ES默认是动态创建索引和索引类型的mapping的,但是在学习的时候还能这样用,在生产中一定是手动制定mapping!在生产中经常会遇到这样的需求,想用某个字段进行统计,又想对该字段进行模糊查询,解决这种需求的方法就是对该字段创建别名!
mapping结构如下:
1 { 2 "settings" : { 3 "index" : { 4 "analysis" : { 5 "filter" : { 6 "english_keywords" : { 7 "type" : "keyword_marker", 8 "keywords" : [ 9 "topsec" 10 ] 11 }, 12 "english_stemmer" : { 13 "type" : "stemmer", 14 "language" : "english" 15 }, 16 "english_possessive_stemmer" : { 17 "type" : "stemmer", 18 "language" : "possessive_english" 19 }, 20 "english_stop" : { 21 "type" : "stop", 22 "stopwords" : "_english_" 23 } 24 }, 25 "analyzer" : {29 "english" : { 30 "type" : "custom", 31 "filter" : [ 32 "lowercase", 33 "english_stop" 34 ], 35 "tokenizer" : "standard" 36 }, 37 "ik" : { 38 "filter" : ["lowercase"], 39 "type" : "custom", 40 "tokenizer" : "ik_max_word" 41 }, 42 "html" : { 43 "filter" : [ 44 "lowercase", 45 "english_stop" 46 ], 47 "char_filter" : [ 48 "html_strip" 49 ], 50 "type" : "custom", 51 "tokenizer" : "standard" 52 }, 53 "lower" : { 54 "filter" : "lowercase", 55 "type" : "custom", 56 "tokenizer" : "keyword" 57 } 58 } 59 }, 60 "number_of_shards" : "1", 61 "number_of_replicas" : "0" 62 } 63 }, 64 "mappings" : { 65 "test" : { 66 "_all" : { 67 "enabled" : false 68 }, 69 "properties" : { 70 "name" : { 71 "type" : "keyword" 72 }, 73 "age" : { 74 "type" : "keyword", 75 "fields" : { 76 "cn" : { 77 "analyzer" : "ik", 78 "type" : "text" 79 } 80 } 81 }, 82 83 "address" : { 84 "type" : "text" 85 } 86 } 87 } 88 } 89 }
字段age的"type" : "keyword",不分词,然后起个别名cn,对它使用ik分词器进行分词!插入四条数据
用age字段对数据进行统计的时候,需要用不分词的age,并且需要使用全匹配规则,语句:
1 { 2 "query": { 3 "bool": { 4 "must": [ 5 { 6 "term": { 7 "age": "北京市海淀区西二旗中关村西门" 8 } 9 } 10 ], 11 "must_not": [], 12 "should": [] 13 } 14 }, 15 "from": 0, 16 "size": 10, 17 "sort": [], 18 "aggs": {} 19 }
结果:
使用age的分词age.cn进行统计是有问题的,运行的结果说明对age的别名age.cn进行分词,查询条件必须匹配分词器对age的内容进行分词的结果进行匹配,
1 { 2 "query": { 3 "bool": { 4 "must": [ 5 { 6 "term": { 7 "age.cn": "北京市海淀区西二旗中关村西门" 8 } 9 } 10 ], 11 "must_not": [], 12 "should": [] 13 } 14 }, 15 "from": 0, 16 "size": 10, 17 "sort": [], 18 "aggs": {} 19 }
结果:
1 { 2 "query": { 3 "bool": { 4 "must": [ 5 { 6 "term": { 7 "age.cn": "北京市" 8 } 9 } 10 ], 11 "must_not": [], 12 "should": [] 13 } 14 }, 15 "from": 0, 16 "size": 10, 17 "sort": [], 18 "aggs": {} 19 }
结果:
如果使用match来统计的话也会有问题,会把不正确的数据也统计出来,使用 match进行统计会把查询条件与内容进行匹配,根据匹配度进行打分,分数高的说明匹配度高,会排在上面
1 { 2 "query": { 3 "bool": { 4 "must": [ 5 { 6 "match": { 7 "age.cn": "北京市海淀区西二旗中关村" 8 } 9 } 10 ], 11 "must_not": [], 12 "should": [] 13 } 14 }, 15 "from": 0, 16 "size": 10, 17 "sort": [], 18 "aggs": {} 19 }
结果:
下面就是按匹配度打分排名的结果
1 { 2 "query": { 3 "bool": { 4 "must": [ 5 { 6 "match": { 7 "age.cn": "北京市昌平区" 8 } 9 } 10 ], 11 "must_not": [], 12 "should": [] 13 } 14 }, 15 "from": 0, 16 "size": 10, 17 "sort": [], 18 "aggs": {} 19 }
结果:
总结:统计就用term,不分词,全匹配;模糊查询就用match,分词,不用全匹配!
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