1、term vector介绍 获取document中的某个field内的各个term的统计信息 term information: term frequency in the field, term positions, start and end offsets, term payloads term statistics: 设置term_statistics=true; total term frequency, 一个term在所有document中出现的频率; document frequency，有多少document包含这个term field statistics: document count，有多少document包含这个field; sum of document frequency，一个field中所有term的df之和; sum of total term frequency，一个field中的所有term的tf之和 GET /twitter/tweet/1/_termvectors GET /twitter/tweet/1/_termvectors?fields=text term statistics和field statistics并不精准，不会被考虑有的doc可能被删除了 我告诉大家，其实很少用，用的时候，一般来说，就是你需要对一些数据做探查的时候。比如说，你想要看到某个term，某个词条，大话西游，这个词条，在多少个document中出现了。或者说某个field，film_desc，电影的说明信息，有多少个doc包含了这个说明信息。 2、index-iime term vector实验 term vector，涉及了很多的term和field相关的统计信息，有两种方式可以采集到这个统计信息 （1）index-time，你在mapping里配置一下，然后建立索引的时候，就直接给你生成这些term和field的统计信息了 （2）query-time，你之前没有生成过任何的Term vector信息，然后在查看term vector的时候，直接就可以看到了，会on the fly，现场计算出各种统计信息，然后返回给你 这一讲，不会手敲任何命令，直接copy我做好的命令，因为这一讲的重点，不是掌握什么搜索或者聚合的语法，而是说，掌握，如何采集term vector信息，然后如何看懂term vector信息，你能掌握利用term vector进行数据探查 PUT /my_index { "mappings": { "my_type": { "properties": { "text": { "type": "text", "term_vector": "with_positions_offsets_payloads", "store" : true, "analyzer" : "fulltext_analyzer" }, "fullname": { "type": "text", "analyzer" : "fulltext_analyzer" } } } }, "settings" : { "index" : { "number_of_shards" : 1, "number_of_replicas" : 0 }, "analysis": { "analyzer": { "fulltext_analyzer": { "type": "custom", "tokenizer": "whitespace", "filter": [ "lowercase", "type_as_payload" ] } } } } } PUT /my_index/my_type/1 { "fullname" : "Leo Li", "text" : "hello test test test " } PUT /my_index/my_type/2 { "fullname" : "Leo Li", "text" : "other hello test ..." } GET /my_index/my_type/1/_termvectors { "fields" : ["text"], "offsets" : true, "payloads" : true, "positions" : true, "term_statistics" : true, "field_statistics" : true } { "_index": "my_index", "_type": "my_type", "_id": "1", "_version": 1, "found": true, "took": 10, "term_vectors": { "text": { "field_statistics": { "sum_doc_freq": 6, "doc_count": 2, "sum_ttf": 8 }, "terms": { "hello": { "doc_freq": 2, "ttf": 2, "term_freq": 1, "tokens": [ { "position": 0, "start_offset": 0, "end_offset": 5, "payload": "d29yZA==" } ] }, "test": { "doc_freq": 2, "ttf": 4, "term_freq": 3, "tokens": [ { "position": 1, "start_offset": 6, "end_offset": 10, "payload": "d29yZA==" }, { "position": 2, "start_offset": 11, "end_offset": 15, "payload": "d29yZA==" }, { "position": 3, "start_offset": 16, "end_offset": 20, "payload": "d29yZA==" } ] } } } } } 3、query-time term vector实验 GET /my_index/my_type/1/_termvectors { "fields" : ["fullname"], "offsets" : true, "positions" : true, "term_statistics" : true, "field_statistics" : true } 一般来说，如果条件允许，你就用query time的term vector就可以了，你要探查什么数据，现场去探查一下就好了 4、手动指定doc的term vector GET /my_index/my_type/_termvectors { "doc" : { "fullname" : "Leo Li", "text" : "hello test test test" }, "fields" : ["text"], "offsets" : true, "payloads" : true, "positions" : true, "term_statistics" : true, "field_statistics" : true } 手动指定一个doc，实际上不是要指定doc，而是要指定你想要安插的词条，hello test，那么就可以放在一个field中 将这些term分词，然后对每个term，都去计算它在现有的所有doc中的一些统计信息 这个挺有用的，可以让你手动指定要探查的term的数据情况，你就可以指定探查“大话西游”这个词条的统计信息 5、手动指定analyzer来生成term vector GET /my_index/my_type/_termvectors { "doc" : { "fullname" : "Leo Li", "text" : "hello test test test" }, "fields" : ["text"], "offsets" : true, "payloads" : true, "positions" : true, "term_statistics" : true, "field_statistics" : true, "per_field_analyzer" : { "text": "standard" } } 6、terms filter GET /my_index/my_type/_termvectors { "doc" : { "fullname" : "Leo Li", "text" : "hello test test test" }, "fields" : ["text"], "offsets" : true, "payloads" : true, "positions" : true, "term_statistics" : true, "field_statistics" : true, "filter" : { "max_num_terms" : 3, "min_term_freq" : 1, "min_doc_freq" : 1 } } 这个就是说，根据term统计信息，过滤出你想要看到的term vector统计结果 也挺有用的，比如你探查数据把，可以过滤掉一些出现频率过低的term，就不考虑了 7、multi term vector GET _mtermvectors { "docs": [ { "_index": "my_index", "_type": "my_type", "_id": "2", "term_statistics": true }, { "_index": "my_index", "_type": "my_type", "_id": "1", "fields": [ "text" ] } ] } GET /my_index/_mtermvectors { "docs": [ { "_type": "test", "_id": "2", "fields": [ "text" ], "term_statistics": true }, { "_type": "test", "_id": "1" } ] } GET /my_index/my_type/_mtermvectors { "docs": [ { "_id": "2", "fields": [ "text" ], "term_statistics": true }, { "_id": "1" } ] } GET /_mtermvectors { "docs": [ { "_index": "my_index", "_type": "my_type", "doc" : { "fullname" : "Leo Li", "text" : "hello test test test" } }, { "_index": "my_index", "_type": "my_type", "doc" : { "fullname" : "Leo Li", "text" : "other hello test ..." } } ] }