前言：
目前大部分业务开发中，ElasticSearch次要还是用来做搜寻。而撑持搜寻性能的数据结构比拟繁多，不会有数据嵌套或者多种关联之类的。只管没有，然而有些小众需要可能还会有一对多查问的场景。为了实现和MySQL的Join相似的查问形式，以下以ES的父子文档形式贮存，并具体演示Logstash如何将MySQL的多张有关联的表同步到ES的父子文档。
手动演示：
以下以restful形式创立父子文档索引，并以简略的形式查问相似join的数据返回。上面所有演示的索引名称都为 "my_join_index"。
1. 创立父子关联索引PUT my_join_index
{ "mappings": { "properties": { "my_join_field": { "type": "join", "relations": { "question": "answer" } } } }}2. 创立父文档PUT my_join_index/_doc/1?refresh
PUT my_join_index/_doc/1?refresh{ "text": "This is a question", "my_join_field": "question" }PUT my_join_index/_doc/2?refresh{ "text": "This is another question", "my_join_field": "question"}3. 创立子文档PUT my_join_index/_doc/3?routing=1&refresh
PUT my_join_index/_doc/3?routing=1&refresh { "text": "This is an answer", "my_join_field": { "name": "answer", "parent": "1" }}PUT my_join_index/_doc/4?routing=1&refresh{ "text": "This is another answer2", "my_join_field": { "name": "answer", "parent": "2" }}4. 全局检索GET my_join_index/_search
GET my_join_index/_search{ "query": { "match_all": {} }, "sort": ["_id"]}5. 依据父文档查找子文档GET my_join_index/_search
GET my_join_index/_search{ "query": { "has_parent" : { "parent_type" : "question", "query" : { "match" : { "text" : "This is" } } } }}6. 依据子文档查找父文档GET my_join_index/_search
GET my_join_index/_search{"query": { "has_child" : { "type" : "answer", "query" : { "match" : { "text" : "This is question" } } } }}7. Join聚合GET my_join_index/_search
GET my_join_index/_search{ "query": { "parent_id": { "type": "answer", "id": "1" } }, "aggs": { "parents": { "terms": { "field": "my_join_field#question", "size": 10 } } }, "script_fields": { "parent": { "script": { "source": "doc['my_join_field#question']" } } }}8. 单条联结查问, 能够是一条父文档对应多个子文档GET my_join_index/_search
GET my_join_index/_search{ "query": { "bool": { "must": [ { "match": { "title": "历史圈" } }, { "has_child": { "type": "answer", "query": { "match": { "text":"是的" } }, "inner_hits":{} } } ] } }}Logstash同步：
以下以文章分类表和文章表为例，二者系一对多的关系。同步文档时，文章分类作为父文档，文章作为子文档，关联字段为 “my_join_field”。
1. 创立有父子文档的索引PUT hhyp_article
PUT hhyp_article{ "mappings": { "properties": { "my_join_field": { "type": "join", "relations": { "article_cate": "article" } } } }}2. 配置同步代码
input { stdin { } jdbc { # mysql 数据库链接,shop为数据库名 jdbc_connection_string => "jdbc:mysql://127.0.0.1:3306/rebuild?characterEncoding=UTF-8&useSSL=false" # 用户名和明码 jdbc_user => "root" jdbc_password => "root" # 驱动 jdbc_driver_library => "E:/2setsoft/1dev/logstash-7.8.0/mysqletc/mysql-connector-java-5.1.7-bin.jar" # 驱动类名 jdbc_driver_class => "com.mysql.jdbc.Driver" jdbc_paging_enabled => "true" jdbc_page_size => "50000" parameters => {"number" => "200"} statement => "SELECT * FROM `hhyp_article` WHERE delete_time = 0" # 是否将字段名转换为小写，默认true（如果有数据序列化、反序列化需要，倡议改为false）； lowercase_column_names => false # Value can be any of: fatal,error,warn,info,debug，默认info； sql_log_level => warn # 设置监听距离 各字段含意（由左至右）分、时、天、月、年，全副为*默认含意为每分钟都更新 schedule => "* * * * *" # 索引类型 type => "article" } jdbc { # mysql 数据库链接,shop为数据库名 jdbc_connection_string => "jdbc:mysql://127.0.0.1:3306/rebuild?characterEncoding=UTF-8&useSSL=false" # 用户名和明码 jdbc_user => "root" jdbc_password => "root" # 驱动 jdbc_driver_library => "E:/2setsoft/1dev/logstash-7.8.0/mysqletc/mysql-connector-java-5.1.7-bin.jar" # 驱动类名 jdbc_driver_class => "com.mysql.jdbc.Driver" jdbc_paging_enabled => "true" jdbc_page_size => "50000" parameters => {"number" => "200"} statement => "SELECT * FROM `hhyp_article_cate` WHERE delete_time = 0" # 是否将字段名转换为小写，默认true（如果有数据序列化、反序列化需要，倡议改为false）； lowercase_column_names => false # Value can be any of: fatal,error,warn,info,debug，默认info； sql_log_level => warn # 设置监听距离 各字段含意（由左至右）分、时、天、月、年，全副为*默认含意为每分钟都更新 schedule => "* * * * *" # 索引类型 type => "article_cate" } } filter { if [type]=="article_cate" { mutate { add_field => { "my_join_field" => "article_cate" } } } if [type]=="article" { mutate { add_field => {"[my_join_field][name]" => "article"} #catalog_id 子表的父id add_field => {"[my_join_field][parent]" => "%{cid}"} } } } output { if[type] == "article_cate" { elasticsearch { hosts => "localhost:9200" index => "hhyp_article" document_type => "_doc" document_id => "%{id}" } } if[type] == "article" { elasticsearch { hosts => "localhost:9200" index => "hhyp_article" document_type => "_doc" document_id => "%{id}" routing => "%{cid}" } } stdout { codec => json_lines } }3. 运行命令开始同步
bin\logstash -f mysql\mysql.conf
4. 通过搜寻父文档题目查问子文档数据