Logstash同步MySQL一对多关联表到Elasticsearch父子文档

前言：

         目前大部分业务开发中，ElasticSearch主要还是用来做搜索。而支撑搜索功能的数据结构比较单一，不会有数据嵌套或者多种关联之类的。尽管没有，但是有些小众需求可能还会有一对多查询的场景。为了实现和MySQL的Join类似的查询方式，以下以ES的父子文档方式储存，并详细演示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方式创建父子文档索引，并以简单的方式查询类似join的数据返回。下面所有演示的索引名称都为 "my_join_index"。

1. 创建父子关联索引

PUT my_join_index
{
  "mappings": {
    "properties": {
        "my_join_field": { 
          "type": "join",
          "relations": {
            "question": "answer" 
          }
        }
      }
  }
}

2. 创建父文档

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. 创建子文档

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
{
  "query": {
    "match_all": {}
  },
  "sort": ["_id"]
}

5. 根据父文档查找子文档

GET my_join_index/_search
{
    "query": {
        "has_parent" : {
            "parent_type" : "question",
            "query" : {
                "match" : {
                    "text" : "This is"
                }
            }
        }
    }
}

6. 根据子文档查找父文档

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
{
  "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. 单条联合查询, 可以是一条父文档对应多个子文档

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.  创建有父子文档的索引

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"
	  
      # 用户名和密码
      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、反序列化需求，建议改为false）；
      lowercase_column_names => false
		
	  # Value can be any of: fatal,error,warn,info,debug，默认info；
	  sql_log_level => warn
		
	  
      # 设置监听间隔  各字段含义（由左至右）分、时、天、月、年，全部为*默认含义为每分钟都更新
      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"
	  
      # 用户名和密码
      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、反序列化需求，建议改为false）；
      lowercase_column_names => false
		
	  # Value can be any of: fatal,error,warn,info,debug，默认info；
	  sql_log_level => warn
	  
      # 设置监听间隔  各字段含义（由左至右）分、时、天、月、年，全部为*默认含义为每分钟都更新
      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.  通过搜索父文档标题查询子文档数据

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