Elasticserach Tips
elasticsearch升级到7.x；改动不小，命令从头再捋一遍；
PS:感叹elasticsearch在搜索和大数据聚合上面做的了不起的工作！ 细致入微，基本上在工程层面解决了数不清的细节问题，了不起的产品设计和再创造，了不起的工作量！ 就像docker重新唤醒容器技术一样，elasticsearch在Lucene之上的构建为个人数据分析和企业数据梳理开创新时代。 如果有条件，我是极为愿意买入他们的股票的。
文档操作
增加一条记录
1 2 3 4 5 6 | PUT /website/_doc/1 { "title": "My 2 blog entry", "text": "I am starting to get the hang of this...", "date": "2014/01/02" } |
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修改
1 2 3 4 5 6 7 | POST /website/_update/1 { "doc" : { "tags" : [ "testing..." ], "views": 0 } } |
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查询
1 2 3 4 5 6 7 8 9 10 11 12 13 | GET /website/_search GET /website/_source/1 GET /website/_mget { "ids" : [ "2", "1" ] } GET /_search { "query": YOUR_QUERY_HERE } |
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删除
1 | DELETE /website/_doc/1 |
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文档功能API
获取映射信息
1 | GET /website/_mapping |
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测试分析器
1 2 3 4 5 | GET /website/_analyze { "field": "tweet", "text": "Black-cats" } |
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多层级对象用扁平化的方法来存储，比如
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | { "gb": { "tweet": { "properties": { "tweet": { "type": "string" }, "user": { "type": "object", "properties": { "id": { "type": "string" }, "gender": { "type": "string" }, "age": { "type": "long" }, "name": { "type": "object", "properties": { "full": { "type": "string" }, "first": { "type": "string" }, "last": { "type": "string" } } } } } } } } } |
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会被转换为如下内部对象:
1 2 3 4 5 6 7 8 9 | { "tweet": [elasticsearch, flexible, very], "user.id": [@johnsmith], "user.gender": [male], "user.age": [26], "user.name.full": [john, smith], "user.name.first": [john], "user.name.last": [smith] } |
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内部对象数组会丢失一部分相关信息，我们需要用嵌套对象(nested object)来处理
查询
查询语句的结构
- 一个查询语句 的典型结构：
1 2 3 4 5 6 | { QUERY_NAME: { ARGUMENT: VALUE, ARGUMENT: VALUE,... } } |
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- 如果是针对某个字段，那么它的结构如下：
1 2 3 4 5 6 7 8 | { QUERY_NAME: { FIELD_NAME: { ARGUMENT: VALUE, ARGUMENT: VALUE,... } } } |
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- 一条复合语句
1 2 3 4 5 6 7 8 9 10 11 12 13 | { "bool": { "must": { "match": { "email": "business opportunity" }}, "should": [ { "match": { "starred": true }}, { "bool": { "must": { "match": { "folder": "inbox" }}, "must_not": { "match": { "spam": true }} }} ], "minimum_should_match": 1 } } |
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实战查询
- 精确查询
1 2 3 4 5 6 7 8 9 10 11 12 | GET /website/_search { "query": { "constant_score" : { "filter":{ "term": { "title": "helloworld" } } } } } |
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- 多词组合
1 2 3 4 5 6 7 8 9 10 11 | GET /website/_search { "query": { "match": { "title": { "query": "BROWN DOG!", "operator": "and" } } } } |
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- 短语匹配
1 2 3 4 5 6 7 8 | GET /website/_search { "query": { "match_phrase": { "title": "quick brown fox" } } } |
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- 混合短语匹配
1 2 3 4 5 6 7 8 9 10 11 | GET /website/_search { "query": { "match_phrase": { "title": { "query": "quick fox", "slop": 1 } } } } |
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- 正则查询 (性能慢)
1 2 3 4 5 6 7 8 | GET /my_index/_search { "query": { "wildcard": { "postcode": "W?F*HW" } } } |
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- 智能匹配
1 2 3 4 5 6 7 8 9 10 11 | GET /my_index/_search { "query": { "match_phrase_prefix" : { "brand" : { "query": "johnnie walker bl", "max_expansions": 50 } } } } |
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- 控制精度
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | GET /website/_search { "query": { "match": { "title": { "query": "quick brown dog", "minimum_should_match": "75%" } } } } GET /website/_search { "query": { "bool": { "should": [ { "match": { "title": "brown" }}, { "match": { "title": "fox" }}, { "match": { "title": "dog" }} ], "minimum_should_match": 2 } } } |
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- 按受欢迎度提升权重
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | GET /blogposts/post/_search { "query": { "function_score": { "query": { "multi_match": { "query": "popularity", "fields": [ "title", "content" ] } }, "field_value_factor": { "field": "votes" } } } } 微调: https://www.elastic.co/guide/cn/elasticsearch/guide/current/boosting-by-popularity.html |
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排障
1 2 3 4 5 6 7 8 | GET /website/_validate/query?explain { "query": { "match" : { "text" : "really powerful" } } } |
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结果排序
1 2 3 4 5 6 7 8 9 | GET /website/_search { "query" : { "bool" : { "filter" : { "term" : { "_id" : 1 }} } }, "sort": { "date": { "order": "desc" }} } |
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索引操作
增加
1 2 3 4 5 6 7 8 9 | PUT /my_index { "settings": { ... any settings ... }, "mappings": { "type_one": { ... any mappings ... }, "type_two": { ... any mappings ... }, ... } } |
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删除
1 2 3 4 | DELETE /my_index DELETE /index_one,index_two DELETE /index_* DELETE /_all |
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配置
- number_of_shards
每个索引的主分片数，默认值是 5 。这个配置在索引创建后不能修改。
- number_of_replicas
每个主分片的副本数，默认值是 1 。对于活动的索引库，这个配置可以随时修改。
重新索引
1 2 3 4 5 6 7 8 9 10 | POST _reindex { "source": { "index": "twitter" }, "dest": { "index": "new_twitter" } } |
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释放空间
1 | POST /_all/_forcemerge?only_expunge_deletes=true |
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