多个词元过滤器时，按顺序应用。 分析器analyze API的使用 分析器analyze API可验证分析器的分析效果并解释分析过程。 # text: 待分析文本 # explain:解释分析过程 # char_filter:字符过滤器 # tokenizer:分词器 # filter:词元过滤器 GET _analyze { "char_filter" : ["html_strip"], "tokenizer": "standard", "filter": [ "lowercase"], "text": "<p><em>No <b>dreams</b>, why bother <b>Beijing</b> !</em></p>", "explain" : true } 自定义多个分析器 创建索引并自定义多个分析器 这里对一个索引同时定义了多个分析器。 PUT my_index { "settings": { "number_of_shards": 3, "number_of_replicas": 1, "analysis": { "char_filter": { //自定义多个字符过滤器 "my_charfilter1": { "type": "mapping", "mappings": ["& => and"] }, "my_charfilter2": { "type": "pattern_replace", "pattern": "(\d+)-(?=\d)", "replacement": "$1_" } }, "tokenizer":{ //自定义多个分词器 "my_tokenizer1": { "pattern":"\s+", "type":"pattern" }, "my_tokenizer2":{ "pattern":"_", "type":"pattern" } }, "filter": { //自定义多个词元过滤器 "my_tokenfilter1": { "type": "stop", "stopwords": ["the", "a","an"] }, "my_tokenfilter2": { "type": "stop", "stopwords": ["info", "debug"] } }, "analyzer": { //自定义多个分析器 "my_analyzer1":{ //分析器my_analyzer1 "char_filter": ["html_strip", "my_charfilter1","my_charfilter2"], "tokenizer":"my_tokenizer1", "filter": ["lowercase", "my_tokenfilter1"] }, "my_analyzer2":{ //分析器my_analyzer2 "char_filter": ["html_strip"], "tokenizer":"my_tokenizer2", "filter": ["my_tokenfilter2"] } } } } } 验证索引my_index的多个分析器 验证分析器my_analyzer1分析效果 GET /my_index/_analyze { "text": "<b>Tom </b> & <b>jerry</b> in the room number 1-1-1", "analyzer": "my_analyzer1"//, //"explain": true } #返回结果 { "tokens": [ { "token": "tom", "start_offset": 3, "end_offset": 6, "type": "word", "position": 0 }, { "token": "and", "start_offset": 12, "end_offset": 13, "type": "word", "position": 1 }, { "token": "jerry", "start_offset": 17, "end_offset": 26, "type": "word", "position": 2 }, { "token": "in", "start_offset": 27, "end_offset": 29, "type": "word", "position": 3 }, { "token": "room", "start_offset": 34, "end_offset": 38, "type": "word", "position": 5 }, { "token": "number", "start_offset": 39, "end_offset": 45, "type": "word", "position": 6 }, { "token": "1_1_1", "start_offset": 46, "end_offset": 51, "type": "word", "position": 7 } ] } 验证分析器my_analyzer2分析效果 GET /my_index/_analyze { "text": "<b>debug_192.168.113.1_971213863506812928</b>", "analyzer": "my_analyzer2"//, //"explain": true } #返回结果 { "tokens": [ { "token": "192.168.113.1", "start_offset": 9, "end_offset": 22, "type": "word", "position": 1 }, { "token": "971213863506812928", "start_offset": 23, "end_offset": 45, "type": "word", "position": 2 } ] } 添加Mapping并为不同字段设置不同分析器 PUT my_index/_mapping/my_type { "properties": { "my_field1": { "type": "text", "analyzer": "my_analyzer1", "fields": { "keyword": { "type": "keyword" } } }, "my_field2": { "type": "text", "analyzer": "my_analyzer2", "fields": { "keyword": { "type": "keyword" } } } } } 创建文档 PUT my_index/my_type/1 { "my_field1":"<b>Tom </b> & <b>jerry</b> in the room number 1-1-1", "my_field2":"<b>debug_192.168.113.1_971213863506812928</b>" } Query-Mathch全文检索 查询时，ES会根据字段使用的分析器进行分析，然后检索。 #查询my_field2字段包含IP:192.168.113.1的文档 GET my_index/_search { "query": { "match": { "my_field2": "192.168.113.1" } } } #返回结果 { "took": 22, "timed_out": false, "_shards": { "total": 3, "successful": 3, "skipped": 0, "failed": 0 }, "hits": { "total": 1, "max_score": 0.2876821, "hits": [ { "_index": "my_index", "_type": "my_type", "_id": "1", "_score": 0.2876821, "_source": { "my_field1": "<b>Tom </b> & <b>jerry</b> in the room number 1-1-1", "my_field2": "<b>debug_192.168.113.1_971213863506812928</b>" } } ] } }